Fuel whole-process data management and control method, system, equipment and medium

Abstract

The invention discloses a fuel whole-process data management and control method, system, equipment and medium, and the method comprises the steps: obtaining the data of each data source system, and carrying out the convergence and preprocessing of the data of each data source system; analyzing, mining and displaying the preprocessed data of each data source system; according to the method, the system, the equipment and the medium, intelligent combustion and automatic control optimization can be carried out on the basis of statistical monitoring of the quality of coal entering the plant and the furnace of the thermal power plant.

Description

Technical Field

[0001] The present invention belongs to the technical field of fuel management in coal-fired power generation enterprises, and relates to a method, system, equipment and medium for data control over the entire fuel process. Background Art

[0002] The rapid development of technologies such as big data, the Internet of Things (IoT), mobile internet, cloud computing, 5G mobile applications, artificial intelligence, and 3D visualization has laid the foundation for power generation companies to move from primarily building digital physical infrastructure to developing cleaner, more efficient, and more reliable smart power plants. After more than a decade of development, my country has achieved substantial digitalization at both the plant and unit levels, paving the way for building smart power plants. Some power generation groups have already begun preliminary planning and demonstration for smart power plant construction and are gradually implementing specific technical solutions and pilot projects.

[0003] In recent years, companies such as Huadian, State Power Investment Corporation, State Power Investment Corporation, Zhejiang Energy, Beijing Energy, China General Nuclear Power Group, and China Resources Power have conducted systematic research and individual demonstrations on smart power plants, focusing primarily on the development of big data platforms, mobile internet and 5G applications, 3D visualization, personnel location tracking, safety identification management, and intelligent control technologies. For example, a Beijing-based gas-fired thermal power company has built a multi-dimensional integrated gas-fired smart power plant featuring an integrated cloud platform, seamless one-touch start and stop, full-service mobile applications, 3D fire protection and security, and full-lifecycle equipment data management. A gas-fired power plant in Jiangsu has also implemented a smart power plant that includes an "Internet+"-based safety production management system, an operation optimization system based on big data analysis, an expert system-based 3D visual fault diagnosis system, and a 3D digital archive and visualization training system. In coal-fired power plants, research focuses on 3D technology, intelligent security, fault diagnosis, intelligent monitoring, operation optimization and control, the in-depth development and application of SIS systems, smart coal yards, boiler combustion system optimization, APS systems, generalized artificial intelligence technologies, and other bus and mobile terminal development and applications.

[0004] With the development of the domestic thermal power market, national policies requiring improved power plant efficiency and energy conservation and emission reduction are becoming increasingly stringent. Therefore, optimizing the combustion control systems of large-capacity, high-parameter ultra-supercritical boilers has become highly relevant. Currently, domestic systems utilize combustion control systems based on various operating conditions for a specific coal quality. Data generated guides operational adjustments based on these adjustments. However, when coal quality fluctuates significantly, operators can only make minor adjustments based on parameters monitored by the DCS. This prevents boiler combustion adjustments from achieving optimal combustion conditions in the shortest possible time, significantly reducing boiler combustion efficiency. Currently, combustion adjustments for large units are automatically performed via DCS control. All design air-to-coal ratios, coal-to-water ratios, and power-to-coal ratios are based on the design coal type. Fine-tuning of the air-to-coal ratio and coal-to-water ratio is then performed based on oxygen content, midpoint temperature, or enthalpy. However, when coal quality fluctuates significantly, the correction range and speed are limited, and the system is subject to a hysteresis, severely impacting the effectiveness of combustion control. Consequently, there are few successful cases in China where real-time online monitoring of incoming coal quality has been applied to boiler combustion optimization.

[0005] At present, the application of combustion optimization and adjustment in thermal power units abroad is mainly based on the precise detection and adjustment of air volume. This is because the designed coal types of most power plants abroad remain basically unchanged in actual application. Unlike in China, due to the fluctuations in coal price market and the uncertainty of coal raw material collection, the coal quality fluctuates greatly. Therefore, the combustion optimization and adjustment abroad are mainly based on air volume detection and regulation. There are few research reports and cases on intelligent combustion and automatic control optimization systems based on online monitoring of coal quality entering the furnace of thermal power plants. Summary of the Invention

[0006] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a method, system, equipment and medium for data management and control of the entire fuel process. This method, system, equipment and medium can perform intelligent combustion and automatic control optimization based on statistical monitoring of coal quality entering the power plant and entering the furnace.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] In one aspect, the present invention provides a method for controlling fuel whole-process data, comprising:

[0009] Obtain data from various data source systems, aggregate and pre-process the data from various data source systems;

[0010] Analyze, mine and display the pre-processed data from various data source systems;

[0011] The results of the analysis and mining are forwarded.

