DESC:CROSS REFERENCE TO RELATED APPLICATIONS
[0001] The following patent application claims priority from an Indian Patent Application having application number 202241018942, filed on March 30, 2022, incorporated by reference herein.

TECHNICAL FIELD
[0002] The present disclosure relates to the field of management and specifically to the field of optimizing complex problems by automatically generating Dynamic Models of systems and then using them for system optimization.

BACKGROUND
[0003] The following description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0004] From an engineering standpoint, it is generally known that complex systems are difficult to control and analyze, especially the systems in the field of logistics and manufacturing. There are difficulties in comprehending from the standpoint of physics, computing, and management, since the complexity of such systems varies from with respect to time as well as the system.
[0005] There is, therefore, a need to provide a system and a method for automodelling and optimization of complex operations of such complex systems.
[0006] Further, these optimization systems use models (digital twins) to represent the system behaviour with such models taking considerable time, effort and cost to create and maintain.
[0007] There is, therefore a need to provide an approach to automatically create and maintain the models (or digital twins) significantly reducing the time, effort and cost of creation and maintenance of such models.
[0008] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.

OBJECTS OF THE INVENTION
[0009] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0010] It is an object of the present disclosure to provide a system for automodelling and optimization of complex operations.
[0011] It is an object of the present disclosure to provide a method for automodelling and optimization of complex operations.
[0012] It is an object of the present disclosure to provide a system for storing and accessing a historical asset data, corresponding to the assets of the system, as a time series asset data.
[0013] It is an object of the present disclosure to provide a system for pre-processing of a historical asset data into a processed time series data.
[0014] It is an object of the present disclosure to provide a dynamic automatic model generator (DAM-G) for generating analysed processed time series data.
[0015] It is an object of the present disclosure to provide a dynamic automatic model generator (DAM-G) for creating a generated model based on the analysed processed time series data.
[0016] It is an object of the present disclosure to provide an optimization system for generating optimization recommendations based on a generated model.
[0017] It is an object of the present disclosure to provide an operation management system for generating an advanced operation plan with an optimized operating efficiency based on the generated model.

SUMMARY
[0018] The present disclosure relates to the field of management and specifically to the field of optimizing complex problems by automatically generating Dynamic Models of systems and then optimizing them.
[0019] In an embodiment of the present disclosure, a system for automodelling and optimization of complex operations is disclosed, The system largely comprises a data pre-processing unit, a dynamic automatic model generator (DAM-G), an optimization system, and an operation management system. The data pre-processing unit may access and pre-process a historical asset data into a processed time series data, wherein the historical asset data corresponds to one or more assets of the system. The dynamic automatic model generator (DAM-G) may apply one or more analysing techniques to the processed time series data for generating an analysed processed time series data. The DAM-G may further create a generated model based on the analysed processed time series data. The optimization system may generate and then send one or more optimization recommendations based on the generated model to the operation management system for further processing and/or decision making. The operation management system may then generate an advanced operation plan with an optimized operating efficiency based on the generated model.
[0020] In another embodiment of the present disclosure, a method for automodelling and optimization of complex operations is disclosed. The method comprises the steps of: storing, an asset data corresponding to one or more assets as a historical asset data; accessing, the historical asset data as a time series asset data; pre-processing, by a data pre-processing unit, the time series asset data into a processed time series data; applying, by a dynamic automatic model generator (DAM-G), one or more analysing techniques to the processed time series data for generating an analysed processed time series data; creating, by the DAM-G, a generated model based on the analysed processed time series data; generating, by an optimization system, one or more optimization recommendations based on the generated model; sending, by the optimization system, the one or more optimization recommendations to an operation management system; and generating, by the operation management system, an advanced operation plan with an optimized operating efficiency based on the generated model.
[0021] In an aspect, the present disclosure provides a system and method for automodelling and optimization of complex operations to provide optimised management for highly complex data-rich operational systems. Utilising a model of the system being managed as a key input for multi-objective optimisation that allows for a rapid determination of potentially conflicting recommendations that may be enacted on by an Operational Management System, whereby the Model of the system may be automatically created and updated by a Dynamic-Auto-Modelling Generator (DAM-G) using processed historical asset data leading to a significantly reduced effort and cost for the deployment and operation of the system leading to a reduction in operational waste for the system operator.

BRIEF DESCRIPTION OF DRAWINGS
[0022] The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure. The diagrams are for illustration only, which thus is not a limitation of the present disclosure.
