Regional power grid equipment maintenance-oriented intelligent decision management method and system

Abstract

The invention discloses a regional power grid equipment maintenance-oriented intelligent decision management method and system, and relates to the technical field of power system dispatching automation, and the method comprises the steps: breaking through a business flow through a service interface technology, and dividing a first object into a pre-event stage, an in-event stage and a post-event stage according to a business process; the first object is optimized and balanced in the pre-stage; performing management and control early warning on the first object in the event stage; and performing intelligent evaluation on the first object item and the index in the post-event stage. According to the intelligent decision management method for regional power grid equipment maintenance provided by the invention, fine management of the maintenance process is realized by performing whole-process management on the maintenance plan; repeated power failure and fault power failure time are reduced to the maximum extent by optimizing maintenance plans of different time scales, an intelligent maintenance decision system is constructed through an intelligent decision and management method, active balance of the maintenance plans is achieved, and the maintenance management level is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching automation, and in particular to an intelligent decision-making management method and system for regional power grid equipment maintenance. Background Art

[0002] Equipment maintenance is the most common business in the power system, involving planning, operation, dispatching and other disciplines. Each discipline handles its own business according to the maintenance business process. Maintenance declaration, operation mode, dispatching operation and other links have business interconnection. These interconnected businesses generally have perfect management regulations in their respective systems. However, the management of overlapping business is often easily overlooked, and due to the isolation of each system, it is impossible to automatically achieve closed-loop management of maintenance business.

[0003] At the same time, the continuous deepening of the electricity marketization process and the continuous improvement of users' requirements for electricity reliability and quality require that future power grids must be able to provide safer, more reliable, cleaner and higher-quality electricity supply. It is necessary to accurately locate power outage users in advance, prepare maintenance plans and pre-connect multiple business systems to achieve efficient and reasonable arrangements and releases for power outage users, improve the standardization, process and automation of operation and maintenance, and improve the power supply service level of power supply companies.

[0004] The maintenance business process management is based on the maintenance order information provided by multi-dimensional data. It arranges reasonable maintenance methods according to the maintenance equipment and maintenance time provided in the maintenance order, analyzes the impact of power outages before maintenance on users and load transfer strategies, equipment power outage status during maintenance, and operator recovery and power grid risk after maintenance, realizes the release of risk warnings and power outage information, and evaluates work links, providing advice and support for future power outage maintenance plans and application processing. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is that the relationship between the overlapping parts of the business of each link in the power system is easily overlooked, and how to solve the problem that the business of each profession is isolated by each system and the closed-loop management of the maintenance business cannot be automatically realized.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: an intelligent decision-making management method for regional power grid equipment maintenance, comprising opening up business flows through service interface technology, dividing a first object into three stages: before, during, and after according to the business process; optimizing and balancing the first object in the before stage; managing, controlling, and warning the first object in the during stage; and intelligently evaluating the first object's matters and indicators in the after stage.

[0008] As a preferred solution of the intelligent decision-making management method for regional power grid equipment maintenance described in the present invention, wherein: the opening of business flow through service interface technology includes establishing a system interface through service interface technology, integrating the data of the first object system application module, breaking the information barriers between systems to establish a smooth data environment, and opening up the business flow.

[0009] As a preferred solution of the intelligent decision-making management method for regional power grid equipment maintenance described in the present invention, the business process includes first object management, first object optimization, first object execution status and indicator assessment business process optimization.

[0010] As a preferred solution of the intelligent decision-making management method for regional power grid equipment maintenance described in the present invention, the optimizing balance of the first object in the prior stage includes automatically, intelligently balancing and actively arranging the first object according to optimization principles based on different time scales.

[0011] As a preferred solution of the intelligent decision-making management method for regional power grid equipment maintenance described in the present invention, the automatic, intelligent balancing and active arrangement include: the system classifies, coordinates and optimizes first objects of different time scales; the system reasonably coordinates and arranges the limited resources of the first objects; the system integrates equipment that has a topological link relationship with the first objects.

[0012] As a preferred solution of the intelligent decision-making management method for regional power grid equipment maintenance described in the present invention, wherein: the control and early warning of the first object during the in-process stage includes: focusing on the implementation of the first object in the past few days during the in-process stage; carrying out lean control of each first object; conducting safety verification of the first object; and conducting maintenance analysis, early warning and pre-control of the risks in the implementation process of the first object based on risk prevention measures.

