Description:
PREAMBLE OF THE DESCRIPTION- COMPLETE
The following specification particularly describes the invention and the manner in which it is to be performed.
MICRO-CONTAINER BASED SYSTEM AND METHOD FOR MANAGING PROJECT EXECUTION IN A COMPUTING ENVIRONMENT
FIELD OF INVENTION
[0001] The present subject matter generally relates to project management systems, more particularly relates to a micro-container-based system for managing project execution in a computing environment.
BACKGROUND
[0002] Financial management within government entities, particularly at the state level, involves complex processes of budget allocation, fund disbursement, and monitoring. The conventional methods for managing these processes often suffer from inefficiencies, delays, and security vulnerabilities. In the context of state finance commissions, ensuring the effective utilization of funds for various projects, services, and staff salaries is paramount for the smooth functioning of public services and infrastructure development. Traditional systems lack the integration and automation necessary to streamline these processes comprehensively.
[0003] Manual interventions often lead to errors, delays, and susceptibility to fraudulent activities. Additionally, tracking staff attendance and managing committed liabilities, such as salaries, add further layers of complexity to financial management systems. In light of these challenges, there exists a need for a comprehensive financial management tool tailored specifically for state finance commissions. Such a system should incorporate features for efficient fund allocation, secure disbursement, real-time monitoring, and staff attendance tracking to enhance transparency, efficiency, and accountability in financial operations.
[0004] Hence, there is a need for an improved system and method for managing project execution in a computing environment in order to address the aforementioned issues.
SUMMARY
[0005] This summary is provided to introduce a selection of concepts, in a simple manner which is further described in the detailed description of the disclosure. This summary is neither intended to identify key or essential inventive concepts of the subject matter nor to determine the scope of the disclosure.
[0006] In accordance with an embodiment of the present disclosure, a micro-container-based system for managing project execution in a computing environment is disclosed. The micro-container-based system receives a request for creating and managing a project workflow for a specific requirement from one or more user devices. Further, the micro-container-based system determines one or more project execution nodes for the received request based on the specific requirements. Each of the one or more project execution nodes corresponds to a plurality of functional tasks to be performed. Each of the determined one or more project execution nodes correspond to containerized microservices. Further, the micro-container-based system determines hierarchy and priority of execution for each of the determined one or more project execution nodes. Further, the micro-container-based system identifies one or more third-party entities for performing the plurality of functional tasks corresponding to the determined one or more project execution nodes based on the determined hierarchy and priority of execution. Furthermore, the micro-container-based system generates a project execution workflow based on the one or more project execution nodes, the determined hierarchy and priority of execution, and the identified one or more third-party entities.
[0007] Further, micro-container-based system validates the generated project execution workflow for identifying one or more risks, exceptions and fallback scenarios using an artificial-intelligence based exception handling model. Further, micro-container-based system establishes communications with the identified one or more third-party entities for executing the plurality of functional tasks in real-time based on successful validation. Further, the micro-container-based system continuously monitors the status of the project execution workflow at each of the one or more project execution nodes at real-time upon establishing the communication. Furthermore, the micro-container-based system predicts possible outcomes and expected errors associated with each of the one or more project execution nodes based on the monitoring using an artificial intelligence-based prediction model and performs one or more actions at each of the one or more project execution nodes based on the predicted outcomes and errors.
[0008] Further, the micro-container-based method for managing project execution in a computing environment includes receiving a request for creating and managing a project workflow for a specific requirement from one or more user devices. Further, the micro-container-based method includes determining one or more project execution nodes for the received request based on the specific requirements. Further, each of the one or more project execution nodes correspond to a plurality of functional tasks to be performed. Further, each of the determined one or more project execution nodes correspond to containerized microservices. Further, the micro-container-based method includes determining hierarchy and priority of execution for each of the determined one or more project execution nodes. Further, the micro-container-based method includes identifying one or more third-party entities for performing the plurality of functional tasks corresponding to the determined one or more project execution nodes based on the determined hierarchy and priority of execution. Further, the micro-container-based method includes generating a project execution workflow based on the one or more project execution nodes, the determined hierarchy and priority of execution, and the identified one or more third-party entities.
[0009] Further, the micro-container-based method includes validating the generated project execution workflow for identifying one or more risks, exceptions and fallback scenarios using an artificial-intelligence based exception handling model. Further, the micro-container-based method includes establishing communications with the identified one or more third-party entities for executing the plurality of functional tasks in real-time based on successful validation. Further, the micro-container-based method includes continuously monitoring status of the project execution workflow at each of the one or more project execution nodes at real-time upon establishing the communication. Furthermore, the micro-container-based method includes predicting possible outcomes and expected errors associated with each of the one or more project execution nodes based on the monitoring using an artificial intelligence-based prediction model and performing one or more actions at each of the one or more project execution nodes based on the predicted outcomes and errors.
[0010] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.
BRIEF DESCRIPTION OF DRAWINGS
[0011] The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:
[0012] FIG. 1 illustrates an exemplary block diagram representation of a micro-container-based system for managing project execution in a computing environment, in accordance with an embodiment of the present disclosure;
[0013] FIG. 2 illustrates an exemplary block diagram representation of a computing unit of a micro-container-based system for managing project execution in a computing environment, in accordance with an embodiment of the present disclosure;
[0014] FIG. 3 illustrates an exemplary schematic diagram representation of a micro-container-based system for managing project execution in a computing environment, in accordance with another embodiment of the present disclosure;
[0015] FIG. 4 illustrates an exemplary flow diagram representation of a micro-container-based method for managing project execution in a computing environment, in accordance with an embodiment of the present disclosure;
[0016] FIG. 5 illustrates an exemplary snapshot diagram representation of Graphical User Interface (GUI) of a micro-container-based system for managing project execution in a computing environment depicting districts performance, in accordance with another embodiment of the present disclosure; `
[0017] FIG. 6 illustrates an exemplary snapshot diagram representation of Graphical User Interface (GUI) of a micro-container-based system for managing project execution in a computing environment depicting top bidding items, in accordance with another embodiment of the present disclosure; and
[0018] FIG. 7 illustrates an exemplary snapshot diagram representation of Graphical User Interface (GUI) of a micro-container-based system for managing project execution in a computing environment depicting auctions status, in accordance with another embodiment of the present disclosure.
[0019] Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.

