Method for supervising fuel data of grassroots thermal power plant by regional branch companies and related device

Abstract

The invention discloses a method and a related device for supervising fuel data of a grassroots thermal power plant by regional branch companies, and belongs to the technical field of regional branch company-level fuel supervising. The method comprises the following steps: defining a plurality of supervising modules and setting supervising rules based on demand analysis of a grassroots thermal power plant fuel management process; abnormal operation or data tampering behaviors in the fuel management process of the basic-level thermal power plant are recognized; checking the supervision basic data corresponding to the behavior data, and completing the collection of incremental data when the data is updated; the supervision data is intelligently analyzed, a visual report is generated, and potential risk fraud behaviors are judged and prompted; and combining a video monitoring module to confirm risk data of the risk point, and inputting the risk data into the intelligent learning training library to complete fuel supervision and control of the basic-level thermal power plant. Through linkage of data safety and intelligent analysis, supervision and intelligent analysis of the whole process of fuel of the basic-level thermal power plant are realized, and the fuel supervision efficiency of the basic-level thermal power plant is optimized.

Description

Technical Field

[0001] The present invention belongs to the technical field of fuel supervision at the regional branch level, and particularly relates to a method and related device for a regional branch to supervise fuel data of grass-roots thermal power plants. Background Art

[0002] In the context of the current energy transformation and the increasingly fierce competition in the thermal power industry, thermal power enterprises are facing problems such as high fuel costs, decentralized management, and low supervision efficiency. As a regional thermal power enterprise, the regional company has a wide jurisdiction range and is involved in coal procurement and management in multiple regions. The traditional supervision mode relies on manual reporting and on-site supervision, resulting in lagging data, insignificant effects of abnormal rectification, and high travel costs. Although grass-roots thermal power units have currently built fuel management systems, the regional company still lacks a unified plant-level supervision platform, making it difficult to achieve centralized supervision and intelligent analysis of the entire fuel process. There is an urgent need to build an advanced plant-level supervision system to optimize fuel management and improve supervision efficiency.

[0003] As an improvement, there have emerged some automated fuel management systems for grass-roots thermal power plants in the prior art. However, although grass-roots thermal power units have currently built fuel management systems, the regional branch still lacks a unified plant-level supervision platform, making it difficult to achieve centralized supervision and intelligent analysis of the entire fuel process, resulting in low fuel supervision efficiency for grass-roots thermal power plants. Summary of the Invention

[0004] The present invention provides a method and related device for a regional branch to supervise fuel data of grass-roots thermal power plants, aiming to solve the problem that although grass-roots thermal power units have currently built fuel management systems, the regional branch still lacks a unified plant-level supervision platform, making it difficult to achieve centralized supervision and intelligent analysis of the entire fuel process, resulting in low fuel supervision efficiency for grass-roots thermal power plants.

[0005] To achieve the above object, the present invention adopts the following technical solutions: The present invention provides a method for a regional branch to supervise fuel data of grass-roots thermal power plants, including the following steps: S1. Based on the requirement analysis of the fuel management process of grass-roots thermal power plants, define several supervision modules; The supervision modules include a metering supervision module, a sampling supervision module, a coal quality supervision module, an event supervision module, and a video monitoring module; S2. Based on the defined several supervision modules, set supervision rules for each supervision module, and adopt data intelligent acquisition and comparative analysis technology to identify abnormal operations or data tampering behaviors in the fuel management process of each grass-roots thermal power plant, and identify the behavior data; S3. Use data encryption and decryption technology and timestamp comparison technology to verify the basic supervisory data corresponding to the behavioral data, and complete the collection of incremental data in real time when the data is updated to obtain supervisory data; S4. Conduct intelligent analysis of supervisory data, generate visual reports, and use artificial intelligence technology to identify potential fraudulent activities and issue early warning alerts for identified risk points. S5. Combined with the video surveillance module, the risk data of risk points are confirmed through the video surveillance platform, and the risk data is input into the intelligent learning training library to complete the fuel supervision and control of grassroots thermal power plants.