[0012] The fuel whole process data control method of the present invention is further improved in that:

[0013] Furthermore, the process of obtaining data from each data source system and aggregating and preprocessing the data from each data source system is as follows:

[0014] By supporting standard transmission protocols, data from various data source systems is obtained, and then the obtained data is cleaned, converted and standardized to form a unified data view.

[0015] Furthermore, the process of analyzing, mining and displaying the pre-processed data from each data source system is as follows:

[0016] Adopt intelligent caching strategies and use BI visual analysis tools to analyze and mine the pre-processed data of each data source system to identify potential optimization opportunities and risk points. Then, the pre-processed data of each data source system and the analysis and mining results are displayed in the form of charts, graphs and dashboards.

[0017] Furthermore, the process of forwarding the analysis and mining results is as follows:

[0018] Based on encryption technology and identity authentication mechanism, a real-time subscription protocol is adopted to integrate the analysis and mining results and the data of each data source system, and then send the integrated results to the external system and the data source system.

[0019] In a second aspect, the present invention provides a fuel whole process data management and control system, comprising:

[0020] The data aggregation function module is used to obtain data from various data source systems, aggregate and pre-process the data from various data source systems;

[0021] Data processing function module, used to analyze, mine and display the pre-processed data from various data source systems;

[0022] The data forwarding function module is used to forward the results of the analysis and mining.

[0023] The fuel full process data control system of the present invention is further improved in that:

[0024] Furthermore, it also includes a data asset center for storing and forwarding pre-processed data, wherein the output end of the data aggregation function module is connected to the data asset center, and the data asset center is connected to the data processing function module and the data forwarding function module.

[0025] Furthermore, it also includes a security assurance function module, and the output end of the data aggregation function module is connected to the data asset center via the security assurance function module.

[0026] Furthermore, it also includes a security assurance module for filtering, verifying, real-time monitoring and auditing of data. Among them, when the data aggregation function module, the data processing function module and the data forwarding function module interact with data, the security assurance module is used to filter, verify, real-time monitor and audit data.

[0027] In the third aspect of the present invention, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the fuel full-process data control method when executing the computer program.

[0028] In a fourth aspect of the present invention, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the fuel full-process data control method are implemented.

[0029] The present invention has the following beneficial effects:

[0030] During specific operation, the fuel whole-process data control method, system, equipment and medium described in the present invention obtain data from each data source system, aggregate and preprocess the data from each data source system, analyze, mine and display the preprocessed data from each data source system, and forward the results of the analysis and mining to achieve integrated data control and processing, solve the data island problem between fuel systems, achieve efficient data flow, improve the operation management level and operation efficiency of each system for system data, provide support for fuel system operation and maintenance decision-making, and reduce the cost and pollution emissions of thermal power plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0032] Figure 1 It is a system structure diagram of the present invention;

[0033] Figure 2 It is a structural diagram of the present invention. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only embodiments of a part of the present invention, not all embodiments, and are not intended to limit the scope of the present invention. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts disclosed in the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present invention.

[0035] The accompanying drawings illustrate schematic diagrams of the structures of the disclosed embodiments of the present invention. These figures are not drawn to scale; for the purpose of clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0036] refer to Figure 2 The hierarchical collaboration concept proposed in the present invention draws on the layered ideas of IaaS (Infrastructure as a Service), SaaS (Software as a Service), and PaaS (Platform as aService) in the cloud computing service model and applies them to the system construction of the present invention. The bottom layer (IaaS) is the hardware device layer, which is composed of various physical devices such as sensors, actuators, monitoring cameras, data acquisition cards, etc., and is responsible for real-time collection of operating data of third-party fuel systems. The middle layer (PaaS) is the third-party business system layer as a platform layer. This layer integrates data from the underlying hardware devices and acts as the manager of each system, participating in the management of the operation and maintenance and decision-making of each system. Layer 2.5 is the smart power plant asset center layer. This layer serves as a bridge between the middle layer and the upper layer and is responsible for the two-way circulation and conversion of data. It includes data adapters, interface services, API management, etc. to ensure the effective transmission and interoperability of data between different systems. The third layer (SaaS) is the smart power plant layer, which can be realized as a service layer for end users for data governance, analysis, decision support and visualization. It includes applications such as dashboards, reporting tools, predictive maintenance, and energy efficiency management, providing users with intuitive operational views and decision support. In this hierarchical collaborative architecture, each layer has distinct responsibilities and works closely with each other to ensure smooth data flow and effective utilization.