DETAILED DESCRIPTION OF THE DISCLOSURE
[0020] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure. It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the disclosure and are not intended to be restrictive thereof.
[0021] In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
[0022] The terms "comprise", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that one or more devices or sub-systems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, additional sub-modules. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.
[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.
[0024] A computer system (standalone, client or server computer system) is configured by an application may constitute a “module” (or “subsystem”) that is configured and operated to perform certain operations. In one embodiment, the “module” or “subsystem” may be implemented mechanically or electronically, so a module includes dedicated circuitry or logic that is permanently configured (within a special-purpose processor) to perform certain operations. In another embodiment, a “module” or “subsystem” may also comprise programmable logic or circuitry (as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations.
[0025] Accordingly, the term “module” or “subsystem” should be understood to encompass a tangible entity, be that an entity that is physically constructed permanently configured (hardwired) or temporarily configured (programmed) to operate in a certain manner and/or to perform certain operations described herein.
[0026] Referring now to the drawings, and more particularly to FIG. 1 through FIG. 7, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments, and these embodiments are described in the context of the following exemplary system and/or method.
[0027] FIG. 1 illustrates an exemplary block diagram representation of a micro-container-based system 100 for managing project execution in a computing environment, in accordance with an embodiment of the present disclosure. The micro-container-based system 100 may include components such as, but not limited to, a plurality of user devices 102-1, …, 102-N, a plurality of databases 104-1, …, 104-N, a plurality of third-party devices 106-1, …, 106-N, and a computing unit 108. Further, the components of the container-based system 100 are connected over a network 112.
[0028] Further, the micro-container-based system 100 may be configured to receive a request for creating and managing a project workflow for a specific requirement from one or more user devices 102-1, …, 102-N. The user devices 102-1, …, 102-N may include, but not limited to, mobile phones, laptops, personal computers and the like.
[0029] Further, the micro-container-based system 100 may be configured to determine one or more project execution nodes for the received request based on the specific requirements. The one or more project execution nodes may include, but not limited to, an automated Request for Quotation (RFQ) generation node, a contract management node, a project creation and monitoring node, an integrated payroll management node, an attendance management node, an asset management node, and an e-auction node. The specific requirements may include, but not limited to, requirement of development works or the like. For example, the micro-container-based system 100 has a built-in decision-making logic to determine the appropriate procurement process based on the work amount. For work amounts greater than 5 lakhs, the system integrates with the "Tenders Haryana" e-procurement platform, allowing vendors to submit their quotes and bids. For work amounts less than 5 lakhs, the micro-container-based system 100 (also referred herein as “system” or “FDMS”) generates and manages the Request for Quotation (RFQ) process, streamlining the vendor selection and assignment.
[0030] Further, each of the one or more project execution nodes correspond to a plurality of functional tasks to be performed. The plurality of functional task may include, but not limited to, assignment and management of contract, project creation and monitoring, geo tagged attendance capturing, creation of attendance register, salary calculation based on the attendance, management of integrated payroll, management of assets and the like. The each of the determined one or more project execution nodes correspond to containerized microservices. Further, the micro-container-based system 100 may be configured to determine hierarchy, and priority of execution for each of the determined one or more project execution nodes. Further, the micro-container-based system 100 may be configured to identify one or more third-party entities for performing the plurality of functional tasks corresponding to the determined one or more project execution nodes based on the determined hierarchy and priority of execution. The third-party entities 106-1, …, 106-N may include, but not limited to, vendors, contractors, employees and the like. Further, the micro-container-based system 100 may be configured to generate a project execution workflow based on the one or more project execution nodes, the determined hierarchy and priority of execution, and the identified one or more third-party entities 106-1, …, 106-N.
[0031] The system provides a digital platform for creating and managing resolutions or decisions to allocate funds. It implements a robust workflow management system with defined user roles, permissions, and approvals. The workflow follows a "maker-checker" model, where a maker (e.g., department officer) prepares the disbursement proposal, and a checker (e.g., finance officer) verifies and approves it. Further, the maker-checker model may include may be used for authenticity. The workflow is designed to ensure proper segregation of duties and maintain the integrity of the financial decision-making process on plurality of levels.
[0032] Further, the micro-container-based system 100 may be configured to validate the generated project execution workflow for identifying one or more risks, exceptions and fall-back scenarios using an artificial-intelligence based exception handling model. The risks may include, but not limited to, financial, operational, or legal risks. Further, the exceptions may include, but not limited to database connection failures, network timeouts, Application Programming Interface (API) integration issues, and other runtime exceptions. Further, the fall-back scenarios may include, but not limited to, regular backups of the database, replication of critical data, and the ability to restore the system to a known good state in the event of a major failure or disaster. The error handling and exception handling component is responsible for handling errors and exceptions that may occur during the fund disbursement process. Error and exception handling ensures that the fund disbursement process continues to run smoothly even in the event of errors and exceptions. Further, the errors may include, but not limited to, transaction failure, error due to server of banks, and the like. Further, the exceptions handling may include, but not limited to, repeated payment deduction, and the like. The micro-container-based system 100 implements robust input validation mechanisms to ensure that the data entered by users or received from integrated systems is within the expected range and format. This includes validating the integrity of RFQ details, contact information, payment data, attendance records, and other critical inputs. Any invalid or inconsistent data is identified and handled appropriately to prevent it from propagating through the system. The FDMS has a centralized error handling and exception management module that intercepts and processes any errors or exceptions that may occur during the execution of various processes. Further, the centralized error handling and exception management module may include exception handlers to catch unhandled exceptions such as, but not limited to, try-catch blocks, asynchronous error handling, database connection failures, network timeouts, Application Programming Interface (API)integration issues, other runtime exceptions, and the like. Further, the try-catch blocks handler may be configured to determine the error and process the error at the local level. The local level may include, but not limited to, microcontainer based service, microcontainer based module, and the like. Further, the try-catch blocks handler may be configured to escalate error or exception. Further, the asynchronous error handler may be configured to determine errors and exceptions for asynchronously running processes. Further, the asynchronous error handler may be configured to report the determined errors and exceptions centrally. Further, the synchronously running processes may include, but not limited to, background jobs, scheduled tasks, and the like. Further, the database connection failures may include monitoring database connections and retrying connections before determining the error. Further, the database connection failures may include notifying the administrator about the errors and exceptions. Further, the network timeouts may include determining timeout exceptions. Further, the timeout exceptions handled by retrying the request or by switching to a fallback mechanism and by performing detailed logging for any network issues. Further, the Application Programming Interface (API) integration issues may include, but not limited to, retrying requests, switching to alternative endpoints, or queuing the requests for later processing, and the like. Further, the integration with external API may include logic to handle plurality of Hypertext Transfer Protocol (HTTP) status codes, and the like.
[0033] The error handling component ensures that the system responds gracefully to these exceptions, providing informative error messages and logging the details for further investigation. Further, the response of the system to the exceptions may include, but not limited to, fallback mechanism, transaction management, and the like. Further, the system may switch to fallback mechanism to maintain functionality of the system. Further, in case of failure of the system to connect with API while maintaining the functionality, the maintenance of the functionality may include, but not limited to, falling back on cached data, secondary API, and the like. Further, the transaction management may include reinstating the system for maintaining the data integrity in case of partial failure. Further, the transaction management may include ensuring partial failures do not retain the system in an inconsistent state.