[0023] In the figures, similar components and/or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label with a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.
[0024] FIG. 1A illustrates a system 100 for automodelling and optimization of complex operations, in accordance with an embodiment of the present invention.
[0025] FIG. 1B illustrates an exemplary method 150 for automodelling and optimization of complex operations, in accordance with an embodiment of the present invention, to elaborate upon its working.
[0026] FIG. 2 illustrates an exemplary computer system 200 in which or with which embodiments of the present disclosure may be implemented.
[0027] The foregoing shall be more apparent from the following more detailed description of the disclosure.

DETAILED DESCRIPTION
[0028] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.
[0029] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.
[0030] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
[0031] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[0032] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive—in a manner similar to the term “comprising” as an open transition word—without precluding any additional or other elements.
[0033] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0034] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.
[0035] Certain terms and phrases have been used throughout the disclosure and will have the following meanings in the context of the ongoing disclosure.
[0036] The term “network” may refer to a computing environment in which physical objects are embedded with devices which enable the physical objects to achieve greater value and service by exchanging data with other systems and/or other connected devices. Each physical object is uniquely identifiable through its embedded device(s) and is able to interoperate within an Internet infrastructure.
[0037] The term “real time” may refer to a level of processing responsiveness that a user or system senses as sufficiently immediate for a particular process or determination to be made, or that enables a processor to keep up with some external process.
[0038] The term “automatically” may refer to without user intervention.
[0039] Embodiments of the present invention may be provided with a machine-readable storage medium tangibly embodying thereon instructions, which may be used to program one or more processors to perform a process. The machine-readable medium may include, but is not limited to, fixed (hard) drives, magnetic tape, floppy diskettes, optical disks, compact disc read-only memories (CD-ROMs), and magneto-optical disks, semiconductor memories, such as ROMs, PROMs, random access memories (RAMs), programmable read-only memories (PROMs), erasable PROMs (EPROMs), electrically erasable PROMs (EEPROMs), flash memory, magnetic or optical cards, or other type of media/machine-readable medium suitable for storing electronic instructions.
[0040] Exemplary embodiments will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. These embodiments are provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those of ordinary skill in the art. Moreover, all statements herein reciting embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (i.e., any elements developed that perform the same function, regardless of structure).
[0041] The present disclosure relates to the field of management and specifically to the field of optimizing complex problems by automatically generating Dynamic Models of systems and then optimizing them.
[0042] In an embodiment of the present disclosure, a system for automodelling and optimization of complex operations is disclosed, The system largely comprises a data pre-processing unit, a dynamic automatic model generator (DAM-G), an optimization system, and an operation management system. The data pre-processing unit may access and pre-process a historical asset data into a processed time series data, wherein the historical asset data corresponds to one or more assets of the system. The dynamic automatic model generator (DAM-G) may apply one or more analysing techniques to the processed time series data for generating an analysed processed time series data. The DAM-G may further create a generated model based on the analysed processed time series data. The optimization system may generate and then send one or more optimization recommendations based on the generated model to the operation management system for further processing and/or decision making. The operation management system may then generate an advanced operation plan with an optimized operating efficiency based on the generated model.
[0043] In another embodiment of the present disclosure, a method for automodelling and optimization of complex operations is disclosed. The method comprises the steps of: storing, an asset data corresponding to one or more assets as a historical asset data; accessing, the historical asset data as a time series asset data; pre-processing, by a data pre-processing unit, the time series asset data into a processed time series data; applying, by a dynamic automatic model generator (DAM-G), one or more analysing techniques to the processed time series data for generating an analysed processed time series data; creating, by the DAM-G, a generated model based on the analysed processed time series data; generating, by an optimization system, one or more optimization recommendations based on the generated model; sending, by the optimization system, the one or more optimization recommendations to an operation management system; and generating, by the operation management system, an advanced operation plan with an optimized operating efficiency based on the generated model.
[0044] In an aspect, the present disclosure provides a system and method for automodelling and optimization of complex operations to provide optimised management for highly complex data-rich operational systems. Utilising a model of the system being managed as a key input for multi-objective optimisation that allows for a rapid determination of potentially conflicting recommendations that may be enacted on by an Operational Management System, whereby the Model of the system may be automatically created and updated by a Dynamic-Auto-Modelling Generator (DAM-G) using processed historical asset data leading to a significantly reduced effort and cost for the deployment and operation of the system leading to a reduction in operational waste for the system operator.