[0013] As a preferred solution of the intelligent decision-making management method for regional power grid equipment maintenance described in the present invention, the intelligent evaluation of the matters and indicators of the first object in the post-event stage includes: conducting a full-process evaluation of all matters and indicators related to the first object in the post-event stage; and using an intuitive and friendly visual interface to display relevant indicators; evaluating the work links according to professional management requirements and forming a report.

[0014] Another object of the present invention is to provide an intelligent decision-making management system for regional power grid equipment maintenance, which can establish a system interface through service interface technology, break down information barriers, establish a smooth data environment, and solve the problem of each system in the power system being unconnected and isolated from each other.

[0015] As a preferred solution of the intelligent decision-making management system for regional power grid equipment maintenance described in the present invention, it includes: a data integration module, a pre-optimization and balancing module, an in-process control and early warning module, and a post-intelligent evaluation module; the multi-data integration module is used to open up the business flow through the service interface technology, and divide the first object into three stages: pre-event, in-event, and post-event according to the business process; the pre-optimization and balancing module is used to optimize and balance the first object in the pre-event stage; the in-event visualization control module is used to control and warn the first object in the in-event stage; the post-event intelligent evaluation module is used to intelligently evaluate the first object's matters and indicators in the post-event stage.

[0016] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of an intelligent decision-making management method for regional power grid equipment maintenance.

[0017] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of an intelligent decision-making management method for regional power grid equipment maintenance.

[0018] Beneficial effects of the present invention: The intelligent decision-making management method for regional power grid equipment maintenance provided by the present invention realizes the rapid declaration, approval, verification and release of maintenance plans through data integration and business process automation, improves the circulation efficiency of maintenance plans, and at the same time, the system automatically balances and optimizes maintenance plans, reduces manual intervention, and improves work efficiency; through the whole process management of maintenance plans, not only the refined management of the maintenance process is realized, but also the operation management level is improved; through the system to monitor the operation status of the power grid in real time and conduct risk assessment, problems can be discovered in time and early warning can be carried out, ensuring the safe and stable operation of the power grid; by optimizing maintenance plans of different time scales, repeated power outages and fault power outages are minimized, and the system reasonably arranges maintenance time and maintenance methods according to the operation status of the power grid and maintenance needs, thereby improving maintenance efficiency; through intelligent decision-making and management methods, an intelligent maintenance decision-making system is constructed, the active balance of maintenance plans is realized, and the maintenance management level is improved. The present invention achieves better results in automation, intelligence and data integration. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0020] Figure 1An overall flow chart of an intelligent decision-making management method for regional power grid equipment maintenance provided in the first embodiment of the present invention.

[0021] Figure 2 A data interaction and business flow chart of an intelligent decision-making management method for regional power grid equipment maintenance provided in the first embodiment of the present invention.

[0022] Figure 3 A maintenance plan optimization diagram at different time scales for an intelligent decision-making management method for regional power grid equipment maintenance is provided in the first embodiment of the present invention.

[0023] Figure 4 An overall flow chart of an intelligent decision-making management system for regional power grid equipment maintenance provided in the fourth embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0025] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides an intelligent decision-making management method for regional power grid equipment maintenance, including: opening up the business flow through the service interface technology, dividing the first object into three stages: before, during, and after according to the business process;

[0026] In the ex ante stage, the first object is optimally balanced;

[0027] During the event, control and early warning are carried out on the first object;

[0028] In the post-event stage, intelligent evaluation is conducted on the first object matters and indicators.

[0029] Furthermore, the service interface technology can adopt WebService network service architecture technology, and allow communication and data exchange between different operating systems and programming languages ​​by using standards such as XML, SOAP, WSDL and UDDI; it can also adopt RESTfulAPI network service architecture technology, and use standard methods such as GET, POST, PUT, DELETE of the HTTP protocol to add, delete, modify and query data, and exchange data in JSON and XML formats.

[0030] It should be noted that the first object may be an equipment inspection plan, an equipment operation and maintenance plan, a power outage plan, an equipment transformation plan, an equipment retirement plan, or a power grid equipment preventive maintenance plan.

[0031] It should also be noted that the optimization and balancing of the first object in the ex ante stage includes automatic, intelligent balancing and proactive arrangement of the first object according to the optimization principle at different time scales; the different time scales are based on the first object according to different time periods, divided into annual implementation plan, quarterly implementation plan, monthly implementation plan, and day-ahead implementation plan.

[0032] Example 2, reference Figure 1 , which is an embodiment of the present invention, provides an intelligent decision-making management method for regional power grid equipment maintenance, including:

[0033] S1: Open up the business flow through service interface technology and divide the first object into three stages: before, during and after the business process.

