DESC:EARLIEST PRIORITY DATE:
This Application claims priority from a Provisional patent application filed in India having Patent Application No. 202441070953, filed on September 19, 2024, and titled “A SYSTEM AND A METHOD WITH INTEGRATED IOT SOLUTION FOR EFFICIENT CNG STATION MANAGEMENT”.
FIELD OF INVENTION/TECHNICAL FIELD
[0001] The present invention relates to the field of supply chain management. More particularly, the present invention relates to an internet-of-things integrated system and method for efficient gas station management.
BACKGROUND
[0002] The evolution of the fuel supply chain has been pivotal in meeting the growing global energy demands since the industrial revolution. Initially, the supply chain for fuels including coal, oil, and gas was rudimentary, involving basic extraction and transportation methods. There were significant advancements with the development of pipelines, tankers, and refineries, which allowed for more efficient and scalable distribution of liquid fuels. Innovations including the introduction of standardized storage and distribution systems, including the use of railways and trucks for transportation, helped streamline the movement of fuels from production sites to end-users.
[0003] Gas is a cleaner alternative to gasoline and diesel, which has become popular due to its lower environmental impact and abundant supply. The management of compressed natural gas (CNG) across the supply chain has evolved significantly since its early days. The early management systems for the CNG focused primarily on the development of safe and efficient compression and storage technologies. This included innovations in high-pressure cylinders and compressor stations, which were crucial for transporting and storing natural gas in a compact form.
[0004] As CNG gained traction, the focus shifted to optimize the supply chain to address challenges including distribution efficiency and infrastructure limitations. The advanced technologies were developed to enhance logistics, including the design of specialized transport vehicles and refuelling stations. These developments aimed to reduce the costs associated with CNG distribution and improve accessibility. Additionally, the integration of real-time data management systems allowed for better monitoring and control of the supply chain, enabling more efficient scheduling and inventory management.
[0005] In recent years, the management of gas across the supply chain has continued to advance with the incorporation of digital technologies and sustainable practices. The rise of smart grids and IoT (Internet of Things) devices has facilitated more precise tracking and analysis of CNG flows from production to end-use. Furthermore, efforts to develop infrastructure for CNG refuelling and distribution have expanded, with an increasing emphasis on renewable energy integration and reducing carbon footprints. These advancements reflect a broader trend towards optimizing energy resources and addressing environmental concerns while maintaining the economic viability of CNG as a fuel source.
[0006] However, with recent developments in the technological aspects managing compressed natural gas (CNG) throughout the supply chain from mother stations to daughter stations and retail outlets presents difficulties in equipment monitoring, precise tracking of gas flow, and aligning distributed gas quantities with actual sales. The absence of integrated monitoring and reconciliation systems results in inefficiencies, inaccuracies, and possible revenue shortfalls. Additionally, these factors result in complicating the maintenance of CNG distribution integrity.
[0007] To address these issues, there is a critical need for a more sophisticated solution that provides improved technological and logistical capabilities including advanced infrastructure, real-time monitoring systems, and sophisticated inventory management to optimize efficiency and meet the evolving needs of global energy markets.
[0008] Hence, there is a need for an improved internet-of-things integrated system and method for efficient gas station management which addresses the aforementioned issue(s).
OBJECTIVES OF THE INVENTION
[0009] The primary objective of the invention is to develop a system and method capable of managing gas distribution across networks, thereby ensuring safe, transparent, and efficient transmission of gas from the source to the destination.
[0010] Another objective of the invention is to implement an IoT-enabled infrastructure that integrates sensors to collect and transmit real-time data to a centralized cloud platform for analysis and monitoring.
[0011] Yet another objective of the invention is to enable end-to-end reconciliation of gas quantities by comparing the gas transferred and sold at various stages, thereby detecting discrepancies such as gas loss, theft, or inaccurate measurements.
SUMMARY
[0012] In accordance with an embodiment of the present disclosure, an Internet-of-Things integrated system for efficient gas station management is disclosed. The system includes a processor, and a memory coupled to the processor, wherein the memory comprises instructions that when executed by the processor cause the processor to receive data from a plurality of sensors installed at each of the multiple stages of a gas distribution network. The processor also executes instructions to transmit the data to a centralized cloud platform for further processing. The processor also executes instructions to reconcile the data related to the quantities of gas measured at the various stages of the gas distribution network. The processor also executes instructions to monitor and analyze flow rate, and the pressure data associated with the gas throughout the gas distribution network. The processor also executes instructions to compare the reconciled data with recorded transactions, expected values, historical baselines, and predefined operational thresholds to verify if the amount of gas sold matches the volume transferred throughout the gas distribution network and to detect one or more discrepancies indicative of operational inefficiencies, the data anomalies, and potential losses. The processor also executes instructions to provide a real-time, cloud-based dashboard on the centralized cloud platform for monitoring operational status of equipment and the gas flow across the gas distribution network. Further, the processor also executes instructions to generate an alert upon detection of the one or more discrepancies in the reconciled data, thereby enabling immediate intervention to mitigate potential gas loss, equipment malfunction, and operational failure. Furthermore, the processor also executes instructions to generate detailed reports based on the reconciled data, monitored operational parameters, and the recorded transaction data collected across the gas distribution network. Moreover, the processor also executes instructions to analyze the historical and the real-time data to identify operational trends, detect inefficiencies, optimize supply chain performance, and reduce overall operational costs within the gas distribution network.