[0006] In some implementation methods, in S1, the metering supervision module is used to collect the metering data and mine data of each grassroots thermal power plant under the transportation mode, the sampling supervision module is used to summarize the sampling rate and plan execution data of the coal samples entering the plant and the furnace of each power plant, the coal quality supervision module is used to compare and analyze the quality of the coal inspected, re-inspected and arriving at the plant, the event supervision module is used to collect fuel abnormal event data and classify and count them, and the video monitoring module is used to access the real-time video monitoring data of the entire fuel process.

[0007] In some implementations, in S2, the supervision rules include: comparative analysis rules for mine data and factory metering data, abnormal sampling rate alarm rules, abnormal analysis model rules based on coal quality indicators, event classification rules, and closed-loop rectification tracking rules; The intelligent data acquisition and comparative analysis technology includes automatic profit and loss calculation of automobile and train measurement data and mining data in the measurement supervision module.

[0008] In some implementations, in S3, the data encryption and decryption technology includes encrypting and storing key indicators according to a preset encryption method, and performing reverse decryption verification during data collection; the timestamp comparison technology includes monitoring changes in data timestamps to trigger real-time upload.

[0009] In some implementations, in S4, intelligent analysis includes: correlating and analyzing real-time monitoring data with historical data, and troubleshooting root causes of problems through big data statistics and artificial intelligence algorithms.

[0010] In some embodiments, in S4, the visual report includes fuel consumption calculation results, fuel allocation strategy data, multi-dimensional data analysis results and trend forecast information.

[0011] In some implementations, in S5, the video surveillance platform retrieves historical operation videos of corresponding business nodes based on the determined potential risk fraud behavior; the intelligent learning training library builds a training material library by storing confirmed risk data.

[0012] The present invention also provides a supervision system for fuel data of grass-roots thermal power plants by regional branches. The system includes a definition module, a data identification module, a data collection module, a data analysis module, and a supervision and control module, where: The definition module: is used to define a number of supervision modules based on the requirement analysis of the fuel management process of grass-roots thermal power plants; The supervision modules include a metering supervision module, a sampling supervision module, a coal quality supervision module, an event supervision module, and a video monitoring module; The data identification module: is used to set supervision rules for each supervision module based on the defined number of supervision modules, and use data intelligent acquisition and comparative analysis technology to identify abnormal operations or data tampering behaviors in the fuel management process of each grass-roots thermal power plant, and identify the behavior data; The data collection module: is used to verify the supervision basic data corresponding to the behavior data by using data encryption and decryption technology and timestamp comparison technology, and complete the real-time acquisition of incremental data when the data is updated to obtain supervision data; The data analysis module: is used to perform intelligent analysis on the supervision data, generate a visual report, and use artificial intelligence technology to judge potential risk fraud behaviors, and give early warning prompts for the judged risk points; The supervision and control module: is used to combine with the video monitoring module to confirm the risk data of the risk points through the video monitoring platform, and input the risk data into the intelligent learning training library to complete the fuel supervision and control of grass-roots thermal power plants.

[0013] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor is used to implement the steps of the supervision method for fuel data of grass-roots thermal power plants by regional branches when executing the computer program.

[0014] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the supervision method for fuel data of grass-roots thermal power plants by regional branches are implemented.

[0015] Compared with the prior art, the supervision method and related devices for fuel data of grass-roots thermal power plants by regional branches of the present invention have the following beneficial effects: The fuel supervision and control method of the present invention is based on the fuel data of each grass-roots thermal power plant, uses big data models and artificial intelligence technologies to complete the collection and intelligent analysis of system data, establishes a fuel supervision and management system and method, and intelligently identifies fraud risk points. The present invention improves the supervision efficiency, realizes the centralized supervision of the whole process of fuel management, and reduces the dependence on manual reporting and on-site supervision. It automatically generates an abnormal analysis report, shortens the problem response time; optimizes the fuel cost, reduces the economic losses caused by measurement errors and coal quality deviations through measurement, coal quality and sampling supervision, accurately calculates the fuel consumption of each power plant, and optimizes the allocation strategy; enhances decision-making support, provides multi-dimensional data analysis and trend prediction, and provides a scientific basis for fuel procurement and cost control. It visually displays supervision indicators and supports cross-departmental collaborative management; reduces management risks, discovers and processes abnormal events in fuel management in a timely manner through video monitoring and data early warning. A closed-loop event management mechanism is established to ensure the rectification effect. The present invention can also identify intelligent fraud behaviors. By using big data technology and artificial intelligence algorithms, statistical analysis is carried out on each supervision data to discover potential fraud risks, which facilitates the fuel risk management of regional companies.