[0034] Furthermore, opening up the business flow through service interface technology includes establishing a system interface through service interface technology, integrating the data of the first object system application module, breaking down the information barriers between systems, establishing a smooth data environment, and opening up the business flow.

[0035] It should be noted that the business process includes first object management, first object optimization, first object execution status and indicator assessment business process optimization.

[0036] It should also be noted that data can be subdivided into data input, data update, data integration, and data sharing.

[0037] S2: In the preliminary stage, the first object is optimized and balanced.

[0038] Furthermore, optimizing and balancing the first object in the ex ante stage includes automatically, intelligently balancing and actively arranging the first object according to optimization principles based on different time scales.

[0039] It should be noted that automatic, intelligent balancing and active arrangements include: the system classifies, coordinates and optimizes first objects of different time scales; the system rationally coordinates and arranges limited resources of the first objects; and the system integrates devices that have topological links to the first objects.

[0040] It should also be noted that the specific implementation steps for optimizing and balancing the first object in the ex ante stage are that the system divides the first object into an annual implementation plan, a quarterly implementation plan, a monthly implementation plan, and a day-ahead implementation plan according to different time scales.

[0041] There are correlations and dependencies between different implementation plans. The annual implementation plan includes the first quarter implementation plan. The quarterly implementation plan further amends and adjusts the annual implementation plan, while the monthly implementation plan is modified and adjusted based on the quarterly implementation plan. The current implementation plan is the implementation of the determined implementation plan.

[0042] After each specialty and unit submits the preliminary balanced annual implementation plan, the system enters all annual implementation plans according to the annual implementation plan module. The system reorders the annual implementation plans according to the optimization principle, and marks the merged implementation plans with the same color. According to the results of the system's preliminary optimization, manual intervention and modification are carried out. After the final annual implementation plan is determined, it is submitted for review and transferred to the quarterly implementation plan according to the template.

[0043] The quarterly implementation plan is rolled over and optimized. The optimization principles are the same as those for the annual implementation plan. The system obtains the key optimization points of the previous two months and analyzes whether there are repeated optimization points. If so, implementation suggestions are given.

[0044] The system automatically optimizes according to the optimization principles of the monthly implementation plan and conducts a balance analysis. The optimized monthly implementation plan is managed and displayed according to certain principles. Manual intervention and modification are performed according to the results of the system optimization to form the final monthly implementation plan. The monthly implementation plan is exported according to the template and issued. At the same time, monthly technical risk warning data is generated and pushed to the supply and service system and risk warning module respectively.

[0045] S3: Conduct control and early warning on the first object during the event.

[0046] Furthermore, the management, control and early warning of the first object during the in-process stage include: focusing on the implementation status of the first object in the past few days; conducting lean management and control of each first object; conducting safety verification of the first object; and inspecting, analyzing, warning and preventing risks in the implementation process of the first object based on risk prevention measures.

[0047] S4: Conduct intelligent evaluation on the first object items and indicators in the post-event stage.

[0048] Furthermore, the intelligent evaluation of the matters and indicators of the first object in the post-event stage includes a full-process evaluation of all matters and indicators involved in the first object in the post-event stage; and the use of an intuitive and friendly visual interface to display relevant indicators; and the evaluation of work links according to professional management requirements, and the formation of a report.

[0049] It should be noted that the annual, quarterly and monthly implementation plans are compared to evaluate the overall quality and efficiency of the operating units, compare the completion rate of each item indicator, compare the monthly implementation plans, and evaluate the quality of preliminary preparations of the operating units and construction units based on changes in the implementation items. The implementation plan execution rate and the completion rate of each indicator are automatically calculated, and the cause analysis is conducted, and the rationality of the implementation period of the current month is statistically analyzed.

[0050] Example 3, reference Figure 2-Figure 3 , which is an embodiment of the present invention, provides an intelligent decision-making management method for regional power grid equipment maintenance. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0051] First, the maintenance business involves planning, transportation, dispatching and corresponding system application module data. Through the WebService network service architecture technology, the system interface is established, a smooth data environment is established, and the business flow is opened up by integrating data. The data interaction mode and business process between the systems are as follows: Figure 2 As shown, the system divides the maintenance plan into three stages: before, during, and after according to the business process.

[0052] The system will manage maintenance plans in advance based on different time scales such as annual, quarterly and monthly.