[0013] In accordance with an embodiment of the present disclosure, method for efficient gas station management is disclosed. The method includes receiving, data from a plurality of sensors installed at each of the multiple stages of a gas distribution network. The method also includes transmitting, the data to a centralized cloud platform for further processing. The method also includes reconciling, the data related to the quantities of gas measured at the various stages of the gas distribution network, using processing techniques. The method also includes monitoring and analyzing, flow rate and the pressure data associated with the gas throughout the gas distribution network. The method also includes comparing, the reconciled data with recorded transactions, expected values, historical baselines, and predefined operational thresholds to verify if the amount of gas sold matches the volume transferred throughout the gas distribution network and to detect one or more discrepancies indicative of operational inefficiencies, the data anomalies, and potential losses. The method also includes providing, a real-time, cloud-based dashboard on the centralized cloud platform for monitoring operational status of equipment and the gas flow across the gas distribution network. Further, the method also includes generating, an alert upon detection of the one or more discrepancies in the reconciled data, thereby enabling immediate intervention to mitigate potential gas loss, equipment malfunction, and operational failure. Furthermore, the method also includes generating, detailed reports based on the reconciled data, monitored operational parameters, and the recorded transaction data collected across the gas distribution network. Moreover, the method also includes analyzing, the historical and the real-time data to identify operational trends, detect inefficiencies, optimize supply chain performance, and reduce overall operational costs within the gas distribution network.
[0014] 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 THE DRAWINGS
[0015] The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:
[0016] FIG. 1 illustrates a network environment for implementing example techniques for internet-of-things integrated system for efficient gas station management in accordance with an embodiment of the present disclosure;
[0017] FIG. 2 illustrates a schematic diagram of a user device of FIG. 1, in accordance with an example implementation of the present subject matter;
[0018] FIG. 3 illustrates a schematic diagram of an Internet-of-Things integrated system for efficient gas station management of FIG. 1, in accordance with an embodiment of the present disclosure;
[0019] FIG. 4(a) illustrates a flow chart representing the steps involved in a method for efficient gas station management, in accordance with an embodiment of the present disclosure; and
[0020] FIG. 4(b) illustrates continued steps of the method of FIG. 4(a) in accordance with an embodiment of the present disclosure.
[0021] 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
[0022] 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.

[0023] The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or subsystems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. 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.

[0024] 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.

[0025] In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.
[0026] FIG. 1 illustrates a network environment for implementing example techniques for internet-of-things integrated system for efficient gas station management in accordance with an embodiment of the present disclosure.
[0027] Referring to FIG. 1, a user device (105) corresponding to a user (115) may be communicatively coupled to a system (120). Further, the user (115) may access the system (120) over a network (110). Examples of the user device (105) includes, but is not limited to, a mobile phone, desktop computer, portable digital assistant (PDA), smart phone, tablet, ultra-book, netbook, laptop, multi-processor system, microprocessor-based or programmable consumer electronic system, or any other communication device that a user (115) may use. It will be appreciated that the system (120) may be presented to the user (115) on a corresponding user device (105) as a web application accessed through a browser, through a software application on the user device (105), or, particularly for smartphones, through a mobile application installed at the smartphone. It will be appreciated that, within the context of the disclosure herein, web application refers to a utility implemented on a networked computing system accessible by user device (105) over the Internet (e.g. through browsers) wherein the bulk of the processing takes place at the networked computing system, mobile applications refer to applications installed on smartphones that may communicate with a networked computing system, and a “software” application refers generally to applications other than web browsers installed on other types of user device (105) that may communicate with a networked computing system over the network (110).
[0028] The network (110) may be a single communication network or a combination of multiple communication networks and may use a variety of different communication protocols. The personalized network may be a wireless network, a wired network, or a combination thereof. Examples of such individual personalized networks include, but are not limited to, Global System for Mobile Communication (GSM) network, Universal Mobile Telecommunications System (UMTS) network, Personal Communications Service (PCS) network, Time Division Multiple Access TDMA) network, Code Division Multiple Access (CDMA) network, Next Generation Network (NON), Public Switched Telephone Network (PSTN). Depending on the technology, the personalized network may include various network entities, such as gateways and routers; however, such details have been omitted for the sake of brevity of the present description.
[0029] The system (120) may have a homepage that is presented to the user (115) accessing a top-level web address for web applications presented to the user (115) in a browser or a welcome screen for software and mobile applications. The homepage may include links to a user (115) log-in interface or general information about the system (120) and the option to register as user (115). It will be appreciated that the presentation of a homepage may not be necessary, for example, if a user (115) bypasses it by directly inputting a web address corresponding to a user (115) log-in page, or if a separate mobile application is designed for users (115).
[0030] A new or unregistered user (115) can access the user log-in interface, fill out the log-in information corresponding to the user's (115) account, and indicate that the user (115) wishes to sign in. It will be appreciated that any conventional registration and log-in techniques for web applications, software application, and mobile applications may be used, whichever is appropriate for the user (115). While registering the user (115) may be prompted to provide username and corresponding user (115) credentials, not limited to, password, geographical location, and contact information and upon receipt of the foregoing information, a corresponding user-profile may be created and stored on a respective database (385) of the system (120).
[0031] In accordance with an embodiment of the present disclosure, an internet-of-things integrated system (120) and method (400) for efficient gas station management is provided. The system (120) comprises a processor (305) and a machine-readable storage medium comprising instructions that, when executed by the processor (305), cause the processor (305) to receive data (370) from a plurality of sensors installed at each of the multiple stages of a gas distribution network. The processor (305) also executes instructions to transmit the data (305) to a centralized cloud platform for further processing. The processor (305) also executes instructions to reconcile the data (370) related to the quantities of gas measured at the various stages of the gas distribution network. The processor (305) also executes instructions to monitor and analyze flow rate, and the pressure data associated with the gas throughout the gas distribution network. The processor (305) also executes instructions to compare the reconciled data (355) with recorded transactions (340), expected values (375), historical baselines (360), and predefined operational thresholds (345) to verify if the amount of gas sold matches the volume transferred throughout the gas distribution network and to detect one or more discrepancies indicative of operational inefficiencies, the data (370) anomalies, and potential losses. The processor (305) also executes instructions to provide a real-time, cloud-based dashboard on the centralized cloud platform for monitoring operational status of equipment and the gas flow across the gas distribution network. Further, the processor (305) also executes instructions to generate an alert upon detection of the one or more discrepancies in the reconciled data, thereby enabling immediate intervention to mitigate potential gas loss, equipment malfunction, and operational failure. Furthermore, the processor (305) also executes instructions to generate detailed reports based on the reconciled data (355), monitored operational parameters (365), and the recorded transaction data (380) collected across the gas distribution network. Moreover, the processor (305) also executes instructions to analyze the historical and the real-time data (350) to identify operational trends, detect inefficiencies, optimize supply chain performance, and reduce overall operational costs within the gas distribution network.