[0016] On the other hand, the present invention provides a fuel supervision and control system based on this method, and provides supporting computer equipment and computer-readable storage media. When performing data processing tasks or loading these programs onto any compatible computer equipment, it can accurately perform numerical calculations and logical judgments, ensure the accuracy of data, and improve the effectiveness of fuel management and supervision in grass-roots thermal power plants. Brief Description of the Drawings

[0017] The accompanying drawings in the specification are used to provide a further understanding of the present invention, and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0018] Figure 1 It is a flow chart of the supervision method of the fuel data of grass-roots thermal power plants by the regional branch of the present invention; Figure 2 It is a flow chart of the embodiment in the supervision method of the fuel data of grass-roots thermal power plants by the regional branch of the present invention. Detailed Embodiments

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and illustrated here can be arranged and designed in various different configurations.

[0020] Accordingly, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0021] It should be noted that like reference numerals and letters denote like items in the following drawings. Therefore, once an item is defined in one drawing, it does not require further definition and explanation in subsequent drawings.

[0022] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper", "lower", "horizontal", "inner", etc. are used to indicate the orientation or positional relationship, it is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed during use. This is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, terms such as "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0023] In addition, if the term "horizontal" appears, it does not mean that the component is required to be absolutely horizontal, but it can be slightly inclined. For example, "horizontal" only means that its direction is more horizontal relative to "vertical", and does not mean that the structure must be completely horizontal, but it can be slightly inclined.

[0024] In the description of the embodiments of the present invention, it should also be noted that unless otherwise clearly specified and limited, if terms such as "set", "installed", "connected", "connected" are used, they should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0025] How to realize the efficient and accurate supervision of the fuel of the grass-roots thermal power plant through data integration of the plant-side management system of the existing grass-roots thermal power plant, data integration, intelligent analysis and centralized display based on the concept of integrated control and intelligent analysis.

[0026] As Figure 1 shown, the supervision method of the fuel data of the grass-roots thermal power plant by the regional branch of the present invention includes the following steps: S1. Define several supervision modules based on the requirement analysis of the fuel management process of the grass-roots thermal power plant; The supervision module includes measurement supervision module, sampling supervision module, coal quality supervision module, event supervision module and video monitoring module; S2. Based on the defined supervision modules, set supervision rules for each module and use intelligent data acquisition and comparative analysis technology to identify abnormal operations or data tampering in the fuel management process of each grassroots thermal power plant and identify behavioral data; S3. Use data encryption and decryption technology and timestamp comparison technology to verify the basic supervisory data corresponding to the behavioral data, and complete the collection of incremental data in real time when the data is updated to obtain supervisory data; S4. Conduct intelligent analysis of supervisory data, generate visual reports, and use artificial intelligence technology to identify potential fraudulent activities and issue early warning alerts for identified risk points. S5. Combined with the video surveillance module, the risk data of risk points are confirmed through the video surveillance platform, and the risk data is input into the intelligent learning training library to complete the fuel supervision and control of grassroots thermal power plants.

[0027] The present invention defines a supervision system including a metering supervision module, a sampling supervision module, a coal quality supervision module, an event supervision module, and a video monitoring module, and constructs a module framework covering the entire fuel management process based on demand analysis, thereby achieving standardized management and providing a unified basis for subsequent rule setting. By setting supervision rules for each supervision module, including mine data comparison, abnormal sampling rate alarm, coal quality index analysis, event classification, and closed-loop rectification tracking, combined with data intelligent acquisition and comparative analysis technology, abnormal operations or data tampering in fuel management are automatically identified, reducing manual intervention and improving the efficiency of abnormality detection. The present invention uses data encryption and decryption technology to encrypt and store key indicators, combines timestamp comparison technology to monitor data updates in real time and trigger uploads, ensuring the security of data during transmission and storage, and solving the lag problem of traditional manual reporting.