[0045] FIG. 1A illustrates a system 100 for optimizing complex operations, in accordance with an embodiment of the present invention.
[0046] Referring FIG. 1A, a system 100 for optimizing complex operations mainly comprises one or more assets 111a and 111b, a data pre-processing unit 112, a dynamic automatic model generator (DAM-G) 113, an optimization system 114, and an operation management system 115.
[0047] In an aspect, the data pre-processing unit 112 may access and then pre-process a historical asset data 102 into a processed time series data 104, wherein the historical asset data 102 corresponds to one or more assets111a and 111b of the system 100. The dynamic automatic model generator (DAM-G) 113 may apply one or more analysing techniques to the processed time series data 104 for generating an analysed processed time series data 105. The DAM-G 113 may further create a generated model 106 based on the analysed processed time series data 105.
[0048] Correspondingly, the optimization system 114 may then generate and send one or more optimization recommendations 107 based on the generated model 106 to the operation management system 115. The operation management system 115 may further generate an advanced operation plan 108 with an optimized operating efficiency based on the generated model 106.
[0049] In an aspect, the Dynamic Automatic Model Generator (DAM-G) 113 may pertain to a software-based platform that?automatically creates a model of a system (Generated Model) 106 that is then used by an optimisation system 114 to improve the operating efficiency of the system 100 by either recommendations or control decisions 107 that are instigated by an operational management system 115.
[0050] In an aspect, auto-modeling and optimisation may be applied to the system 100 where historic data (Asset Data 101a or 101b) 102 can be provided, optimisation objectives/targets 109 can be defined, and an operational management system 115 exists to effect change in the system 100, in advance to improve operations planning, in a dynamic near real-time capacity to improve operations management.
[0051] In an aspect, auto-modeling and optimisation may be a combination of advanced planning and dynamic operations management 115.
[0052] In an aspect, auto-modeling and optimisation may be creating a generated model 106 such that in the process of automatically creating a generated model 106, DAM-G 113 ingests: Historical data (Processed Time Series Asset Data) 105 derived from pre-processed time-stamped Asset Data 104, relating to the performance of the system 100 to be optimised (and managed).
[0053] Asset Data 101a and 101b includes data from: Connected devices such as Internet of Things (IoT), personal mobile, vehicles, legacy control systems third party APIs, Third party subsystems, Cloud based services, any other device or service that provides data that can be associated with a timestamp, asset Data is time stamped and stored as Historical Asset Data 102 and accessed as Time Series Asset Data 103.
[0054] In an aspect, pre-processing of time stamped Time Series Asset Data 103 includes data-cleansing, filtering, augmentation, interpolation and any other technique necessary to ensure the data is usable and relevant, it is stored as Processed Historical Asset Data and accessed as Processed Time Series Data 105.
[0055] Filtering (e.g. Kalman filtering), Outlier Detection, Interpolation, Dimensionality Reduction (PCA Principal Component Analysis), Data augmentation, Supplementary models to represent how additional data sources (which may be new) change in response to other stimuli. DAM-G 113 applies one or more AI based techniques using the Processed Time Series Data 105 to create the Generated Model 106, these techniques include one or more of: Neural networks, Statistical analysis/multivariate forecasting, Reinforcement learning, End to end model optimisation such as: Hyperparameter Optimisation (i.e. Configuration of Neural Net), Optimisation of Neural Network Training using state of the art Algorithms (Gradient Descent, Adam and more).
[0056] Automated Model Architecture of neural network (i.e. Auto-modeling inside Auto-modeling). Final stage of model optimisation for deployment (i.e. using Tensorflow’s toolkit, or Third-Party like Intel’s OpenVINO platform). Meta-heuristic optimization. Multi-criteria decision making.
[0057] DAM-G 113 may use reinforcement learning techniques on a Generated Model 106 alongside updated Processed Time Series Data105 to create an updated Generated Model 106. During creation of the Generated Model 106 by DAM-G 113, an Auto-modeling Explanation Record may be generated, this record provides a mechanism to explain why DAM-G 113 created the Generated Model 106 that it did.
[0058] The Generated Model 106 may take one or more of the following forms or other forms as appropriate for the operations and optimisation environment in which it is being used: Deep learning model, Mathematical representation, Simulation model.