[0032] In one embodiment of the present invention, the system includes multiple stages of the gas distribution network include, but are not limited to, mother stations (120a), daughter stations (120b), and retail outlets (120c). The mother stations (120a) serve as the primary source of the gas, where the gas is compressed and stored before being dispatched to the downstream stations. The daughter stations (120b) act as intermediate hubs, receiving the gas from the mother stations (120a) and redistributing it to the retail outlets (120c). The retail outlets (120c) represent the final stage in the distribution network, where the gas is dispensed directly to consumers. The IoT module (315) installed at the mother stations (120a), the daughter stations (120b), and the retail outlets (120c) are configured to interface with the plurality of sensors to ensure data (370) is captured across all the stages.
[0033] In another embodiment of the present invention, the plurality of sensors includes, but are not limited to, flow meters (135a) (135b) (135c), pressure sensors (130a) (130b) (130c) associated with compressors, and dispensing unit monitors (140c) installed throughout the gas distribution network. Each of the plurality of sensors plays a distinct role in capturing critical data at various stages of the distribution process. The flow meters (135a) (135b) (135c) deployed at the mother stations (120a), daughter stations (120b), and the retail outlets (120c) measure the exact volume of gas being transferred or sold. This data (370) is essential for tracking gas movement and ensuring that quantities recorded at each stage align with actual physical transfers. The pressure sensors (130a) (130b) (130c), particularly those integrated with compressors, deployed at the mother (120a) and daughter stations (120b), monitors pressure levels during the gas compression and transfer. The flow meters (135a) (135b) (135c), pressure sensors (130a) (130b) (130c) associated with compressors, and dispensing unit monitors (140c) help detect anomalies that may indicate equipment malfunction or leakage. The dispensing unit monitors (140c), installed at the retail outlets (120c), track the volume of gas dispensed to the end consumers. The dispensing unit monitors (140c) ensure that recorded sales transactions accurately reflect the gas delivered. The plurality of sensors generates a variety of data (370), which is transmitted to the IoT module (315) for further processing.
[0034] It may be noted that the foregoing system (120) is an exemplary system (120) and may be implemented as computer executable instructions in any computing or processing environment, including in digital electronic circuitry or in computer hardware, firmware, device driver, or software. As such, the system (120) is not limited to any specific hardware or software configuration.
[0035] FIG. 2 illustrates a schematic diagram of a user device (105), in accordance with an example implementation of the present subject matter. Referring to FIG. 2, the user device (105) may comprise a processor(s) (205), a memory(s) (210) coupled to and accessible by the processor(s) (205), and an interface (225) coupled to the memory(s) (210). The user device (105) disclosed herein may be same as the user device (105) described in FIG. 1. The functions of various elements shown in the figs., including any functional blocks labelled as "processor(s) (205)", may be provided through the use of dedicated hardware as well as hardware capable of executing instructions. When provided by a processor (205), the functions may be provided by a single dedicated processor (205), by a single shared processor (205), or by a plurality of individual processors (205), some of which may be shared. Moreover, explicit use of the term "processor" (205) would not be construed to refer exclusively to hardware capable of executing instructions, and may implicitly comprise, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA). Other hardware, standard and/or custom, may also be coupled to the processor(s) (205). The user device (105) may further include a display (215) in addition to other components such as, but not limited to, keyboard, sensors, logic circuits etc. Further, the user device (105) may include data (220) which may include data (220) that may be stored, utilized or generated during the operation of the user device (105).
[0036] The memory(s) (210) may be a computer-readable medium, examples of which comprise volatile memory (e.g., RAM), and/or non-volatile memory (e.g., Erasable Programmable read-only memory, i.e. EPROM, flash memory, etc.). The memory(s) (210) may be an external memory, or internal memory, such as a flash drive, a compact disk drive, an external hard disk drive, or the like. The user device (105) may further include an interface (225) that may allow the connection or coupling of the user device (105) with one or more other devices, through a wired (e.g., Local Area Network, i.e., LAN) connection or through a wireless connection (e.g., Bluetooth®, Wi-Fi), for example, for connecting to the system (120) shown in FIG. 1. The interface (225) may also enable intercommunication between different logical as well as hardware components of the user device (105).
[0037] FIG. 3 illustrates a schematic diagram of an Internet-of-Things integrated system for efficient gas station management of FIG. 1, in accordance with an embodiment of the present disclosure. Referring to FIG. 3, the system (120) includes a processor(s) (305), a memory(s) (310) coupled to and accessible by the processor(s) (305), database (385) and a user interface (390) coupled to the memory(s) (310).
[0038] The system (120) disclosed herein is the same as the system (120) described in FIG. 1. The functions of various elements shown in the figs., including any functional blocks labelled as "processor(s)" (305), may be provided through the use of dedicated hardware as well as hardware capable of executing instructions. When provided by a processor (305), the functions may be provided by a single dedicated processor (305), by a single shared processor (305), or by a plurality of individual processors (305), some of which may be shared. Moreover, explicit use of the term "processor" (305) would not be construed to refer exclusively to hardware capable of executing instructions, and may implicitly comprise, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA). Other hardware, standard and/or custom, may also be coupled to the processor(s) (305). The system (120) may further include other components such as, but not limited to, keyboard, sensors, logic circuits, input/output interfaces etc. Further, the system (120) may include data (not shown) which may include data that may be stored, utilized or generated during the operation of the computer implemented system (120).