[0034] For certain critical operations, such as payment disbursements or data synchronization with external systems, the micro-container-based system 100 implements a retry mechanism to handle temporary failures.
[0035] In the event of a failed transaction, the system initiates a compensating transaction to undo the partial or incomplete operation, maintaining data consistency and integrity. This retry and compensating transaction logic helps ensure the reliability and robustness of the micro-container-based system 100, even in the face of intermittent system or network failures.
[0036] The micro-container-based system 100 incorporates fallback mechanisms and disaster recovery strategies to ensure the system's resilience and availability. This includes regular backups of the database, replication of critical data, and the ability to restore the system to a known good state in the event of a major failure or disaster. These measures help maintain the continuity of the FDMS operations and safeguard the State Finance Commission's financial data and processes.
[0037] The micro-container-based system 100 analyses the available funds, budget constraints, and resource requirements for ongoing projects. It employs optimization algorithms and models to allocate resources (e.g., budgets, personnel, equipment) in a way that maximizes the utilization and impact of the available resources. This could involve prioritizing critical projects, shifting resources between projects based on needs, and ensuring a balanced distribution of funds and efforts. The FDMS continuously monitors the progress of ongoing projects and identifies potential delays or bottlenecks. It utilizes project management techniques, such as critical path analysis, resource levelling, and schedule optimization, to adjust project timelines and ensure timely completion. This could involve rescheduling tasks, reallocating resources, or implementing mitigation strategies to address delays and maintain project deadlines. Further, the project management techniques may include plurality of Artificial Intelligence (AI) based techniques and Machine Learning (ML) based techniques. Further, the AI/ML based techniques may include, but not limited to, Reinforcement Learning (RL), Neural Networks (NN), Genetic Algorithms (GA), Constraint Satisfaction Problems (CSP) Solvers, Optimization Algorithms, Bayesian Networks, and the like. Further the micro-container-based system 100 may be configured to utilise Reinforcement Learning (RL) technique for critical path analysis. Further, the RL may be configured to dynamically adjust the project schedule by learning the best actions to respond to changes and uncertainties in the project timeline. Further, the micro-container-based system 100 may be configured to utilise Neural Networks (NN) technique to predict the impact of potential delays on the overall project schedule. Further, the micro-container-based system 100 may be configured to utilise Genetic Algorithms (GA) to find the optimal allocation of resources to tasks while minimizing conflicts and over-allocation for resource levelling. Further, the micro-container-based system 100 may be configured to utilise Constraint Satisfaction Problems (CSP) Solvers to ensure resources are allocated without exceeding availability and maintaining project timelines. Further, the micro-container-based system 100 may be configured to utilise Optimization Algorithms such as, but not limited to, Linear Programming, Mixed-Integer Linear Programming, and the like to find the most efficient schedule that minimizes the project duration and cost while meeting all constraints. Further, the micro-container-based system 100 may be configured to utilise Bayesian Networks to model uncertainties in task durations and resource availability and optimize the schedule considering the uncertainties.
[0038] Further, the micro-container-based system 100 may be configured to establish communications with the identified one or more third-party entities 106-1, …, 106-N for executing the plurality of functional tasks in real-time based on successful validation. Furthermore, the micro-container-based system 100 may be configured to predict possible outcomes and expected errors associated with each of the one or more project execution nodes based on the monitoring using an artificial intelligence-based prediction model and perform one or more actions at each of the one or more project execution nodes based on the predicted outcomes and errors. Further, the artificial intelligence-based prediction model may include, but not limited to, Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) layers, and the like. Further, the Long Short-Term Memory (LSTM) layers may be a special type of layer used in Recurrent Neural Network (RNN). Further, the Long Short-Term Memory (LSTM) layer may be configured to function on the principle of a cell. Further, the cell may be configured to store information for longer period. Further, the LSTM layer may be configured with special gates that control flow of stored information. Further, the special gates may include, but not limited to, a forget gate, an input gate, an output gate, and the like. Further, the forget gate may be configured to determine the information to be discarded from previous state. Further, the input gate may be configured to determine new information new information to store in the cell. Further, the output gate may be configured to control information from the cell to be passed to a next step. Further, the artificial intelligence-based prediction model may be configured to capture long-term dependencies and are designed to avoid the vanishing gradient problem. Further the vanishing gradient problem may refer to the diminishing magnitude of gradients of data over a period of time. Further, the micro-container-based system 100 may use Reinforcement Learning for real-time decision-making and optimization. Further, the micro-container-based system 100 may be configured to combine LSTM for prediction and RL for decision-making.
[0039] Further, the micro-container-based system 100 may include the plurality of databases 104-1, …, 104-N. The plurality of databases may include, but not limited to, relational database and the like. Further, the plurality of databases 104-1, …, 104-N may be utilised for, but not limited to, maintaining a database of registered vendors, contractors, and profiles of respective registered vendors and contractors. Further, the plurality of databases 104-1, …, 104-N are utilized during the procurement and contract assignment processes. The database such as, but not limited to, MySQL, and POSTGRESQL may be extensively utilized in the Funds Disbursement and Management System (FDMS) of the State Finance Commission to address a wide range of data management and storage requirements. In Transactional Data Management: MySQL serves as the primary relational database for storing and managing the core transactional data of the FDMS, such as: RFQ records and bidding details, Contract and work order information, Payment transactions and disbursements, Vendor and contractor profiles and MySQL's ACID (Atomicity, Consistency, Isolation, Durability) compliance ensures the integrity and reliability of these critical financial records, which are essential for the State Finance Commission's operations.
[0040] In User and Access Management, MySQL is used to store and manage user accounts, roles, and permissions for the FDMS, ensuring secure access to the system's functionalities. This includes maintaining employee profiles, login credentials, and authorization levels for the various stakeholders of the State Finance Commission, such as finance officers, project managers, and administrative staff.
[0041] In Financial Reporting and Analytics, the structured data stored in the MySQL database can be leveraged for generating comprehensive financial reports, dashboards, and analytical insights for the State Finance Commission. This can include budget planning, fund allocation, project expenditure tracking, and staff-related expense management, which are critical for the Commission's decision- making processes and compliance requirements.
[0042] In Integration with Other Components: MySQL is integrated with other modules and components of the FDMS, such as the attendance tracking mobile application, to establish a centralized data repository. This allows for seamless data exchange and synchronization, enabling accurate payroll calculations and staff expense management for the Commission's employees.
[0043] MySQL's scalability and high availability features, such as replication, sharding, and clustering, are utilized to ensure that FDMS handles growing data volumes and user demands, as the State Finance Commission's operations expand and evolve over time.
[0044] MySQL's robust backup and recovery mechanisms is leveraged to implement a comprehensive data protection and disaster recovery strategy for the FDMS, ensuring the Commission's critical financial data is safeguarded and can be restored in the event of system failures or unexpected events.
[0045] Those of ordinary skilled in the art will appreciate that the hardware depicted in FIG. 1 may vary for particular implementations. For example, other peripheral devices such as an optical disk drive and the like, Local Area Network (LAN), Wide Area Network (WAN), Wireless (e.g., Wi-Fi) adapter, graphics adapter, disk controller, input/output (I/O) adapter also may be used in addition or in place of the hardware depicted. The depicted example is provided for the purpose of explanation only and is not meant to imply architectural limitations with respect to the present disclosure.
[0046] FIG. 2 illustrates an exemplary block diagram representation of a computing unit 108 of a micro-container-based system 100 for managing project execution in a computing environment, in accordance with an embodiment of the present disclosure. The computing unit 108 may include a hardware processor 202 and a memory 204 coupled to the hardware processor 202. The memory 204 may include a plurality of modules 110 in the form of programmable instructions executable by the hardware processor 202. The plurality of modules 110 may include, but not limited to, a request receiver module 206, a container-based project management module 208, a validation module 210, a communication module 212, a project monitoring module 214, and a predictive analytics module 216.
[0047] Further, the request receiver module 206 may be configured to receive a request for creating and managing a project workflow for a specific requirement from one or more user devices 102-1, …, 102-N. The specific requirements may include, but not limited to, requirement of development works or the like. Further, the container-based project management module 208 may be configured to determine one or more project execution nodes for the received request based on the specific requirements. The nodes may include, but not limited to, an automated Request for Quotation (RFQ) generation node, a contract management node, a project creation and monitoring node, an integrated payroll management node, an attendance management node, an asset management node, and an e-auction node. Further, each of the one or more project execution nodes correspond to a plurality of functional tasks to be performed. Further, each of the determined one or more project execution nodes correspond to containerized microservices. Further, the container-based project management module 208 may be configured to determine hierarchy, and priority of execution for each of the determined one or more project execution nodes. Further, the container-based project management module 208 may be configured to identify one or more third-party entities 106-1, …, 106-N for performing the plurality of functional tasks corresponding to the determined one or more project execution nodes based on the determined hierarchy and priority of execution. Further, the container-based project management module 208 may be configured to generate a project execution workflow based on the one or more project execution nodes, the determined hierarchy and priority of execution, and the identified one or more third-party entities 106-1, …, 106-N. The container-based project management module 208 further comprising a user and Access Management module (not shown) configured to authenticate one or more users accessing the one or more project execution nodes based on user profiles, login credentials, and access privileges. The user profiles may include information about one or more registered vendors and contractors, one or more employees, various stakeholders of the State Finance Commission such as finance officers, project managers, and administrative staff. The login credentials may include, but not limited to, usernames, passwords and the like.
[0048] Further, the container-based project management module 208 may be configured to determine a winning third-party entity 106 of the project execution workflow based on a vendor selection criterion. The vendor selection criterion is included within the specific requirements. Further, the container-based project management module 208 may be configured to generate contract documentation, work orders, and project tracking routes for the determined winning third-party entity 106 based on pre-defined rules and the plurality of functional tasks associated with the contract management node. Further, the container-based project management module 208 may be configured to transmit the generated contract documentation, work orders and project tracking routes to other project execution nodes for approval using a blockchain network and update a database 104 associated with the contract management node with the approved contract documentation, work orders and project tracking routes. Further, the container-based project management module 208 may be configured to prioritize critical projects and shifting the resources between the projects based on needs upon successful validation of the project execution workflow.
[0049] In an exemplary embodiment, the system maintains a database of registered vendors, contractors, and their profiles, which is utilized during the procurement and contract assignment processes. Upon the selection of a winning vendor, the FDMS automatically generates the necessary contract documentation, work orders, and project tracking mechanisms. Further, the micro-container-based system 100 may be configured to store and automatically generate templates of the contract documents. Further, the one or more third party entities 106 are assigned Letter of Intent (LOI) document automatically. Further, the micro-container-based system 100 may be configured generate LOI automatically with the third-party entity name and details after the third-party entity wins the bid.
[0050] In an additional embodiment, the FDMS provides a centralized platform for the creation and monitoring of development works, involving various administrative bodies (e.g., XEN, SDO, JE). It enables the tracking of project milestones, progress, and any delays or bottlenecks, facilitating timely interventions and issue resolution. The system generates comprehensive reports on the status of ongoing projects, enabling data-driven decision-making and oversight.
[0051] Further, the validation module 210 may be configured to validate the generated project execution workflow for identifying one or more risks, exceptions and fall-back scenarios using an artificial intelligence-based exception handling model. Further, in validating the generated project execution workflow for identifying one or more risks, exceptions and fall-back scenarios using an artificial intelligence-based exception handling model, the predictive analytics module (not shown) is configured to validate input data associated with each of the one or more project execution nodes within the project execution workflow. Further, the predictive analytics module (not shown) is configured to identify one or more invalid and inconsistent data within the input data by comparing the input data with pre-stored data table. Further, the predictive analytics module (not shown) is configured to halt the execution of the project workflow if at least one of the project execution nodes are identified with one or more invalid and inconsistent data. Further, the predictive analytics module (not shown) is configured to determine a resolution action for correcting the invalid and inconsistent data using the pre-stored data table. Furthermore, the predictive analytics module (not shown) is configured to perform the resolution action at the corresponding project execution node and resume the execution of the project workflow upon completion of the resolution action.