[0028] Moreover, the present invention conducts correlation analysis on real-time and historical supervision data through the regional company's big data analysis platform, uses artificial intelligence technology to judge potential fraud risks and generate visual reports including fuel consumption accounting, allocation strategy, multi-dimensional analysis and trend forecasting, providing data support for decision-making; through the video monitoring platform, historical operation videos corresponding to risk points are retrieved for verification, and the confirmed data is input into the intelligent learning training library to form risk identification and algorithm optimization, thereby continuously improving the accuracy of fuel supervision analysis and control.

[0029] In some embodiments, the supervised basic data of the present invention serves as the data support for the big data analysis of regional companies. The accuracy and timeliness of the data are particularly important. In the data collection link, data encryption and decryption technology and timestamp comparison technology are used to verify the key indicators of the data, and when the data is updated, the incremental data is collected in real time.

[0030] From the perspective of the regional company, the supervision data collection and display of each power plant are completed. Using big data technology, the collected supervision data is intelligently analyzed to output visual reports, providing data support for leadership decision-making. At the same time, artificial intelligence technology is adopted to intelligently judge possible risk fraud behaviors and give corresponding prompts, achieving the function of intelligent identification of fuel management risk fraud.

[0031] Furthermore, the measurement supervision module of the present invention automatically calculates transportation profits and losses, the sampling supervision module real-time verifies the sampling plan, the coal quality supervision module cross-verifies the authenticity of coal quality through spot-check / re-inspection data, the event supervision module classifies and counts abnormal events, and the video monitoring module provides a record of the entire operation process. Combined with the data intelligent acquisition technology, the abnormal operation nodes can be quickly located, improving the supervision efficiency.

[0032] The present invention uses encryption technology to encrypt and store key indicators, combined with a reverse decryption verification mechanism to effectively prevent data tampering. The timestamp comparison technology real-time monitors the data update timestamp, triggering the real-time upload of incremental data to ensure the integrity and timeliness of the supervision data. And the present invention uses association analysis technology to compare the real-time supervision data with historical data, industry standards, and regional benchmarks in multiple dimensions, automatically detecting problems such as systematic deviations or fraud behaviors.

[0033] The visual report of the present invention covers content such as fuel consumption accounting, allocation strategy optimization, multi-dimensional data analysis, and trend prediction, supporting the management to intuitively evaluate risks and adjust decisions. And through the coordinated action of the video monitoring platform and the risk warning system of the present invention, the historical videos of the corresponding business nodes are automatically retrieved for potential fraud behaviors, realizing the reproduction and traceability of the operation process, and optimizing the supervision and control of fuels.

[0034] The present invention also provides a supervision system for the fuel data of grass-roots thermal power plants by regional branch companies. The system includes a definition module, a data identification module, a data collection module, a data analysis module, and a supervision and control module, where: Definition module: used to define a number of supervision modules based on the requirements analysis of the fuel management process of grass-roots thermal power plants; The supervision modules include a measurement supervision module, a sampling supervision module, a coal quality supervision module, an event supervision module, and a video monitoring module; Data identification module: Used to set supervision rules for each of several defined supervision modules, and adopt data intelligent acquisition and comparative analysis technology to identify abnormal operations or data tampering behaviors in the fuel management process of each grass-roots thermal power plant, and identify behavior data; Data acquisition module: Used to verify the supervision basic data corresponding to the behavior data by using data encryption and decryption technology and timestamp comparison technology, and complete the acquisition of incremental data in real time when the data is updated to obtain supervision data; Data analysis module: Used to perform intelligent analysis on the supervision data, generate visual reports, and use artificial intelligence technology to judge potential risk fraud behaviors, and give early warning prompts for the judged risk points; Supervision and control module: Used to combine with the video monitoring module, confirm the risk data of the risk points through the video monitoring platform, and input the risk data into the intelligent learning training library to complete the fuel supervision and control of the grass-roots thermal power plant.

[0035] The following further details a supervision method and related device for the fuel data of grass-roots thermal power plants by a regional branch of the present invention through specific embodiments.