[0037] Example 1

[0038] The present invention provides a method for controlling fuel whole-process data, comprising:

[0039] 1) Obtain data from each data source system, aggregate and pre-process the data from each data source system;

[0040] Specifically, data from various data source systems is obtained by supporting standard transmission protocols, and then the obtained data is cleaned, converted and standardized to form a unified data view.

[0041] 2) Analyze, mine and display the pre-processed data from each data source system;

[0042] Specifically, an intelligent caching strategy is adopted, and BI visual analysis tools are used to analyze and mine the pre-processed data of each data source system to identify potential optimization opportunities and risk points. The pre-processed data of each data source system and the analysis and mining results are then displayed in the form of charts, graphs and dashboards.

[0043] 3) Forwarding the results of the analysis and mining.

[0044] Specifically, based on encryption technology and identity authentication mechanism, a real-time subscription protocol is adopted to integrate the analysis and mining results and the data of each data source system, and then the integrated results are sent to the external system and the data source system.

[0045] Example 2

[0046] refer to Figure 1 The fuel whole process data control system of the present invention includes:

[0047] 1. Data aggregation function module

[0048] The data aggregation function module collects and aggregates multi-source heterogeneous data through a standard transmission protocol. The data aggregation function module supports standard transmission protocols and connects to the underlying hardware through standard transmission protocols to obtain data from the underlying hardware, such as Modbus, OPC UA, MQTT, and HTTP, etc., to ensure compatibility with different devices and systems, and to enable seamless docking and data transmission. In addition, the data collected from each data source system is cleaned, converted, and standardized to form a unified data view. In addition, data storage, security, scalability, and compatibility support are provided, and the collected data is displayed at the same time. In addition, it should be noted that the data aggregation function module will not interfere with the data transmission process.

[0049] The data aggregation function module transmits the collected data to the data asset center through the security assurance function module, and the security assurance function module can ensure the security and integrity of data transmission; it should be noted that the present invention adopts the real-time transmission protocol MQTT, so that the data asset center can receive and store data from the data aggregation function module in real time, ensuring the real-time and accuracy of the data. Among them, the lightweight characteristics and low bandwidth consumption of the MQTT protocol make it particularly suitable for real-time data transmission, especially in an environment with unstable network conditions.

[0050] In addition, the data aggregation functional module of the present invention is highly scalable and flexible, and can adapt to the possible future expansion needs of the power plant, including the addition of new data sources and changes to data protocols. This design enables this patent to not only meet current power plant management needs, but also adapt to future technological developments and changes, providing power plants with a long-term, stable, and secure data aggregation solution. In this way, the present invention can provide strong technical support for data management and decision support in smart power plants, promoting the automation and intelligent development of power plant management.

[0051] 2. Data processing function module

[0052] The data processing function module is a core component of the centralized management and control tool. The data processing function module is integrated with advanced business intelligence (BI) visualization analysis tools and a unified management portal. The design of the data processing function module takes into account the user's operational convenience, allowing users to access all fuel-related systems through a single login point to achieve one-stop operation and maintenance management. The BI visualization analysis tool is one of the highlights of the data processing function module. The BI visualization analysis tool obtains the operating data of each system in real time through interaction with the data asset center. The system's operating data not only includes basic operating parameters, but also covers key performance indicators and trend information. By using big data technology and artificial intelligence technologies such as machine learning, the BI visualization analysis tool can conduct in-depth analysis and mining of massive data to identify potential optimization opportunities and risk points. The present invention adopts an intelligent caching strategy to improve the data access speed and system performance of the BI visualization tool, by automatically identifying and storing frequently accessed hot spot data, preloading data that may be needed, and implementing an effective cache invalidation and update mechanism to ensure rapid retrieval and consistency of data. In addition, intelligent caching also involves advanced functions such as personalized caching, hierarchical caching, cache space management, granular control and distributed caching, as well as continuous monitoring and optimization of cache performance, enabling BI visual analysis tools to process data more efficiently, reduce latency, and improve the efficiency and accuracy of user decision-making.