[0052] Further, the computing unit 108 may include the communication module 212 configured to establish communications with the identified one or more third-party entities 106-1, …, 106-N for executing the plurality of functional tasks in real-time based on successful validation.
[0053] Further, the computing unit 108 may include the project monitoring module 214 configured to continuously monitor status of the project execution workflow at each of the one or more project execution nodes at real-time upon establishing the communication. Establishing the communication may include, but not limited to, identifying one or more delays and issues in each of the one or more project execution nodes. Further, the project monitoring module 214 is configured to extract raw project data associated with each of the one or more project execution nodes from corresponding one or more third-party entities 106-1, …, 106-N at real-time using a communication network 110. Further, the raw project data may include, but not limited to, JavaScript Object Notation (JSON), images, traditional database-like structure, NO-SQL, and the like. Further, the project monitoring module 214 is configured to transform the extracted raw project data into normalized project data using one or more data normalization models and a natural language processing model.
[0054] In an exemplary embodiment, by ensuring the proper extraction, transformation, and mapping of data across the various modules and source systems, the FDMS data flows and mapping component plays a crucial role in maintaining data integrity, consistency, and reliability, which is essential for the efficient and transparent management of the State Finance Commission's funds disbursement and financial operations.
[0055] In Resolution and Workflow Data Management: The FDMS extracts digitized records of fund allocation resolutions and decisions from the source database. The system transforms the raw resolution data into a standardized format, ensuring consistency with the workflow management module's data model. The transformed resolution data is then mapped to the appropriate tables in the destination database, linking it to user roles, access controls, and audit trail information.
[0056] In Procurement and Vendor Management Data Integration, the FDMS extracts vendor profiles, bid details, and contract award information from the "Tenders Haryana" e-procurement platform and internal RFQ processes. The extracted data is normalized and structured to align with the contract management, work order, and payment disbursement data models. The transformed vendor and procurement data is mapped to the corresponding tables in the FDMS database, enabling seamless integration across the modules.
[0057] In Project Execution and Monitoring Data Aggregation, the system extracts project planning, implementation, and progress tracking data from the administrative bodies (XEN, SDO, JE). The raw project data is transformed into a format that can be easily integrated with the work order management, milestone tracking, and reporting modules. The transformed project data is mapped to the appropriate tables in the FDMS database, ensuring a comprehensive view of the development works.
[0058] In Payment Disbursement and Banking Data Synchronization: The FDMS extracts payment-related data, such as invoices, approvals, and transaction details, from the integrated banking systems. The extracted banking data is transformed to match the payment disbursement, vendor, and general ledger data models used in the FDMS. The transformed payment data is mapped to the corresponding vendor profiles, work orders, and financial accounting tables in the FDMS database, maintaining data integrity and accuracy.
[0059] In Audit and Compliance Data Management, the system extracts audit findings, recommendations, and corrective actions from the online audit process. The raw audit data is transformed into a format that can be easily correlated with financial records, project details, and user activities. The transformed audit data is mapped to the appropriate tables in the FDMS database, enabling comprehensive tracking of compliance and transparency measures. The FDMS maintains a comprehensive logging mechanism that records all errors, exceptions, and system events, including the relevant contextual information (e.g., user actions, data inputs, system states). This detailed audit trail helps in the investigation of issues, root cause analysis, and compliance reporting. The logged data is also used to generate reports and analytics, enabling the identification of recurring problems and the implementation of preventive measures.
[0060] In Committed Liabilities and HR Data Integration, the FDMS extracts staff-related expense data, such as salaries and attendance, from the integrated Human Resource Management System (HRMS). The extracted HR data is transformed to align with the budget planning, fund allocation, and payroll processing data models used in the FDMS. The transformed committed liabilities data is mapped to the corresponding tables in the FDMS database, ensuring accurate financial forecasting and management.
[0061] In Asset Management and E-Auction Data Handling, the system extracts data related to created assets, their maintenance, and the e-auction process for asset disposal or reallocation. The raw asset data is transformed into a format that can be easily integrated with the financial records, project details, and revenue generation data. The transformed asset data is mapped to the appropriate tables in the FDMS database, maintaining a comprehensive view of the asset lifecycle and any associated financial implications.
[0062] In Reporting and Analytics Data Aggregation: The FDMS extracts data from all modules, including RFQs, works, payments, attendance, auctions, and audits, for reporting and analytics purposes. The extracted data is transformed and normalized to ensure consistency and integrity, enabling the generation of comprehensive reports and analytics. The transformed data is mapped to the appropriate tables in the FDMS database, ensuring the traceability and reliability of the information used for decision-making and stakeholder reporting.
[0063] Further, the computing unit 108 may include the predictive analytics module 216 configured to predict possible outcomes and expected errors associated with each of the one or more project execution nodes based on the monitoring using an artificial intelligence-based prediction model. Furthermore, the predictive analytics module 216 is configured to perform one or more actions at each of the one or more project execution nodes based on the predicted outcomes and errors. Performing one or more actions at each of the one or more project execution nodes based on the predicted outcomes and errors may include, but not limited to, performing rescheduling tasks, reallocating resources, implementing mitigation strategies to address delays, maintain project deadlines, analysing quality of project deliverables, overall satisfaction of stakeholders, regular inspection and testing, risk response planning, risk monitoring, automatically trigging alerts, generating reports, and dashboards.
[0064] Further, the predictive analytics module 216 may be configured to identify and assess risks associated with a plurality of projects. The risks may include, but not limited to, financial risk, an operational risk, a legal, compliance risk, and the like. Further, the predictive analytics module 216 may be configured to store geo-tagged images of completed work in the system 100 through, but not limited to, mobile application, web portal, and the like. Further, the predictive analytics module 216 may be configured to identify geo-codes of the completed work and determine the type of work and the Geo-codes of the workplace Whenever a new project is logged into the system 100. Further, the predictive analytics module 216 may be configured to detect similar work and similar locality. Further, the predictive analytics module 216 may be configured to display an error on determining the similar project and similar locality.
[0065] Further, in accordance with an embodiment of the present disclosure. The computing unit 108 may also include instruction execution modules, such as, but not limited to, the user and access module (not shown), and the predictive analytics module (not shown).
[0066] FIG. 3 illustrates an exemplary schematic diagram representation of a micro-container-based system 300 for managing project execution in a computing environment, in accordance with another embodiment of the present disclosure. The system 300 may include a plurality of specific requirements 316, a plurality of project execution nodes, a plurality of third-party entities 106-1, …, 106-N, an administrative body 322, an engineering body 320, and a combined dashboard 318.
[0067] The micro-container-based system 300 may include project execution nodes such as, but not limited to, an automated Request for Quotation (RFQ) generation node 302, a contract management node 304, a project creation and monitoring node 306, an integrated payroll management node 308, an attendance management node 310, an asset management node 312, and an e-auction node 314. Further, the micro-container-based system 300 may be configured to operate on the architecture which uses microservices deployed in plurality of microcontainers. Further, the one or more microcontainers may be configured to operate independently on one or more services. Further, the one or more services are associated with one or more project execution nodes.
[0068] Further, the automated Request for Quotation (RFQ) generation node 302 may be configured to generate an automatic Request for Quotation (RFQ) based on the specific requirement 316 from the administrative body 322. The specific requirement May 316 includes requirement for development work. Further, the automated Request for Quotation (RFQ) generation node 302 may be configured to incorporate logic and processes required for automatically generating Request for Quotations (RFQs). Further, the (RFQ) generation node 302 may include, but not limited to, all necessary software libraries, configurations, dependencies, and the like. Further, the (RFQ) generation node 302 container may ensure consistent performance across different environments.
[0069] Further, the contract management node 304 may be configured to maintain a database of registered third-party entities 106-1, …, 106-N such as, but not limited to, vendors, contractors, and profiles of respective vendors and contractors to utilize during the procurement and contract assignment processes. Further, the contract management node 304 may be configured to manage contracts and handle tasks such as, but not limited to, creation of contracts, tracking of contracts, and compliance monitoring of contracts, and the like. Further, the contract management node 304 may be configured to operate independently and efficiently by isolating it from other microcontainers. Further, contract management node 304 may be configured to assign the contract to a winning third-party entity 106 of the project execution workflow based on a vendor selection criterion. The vendor selection criterion is included within the specific requirement 316. Further, the contract management node 304 may be configured to generate contract documentation, work orders, and project tracking routes for the determined winning third-party entity 106 based on pre-defined rules and the plurality of functional tasks associated with the contract management node 304. Further, the contract management node 304 may be configured to transmit the generated contract documentation, work orders and project tracking routes to other project execution nodes for approval using a blockchain network. Furthermore, the contract management node 304 may be configured to update a database associated with the contract management node with the approved contract documentation, work orders and project tracking routes.
[0070] Further, the micro-container-based system 300 may include the project creation and monitoring node 306 configured to provide a centralized platform for the creation of development work and monitoring of development works. Further, project creation and monitoring node 306 may be configured to create projects and monitor ongoing projects. Further, project creation and monitoring node 306 may be configured to contain all relevant data and processes within a specific environment. Further, the project creation and monitoring node 306 may include engineering body 32 such as, but not limited to, an Executive Engineer (XEN), a Sub Divisional Officer (SDO), and a Junior Engineer (JE). Further, the project creation and monitoring node 306 may be configured enable the micro-container-based system 300 to track milestones of project, progress of project, and any delays or bottlenecks in project, facilitating timely interventions and issue resolution. Further, the project creation and monitoring node 306 may be configured to generate comprehensive reports on the status of ongoing projects, enabling data-driven decision-making and oversight.
[0071] Further, the micro-container-based system 300 may include the integrated payroll management node 308 configured to integrate the micro-container-based system 300 with banking systems to facilitate secure and timely disbursement of payments to one or more third party entities 106-1, …, 106-N such as, but not limited to, vendors, contractor and the like. Further, the integrated payroll management node 308 may be configured to implement a maker-checker approval workflow for the payment process, with the ability to integrate Digital Signature Certificates (DSCs) from the concerned authorities for enhanced security and non-repudiation. Further, the integrated payroll management node 308 may include an online audit module configured to provide transparency and accountability in the fund disbursement and project completion processes. Further, the integrated payroll management node 308 may be configured to manage payroll related tasks. Further, the integrated payroll management node 308 may be configured to integrate with other nodes to ensure correct handling of records and payments of one or more employees.
[0072] The FDMS integrates with banking systems to facilitate secure and timely disbursement of payments to vendors and contractors within a Double Accounting framework. This ensures all transactions are recorded independently in both a general ledger and subsidiary ledgers, providing a robust system of checks and balances. The system implements a maker-checker approval workflow for the payment process, with the ability to integrate digital signature certificates (DSCs) for enhanced security and non-repudiation. Additionally, an online audit module provides transparency and accountability in the fund’s disbursement and project completion processes. FDMS also records financial commitments, such as staff salaries and other expenses, in an integrated Human Resource Management System (HRMS). This ensures accurate tracking and management of committed liabilities, enabling better budget planning and forecasting. Further, the micro-container-based system 300 may include the attendance management node 310 configured to integrate the micro-container-based system 100 with a geo-tagged attendance tracking system to capture employee attendance data. Further, the attendance management node 310 may be configured to create a register of captured attendance. Further, the attendance data is then used to automate the salary calculation and processing, ensuring accurate and timely payments to the employees. The FDMS integrates with a geo-tagged attendance tracking system, which captures employee attendance data. This attendance data is then used to automate the salary calculation and processing, ensuring accurate and timely payments to the staff. Further, the attendance management node 310 may be configured to manage attendance tracking, secure data storage, and integrate with other project execution nodes.