[0039] The memory(s) (310) may be a computer-readable medium, examples of which comprise volatile memory (e.g., RAM), and/or non-volatile memory (e.g., Erasable Programmable read-only memory, i.e. EPROM, flash memory, etc.). The memory(s) (310) may be an external memory, or internal memory, such as a flash drive, a compact disk drive, an external hard disk drive, or the like. The system (120) may further include the user interface (390) that may allow the connection or coupling of the system (120) with one or more other devices, through a wired (e.g., Local Area Network, i.e., LAN) connection or through a wireless connection (e.g., Bluetooth®, Wi-Fi)., for example, for connecting to the user device (105) as shown in FIG. 1. The user interface (390) may also enable intercommunication between different logical as well as hardware components of the system (120).
[0040] The system (120) may be provided with a database (385) to store data (370), reconciled data (355), recorded transactions (340), expected values (375), historical baselines (360), predefined operational thresholds (345), recorded transaction data (380), monitored operational parameters (365) and real-time data (355). In an example implementation of the system (120) including one or more servers, the databases (385) may databases (385) local to the server or may be remote to the server. It may be noted that the data in the databases (385) may be stored as a table or may be pre-stored as a mapping with the other. This application is not limited thereto.
[0041] In one embodiment of the present invention the memory (310) causes the processor (305) to handle the data (370), the reconciled data (355), the recorded transactions (340), the expected values (375), the historical baselines (360), the predefined operational thresholds (345), the recorded transaction data (380), the monitored operational parameters (365) and the real-time data (350) through the database (385) integrated within the system (120). The database (385) allows the memory (310) to manipulate the data stored in the database (385). The manipulation is required for analysis and monitoring of the data.
[0042] The system (120) may include module(s). The module(s) may include an Internet-of-Things module (315), cloud-integrated data management module (320), a reconciliation module (325), a monitoring and alerts module (330) and an analytics and reporting module (335). In one example, the module(s) may be implemented as a combination of hardware and firmware. In an example described herein, such combinations of hardware and firmware may be implemented in several different ways. For example, the firmware for module(s) may be processor (305) executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the module(s) may include a processing resource (for example, implemented as either single processor or combination of multiple processors), to execute such instructions. Further, the hardware for the module(s) may include communication apparatuses, control circuitries involving electrical and electronics components, sensors, and interface devices, which may be in communication with each other for multi-directional communication therebetween.
[0043] Further, the system (120) includes data. The data may include data that is either stored or generated as a result of functions implemented by the system. It may be further noted that information stored and available in data may be utilized by the engine(s) for performing various functions by the system (120). In an example, data may include data (370), reconciled data (355), recorded transactions (340), expected values (375), historical baselines (360), predefined operational thresholds (345), recorded transaction data (380), monitored operational parameters (365) and real-time data (350). It may be noted that such examples of the various functions are only indicative. The present approaches may be applicable to other examples without deviating from the scope of the present subject matter.
[0044] In the present examples, the non-transitory machine-readable storage medium may store instructions that, when executed by the processing resource, implement the functionalities of modules(s). In such examples, the system (120) may include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions. In other examples of the present subject matter, the machine-readable storage medium may be located at a different location but accessible to the system (120) and the processor(s) (305).
[0045] In operation, the Internet-of-Things (IoT) module (315) are configured to receive data (370) from a plurality of sensors installed at each of the multiple stages. The IoT module (315) used herein refers to a hardware and software-enabled device. Typically, each IoT module (125a) (125b) (125c) includes components such as a microcontroller and embedded firmware which handles the reception of the data (370). Additionally, the IoT module (315) may support remote configuration, updates, and diagnostics, thereby enhancing the automation and intelligence of the gas distribution network. By enabling automated data (370) acquisition, the IoT module (315) deployed across the gas distribution network, streamline operational oversight.
[0046] In one embodiment of the present invention, the data (370) generated by the plurality of sensors includes but are not limited to, flow rate, pressure readings, gas volume transferred, and dispensing transaction data each associated with corresponding timestamps. This data (370) forms the backbone of the system’s (120) monitoring and reconciliation capabilities, enabling a detailed and time-sensitive understanding of the gas movement and the station performance. The flow rate data offers insight into the speed and consistency of the gas transfer across the distribution network, helping identify potential bottlenecks or irregularities. The pressure readings, particularly those taken during compression and transfer, are critical for verifying that the gas is handled within safe operational limits. The volume of gas transferred serves as a direct indicator of supply and consumption. When paired with the dispensing transaction data from the retail outlets (120c), it enables accurate reconciliation between distributed and sold quantities.
[0047] Further, the inclusion of the timestamps with each data (370) point ensures that all measurements are temporally aligned, allowing the system (120) to track changes over time and correlate events across different stations. For instance, a sudden drop in pressure at the daughter station (120b) or the retail outlet (120c) can be correlated with the flow data from the mother station (120a) to determine whether the issue originated upstream. This time-linked data (370) also facilitates historical analysis, trend identification, and predictive maintenance making the system (120) not only reactive but also proactive in managing the gas distribution network.
[0048] At each stage of the gas distribution network, the IoT module (315) receive the variety of data (370) from the plurality of sensors and transmit it for further processing, thereby enabling real-time monitoring of gas movement and equipment performance. For example, when gas is transferred from the mother station (120a) to the daughter station (120b), IoT module (315) at both ends capture synchronized data (370) reflecting the quantity and pressure of gas transferred. The continuous and automated nature of the data (370) collection through these IoT module (315) eliminates the need for manual logging, significantly reducing human error and enhancing operational transparency.