[0059] In an aspect, the Optimising Operations 114 may include: all elements of the system 100 are deployed in a scalable manner using a combination that includes one or more of: Cloud scalable technologies like containerization (e.g. Docker), Hardware acceleration for Deployment (FPGAs), Hardware acceleration for Training (GPUs), Processing capability of massively parallel architectures, Software-defined gateways, Business Insights, Notification services, Managed, scalable cloud services.
[0060] In an aspect, the Operational Management System 115 can access both Operations Plans 116 and Processed Asset Data 104', Processed Asset Data 104' is a cleansed subset of live Asset Data 101a, cleansing activities including: Filtering (e.g. Kalman filtering), Outlier Detection, Interpolation, or Normalisation.
[0061] In an aspect, Inputs to the Operational Management System 115 (i.e. Operations Plans 116 and Processed Asset Data 104') can be in several forms and may vary in nature depending on the operations being optimized. The Operational Management System 115 can make both advanced planning recommendations i.e., Optimised Operations Plans/Recommendations 107 and dynamic recommendations/decisions i.e., Asset Commands/Recommendations 107'.
[0062] In an aspect, Optimisation recommendations 107 and 107' (i.e. Operations Plans/Recommendations & Asset Commands/Recommendations) are generated based on a holistic understanding of the system as represented by the Generated Model 106.
[0063] The Operational Management System 115 has user defined Targets 109 that can be correlated against Asset Data 101a or 101b to assess performance.
[0064] Targets 109 are shared with the Optimisation System 114 to be used as optimisation objectives. The Operational Management System 115 uses Operations Plans 116 and Processed Asset Data 104' as further inputs to the Optimisation System 114, representing the current state of the operational system 100.
[0065] The Optimisation System 114 applies one or more optimisation techniques using the Generated Model 106, Targets 109, and Processed Asset Data 104' to derive a set of recommended changes to effect efficiency improvements in the operations system 100, techniques may include one or more of the following: Statistical analysis/Multivariate (Principal component analysis and dimensionality reduction)
[0066] In an aspect, the system 100 applies Meta-heuristics?optimisation? (Multi-objective tabu search 3, MOTS3), Multi Criterion Decision Making Parallelisation (e.g. Spark and Jax frameworks, Cuda), Genetic algorithms Reinforcement learning and other optimisation techniques not listed.
[0067] In an aspect, during creation of the Optimised Recommendations/Decisions 107 by the Optimisation System 114, an Optimisation Explanation Record 113' may be generated to provide a mechanism to explain why the Optimisation System 114 made the recommendations/decisions 107 that it did.
[0068] In an aspect, the recommended changes are conveyed as a set of revised operations plans 108 for manual intervention and/or commands/decisions to devices for automated intervention as appropriate.
[0069] In an aspect, a system 100 for improving the efficiency of operations comprising one or more element of: an operational management system 115 that monitors a system 100 and interacts with system elements, wherein the system 100 includes assets 111a and 111b, and interaction with third party systems, wherein the operational management system 115 may include a visualisation element representing the status of assets, wherein the operational management system 115 may include a human machine interface enabling human operators to affect change in the system behaviour. Wherein the operational management system 115 may automatically instigate change in the system behaviour Multiple assets, wherein the assets 111a and/or 111b may have associated sensors or devices that can provide information relating to the asset 111a or 111b and its performance wherein the assets 111a and/or 111b may have associated actuators or devices that can provide a means for realising a change in the asset and system behaviour and performance ccommunications channels.
[0070] The communications channels are used to transmit information from assets to a data storage system wherein the communications channels may be used to transmit commands or recommendations to assets, third party systems wherein third-party systems may be included in the system to be managed.
[0071] The third party systems may provide information that can be used to influence the behaviour of other parts of the system, wherein third part systems may provide information that can be used to influence the behaviour of system as a whole wherein APIs may be used to for connecting to third-party systems that can provide both data relating to third-party system performance as well and mechanisms to affect change in the third-party systems wherein APIs may be third-party APIs wherein APIs may be integral parts of the system.
[0072] In an aspect, an optimisation system wherein the optimisation system uses a model of the system to represent the behaviour of the system, wherein the system model of the system may be created by engineers.
[0073] In an aspect, wherein a system model of the system 100 may be created automatically from asset and third-party data by an automatic model creation system, wherein the system model will provide a set of expected system outputs when given a set of inputs wherein the optimisation system 114 generates a set of recommendations 107 used by an operational management system 115 to affect change in the system 100.