[0053] After each specialty and unit submits their optimized maintenance plan, the annual maintenance plan focuses on balanced optimization of repeated power outages, maintenance associations, and maintenance density. The annual maintenance plan module enters all annual maintenance plans, and the system optimizes and coordinates according to "coordinating major repairs and technical reforms, rural power grid projects, municipal projects, business expansion projects, equipment defects, and maintenance pre-tests". The principles of optimization and coordination include not arranging power outages and maintenance work during special periods, not arranging maintenance time for equipment that is difficult to power outage during high-load periods, staggering maintenance time for equipment that cannot be powered off at the same time, and making preliminary judgments on whether the construction period is reasonable, whether the repeated power outage requirements are met, and reasonably arranging maintenance density; the annual maintenance plan is re-arranged according to the optimization principle, and the merged maintenance plan items are put together and marked with the same color. Abnormal power outage plans are reminded, and manual intervention and modification are performed based on the prompts of the preliminary optimization results. After determining the final annual maintenance plan, submit it for review and transfer to the quarterly maintenance plan according to the given template.

[0054] The quarterly maintenance plan is rolled over and optimized. The optimization principle is the same as the annual maintenance plan. It mainly screens for repeated power outages. The system obtains the power outage situation in the previous two months, which includes planned power outages, unplanned power outages, and fault shutdowns. It analyzes whether it is a repeated power outage and whether there is a risk of frequent complaints. Maintenance suggestions are given for lines with more than two power outages.

[0055] The monthly maintenance plan is optimized according to the principle of balancing repeated power outages, maintenance density and maintenance windows. The system automatically optimizes according to the monthly maintenance plan balance principle and power supply protection requirements, and performs a balance analysis at the same time. The balance analysis includes power outage range analysis, risk level analysis, maintenance period and switching operation time rationality assessment, load transfer plan push, equipment defect prompts, important users, sensitive user window period prompts, and daily maintenance density. The optimized monthly maintenance plan is managed and displayed according to certain principles, and prompts are given whether there are repeated power outages, whether the construction period and power outage operation time are reasonable, whether there are important sensitive users (establish a database), whether the workload distribution is reasonable, and equipment defect prompts. Finally, according to the results of the system optimization, manual intervention and modification are performed to form the final monthly maintenance plan, which is exported into a monthly maintenance plan according to the given template and issued. At the same time, the monthly planned power outage information and monthly power grid risk warning are generated and pushed to the supply and service system and risk warning module respectively. The maintenance plan optimization at different time scales is as follows: Figure 3 shown.

[0056] During the process, the system conducts safety verification of the maintenance plan and performs risk prevention.

[0057] Safety verification methods include active power balance analysis, N-1 static safety analysis, and weak operation analysis.

[0058] Active power balance analysis is to analyze the active power balance of the power grid based on the equipment maintenance declaration and equipment ledger information to determine whether there is an active power shortage. If so, the power grid power generation plan or maintenance plan needs to be appropriately adjusted.

[0059] N-1 static safety analysis is to calculate the basic power flow of the power grid according to the maintenance plan and system power generation plan to determine whether there will be power flow exceeding the limit in active sections or lines or important power transmission and transformation equipment during the maintenance period.

[0060] The weak operation analysis is due to the characteristics of the regional power grid structure. During equipment maintenance, weak operation modes are prone to occur. Failures at weak points during maintenance mode can easily lead to serious consequences such as power outages.

[0061] Risk pre-control is to call the power flow calculation, optimal power flow calculation, and topology analysis tools for the power grid faults where the expected faults are concentrated in the maintenance mode, conduct a comprehensive power balance analysis, static safety analysis, and power grid structure analysis, and calculate the severity indicators under the expected faults. The severity indicators include the size (proportion) of load reduction, reliability of high-risk customers, complete shutdown of substations (busbars), power grid decoupling, equipment (section) overload, voltage over-limit, etc., match the rule knowledge base of the application area, and obtain the corresponding deterministic risk level; if the result of the N-1 analysis shows that the active power flow exceeds the limit, the load transfer method is used to adjust the power grid operation mode, and maintenance analysis, early warning, and pre-control are carried out on the visual interface. For the weak points that cannot be eliminated due to various factors, they are used as special instructions for the maintenance plan, requiring the operating personnel to strengthen the control of the risk points.

[0062] In the post-event stage, a full-process evaluation is conducted on all matters and indicators involved in the maintenance plan, and an intuitive and friendly visual interface is used to display relevant indicators. The work links are evaluated according to professional management requirements and a report is generated.

[0063] The plan implementation status and assessment indicators include temporary power outage rate, maintenance plan execution rate, on-time completion rate and other indicators which directly affect the power supply company's benchmarking level against its peers and have always received high attention during the actual operation of the power grid.