[0049] In further operation, the cloud-integrated data management module (320) is operatively coupled to the Internet-of-Things (IoT) module (315) wherein the cloud-integrated data management module (320) is configured to receive the data (370) from the Internet-of-Things module (315) and transmit the data (370) to a centralized cloud platform for further processing. The cloud-integrated data management module (320) handles the aggregation of the data (370) from the IoT module (315) and the transmission of those aggregated data (370) to the centralized cloud platform for further processing. For example, when the flowmeter (135b) at the daughter station (120b) records a specific volume of the gas received, the IoT module (125b) transmits this data (370) to the cloud-integrated data management module (320), which then forwards it to the centralized cloud platform. This enables real-time visibility and supports the reconciliation of the gas quantities across the network. The advantage of this setup lies in its ability to centralize data (370) from geographically dispersed stations, reduce latency in data (370) transmission, and support scalable analytics that enhance operational decision-making and system (120) responsiveness.
[0050] The cloud-integrated data management module (320) is deployed at each stage of the gas distribution network comprises software and communication interfaces which handles the aggregation of the data (370) from the IoT module (315) deployed at each stage and the transmission of those aggregated data (370) to the centralized cloud platform for further processing. The communication interfaces which may be used by the cloud-integrated data management module includes but are not limited to, Global System for Mobile Communication (GSM) network, Universal Mobile Telecommunications System (UMTS) network, Personal Communications Service (PCS) network, Time Division Multiple Access TDMA) network, Code Division Multiple Access (CDMA) network, Next Generation Network (NON), Public Switched Telephone Network (PSTN).
[0051] The cloud-integrated data management module (320) acts as a bridge between the edge-level data (370) acquisition and the centralized cloud platform, ensuring that all sensor generated data (370) is securely and efficiently transmitted for further processing. The centralized cloud platform serves as the repository and processing center where further processing of the data (370) is performed.
[0052] In one embodiment of the present invention, the further processing of the data (370) refers to the operations performed on the raw data (370) after the data (370) has been transmitted from the Internet-of-Things module (315) to the centralized cloud platform. This processing includes but is not limited to aggregating the transmitted data (370) and analyzing the aggregated data (370) using various computational and analytical techniques. The aggregation involves compiling data (370) from multiple sources such as the flowmeters (135a) (135b) (135c), the pressure sensors (130a) (130b) (130c), and the dispensing unit monitors (140c) into a unified dataset that reflects the operational status of the entire gas distribution network.
[0053] The centralized cloud platform stores all the incoming data (370), organizes it based on the timestamps and the station identifiers, and applies computational and analytical techniques to perform real-time analysis. These techniques reconcile the gas quantities measured at different points in the distribution network, ensuring that the amount of gas transferred from the mother stations (120a) matches the quantities received at the daughter stations (120b) and ultimately sold at the retail outlets (120c). By comparing the expected flow rates with actual readings from the IoT module (315), the system (120) can detect discrepancies and measurement inaccuracies. For instance, if the daughter stations (120b) consistently receive less gas than what was dispatched from the mother station (120a), the cloud platform can flag this anomaly for further investigation.
[0054] In further operation, a reconciliation module (325) is operatively coupled to the cloud-integrated data management module (320) wherein the reconciliation module (325) is configured to reconcile the data (370) related to the quantities of gas measured at the various stages of the gas distribution network using processing techniques. This begins with the collection of initial quantity data (370) at the mother station (120a), where the gas is compressed and dispatched. As the gas moves through the distribution network to the daughter stations (120b) and eventually to the retail outlets (120c), the system (120) continuously tracks the flow and the pressure data. The reconciliation module (325) aggregates this data (370) and applies computational techniques to verify that the volume of gas transferred from one stage to the next is consistent and accurately recorded. This ensures that each handoff in the supply network is documented and validated.
[0055] As the initial data (370) is reconciled, the reconciliation module (325) monitors and analyzes the flow rate, and the pressure data associated with the gas distribution network. The flow rate and the pressure data are critical for assessing the operational health of the system. For example, abnormal pressure drops between stations may indicate leakage or equipment malfunction, while irregular flow rates could suggest blockages or unauthorized diversions. By analyzing these metrics in real-time, the reconciliation module (325) helps maintain optimal operating conditions and supports early detection of potential issues.
[0056] The reconciliation module (325) further compares the reconciled data (355) with recorded transactions (340), expected values (375), historical baselines (360), and predefined operational thresholds (345). This comparison is particularly important at the retail outlet (120c) stage, where the amount of gas dispensed to customers must match the volume received from the upstream stations. Any mismatch between the reconciled data (355) and transaction records (340) can indicate one or more discrepancies which are indicative of operational inefficiencies, data anomalies, and potential losses. For instance, if the volume of gas sold at a retail outlet (120c) consistently exceeds the volume recorded as received, the system (120) flags this anomaly for investigation. Through this layered verification process, the reconciliation module (325) ensures that the entire distribution network operates with integrity, efficiency, and accountability.
[0057] In one embodiment of the present invention the one or more discrepancies include but are not limited to the gas losses, the gas theft, leakage and measurement inaccuracies within the gas distribution network. Each of these issues can significantly impact the operational efficiency, safety, and financial integrity of the distribution process. The gas losses refer to the unaccounted reduction in the gas volume as the gas moves from one stage to another, which may occur due to evaporation, improper sealing, or unnoticed leaks. The gas theft involves unauthorized extraction or diversion of the gas, typically occurring at vulnerable points in the network where monitoring is weak or tampering is possible. The leakage is a physical escape of the gas from pipelines, compressors, or storage units, often due to equipment failure, corrosion, or improper maintenance. The measurement inaccuracies arise when the sensors such as the flowmeters (135a) (135a) (135c) and the pressure sensors (130a) (130b) (130c) fail to record the data (370) correctly due to calibration errors, sensor drift, or environmental interference.
[0058] The reconciliation module (325) detects these discrepancies by comparing the actual sensor readings with expected values (377), historical baselines (360), and recorded transactions (380) and flags anomalies that suggest one or more of these issues. Further, by identifying these discrepancies in the real-time, the system (120) enables operators to take immediate corrective actions, thereby safeguarding the integrity of the gas distribution network and minimizing operational and financial risks.