[0073] Further, the micro-container-based system 300 may include the asset management node 312 configured to provide functionalities for managing the one or more assets created through the development works. Further, the asset management node 312 may be configured to manage physical and digital assets. Further, the asset management node 312 may be configured to incorporate asset related data and processes within a specific environment. The system provides functionalities for managing the assets created through the development works, including the ability to conduct e- auctions for asset disposal or reallocation.
[0074] Further, the micro-container-based system 300 may include the e-auction node 314 configured to conduct e-auction for asset disposal or reallocation. Further, the e-auction node 314 may be configured to manage e-auction processes, such as, but not limited to, bid submissions, evaluations, selections, and the like.
[0075] Further, the micro-container-based system 300 may include the dashboard 318 consolidates data from plurality of modules, including RFQs, works, payments, attendance, auctions, and audits. Further, the dashboard 318 may be configured to provide advanced reporting and analytical capabilities, enabling the micro-container-based system 300 to monitor the overall system performance, identify trends, and make data-driven decisions. The FDMS offers a comprehensive dashboard that consolidates data from various modules, including RFQs, works, payments, attendance, auctions, and audits. This dashboard 318 provides advanced reporting and analytical capabilities, enabling the State Finance Commission to monitor the overall system performance, identify trends, and make data-driven decisions.
[0076] FIG. 4 illustrates an exemplary flow diagram representation of a micro-container-based method for managing project execution in a computing environment, in accordance with an embodiment of the present disclosure. As illustrated in FIG. 4, the following steps may be implemented.
[0077] At step 402, the method 400 includes receiving a request for creating and managing a project workflow for a specific requirement from one or more user devices. At step 404, the method 400 includes determining one or more project execution nodes for the received request based on the specific requirements. The each of the one or more project execution nodes correspond to a plurality of functional tasks to be performed. Further, each of the determined one or more project execution nodes correspond to containerized microservices. At step 406, the method 400 includes determining hierarchy, and priority of execution for each of the determined one or more project execution nodes. At step 408, the method 400 includes identifying one or more third-party entities 106-1, …, 106-N for performing the plurality of functional tasks corresponding to the determined one or more project execution nodes based on the determined hierarchy and priority of execution. At step 410, the method 400 includes generating a project execution workflow based on the one or more project execution nodes, the determined hierarchy and priority of execution, and the identified one or more third-party entities 106-1, …, 106-N. At step 412, the method 400 includes validating the generated project execution workflow for identifying one or more risks, exceptions and fall-back scenarios using an artificial intelligence-based exception handling model. At step 414, the method 400 includes establishing communications with the identified one or more third-party entities 106-1, …, 106-N for executing the plurality of functional tasks in real-time based on successful validation. At step 416, the method 400 includes continuously monitoring status of the project execution workflow at each of the one or more project execution nodes at real-time upon establishing the communication. At step 418, the method 400 includes predicting possible outcomes and expected errors associated with each of the one or more project execution nodes based on the monitoring using an artificial intelligence-based prediction model. At step 420, the method 400 includes performing one or more actions at each of the one or more project execution nodes based on the predicted outcomes and errors.
Further, the method 400 may include one or more project execution nodes, such as, but not limited to, Request for Quotation (RFQ) generation node, contract management node, project creation and monitoring node, an integrated payroll management node, an attendance management node, an asset management node, an e-auction node, and the like. Further, the method 400 may include authenticating one or more users accessing the one or more project execution nodes based on user profiles, login credentials, and access privileges. Further, in the method 400, continuously monitoring the status of the project execution workflow at each of the one or more project execution nodes at real-time may include, but not limited to, extracting raw project data associated with each of the one or more project execution nodes from corresponding one or more third-party entities at real-time using a communication network, transforming the extracted raw project data into normalized project data using one or more data normalization models and a natural language processing model, and the like. Further, in the method 400, the contract management node, the method may include, but not limited to, determining a winning third-party entity of the project execution workflow based on a vendor selection criterion, wherein the vendor selection criterion is comprised within the specific requirements, generating contract documentation, work orders, and project tracking routes for the determined winning third-party entity based on pre-defined rules and the plurality of functional tasks associated with the contract management node, transmitting the generated contract documentation, work orders and project tracking routes to other project execution nodes for approval using a blockchain network, updating a database associated with the contract management node with the approved contract documentation, work orders and project tracking routes, and the like.
[0078] Further, in the method 400, the attendance management node may include, but not limited to, capturing real-time employee attendance data and location data associated with an employee deployed in the project using a geo-tagged system, generating an employee payroll data based on the captured real-time employee attendance data and the location data, and the like. Further, in the method 400, validating the generated project execution workflow for identifying one or more risks, exceptions and fall-back scenarios using an artificial intelligence-based exception handling model may include, but not limited to, validating input data associated with each of the one or more project execution nodes within the project execution workflow, identifying one or more invalid and inconsistent data within the input data by comparing the input data with pre-stored data table, halting the execution of the project workflow if at least one of the project execution nodes are identified with one or more invalid and inconsistent data, determining a resolution action for correcting the invalid and inconsistent data using the pre-stored data table, performing the resolution action at the corresponding project execution node, resuming the execution of the project workflow upon completion of the resolution action, and the like. Further, the method 400 may include, determining one or more exceptions while executing the plurality of functional tasks. The one or more exceptions include, but not limited to, database connection failures, network timeouts, API integration issues, other runtime exceptions, and the like. Further, the method 400 may also include resolving each of the determined one or more exceptions by executing corrective actions for each of the one or more exceptions. Further, the method 400 may include a disaster recovery and fallback management module configured to perform regular backups of databases, replicating critical project data, and restoring the system to a good state in event of a major system failure. Further, in the method 400, validating the generated project execution workflow for identifying one or more risks, exceptions and fall-back scenarios using an artificial intelligence-based exception handling model may include, but not limited to, analysing available funds, budget constraints, and resource requirements for ongoing projects, analysing actual costs incurred for each project and comparing the actual costs with budgeted costs, allocating resources to each of the one or more project execution nodes based on the analysed available funds, the budget constraints, and the resource requirements using optimization algorithms and models, validating the generated project execution workflow based on the allocated resources and the comparison of the actual costs with the budgeted costs, and the like.
Further, the method 400 may include prioritizing critical projects and shifting the resources between the projects based on needs upon successful validation of the project execution workflow. Further, in the method 400, continuously monitoring status of the project execution workflow at each of the one or more project execution nodes at real-time upon establishing the communication may include identifying one or more delays and issues in each of the one or more project execution nodes. Further, in the method 400, predicting possible outcomes and expected errors associated with each of the one or more project execution nodes based on the monitoring using an artificial intelligence-based prediction model may include identifying and assessing risks associated with a plurality of projects, wherein the risks comprise a financial risk, an operational risk, a legal and compliance risk. Furthermore, the method 400, performing one or more actions at each of the one or more project execution nodes based on the predicted outcomes and errors may include, but not limited to, performing rescheduling tasks, reallocating resources, implementing mitigation strategies to address delays, maintain project deadlines, analysing quality of project deliverables, overall satisfaction of stakeholders, regular inspection and testing, risk response planning, risk monitoring, automatically trigging alerts, generating reports, dashboards, and the like.
[0079] FIG. 5 illustrates an exemplary snapshot diagram representation of Graphical User Interface (GUI) of a micro-container-based system 100 for managing project execution in a computing environment depicting districts performance, in accordance with another embodiment of the present disclosure. Further, the micro-container-based system 100 may be configured to create projects and monitor ongoing projects using the project creation and monitoring node 306. According to FIG. 5, the micro-container-based system 100 may be configured to manage and track resources allocated to one or more development works. Further, the micro-container-based system 100 may be configured to track the performance of plurality of districts based on the resources allocated for the development work to one or more districts. Further, the micro-container-based system 100 may be configured to generate graphical analysis of the resources allocated to one or more districts. Further, the resources may include, but not limited to, total grant allocated, total grant used, total balance. And the like.
[0080] FIG. 6 illustrates an exemplary snapshot diagram representation of Graphical User Interface (GUI) of a micro-container-based system 100 for managing project execution in a computing environment depicting top bidding items, in accordance with another embodiment of the present disclosure.
[0081] FIG. 7 illustrates an exemplary snapshot diagram representation of Graphical User Interface (GUI) of a micro-container-based system 100 for managing project execution in a computing environment depicting auctions status, in accordance with another embodiment of the present disclosure. Further, the micro-container-based system 300 may include the e-auction node 314 configured to conduct e-auction for asset disposal or reallocation. Further, the e-auction node 314 may be configured to manage e-auction processes, such as, but not limited to, bid submissions, evaluations, selections, and the like. Further, the e-auction node 314 may be configured to generate, but not limited to, auction ID, auction name, and the like. Further, the e-auction node 314 may be configured to determine opening dates and closing dates of the auctions.
[0082] The one or more project execution nodes includes automated Request for Quotation (RFQ) generation node, contract management node, project creation and monitoring node, an integrated payroll management node, an attendance management node, an asset management node, and an e-auction node.
[0083] The method includes authenticating one or more users accessing the one or more project execution nodes based on user profiles, login credentials, and access privileges.
[0084] In continuously monitoring the status of the project execution workflow at each of the one or more project execution nodes at real-time, the method includes extracting raw project data associated with each of the one or more project execution nodes from corresponding one or more third-party entities at real-time using a communication network; and transforming the extracted raw project data into normalized project data using one or more data normalization models and a natural language processing model.
[0085] In the contract management node, the method comprises: determining a winning third-party entity of the project execution workflow based on a vendor selection criterion, wherein the vendor selection criterion is comprised within the specific requirements; generating contract documentation, work orders, and project tracking routes for the determined winning third-party entity based on pre-defined rules and the plurality of functional tasks associated with the contract management node; transmitting the generated contract documentation, work orders and project tracking routes to other project execution nodes for approval using a blockchain network; and updating a database associated with the contract management node with the approved contract documentation, work orders and project tracking routes.
[0086] In the attendance management node, the method includes capturing, real-time employee attendance data and location data associated with an employee deployed in the project using a geo-tagged system; and generating an employee payroll data based on the captured real-time employee attendance data and the location data.
[0087] In validating the generated project execution workflow for identifying one or more risks, exceptions and fall-back scenarios using an artificial intelligence-based exception handling model method includes validating input data associated with each of the one or more project execution nodes within the project execution workflow; identifying one or more invalid and inconsistent data within the input data by comparing the input data with pre-stored data table; halting the execution of the project workflow if at least one of the project execution nodes are identified with one or more invalid and inconsistent data; determining a resolution action for correcting the invalid and inconsistent data using the pre-stored data table; performing the resolution action at the corresponding project execution node; and resuming the execution of the project workflow upon completion of the resolution action.