[0036] As Figure 2 shown, the application of the supervision system for the fuel data of grass-roots thermal power plants by the regional branch of the present invention in specific working conditions. The system architecture includes five parts, namely the plant-level supervision platform, data acquisition, regional company big data analysis platform, video monitoring platform, and intelligent learning training library. The specific details of each part are as follows: Plant-level supervision platform; On this platform, according to the fuel management process, supervision data and process operation specifications are defined. The main supervision data is as follows: Measurement supervision. Collect the in-plant measurement data under various transportation methods of each power plant, and check the quantity of coal shipped from the mine; conduct comparative analysis on the measurement data of trucks and trains and the data of coal shipped from the mine, automatically calculate the profit and loss, and identify abnormal measurement behaviors. Sampling supervision: Summarize the sampling rates and the implementation status of the sampling plans for the incoming and in-furnace coal samples of each power plant; mark and alarm abnormal data and automatically generate supervision reports. Coal quality supervision: Conduct comparative analysis on the spot-check, re-check, and in-plant coal quality; establish a coal quality abnormal analysis model based on coal quality indicators (such as calorific value, sulfur content, etc.) to identify potential risks. Event supervision: Collect the fuel abnormal event data of each power plant, classify and count them according to A / B / C type events and generate event briefings; provide the function of tracking the event handling status to ensure the closed-loop rectification of problems. Video monitoring: Connect to the real-time video monitoring of the whole process of the company's fuel, covering key links such as sampling, acceptance, and storage; through the video early warning mechanism, remotely confirm the abnormal conditions at the fuel site.

[0037] Data collection. This function is mainly based on data encryption / decryption and real-time upload technology through comparison of data timestamps. In the data encryption process, when the factory-side data platform generates supervision data, key indicators are encrypted and stored according to a preset encryption method. When collecting data, reverse decryption verification of the data is required, which greatly improves the accuracy of the data. On the other hand, the database monitors the data timestamps. When the data timestamps change, the data is uploaded in real time, solving the problem of data timeliness.

[0038] Big data analysis platform of regional company. This platform mainly conducts statistical analysis on the supervision data collected from each subsystem, and analyzes the real-time data collected through supervision in combination with historical data. On the one hand, data display is carried out to facilitate supervision and management personnel to master the real-time data situation of each on-site supervision link. On the other hand, using big data statistics and artificial intelligence, intelligent cause investigation of supervision data with higher frequencies and greater risk points is carried out, and systematic suggestions are given so that supervision personnel can track the root cause of problems faster and more accurately, and prevent the occurrence of fuel risk fraud behaviors.

[0039] Video monitoring. Compared with traditional video monitoring, this video monitoring platform can not only view the real-time videos and past videos of each camera, but also adds an abnormal behavior video linkage function. For the fraud behaviors obtained in the big data analysis platform, it can be located to view the historical videos of the on-site operations at that business node at that time to confirm whether there are fraud operations in this behavior.

[0040] Intelligent learning training library. The confirmed behaviors can be stored to gradually form a training library, enabling artificial intelligence to have self-learning materials to improve the reliability of the entire platform in fuel supervision in the later stage.

[0041] Finally, it should be noted that: The above is only the preferred embodiment of the present invention, and does not impose any form of limitation on the present invention; Any person skilled in the art can smoothly implement the present invention according to the description in the specification and the above. Slight changes, modifications and equivalent changes in evolution made by using the technical content disclosed above are all equivalent embodiments of the present invention; At the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A supervision method for the fuel data of grass-roots thermal power plants by regional branches, characterized in that The steps include: S1. Based on the demand analysis of the fuel management process of grassroots thermal power plants, define several supervision modules; The supervision module includes measurement supervision module, sampling supervision module, coal quality supervision module, event supervision module and video monitoring module; S2. Based on the defined supervision modules, set supervision rules for each module and use intelligent data acquisition and comparative analysis technology to identify abnormal operations or data tampering in the fuel management process of each grassroots thermal power plant and identify behavioral data; S3. Use data encryption and decryption technology and timestamp comparison technology to verify the basic supervisory data corresponding to the behavioral data, and complete the collection of incremental data in real time when the data is updated to obtain supervisory data; S4. Conduct intelligent analysis of supervisory data, generate visual reports, and use artificial intelligence technology to identify potential fraudulent activities and issue early warning alerts for identified risk points. S5. Combined with the video surveillance module, the risk data of risk points are confirmed through the video surveillance platform, and the risk data is input into the intelligent learning training library to complete the fuel supervision and control of grassroots thermal power plants.