[0059] In further operation, a monitoring and alerts module (330) is operatively coupled to the reconciliation module (325), wherein the monitoring and alerts module (330) is configured to provide a real-time, cloud-based dashboard on the centralized cloud platform for monitoring operational status of equipment and the gas flow across the gas distribution network. The monitoring and alerts module (330) functions as the system’s (120) real-time oversight and response mechanism and provides the cloud-based dashboard that displays the operational status of the equipment and the gas flow across the entire gas distribution network. This dashboard is accessible through the centralized cloud platform and continuously updates with the live data (370), allowing operators to monitor key parameters such as the flow rate, the pressure, and the gas volume at each stage of the mother stations (120a), the daughter stations (120b), and the retail outlets (120c).
[0060] Additionally, the monitoring and alerts module (330) is configured to generate an alert upon detection of the one or more discrepancies in the reconciled data (355), thereby enabling immediate intervention to mitigate the potential gas loss, the malfunction, and the operational failure. These discrepancies such as the gas loss, the leakage, or the mismatches between transferred and the sold volumes are first identified by the reconciliation module (325). Once flagged, the monitoring and alerts module (330) immediately issues a notification through the dashboard. This alert system enables prompt intervention by the operators, helping to mitigate the potential gas loss, the equipment malfunction, or the operational failure before they escalate. The integration of the real-time monitoring with the automated alerting ensures that the system (120) remains responsive, transparent, and capable of maintaining high operational integrity.
[0061] In further operation, an analytics and reporting module (335) is operatively coupled to the monitoring and alerts module (330) which serves as the intelligence layer of the system (120), transforming the raw data (370) and reconciled data (355) into meaningful insights that support strategic decision-making. The analytics and reporting module (335) generates detailed reports based on the reconciled data (355), monitored operational parameters (365), and the recorded transaction data (380) collected across the network. These reports consolidate information from multiple stages and present it in a structured format that highlights the gas flow volumes, the pressure trends, the equipment performance, and the sales activity. By compiling this data into comprehensive reports, the analytics and reporting module (335) enables operators and stakeholders to review the system (120) performance, validate operational accuracy, and maintain regulatory compliance.
[0062] Additionally, the analytics and reporting module (335) analyzes both historical (360) and real-time data (350) to identify operational trends, detect inefficiencies, optimize supply chain performance, and reduce overall operational costs. This involves examining patterns over time, such as the recurring discrepancies, seasonal demand fluctuations, or pressure anomalies, to uncover underlying issues or opportunities for improvement. For example, if the analytics and reporting module (335) detects that a particular daughter station (120b) consistently receives less gas than expected, it may indicate a persistent leak or calibration issue. Similarly, analysis of the transaction data (380) may reveal underperforming retail outlets (120c) or inefficiencies in delivery scheduling.
[0063] In one embodiment of the present invention, the detailed reports generated by the analytics and reporting module (335) comprise comprehensive documentation on gas distribution, sales, and equipment performance across the entire network. These reports are compiled using the reconciled data (355), the real-time data (350), and the recorded transaction data (380), offering a structured overview of how the system (120) is functioning at each stage.
[0064] The detailed reports on the gas distribution provide insights into the volume of gas transferred between the mother stations (120a), the daughter stations (120b), and the retail outlets (120c). They help verify whether the quantities dispatched and received are consistent and traceable throughout the supply network. The sales reports focus on the amount of gas dispensed to the end consumers at the retail outlets (120c), comparing the transaction data (380) with actual dispensing volumes to ensure accuracy and detect any anomalies. The equipment performance reports evaluate the operational health of critical infrastructure such as the compressors, the flowmeters (135a) (135b) (135c), and the pressure sensors (130a) (130b) (130c), highlighting any deviations from expected behavior that may indicate wear, malfunction, or inefficiency. These reports enable the operators to maintain transparency, ensure regulatory compliance, and make informed decisions to optimize the system (120) performance and reduce operational costs.
[0065] Consider a non-limiting embodiment wherein the gas distribution management system (120) is implemented. The system (120) is designed to ensure high-throughput, low-latency data handling and real-time reconciliation across multiple stages of the gas distribution network, including Mother stations (120a), Daughter stations (120b), and retail outlets (120c). Initially, an Internet-of-Things (IoT) module (125a) (125b) (125c) are deployed at each stage of the gas distribution network which receives data (370) from heterogeneous sensors such as flowmeters (135a) (135b) (125c), compressor pressure sensors (130a) (130b) (130c), and dispensing unit monitors (140c). These IoT modules (315) are configured with high-speed communication interfaces to support continuous data (370) acquisition with minimal latency. The incoming data (370) includes flow rate, pressure readings, gas volume transferred, and dispensing transaction data, each tagged with precise timestamps to ensure temporal alignment. The collected data (370) is transmitted to a cloud-integrated data management module (320), which acts as intermediaries between the edge-level IoT infrastructure and a centralized cloud platform. These modules perform initial data (370) formatting and validation before transmitting the data (370) to the cloud for further processing. The centralized cloud platform aggregates the data (370) and applies processing techniques to reconcile the quantities of gas measured at each stage. This includes comparing the volume of gas dispatched from the mother stations (120a) with the volume received at the daughter stations (120b) and ultimately dispensed at the retail outlets (120c). A reconciliation module (325) operating on the cloud platform performs real-time analysis of the aggregated data (370). It monitors the flow and the pressure metrics, evaluates them against historical baselines (360) and predefined operational thresholds (345), and identifies discrepancies such as gas loss, leakage, theft, or measurement inaccuracies. The reconciliation process is continuous and adaptive, ensuring that any deviation from expected behavior is promptly detected. Upon detection of such discrepancies, the monitoring and alerts module (330) generates real-time notifications via the cloud-based dashboard. This dashboard provides a live view of the equipment status and the gas flow across the network, enabling the operators to intervene immediately and mitigate the potential operational failures or the losses. Subsequently, the analytics and reporting module (335) processes both real-time (350) and historical data (360) to generate detailed reports on the gas distribution, sales, and the equipment performance. It identifies operational trends, detects inefficiencies, and provides actionable insights to optimize supply chain performance and reduce costs. These reports are accessible through the dashboard and can be integrated with enterprise resource planning (ERP) systems for broader operational visibility. The system (120) ensures a robust, scalable, and intelligent framework for managing gas distribution, offering transparency, accuracy, and efficiency across the entire supply chain.