[0088] The method includes determining one or more exceptions while executing the plurality of functional tasks, wherein the one or more exceptions include database connection failures, network timeouts, API integration issues, and other runtime exceptions; and resolving each of the determined one or more exceptions by executing corrective actions for each of the one or more exceptions.
[0089] The method includes performing regular backups of databases, replicating critical project data, and restoring the system to a good state in event of a major system failure.
[0090] In validating the generated project execution workflow for identifying one or more risks, exceptions and fall-back scenarios using an artificial intelligence-based exception handling model method includes analysing available funds, budget constraints, and resource requirements for ongoing projects; analyses actual costs incurred for each project and comparing the actual costs with budgeted costs; allocating resources to each of the one or more project execution nodes based on the analysed available funds, the budget constraints, and the resource requirements using optimization algorithms and models; and validating the generated project execution workflow based on the allocated resources and the comparison of the actual costs with the budgeted costs. The FDMS tracks and analyses the actual costs incurred for each project and compares them with the budgeted amounts. It employs cost optimization techniques, such as value engineering, procurement process optimization, and vendor/contractor performance management, to identify and implement cost-saving opportunities. This could include negotiating better rates with vendors, rationalizing procurement processes, or identifying and mitigating cost overruns.
[0091] The FDMS monitors the quality of project deliverables and the overall satisfaction of stakeholders, such as the government agencies, beneficiaries, and the general public. It implements quality assurance and control measures, including regular inspections, testing, and feedback mechanisms, to ensure that the projects meet the desired standards and requirements. This could involve implementing quality control checkpoints, providing training to project teams, and incorporating feedback loops to continually improve the quality of project outcomes.
[0092] The method includes prioritizing critical projects and shifting the resources between the projects based on needs upon successful validation of the project execution workflow.
[0093] In continuously monitoring status of the project execution workflow at each of the one or more project execution nodes at real-time upon establishing the communication method includes: identifying one or more delays and issues in each of the one or more project execution nodes.
[0094] In predicting possible outcomes and expected errors associated with each of the one or more project execution nodes based on the monitoring using an artificial intelligence-based prediction model method includes: identifying and assessing risks associated with a plurality of projects, wherein the risks comprise a financial risk, an operational risk, a legal and compliance risk. The FDMS proactively identifies and assesses the risks associated with various projects, such as financial, operational, or legal risks. It employs risk optimization techniques, including risk assessment, risk response planning, and risk monitoring, to minimize the impact of risks on project execution and funds disbursement. This could involve implementing contingency plans, diversifying vendor/contractor portfolios, or establishing robust risk monitoring and reporting mechanisms.
[0095] The FDMS leverages the comprehensive data and analytics capabilities to make informed, data-driven decisions regarding project optimization. It uses advanced analytics, predictive modelling, and scenario analysis to forecast project outcomes, identify optimization opportunities, and support decision-making. This could involve generating customized reports, dashboards, and visualizations to provide insights and recommendations for project optimization. By incorporating these project optimization strategies, the FDMS helps the State Finance Commission maximize the impact and efficiency of its fund’s disbursement, ensuring that the available resources are used effectively to achieve the desired developmental goals and outcomes.
[0096] In performing one or more actions at each of the one or more project execution nodes based on the predicted outcomes and errors method includes: performing rescheduling tasks, reallocating resources, implementing mitigation strategies to address delays, maintain project deadlines, analysing quality of project deliverables, overall satisfaction of stakeholders, regular inspection and testing, risk response planning, risk monitoring, automatically trigging alerts, generating reports, and dashboards. The error handling and exception management module of the FDMS is integrated with an alerting and notification system. This system automatically triggers alerts, such as email notifications or SMS alerts, to the relevant stakeholders (e.g., system administrators, finance officers) in the event of critical errors or exceptions. This enables quick response and resolution of issues, minimizing the impact on the FDMS operations and the State Finance Commission's financial management processes.
[0097] The purpose of the Funds Disbursement and Management System (FDMS) system is to design and implement a robust, modular, and cloud-based software solution that streamlines the State Finance Commission's financial management processes, including automated RFQ generation, secure payment workflows, project tracking, and integrated payroll management, leveraging cutting-edge technologies such as microservices architecture, containerization, and business intelligence analytics to drive operational efficiency, data-driven decision-making, and regulatory compliance. The Funds Disbursement and Management System (FDMS) is a comprehensive system designed to modernize the financial management and project execution processes of the State Finance Commission.
[0098] Developed using a microservices-based architecture and containerized deployment, the FDMS comprises modular components for automated RFQ generation, secure contract management, real-time project monitoring, maker-checker payment workflows with DSC integration, attendance-based payroll calculation, and advanced financial reporting and analytics.
[0099] The system uses technologies such as PHP, HTML/CSS 5 and MySQL to ensure scalability, reliability, and seamless integration with the Commission's existing IT infrastructure. Adhering to industry-standard security protocols and compliance guidelines, the FDMS empowers the Commission to streamline operations, enhance transparency, and improve resource allocation in support of the state's development initiatives.
[0100] Funds Disbursement and Management System (FDMS) for the State Finance Commission is a novel solution designed to address the shortcomings of conventional financial management systems. This system encompasses a suite of features aimed at streamlining fund allocation, disbursement, and monitoring processes while ensuring enhanced security, prompt payments, efficient budget management, and improved project oversight. The FDMS facilitates the seamless generation and awarding of Requests for Quotations (RFQs), ensuring transparency and efficiency in vendor selection processes. The FDMS enables the creation and real-time monitoring of project works, allowing for proactive management of project timelines and resource utilization. The FDMS ensures prompt and secure disbursement of payments to vendors, contractors, and staff, thereby fostering positive relationships and improving project outcomes: Enhanced security measures, such as the maker and checker concept integrated with Digital Signature Certificate (DSC) technology, safeguard against unauthorized or fraudulent activities, ensuring the integrity of financial transactions and approvals. The system effectively manages committed liabilities, including staff salaries, chowkidars, tube well operators, and other personnel, facilitating accurate payroll calculations and budget planning. An integrated mobile application allows for seamless tracking of staff attendance, enabling accurate salary calculations based on attendance records and enhancing transparency in payroll management. The FDMS offers several benefits, including enhanced security, timely payments, optimized budget management, and improved project oversight. By providing real-time monitoring capabilities, the system enables proactive problem-solving, ensuring successful project completion and contributing to the state's development goals. Moreover, its user-friendly interface and comprehensive feature set make it an indispensable tool for state finance commissions seeking to modernize and streamline their financial management processes.
[0101] In operation, for receiving a request, docker container with a web server such as, for example, Nginx or Apache may be used. The system may receive project creation requests from user devices via API calls. The container may be configured to accept requests in a specific format (e.g., JSON) and translate them into messages understood by other microservices. The Workflow Orchestrator may be docker container with a workflow engine such as for example, Apache Airflow or Luigi. This part analyses project requirements, defines execution nodes (microservices), assigns hierarchy and priority, identifies suitable third-party services, and generates the project workflow. This container would run the chosen workflow engine to process project data, manage microservice dependencies, and generate the workflow definition. The risk assessment engine may be a docker container with a machine learning library such as for example, but not limited to, TensorFlow or PyTorch. This part analyses the generated workflow to identify potential risks, exceptions, and fall-back scenarios using an AI model. The container may use the AI model trained to analyse workflow data and predict potential issues. The container may communicate risks and suggested actions to the project management microservice. The Project Monitoring may be two Docker containers. One for the monitoring tool (for example, but not limited to, Prometheus) and One for the visualization library (for example, but not limited to, Grafana). This part may continuously monitor the execution status of each project node in real-time and provide a visual dashboard for project progress and potential issues. This container may scrape data from each microservice about task execution. This container may visualize this data, providing a central monitoring dashboard. The Predictive Analytics (Prediction Engine) may be a Docker container with a machine learning library for example, but not limited to, TensorFlow or PyTorch. This part analyses monitoring data to predict potential outcomes and errors for each node using an AI model, and suggests corrective actions based on predictions. Similar to the Validation microservice, this container may run an AI model to analyse real-time monitoring data and predict potential issues. It would communicate predictions and suggested actions to the project management microservice.
[0102] In an embodiment, a container orchestration tool such as for example, but not limited to, Kubernetes may be used to manage the deployment, scaling, and lifecycle of all microservice containers. The system may implement secure communication protocols (e.g., HTTPS) and access control mechanisms (e.g., JWT) within the container network to restrict unauthorized access between microservices. Further, the system may use a central data store (e.g., relational database or NoSQL database) accessible by all microservices to manage project data and facilitate communication.
[0103] This breakdown exemplifies how each functionality can be implemented as a separate, containerized microservice. The specific containerization technologies and tools may be chosen based on project requirements and resource availability.
[0104] Microservices are implemented using containers, which are software packages that bundle an application with its dependencies and configuration. These containers run on physical hardware (processors and memory).
[0105] Microservices address a specific problem of managing complex project workflows. This is achieved through modularity, scalability, and integration with AI for real-time monitoring and prediction. Each microservice is a self-contained unit responsible for a specific task. This allows for independent development, deployment, and scaling of functionalities. Individual microservices can be scaled up or down based on resource requirements. New functionalities can be easily introduced by adding new microservices without modifying existing ones. This allows the system to adapt to changing project needs. Issues in one microservice are isolated and less likely to cascade to other parts of the system. This enhances system reliability and ensures project execution continues even if individual services encounter problems.
[0106] The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open-ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
[0107] Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the embodiments of the present invention are intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.
, Claims:CLAIMS
We claim:
1. A micro-container-based system (100) for managing project execution in a computing environment, the system comprising:
one or more hardware processors (202); and
a memory (204) coupled to the one or more hardware processors, wherein the memory (204) comprises a plurality of modules (110) in the form of programmable instructions executable by the one or more hardware processors (202), wherein the plurality of modules (110) comprises:
a request receiver module (206) configured to receive a request for creating and managing a project workflow for a specific requirement from one or more user devices (102);
a container-based project management module (208) configured to:
determine one or more project execution nodes for the received request based on the specific requirements, wherein each of the one or more project execution nodes correspond to a plurality of functional tasks to be performed, wherein each of the determined one or more project execution nodes correspond to containerized microservices;
determine hierarchy, and priority of execution for each of the determined one or more project execution nodes;
identify one or more third-party entities (106-1, …, 106-N) for performing the plurality of functional tasks corresponding to the determined one or more project execution nodes based on the determined hierarchy and priority of execution;
generate a project execution workflow based on the one or more project execution nodes, the determined hierarchy and priority of execution, and the identified one or more third-party entities (106-1, …, 106-N);
a validation module (210) configured to:
validate the generated project execution workflow for identifying one or more risks, exceptions and fall-back scenarios using an artificial intelligence-based exception handling model;
a communication module (212) configured to:
establish communications with the identified one or more third-party entities (106-1, …, 106-N) for
executing the plurality of functional tasks in real-time based on successful validation;
a project monitoring module (214) configured to continuously monitor status of the project execution workflow at each of the one or more project execution nodes at real-time upon establishing the communication; and
a predictive analytics module (216) configured to:
predict possible outcomes and expected errors associated with each of the one or more project execution nodes based on the monitoring using an artificial intelligence-based prediction model; and
perform one or more actions at each of the one or more project execution nodes based on the predicted outcomes and errors.