[0074] In an aspect, wherein the optimisation system 114 considers the current state of the system 100 as represented by data from one or more assets 111a and/or 111b within it and optionally third-party systems.
[0075] In an aspect, wherein the optimisation system 114 makes recommendations 107 that improve the efficiency of the system 100 based on one or more objectives wherein the optimisation system 114 may assess a system 100 performance against multiple potentially conflicting targets 109 to provide multi-objective recommendations 107 or 108.
[0076] In an aspect, wherein the optimisation system 114 may apply one or more of Statistical analysis/Multivariate (Principal component analysis and dimensionality reduction), Meta-heuristics?optimisation? (Multi-objective tabu search 3, MOTS3), Multi Criterion Decision Making, Parallelisation (e.g. Spark and Jax frameworks, Cuda), Genetic algorithms, Reinforcement learning, meta-heuristics, multi-objective assessment to make recommendations 107.
[0077] In an aspect, wherein the optimisation system 114 may create an explanation as to why the Optimisation System 114 made the recommendations/decisions 107 that it did with a data pre-processing system wherein asset data is time stamped.
[0078] In an aspect, the time stamped asset data 103 is processed to provide relevant and consistent data that wherein the processing of time stamped asset data includes one or more data processing techniques including Filtering (e.g. Kalman filtering), Outlier Detection, Interpolation, Dimensionality Reduction (PCA Principal Component Analysis), Data augmentation, wherein the processed time stamped data is captured over a period as time series data.
[0079] In an aspect, the system 100 wherein the processed time stamped data 104 is stored and made accessible to other system elements.
[0080] In an aspect, an automatic model creation system wherein the time series data 103 is used to create a model 106 of the system. In an aspect, the model creation may apply one or more artificial intelligence techniques to create the system model including Neural networks, Statistical analysis/multivariate forecasting, Reinforcement learning, End to end model optimisation such as: Hyperparameter Optimisation (i.e. Configuration of Neural Net), Optimisation of Neural Network Training using state of the art Algorithms (Gradient Descent, Adam and more), Automated Model Architecture of neural network (e.g. Auto-modelling inside Auto-modelling), Final stage of model optimisation for deployment (e.g. using Tensorflow’s toolkit, or Third-Party like Intel’s Open VINO platform), Meta-heuristic optimisation. Multi-criteria decision making wherein the automatic model creation system may use reinforcement learning techniques to generate a new updated system model 106.
[0081] In an aspect, the automatic model creation system may create an explanation that enables an understanding of why the system model behaves as it does, wherein the automatic model creation may create models in one or more forms as appropriate for the operations and optimisation environment in which they are being used, including: Deep learning model, Mathematical representation and Simulation model.
[0082] FIG. 1B illustrates an exemplary method 150 for optimizing complex operations, in accordance with an embodiment of the present invention, to elaborate upon its working.
[0083] In an embodiment of the present disclosure, the method 150 for optimizing complex operations comprises the following steps:
? At step S151, storing, an asset data 101a and 101b corresponding to one or more assets 101a and 101b as a historical asset data 102;
? At step S152, accessing, the historical asset data 102 as a time series asset data 103;
? At step S153, pre-processing, by a data pre-processing unit 112, the time series asset data 103 into a processed time series data 104;
? At step S154, applying, by a dynamic automatic model generator (DAM-G) 113, one or more analysing techniques to the processed time series data 104 for generating an analysed processed time series data 105;
? At step S155, creating, by the dynamic automatic model generator (DAM-G) 113, a generated model 106 based on the analysed processed time series data 105;
? At step S156, generating, by an optimization system 114, one or more optimization recommendations 107 based on the generated model 106;
? At step S157, sending, by the optimization system 114, the one or more optimization recommendations 107 to an operation management system 115; and
? At step S158, generating, by the operation management system 115, an advanced operation plan 108 with an optimized operating efficiency based on the generated model 106.