[0064] Compare annual, quarterly and monthly maintenance plans, evaluate the quality and efficiency of the overall arrangement of the operating unit, compare the completion rate of power outage plans, the on-time completion rate of maintenance plans, the qualified rate of maintenance plan execution, the repeated power outage rate, etc.; compare the monthly maintenance plan, and evaluate the quality of the preliminary preparations of the operating unit and the construction unit according to the changes in the maintenance application items; automatically count the maintenance plan execution rate, unplanned power outage control rate, repeated power outage rate and on-time power supply rate, and conduct cause analysis; statistically analyze the rationality of the maintenance period of the month, the rationality of the power outage operation time, the correlation between the maintenance window period and user complaints, etc.; count the number of households affected by the power outages in the month and cumulatively.

[0065] Evaluate the work links according to professional management requirements and form an evaluation report to provide advice and support for future power outage maintenance plans and application processing.

[0066] Example 4, reference Figure 4 , which is an embodiment of the present invention, provides an intelligent decision-making management system for regional power grid equipment maintenance, including a data integration module, a pre-optimization and balancing module, an in-process control and early warning module, and a post-intelligent evaluation module;

[0067] Among them, the multi-data integration module is used to open up the business flow through service interface technology, and divide the first object into three stages: before, during, and after according to the business process; the pre-optimization and balance module is used to optimize and balance the first object in the pre-stage; the in-process visualization management and control module is used to perform management and control and early warning on the first object in the in-process stage; the post-intelligent evaluation module is used to perform intelligent evaluation on the matters and indicators of the first object in the post-stage.

[0068] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0069] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0070] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0071] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0072] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent decision-making management method for regional power grid equipment maintenance, characterized in that: include: Through the service interface technology, the business flow is opened up, and the first object is divided into three stages according to the business process: before, during, and after; In the ex ante stage, the first object is optimally balanced; During the event, control and early warning are carried out on the first object; In the post-event stage, intelligent evaluation is conducted on the first object matters and indicators.

2. The intelligent decision-making management method for regional power grid equipment maintenance according to claim 1, characterized in that: The method of opening up the business flow through the service interface technology includes establishing a system interface through the service interface technology, integrating the data of the first object system application module, breaking down the information barriers between systems to establish a smooth data environment, and opening up the business flow.

3. The intelligent decision-making management method for regional power grid equipment maintenance as claimed in claim 2, characterized in that: The business process includes first object management, first object optimization, first object execution status and indicator assessment business process optimization.

4. The intelligent decision-making management method for regional power grid equipment maintenance as claimed in claim 3 is characterized by: The optimizing balance of the first object in the prior stage includes automatically, intelligently balancing and actively arranging the first object according to different time scales in accordance with the optimization principle.

5. The intelligent decision-making management method for regional power grid equipment maintenance as claimed in claim 4, characterized in that: The automatic, intelligent balancing and active arrangement includes the system classifying and coordinating and optimizing first objects of different time scales; The system reasonably coordinates and arranges the limited resources of the first object; The system integrates devices having a topological link relationship with the first object.

6. The intelligent decision-making management method for regional power grid equipment maintenance as claimed in claim 5, characterized in that: The control and early warning of the first object during the mid-event phase includes focusing on the implementation of the first object a few days ago during the mid-event phase; Carry out lean management and control of each first object; Performing a safety check on the first object; The risks in the implementation process of the first object are inspected, analyzed, warned and prevented according to risk prevention measures.

7. The intelligent decision-making management method for regional power grid equipment maintenance according to claim 6, characterized in that: The intelligent evaluation of the matters and indicators of the first object in the post-event stage includes a full-process evaluation of all matters and indicators involved in the first object in the post-event stage; And use an intuitive and friendly visual interface to display relevant indicators; Evaluate work links according to professional management requirements and generate reports.

8. A system using the intelligent decision-making management method for regional power grid equipment maintenance as claimed in any one of claims 1 to 7, characterized in that: It includes data integration module, pre-optimization and balance module, in-process control and early warning module, and post-event intelligent evaluation module; The multi-data integration module is used to open up the business flow through the service interface technology, and divide the first object into three stages: before, during and after the business process; The pre-optimization and balancing module is used to optimize and balance the first object in the pre-stage; The in-process visual control module is used to perform control and early warning on the first object during the in-process stage; The post-event intelligent evaluation module is used to perform intelligent evaluation on the first object items and indicators in the post-event stage.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent decision-making management method for regional power grid equipment maintenance described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent decision-making management method for regional power grid equipment maintenance described in any one of claims 1 to 7 are implemented.

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