2. The system as claimed in claim 1, wherein the one or more project execution nodes comprises automated Request for Quotation (RFQ) generation node (302), contract management node (304), project creation and monitoring node (306), an integrated payroll management node (308), an attendance management node (310), an asset management node (312), and an e-auction node (314).
3. The system as claimed in claim 1, further comprising a user and Access Management module configured to:
authenticate one or more users accessing the one or more project execution nodes based on user profiles, login credentials, and access privileges.
4. The system as claimed in claim 1, wherein in continuously monitoring the status of the project execution workflow at each of the one or more project execution nodes at real-time, the project monitoring module (214) is configured to:
extract raw project data associated with each of the one or more project execution nodes from corresponding one or more third-party entities (106-1, …, 106-N) at real-time using a communication network; and
transform the extracted raw project data into normalized project data using one or more data normalization models and a natural language processing model.

5. The system as claimed in claim 2, wherein in contract management node (304), the container-based project management module (208) is configured to:
determine a winning third-party entity (106-1, …, 106-N) of the project execution workflow based on a vendor selection criterion, wherein the vendor selection criterion is comprised within the specific requirements;
generate contract documentation, work orders, and project tracking routes for the determined winning third-party entity (106-1, …, 106-N) based on pre-defined rules and the plurality of functional tasks associated with the contract management node (304);
transmit the generated contract documentation, work orders and project tracking routes to other project execution nodes for approval using a blockchain network; and
update a database associated with the contract management node with the approved contract documentation, work orders and project tracking routes.