2. The regulatory method of the fuel data of the grass-roots thermal power plants by the regional branch according to claim 1, characterized in that In said S1, the metering supervision module is used to collect the metering data and mine data of each grassroots thermal power plant under the transportation mode; the sampling supervision module is used to summarize the sampling rate and plan execution data of the coal samples entering the plant and the furnace of each power plant; the coal quality supervision module is used to compare and analyze the quality of the coal inspected, re-inspected and arriving at the plant; the event supervision module is used to collect fuel abnormality event data and classify and count them; the video monitoring module is used to access the real-time video monitoring data of the entire fuel process.

3. The regulatory method of fuel data of grass-roots thermal power plants by regional branches according to claim 1, characterized in that In S2, the supervision rules include: comparative analysis rules for mine data and factory metering data, abnormal sampling rate alarm rules, abnormal analysis model rules based on coal quality indicators, event classification rules, and closed-loop rectification tracking rules; The intelligent data acquisition and comparative analysis technology includes automatic profit and loss calculation of automobile and train measurement data and mining data in the measurement supervision module.

4. The regulatory method of the fuel data of the grass-roots thermal power plants by the regional branch according to claim 1, characterized in that, In the S3, the data encryption and decryption technology includes encrypting and storing key indicators according to a preset encryption method, and performing reverse decryption verification during data collection; the timestamp comparison technology includes monitoring changes in data timestamps to trigger real-time upload.

5. The regulatory method of the fuel data of the grass-roots thermal power plants by the regional branch according to claim 1, characterized in that, In said S4, intelligent analysis includes: correlating and analyzing real-time monitoring data with historical data, and troubleshooting the root causes of problems through big data statistics and artificial intelligence algorithms.

6. The supervision method of the fuel data of the grass-roots thermal power plants by the regional branch according to claim 1, characterized in that, In said S4, the visual report includes fuel consumption calculation results, fuel allocation strategy data, multi-dimensional data analysis results and trend forecast information.

7. The supervision method of the fuel data of the grass-roots thermal power plants by the regional branch according to claim 1, characterized in that In S5, the video surveillance platform retrieves the historical operation video of the corresponding business node based on the potential risk fraud behavior identified; The intelligent learning training library builds a training material library by storing confirmed risk data.

8. The system on which the supervision method of the fuel data of the grass-roots thermal power plants by the regional branch company described in any one of claims 1-7 is based, characterized in that, The system includes a definition module, a data identification module, a data acquisition module, a data analysis module and a supervision and control module, wherein: Definition module: used to define several supervision modules based on the demand analysis of the fuel management process of grassroots thermal power plants; The supervision module includes a metering supervision module, a sampling supervision module, a coal quality supervision module, an event supervision module, and a video monitoring module; The data identification module: For a number of defined supervision modules, it sets supervision rules for each supervision module, and uses data intelligent acquisition and comparative analysis technology to identify abnormal operations or data tampering behaviors in the fuel management process of each grass-roots thermal power plant, and identifies the behavior data; The data acquisition module: It is used to verify the supervision basic data corresponding to the behavior data by using data encryption and decryption technology and timestamp comparison technology, and to complete the real-time acquisition of incremental data when the data is updated, so as to obtain supervision data; The data analysis module: It is used to perform intelligent analysis on the supervision data, generate visual reports, and use artificial intelligence technology to judge potential risk fraud behaviors, and give early warning prompts for the judged risk points; The supervision and control module: It is used to combine with the video monitoring module, confirm the risk data of the risk points through the video monitoring platform, and input the risk data into the intelligent learning training library to complete the fuel supervision and control of the grass-roots thermal power plant.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor is used to implement the steps of the supervision method of the fuel data of the grass-roots thermal power plant by the regional branch as described in any one of claims 1-7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the supervision method of the fuel data of the grass-roots thermal power plant by the regional branch as described in any one of claims 1-7.

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