[0053] The data processing function module has the ability to display data. Specifically, it displays the processed data in the form of intuitive charts, graphs and dashboards, allowing power plant operation and maintenance personnel to quickly grasp the system status and make timely decisions. This display method not only improves the readability of the information, but also enhances the user experience, making complex data information easy to understand and operate. In addition, the data processing function module provides flexible configuration options, allowing users to customize the content and format of data display according to specific needs. Users can create personalized data dashboards through drag and drop operations to meet the needs of different roles and scenarios. In addition, the data processing function module supports the access of multiple data sources, including real-time data streams and historical data storage, to ensure the comprehensiveness and diversity of the data.

[0054] 3. Data forwarding function module

[0055] The data forwarding module uses real-time subscription protocols such as MQTT or WebSocket to achieve efficient data flow and dynamic interaction. It integrates processed and raw data to ensure data integrity and consistency. Furthermore, it forwards the integration results to external systems in real time, supporting cross-system data analysis and decision-making.

[0056] To ensure system scalability and maintainability, the data forwarding module adopts a modular design, allowing each component to be independently upgraded and replaced. This is demonstrated by the use of standardized interfaces, such as RESTful APIs or MQTT, to ensure interoperability between different systems. A configuration-driven approach allows for flexible adjustments to system behavior without requiring in-depth code. An abstraction layer isolates underlying changes, simplifying future expansion. A microservices architecture supports horizontal scalability and distributed processing. Load balancing and fault tolerance mechanisms, such as retry logic and the circuit breaker pattern, enhance system stability and reliability.

[0057] Furthermore, the data forwarding module features data downstream capabilities, allowing processed data to flow back to the data source system, enabling two-way data communication. This feedback mechanism not only enhances the system's feedback control capabilities but also enables optimization and adjustment of the existing system based on external system analysis results, thereby achieving more precise and automated control. To ensure the security and reliability of data during transmission, the data forwarding module utilizes advanced encryption technology and authentication mechanisms. Furthermore, the data forwarding module provides detailed logging and monitoring capabilities, enabling every step of data transmission to be tracked and audited, ensuring data transparency and traceability.

[0058] 4. Security Assurance Module

[0059] The security module is an innovative design within the system architecture. Drawing on the concept of a "one-way isolation firewall," it ensures data security and system independence. The core design principle of the security module is to achieve a pseudo-unidirectional data flow, thereby establishing a solid security barrier between the centralized control tool and the fuel-related subsystems. In practice, the security module ensures the security and controllability of data exchange. Through data filtering and verification mechanisms, the module ensures only verified data is transmitted, further enhancing the security of data transmission. Furthermore, through real-time monitoring and auditing capabilities, the security module tracks and records all data exchange activities, ensuring transparency and traceability. If abnormal behavior or potential security threats are detected during monitoring, the security module immediately triggers an alarm and takes appropriate protective measures, such as blocking data transmission or initiating emergency response plans.

[0060] At the same time, in order to enhance the anti-attack capability of the security assurance module, the present invention adopts a multi-layer defense strategy to protect critical systems and data by deploying security measures at multiple levels. The core of the strategy is to establish a series of defense barriers to ensure that even if a certain defense layer is breached, attackers cannot easily penetrate to the next level. Key components include physical security measures, network security controls, host and application security reinforcement, data encryption and access management, continuous security monitoring and auditing, employee security awareness training, and clear security policies and procedures. In addition, disaster recovery plans and business continuity plans are also included to deal with potential security incidents. To adapt to changing security needs, the security assurance module also supports flexible configuration and upgrades. Users can customize data filtering rules and monitoring parameters according to specific security policies and compliance requirements. The design of the module also takes into account compatibility with other security devices, such as firewalls, intrusion detection systems, etc., to achieve more comprehensive security protection.