[0066] FIG. 4(a) illustrates a flow chart representing the steps involved in a method for efficient gas station management, in accordance with an embodiment of the present disclosure and FIG. 4(b) illustrates continued steps of the method of FIG. 4(a) in accordance with an embodiment of the present disclosure. The method (400) includes receiving, data from a plurality of sensors installed at each of the multiple stages of a gas distribution network. The method involves the real-time acquisition of data from the plurality of sensors deployed across various stages of the gas distribution network in step (405).
[0067] In one embodiment, the sensors, including but are not limited to flowmeters, pressure sensors associated with compressors, and dispensing unit monitors, and the multiple stages include but are not limited to Mother stations, Daughter stations, and retail outlets. Further, the data includes but is not limited to flow rate, pressure readings, gas volume transferred and dispensing transaction data each associated with corresponding timestamps.
[0068] The plurality of sensors stream data in real time to the Internet-of-things module deployed at each stage, ensuring that every stage of gas movement from compression and transfer to final dispensing is captured with high temporal accuracy. This streaming process enables continuous visibility into the operational state of the network and forms the basis for further processing, analysis, and reconciliation of gas quantities throughout the distribution network.
[0069] The method (400) also includes, transmitting, the data to a centralized cloud platform for further processing (410). Once the real-time data is received by the IoT module, this data is transmitted to the centralized cloud platform via a cloud integrated data management module. The centralized cloud platform serves as the central repository and processing hub where all data streams from the gas distribution network converge. This centralized architecture enables uniform access, high availability, and scalability of data handling across the entire system.
[0070] The method (400) also includes, reconciling, the data related to the quantities of gas measured at the various stages of the gas distribution network, using processing techniques (415). After the sensor data is transmitted to the centralized cloud platform, an end-to-end reconciliation is processed to ensure consistency and accuracy in the flow of gas across the distribution network. The reconciliation involves comparing the gas quantities measured at each stage from the initial loading at the mother stations, through transfers to the daughter stations, and ultimately to the dispensing at the retail outlets. The cloud platform uses predefined processing techniques and algorithms to correlate the flowmeter readings, the pressure data, and the dispensing records, taking into account time stamps and transaction logs. This reconciliation determines whether the volume of gas dispatched from one stage aligns with the volume received at the next, and whether the amount of gas sold matches the amount distributed. The process accounts for permissible losses and measurement tolerances but flags any irregularities that deviate from expected values.
[0071] The method (400) also includes monitoring and analyzing, flow rate and the pressure data associated with the gas throughout the gas distribution network (420). Along with the reconciliation of the data, the flow rate and the pressure data associated with the gas throughout the gas distribution network is monitored and analyzed. The flow rates and pressure data analysis detect pressure drop or surge and correlate these variations with specific stations or equipment.
[0072] The method (400) also includes comparing the reconciled data with recorded transactions, expected values, historical baselines, and predefined operational thresholds to verify if the amount of gas sold matches the volume transferred throughout the gas distribution network and to detect one or more discrepancies indicative of operational inefficiencies, the data anomalies, and potential losses (425). Once the data is reconciled and the flow is analyzed, the reconciled data is compared against multiple reference datasets to validate operational integrity. These references include recorded transactions, expected values based on scheduled transfers, the historical baselines derived from the past operational trends, and the predefined operational thresholds that define acceptable performance ranges. By performing this comparison, the system checks whether the amount of gas recorded sold at the retail stations aligns with the quantity of gas that was transferred from the upstream stages.
[0073] In one embodiment, the one or more discrepancies comprises at least one of the gas losses, the gas theft, leakage and measurement inaccuracies within the gas distribution network. The gas losses occur due to evaporation, leaks, or poor sealing. The gas theft involves unauthorized diversion at weak monitoring points. The leakage results from equipment failure or poor maintenance and the measurement inaccuracies stem from faulty sensors, calibration errors, or environmental factors, leading to incorrect data on gas flow, pressure, or volume.
[0074] The method (400) also includes providing a real-time, cloud-based dashboard on the centralized cloud platform for monitoring operational status of the equipment and the gas flow across the gas distribution network (430). To ensure continuous visibility and operational oversight, the cloud-based dashboard consolidates and visualizes data from all the stages of the gas distribution network. This dashboard serves as the primary user interface for operators and decision-makers. The data presented is dynamically updated, allowing users to monitor ongoing operations without delay.
[0075] Further, the method (400) includes generating, an alert upon detection of the one or more discrepancies in the reconciled data, thereby enabling immediate intervention to mitigate the potential gas loss, the equipment malfunction, and the operational failure (435). Upon detection of the one or more discrepancies in the reconciled data the system generates the alert. The discrepancies may include inconsistencies between the gas transferred and sold, abnormal pressure or flow readings, or deviations from the expected operational baselines. When such anomalies are identified, the system triggers alerts through the cloud platform, which are then displayed on the centralized cloud dashboard.
[0076] Furthermore, the method (400) includes generating detailed reports based on the reconciled data, monitored operational parameters, and the recorded transaction data collected across the gas distribution network (440). In addition to real-time monitoring and alerting, the system automatically compiles comprehensive reports that consolidate the reconciled data, the operational metrics, and the transactional records collected from all points within the gas distribution network. These reports provide a detailed overview of the system performance, the gas flow integrity, and the equipment status. Each report includes metrics such as the total gas transferred, received, and sold, along with variance analysis to highlight any imbalances or losses. By combining real-time data with historical logs, the reporting function supports internal audits, regulatory compliance, and operational review. These reports are accessible through the cloud dashboard and can be exported in standard formats e.g., Portable Document Format (PDF) and Excel for sharing with stakeholders or integrating into enterprise resource planning (ERP) systems.