6. The system as claimed in claim 1, wherein in the attendance management node (310), the project monitoring module (214) configured to:
capture, real-time employee attendance data and location data associated with an employee deployed in the project using a geo-tagged system; and
generate an employee payroll data based on the captured real-time employee attendance data and the location data.

7. The system as claimed in claim 1, wherein in validating the generated project execution workflow for identifying one or more risks, exceptions and fall-back scenarios using an artificial intelligence-based exception handling model, the predictive analytics module (216) is configured to:
validate input data associated with each of the one or more project execution nodes within the project execution workflow;
identify one or more invalid and inconsistent data within the input data by comparing the input data with pre-stored data table;
halt the execution of the project workflow if at least one of the project execution nodes are identified with one or more invalid and inconsistent data;
determine a resolution action for correcting the invalid and inconsistent data using the pre-stored data table;
perform the resolution action at the corresponding project execution node; and
resume the execution of the project workflow upon completion of the resolution action.

8. The system as claimed in claim 1, further comprising the Error Handling and Exception Handling module configured to:
determine one or more exceptions while executing the plurality of functional tasks, wherein the one or more exceptions comprise database connection failures, network timeouts, API integration issues, and other runtime exceptions; and
resolve each of the determined one or more exceptions by executing corrective actions for each of the one or more exceptions.

9. The system as claimed in claim 1, further comprising a disaster recovery and fallback management module configured to:
perform regular backups of databases, replicating critical project data; and
restore the system to a healthy state in event of a system failure.

10. The system as claimed in claim 1, wherein in validating the generated project execution workflow for identifying one or more risks, exceptions and fall-back scenarios using an artificial intelligence-based exception handling model, the predictive analytics module (216) is configured to:
analyze available funds, budget constraints, and resource
requirements for ongoing projects;
analyze actual costs incurred for each project and comparing the actual costs with budgeted costs;
allocate resources to each of the one or more project execution nodes based on the analyzed available funds, the budget constraints, and the resource requirements using optimization algorithms and models; and
validate the generated project execution workflow based on the allocated resources and the comparison of the actual costs with the budgeted costs.

11. The system as claimed in claim 10, further the container-based project management module (208) configured to:
prioritize critical projects and shifting the resources between the projects based on needs upon successful validation of the project execution workflow.
12. The system as claimed in claim 1, wherein continuously monitoring status of the project execution workflow at each of the one or more project execution nodes at real-time upon establishing the communication comprises:
identify one or more delays and issues in each of the one or more project execution nodes.

13. The system as claimed in claim 1, wherein predicting possible outcomes and expected errors associated with each of the one or more project execution nodes based on the monitoring using an artificial intelligence-based prediction model, the predictive analytics module (216) configured to:
identify and assess risks associated with a plurality of projects, wherein the risks comprise a financial risk, an operational risk, a legal and compliance risk.

14. The method as claimed in claim 1, wherein in performing one or more actions at each of the one or more project execution nodes based on the predicted outcomes and errors comprises:
performing rescheduling tasks, reallocating resources, implementing mitigation strategies to address delays, maintain project deadlines, analysing quality of project deliverables, overall satisfaction of stakeholders, regular inspection and testing, risk response planning, risk monitoring, automatically trigging alerts, generating reports, and dashboards.

15. A micro-container-based method for managing project execution in a computing environment, the method comprising:
receiving a request for creating and managing a project workflow for a specific requirement from one or more user devices (102-1, …, 102-N);
determining one or more project execution nodes for the received request based on the specific requirements, wherein each of the one or more project execution nodes correspond to a plurality of functional tasks to be performed, wherein each of the determined one or more project execution nodes correspond to containerized microservices;
determining hierarchy, and priority of execution for each of the determined one or more project execution nodes;
identifying one or more third-party entities (106-1, …, 106-N) for performing the plurality of functional tasks corresponding to the determined one or more project execution nodes based on the determined hierarchy and priority of execution;
generating a project execution workflow based on the one or more project execution nodes, the determined hierarchy and priority of execution, and the identified one or more third-party entities (106-1, …, 106-N);
validating the generated project execution workflow for identifying one or more risks, exceptions and fall-back scenarios using an artificial intelligence-based exception handling model;
establishing communications with the identified one or more third-party entities for executing the plurality of functional tasks in real-time based on successful validation;
continuously monitoring status of the project execution workflow at each of the one or more project execution nodes at real-time upon establishing the communication;
predicting possible outcomes and expected errors associated with each of the one or more project execution nodes based on the monitoring using an artificial intelligence-based prediction model; and
performing one or more actions at each of the one or more project execution nodes based on the predicted outcomes and errors.

16. The method as claimed in claim 15, wherein the one or more project execution nodes comprises automated Request for Quotation (RFQ) generation node (302), contract management node (304), project creation and monitoring node (306), an integrated payroll management node (308), an attendance management node (310), an asset management node (312), and an e-auction node (314).

17. The method as claimed in claim 15, further comprising:
authenticating one or more users accessing the one or more project execution nodes based on user profiles, login credentials, and access privileges.

18. The method as claimed in claim 15, wherein continuously monitoring the status of the project execution workflow at each of the one or more project execution nodes at real-time comprises:
extracting raw project data associated with each of the one or more project execution nodes from corresponding one or more third-party entities at real-time using a communication network; and
transforming the extracted raw project data into normalized project data using one or more data normalization models and a natural language processing model.

19. The method as claimed in claim 16, wherein in the contract management node, the method comprises:
determining a winning third-party entity of the project execution workflow based on a vendor selection criterion, wherein the vendor selection criterion is comprised within the specific requirements;
generating contract documentation, work orders, and project tracking routes for the determined winning third-party entity (106-1, …, 106-N) based on pre-defined rules and the plurality of functional tasks associated with the contract management node (304);
transmitting the generated contract documentation, work orders and project tracking routes to other project execution nodes for approval using a blockchain network; and
updating a database associated with the contract management node (304) with the approved contract documentation, work orders and project tracking routes.

20. The method as claimed in claim 15, wherein in the attendance management node, the method comprises:
capturing, real-time employee attendance data and location data associated with an employee deployed in the project using a geo-tagged system; and
generating an employee payroll data based on the captured real-time employee attendance data and the location data.

21. The method as claimed in claim 15, wherein validating the generated project execution workflow for identifying one or more risks, exceptions and fall-back scenarios using an artificial intelligence-based exception handling model comprises:
validating input data associated with each of the one or more project execution nodes within the project execution workflow;
identifying one or more invalid and inconsistent data within the input data by comparing the input data with pre-stored data table;
halting the execution of the project workflow if at least one of the project execution nodes are identified with one or more invalid and inconsistent data;
determining a resolution action for correcting the invalid and inconsistent data using the pre-stored data table;
performing the resolution action at the corresponding project execution node; and
resuming the execution of the project workflow upon completion of the resolution action.

22. The method as claimed in claim 15, further comprising:
determining one or more exceptions while executing the plurality of functional tasks, wherein the one or more exceptions comprise database connection failures, network timeouts, API integration issues, and other runtime exceptions; and
resolving each of the determined one or more exceptions by executing corrective actions for each of the one or more exceptions.

23. The method as claimed in claim 15, further comprising a disaster recovery and fallback management module configured to:
performing regular backups of databases, replicating critical project data, and restoring the system to a good state in event of a major system failure.

24. The method as claimed in claim 15, wherein validating the generated project execution workflow for identifying one or more risks, exceptions and fall-back scenarios using an artificial intelligence-based exception handling model comprises:
analysing available funds, budget constraints, and resource
requirements for ongoing projects;
analyses actual costs incurred for each project and comparing the actual costs with budgeted costs;
allocating resources to each of the one or more project execution nodes based on the analysed available funds, the budget constraints, and the resource requirements using optimization algorithms and models; and
validating the generated project execution workflow based on the allocated resources and the comparison of the actual costs with the budgeted costs.

25. The method as claimed in claim 10, further comprising:
prioritizing critical projects and shifting the resources between
the projects based on needs upon successful validation of the project execution workflow.

26. The method as claimed in claim 1, wherein continuously monitoring status of the project execution workflow at each of the one or more project execution nodes at real-time upon establishing the communication comprises:
identifying one or more delays and issues in each of the one or more project execution nodes.

27. The method as claimed in claim 15, wherein predicting possible outcomes and expected errors associated with each of the one or more project execution nodes based on the monitoring using an artificial intelligence-based prediction model comprises:
identifying and assessing risks associated with a plurality of projects, wherein the risks comprise a financial risk, an operational risk, a legal and compliance risk.

28. The method as claimed in claim 15, wherein performing one or more actions at each of the one or more project execution nodes based on the predicted outcomes and errors comprises:
performing rescheduling tasks, reallocating resources, implementing mitigation strategies to address delays, maintain project deadlines, analysing quality of project deliverables, overall satisfaction of stakeholders, regular inspection and testing, risk response planning, risk monitoring, automatically trigging alerts, generating reports, and dashboards.