[0084] Referring FIG. 2, an exemplary computer system 200 in which or with which embodiments of the present disclosure may be utilized, is disclosed. As shown in FIG. 2, the computer system 200 may include an external storage device 210, a bus 220, a main memory 230, a read-only memory 240, a mass storage device 250, one or more communication ports 260, and a processor 270. A person skilled in the art will appreciate that the computer system 200 may include more than one processor and/or communication ports. The processor 270 may include various modules associated with embodiments of the present disclosure. The one or more communication ports 260 may be any of an RS-232 port for use with a modem-based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The one or more communication ports 260 may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system 200 connects. The main memory 230 may be a RAM, or any other dynamic storage device commonly known in the art. The read-only memory 240 may be any static storage device(s) including, but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or basic input/output system (BIOS) instructions for the processor 270. The mass storage device 250 may be any current or future mass storage solution, which may be used to store information and/or instructions.
[0085] The bus 220 communicatively couples the processor 270 with the other memory, storage, and communication blocks. The bus 220 can be, e.g. a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), universal serial bus (USB), or the like, for connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor 270 to the computer system 200.
[0086] Optionally, operator and administrative interfaces, e.g. a display, keyboard, and a cursor control device, may also be coupled to the bus 220 to support direct operator interaction with the computer system 200. Other operator and administrative interfaces may be provided through network connections connected through the one or more communication ports 260. In no way should the aforementioned exemplary computer system 200 limit the scope of the present disclosure.
[0087] A person of ordinary skill in the art will appreciate that these are mere examples, and in no way, limit the scope of the present disclosure.
[0088] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the disclosure and not as limitation.
,CLAIMS:1. A system (100) for automodelling and optimization of complex operations, the system (100) comprising:
a data pre-processing unit (112) configured to access and pre-process a historical asset data (102) into a processed time series data (104);
a dynamic automatic model generator, DAM-G, (113), configured to apply one or more analysing techniques to the processed time series data (104) for generating an analysed processed time series data (105) and create a generated model (106) based on the analysed processed time series data (105);
an optimization system (114) configured to generate and send one or more optimization recommendations (107) based on the generated model (106) to an operation management system (115); and
the operation management system (115) configured to generate an advanced operation plan (108) with an optimized operating efficiency based on the generated model (106).
2. The system (100) as claimed in claim 1, wherein the historical asset data (102) includes asset data (101a, 101b) corresponding to one or more assets (111a, 111b), wherein the one or more assets (111a, 111b) are selected from one or more connected devices such as Internet of Things (IoT), personal mobile, vehicles, legacy control systems, APIs, third-party subsystems, cloud-based services, or any other device or service configured to provide data that can be associated with a timestamp, wherein the asset data (101a, 101b) is time stamped and stored as historical asset data (102).
3. The system (100) as claimed in claim 1, wherein the operation management system (115) includes all elements of the system (100) configured to be deployed in a scalable manner.
4. The system (100) as claimed in claim 1, wherein the operation management system (115) comprises one or more user-defined targets (109) corresponding to the asset data (101a, 101b).

5. The system (100) as claimed in claim 1, wherein the operation management system (115) is configured to access one or more operation plans (116) and a processed asset data (104'), wherein the processed asset data (104') comprises a processed or a filtered subset of the asset data (101a, 101b).
6. The system (100) as claimed in claim 1, wherein the generated model (106) is configured to be one of the deep learning model, a mathematical representation model, or a simulation model.
7. A method (150) for automodelling and optimization of complex operations, the method (150) comprising:
storing (S151), an asset data (101a, 101b) corresponding to one or more assets (111a, 111b) as a historical asset data (102);
accessing (S152), the historical asset data (102) as a time series asset data (103);
pre-processing (S153), by a data pre-processing unit (112), the time series asset data (103) into a processed time series data (104);
applying (S154), by a dynamic automatic model generator, DAM-G, (113), one or more analysing techniques to the processed time series data (104) for generating an analysed processed time series data (105);
creating (S155), by the dynamic automatic model generator, DAM-G, (113), a generated model (106) based on the analysed processed time series data (105);
generating (S156), by an optimization system (114), one or more optimization recommendations (107) based on the generated model (106);
sending (S157), by the optimization system (114), the one or more optimization recommendations (107) to an operation management system (115); and
generating (S158), by the operation management system (115), an advanced operation plan (108) with an optimized operating efficiency based on the generated model (106).

8. The method (150) as claimed in claim 7, wherein pre-processing (S153) the time series asset data (103) comprises data-cleansing, filtering, augmentation, interpolation or any other data processing technique configured to ensure that the processed data is usable and relevant.
9. The method (150) as claimed in claim 1, wherein the dynamic automatic model generator, DAM-G, (113) is configured to generate an explanation record for understanding of the behaviour of the generated model (106).