[0077] Moreover, the method (400) includes analyzing the historical and the real-time data to identify operational trends, detect inefficiencies, optimize supply chain performance, and reduce overall operational costs within the gas distribution network. Lastly, both historical and real-time datasets are analyzed to perform in-depth analytics aimed at improving the long-term efficiency and effectiveness of the gas distribution network (445). By continuously aggregating data from the sensors, the transaction logs, and the reconciliation outcomes, the system builds a rich repository of operational intelligence. These insights help in pinpointing inefficiencies such as frequent over-pressurization, recurring discrepancies in gas volumes, or underperforming equipment. Furthermore, the analysis helps optimize logistics, reduce unnecessary transfers, and balance load distribution between stations, leading to better supply chain coordination.
[0078] Thus, various embodiments of the internet-of-things integrated system (120) and method (400) for efficient gas station management provides several benefits in terms of operational transparency, efficiency, and reliability. The system (120) integrates a network of sensors with cloud-based analytics to enable real-time monitoring and reconciliation of gas flow across multiple stages. By automating data (370) collection and analysis, the system (120) eliminates manual errors and ensures that every unit of gas transfer is accurately tracked and accounted for. The reconciliation module (325) enhances accountability by comparing measured quantities with recorded transactions (380) and historical baselines (360), allowing for the early detection of discrepancies such as gas loss, theft, or equipment malfunction. The monitoring and alerts module (330) provides immediate notifications through a centralized dashboard, enabling operators to respond swiftly to anomalies and prevent operational failures. Furthermore, the analytics and reporting module (335) delivers actionable insights by analyzing both real-time and historical data, helping stakeholders identify inefficiencies, optimize supply chain performance, and reduce operational costs. In essence, the system (120) transforms traditional gas distribution into a smart, data-driven process that supports proactive decision-making, improves resource utilization, and ensures the integrity of the entire distribution network.
,CLAIMS:WE CLAIM:
1. An Internet-of-Things integrated system for efficient gas station management, comprising:
a processor;
a memory coupled to the processor, wherein the memory comprises instructions that when executed by the processor cause the processor to:
receive data from a plurality of sensors installed at each of the multiple stages of a gas distribution network;
transmit the data to a centralized cloud platform for further processing;
reconcile the data related to the quantities of gas measured at the various stages of the gas distribution network;
monitor and analyze flow rate and the pressure data associated with the gas throughout the gas distribution network;
compare the reconciled data with recorded transactions, expected values, historical baselines, and predefined operational thresholds to verify if the amount of gas sold matches the volume transferred throughout the gas distribution network and to detect one or more discrepancies indicative of operational inefficiencies, the data anomalies, and potential losses;
provide a real-time, cloud-based dashboard on the centralized cloud platform for monitoring operational status of equipment and the gas flow across the gas distribution network;
generate an alert upon detection of the one or more discrepancies in the reconciled data, thereby enabling immediate intervention to mitigate potential gas loss, equipment malfunction, and operational failure;
generate detailed reports based on the reconciled data, monitored operational parameters, and the recorded transaction data collected across the gas distribution network; and
analyze the historical and the real-time data to identify operational trends, detect inefficiencies, optimize supply chain performance, and reduce overall operational costs within the gas distribution network.

2. The system as claimed in claim 1, wherein the multiple stages of the gas distribution network comprise mother stations, daughter stations, and retail outlets.
3. The system as claimed in claim 1, wherein the data comprises the flow rate, pressure readings, gas volume transferred, and dispensing transaction data, each associated with corresponding timestamps.

4. The system as claimed in claim 1, wherein the plurality of sensors comprises flow meters, pressure sensors associated with compressors, and dispensing unit monitors installed at the gas distribution network.

5. The system as claimed in claim 1, wherein the processing of the data comprises aggregating the transmitted data and analyzing the aggregated data using the processing techniques.

6. The system as claimed in claim 1, wherein the one or more discrepancies comprises the gas losses, the gas theft, leakage and measurement inaccuracies within the gas distribution network.

7. The system as claimed in claim 1, wherein the detailed reports comprise reports on the gas distribution, sales, and the equipment performance.

8. The system as claimed in claim 1, wherein the memory causes the processor to handle the data, the reconciled data, the recorded transactions, the expected values, the historical baselines, the predefined operational thresholds, the recorded transaction data, the monitored operational parameters and the real-time data through a database integrated within the system.

9. A method for efficient gas station management, comprising:
receiving, data from a plurality of sensors installed at each of the multiple stages of a gas distribution network;
transmitting, the data to a centralized cloud platform for further processing;
reconciling, the data related to the quantities of gas measured at the various stages of the gas distribution network, using processing techniques;
monitoring and analyzing, flow rate and the pressure data associated with the gas throughout the gas distribution network;
comparing, the reconciled data with recorded transactions, expected values, historical baselines, and predefined operational thresholds to verify if the amount of gas sold matches the volume transferred throughout the gas distribution network and to detect one or more discrepancies indicative of operational inefficiencies, the data anomalies, and potential losses;
providing, a real-time, cloud-based dashboard on the centralized cloud platform for monitoring operational status of equipment and the gas flow across the gas distribution network;
generating, an alert upon detection of the one or more discrepancies in the reconciled data, thereby enabling immediate intervention to mitigate potential gas loss, equipment malfunction, and operational failure;
generating, detailed reports based on the reconciled data, monitored operational parameters, and the recorded transaction data collected across the gas distribution network; and
analyzing, the historical and the real-time data to identify operational trends, detect inefficiencies, optimize supply chain performance, and reduce overall operational costs within the gas distribution network.

Dated this 01st day of August 2025

Signature

Prakriti Bhattacharya
Patent Agent (IN/PA-5178)
Agent for applicant