[0061] It should be noted that, through advanced data processing technology, the present invention enables the system to batch process data from different systems and links to ensure the consistency and accuracy of the data. At the same time, the system provides an intuitive visual interface, allowing operation and maintenance personnel to monitor the full life cycle status of the fuel in real time, thereby making more accurate operation and maintenance decisions. This full life cycle visualization effect not only improves the operational management level of thermal power plants, but also provides strong support for energy conservation, emission reduction and environmental compliance of power plants. In short, the present invention provides a comprehensive, efficient and intelligent solution for fuel management in thermal power plants through its unique hierarchical coordination mechanism and the integrated application of advanced technologies, which promotes the development of the thermal power industry towards digital and intelligent transformation.

[0062] The division of modules in the embodiments of the present application is illustrative and is merely a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the present application may be integrated into a single processor, or may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0063] Example 3

[0064] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method for controlling fuel process data. For example, the steps include: acquiring data from various data source systems, aggregating and preprocessing the data from each data source system; analyzing, mining, and displaying the preprocessed data from each data source system; and forwarding the results of the analysis and mining. The memory may include internal memory, such as high-speed random access memory (RAM), or non-volatile memory, such as at least one disk drive. The processor, network interface, and memory are interconnected via an internal bus, which may be an Industrial Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industrial Standard Architecture (ESIA) bus, or other bus types. The bus may be categorized as an address bus, a data bus, or a control bus. The memory is used to store programs. Specifically, the programs may include program code, which includes computer operating instructions. The memory may include both internal memory and non-volatile memory, and provides instructions and data to the processor.

[0065] Example 4

[0066] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for controlling fuel whole-process data, including, for example: acquiring data from each data source system, aggregating and preprocessing the data from each data source system; analyzing, mining, and displaying the preprocessed data from each data source system; and forwarding the results of the analysis and mining. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0067] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0068] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0069] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0070] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for controlling fuel process data, characterized in that: include: Obtain data from various data source systems, aggregate and pre-process the data from various data source systems; Analyze, mine and display the pre-processed data from various data source systems; The results of the analysis and mining are forwarded.

2. The fuel whole process data control method according to claim 1 is characterized in that: The process of obtaining data from each data source system and aggregating and preprocessing the data from each data source system is as follows: By supporting standard transmission protocols, data from various data source systems is obtained, and then the obtained data is cleaned, converted and standardized to form a unified data view.

3. The fuel whole process data control method according to claim 1 is characterized in that: The process of analyzing, mining and displaying the pre-processed data from each data source system is as follows: Adopt intelligent caching strategies and use BI visual analysis tools to analyze and mine the pre-processed data of each data source system to identify potential optimization opportunities and risk points. Then, the pre-processed data of each data source system and the analysis and mining results are displayed in the form of charts, graphs and dashboards.

4. The fuel whole process data control method according to claim 1 is characterized in that: The process of forwarding the analysis and mining results is as follows: Based on encryption technology and identity authentication mechanism, a real-time subscription protocol is adopted to integrate the analysis and mining results and the data of each data source system, and then send the integrated results to the external system and the data source system.

5. A fuel whole process data control system, characterized by: include: The data aggregation function module is used to obtain data from various data source systems, aggregate and pre-process the data from various data source systems; Data processing function module, used to analyze, mine and display the pre-processed data from various data source systems; The data forwarding function module is used to forward the results of the analysis and mining.

6. The fuel process data control system according to claim 5 is characterized in that: It also includes a data asset center for storing and forwarding pre-processed data, wherein the output end of the data aggregation function module is connected to the data asset center, and the data asset center is connected to the data processing function module and the data forwarding function module.

7. The fuel whole process data control system according to claim 5 is characterized in that: It also includes a security assurance function module, and the output end of the data aggregation function module is connected to the data asset center via the security assurance function module.

8. The fuel process data control system according to claim 5, characterized in that: It also includes a security assurance module for filtering, verifying, real-time monitoring and auditing of data. Among them, when the data aggregation function module, the data processing function module and the data forwarding function module interact with data, the security assurance module is used to filter, verify, real-time monitor and audit data.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the fuel whole process data control method as described in any one of claims 1 to 4 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the fuel whole process data control method as described in any one of claims 1 to 4 are implemented.

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