Global Monitoring Method, System, Terminal and Storage Medium for Operating Status of Virtual Power Plant

Abstract

The present invention relates to the technical field of virtual power plants, and specifically provides a method, a system, a terminal and a storage medium for globally monitoring the operating state of a virtual power plant, including: collecting the number of responses and the average response time of each subordinate virtual power plant to scheduling instructions within a monitoring period; calculating the weighted sum of the number of responses and the average response time of the subordinate virtual power plants to obtain a response score; screening out abnormal subordinate virtual power plants whose response scores do not reach the response threshold, and issuing a self-check task to the abnormal subordinate virtual power plants, so that the abnormal subordinate virtual power plants execute the self-check task based on a pre-set self-check strategy; receiving the self-check data returned by the abnormal subordinate virtual power plants, and saving the self-check data to a monitoring log. The present invention reduces the occupation of computing resources for global monitoring and ensures the accuracy of global monitoring.

Description

Technical Field

[0001] The present invention belongs to the technical field of virtual power plants, and particularly relates to a global monitoring method, system, terminal and storage medium for the operating status of a virtual power plant. Background Art

[0002] A virtual power plant is a power coordination management system that realizes the aggregation and coordinated optimization of DERs such as DGs, energy storage systems, controllable loads, and electric vehicles through advanced information and communication technologies and software systems, and participates in the power market and grid operation as a special power plant. The core of the virtual power plant concept can be summarized as "communication" and "aggregation". The key technologies of virtual power plants mainly include coordinated control technology, intelligent metering technology, and information and communication technology. The most attractive function of virtual power plants is their ability to aggregate DERs to participate in the operation of the power market and ancillary service market, and provide management and ancillary services for the distribution network and transmission network.

[0003] Currently, the deployment architecture of virtual power plants is a hierarchical architecture, that is, one upper-level virtual power plant and multiple lower-level virtual power plants. The upper-level virtual power plant has the authority to monitor, dispatch, and manage the lower-level virtual power plants. Monitoring the operating status is the basis for ensuring the stability of the entire system. However, due to the large number of virtual power plants and the large amount of monitoring data, the upper-level virtual power plant needs to consume a large amount of computing power to monitor the lower-level virtual power plants, seriously affecting the processing efficiency of other services. Summary of the Invention

[0004] In view of the above deficiencies of the prior art, the present invention provides a global monitoring method, system, terminal and storage medium for the operating status of a virtual power plant to solve the above technical problems.

[0005] In a first aspect, the present invention provides a global monitoring method for the operating status of a virtual power plant, including:

[0006] Collecting the number of responses and average response time of each lower-level virtual power plant to dispatch instructions within a monitoring period;

[0007] Calculating the weighted sum of the number of responses and average response time of the lower-level virtual power plant to obtain a response score;

[0008] Screening out abnormal lower-level virtual power plants whose response scores do not reach the response threshold, and issuing a self-check task to the abnormal lower-level virtual power plants so that the abnormal lower-level virtual power plants execute the self-check task based on a pre-set self-check strategy;

[0009] Receiving the self-check data returned by the abnormal lower-level virtual power plants and saving the self-check data to the monitoring log.

[0010] In an optional embodiment, collecting the number of responses and average response time of each lower-level virtual power plant to dispatch instructions within a monitoring period includes:

[0011] Record the dispatched scheduling instructions and the dispatch time in the operation log, as well as the received response information, the subordinate virtual power plants to which the response information belongs, and the time of receiving the response information;

[0012] Take the time difference between the time of receiving the response information and the dispatch time as the response time;

[0013] Based on the operation log, count the response times and average response times of each subordinate virtual power plant during the current monitoring period.

[0014] In an optional embodiment, the method further includes:

[0015] Configure a self-checking policy, which includes monitoring metrics and a monitoring metric analysis method based on a clustering algorithm;

[0016] Deploy the self-checking policy to all subordinate virtual power plants.

[0017] In an optional embodiment, configuring a self-checking policy includes:

[0018] Analyze the business types of subordinate virtual power plants, hierarchically sort out the functional module sequences involved in each business type, and write the functional module sequences corresponding to each business type into a target file;

[0019] By configuring probe scripts for the functional modules in the target file, obtain the requests processed by the functional modules and the processing results, and then count the success rate based on the requests processed by the functional modules and the processing results;

[0020] Cluster the success rates and request processing quantities of each functional module, and calculate the weighted sum of the success rate and request processing quantity of the centroid of each cluster. The weight of the request processing quantity is negative and the weight of the success rate is positive, and filter out the abnormal cluster with the smallest weighted sum;

[0021] Match the abnormal functional modules included in the abnormal cluster with the functional module sequence, and output the successfully matched abnormal functional modules as an abnormal module combination;

[0022] Sort each abnormal function module in the abnormal module combination in ascending order of success rate. If the abnormal function module ranked first is the bottom - layer function module, write the bottom - layer function module into the warning message; if the abnormal function module ranked first is not the bottom - layer function module, calculate the difference in success rate between the abnormal function module ranked first and the bottom - layer function module. If the difference in success rate does not exceed the set threshold, write the corresponding bottom - layer function module into the warning message; if the abnormal function module ranked first is not the bottom - layer function module and the difference in success rate exceeds the set threshold, write the abnormal function module ranked first into the warning message;

[0023] Repair the function modules in the warning message, and write the warning message and repair operations into the self - inspection log.

[0024] In an alternative embodiment, receive the self - inspection data returned by the abnormal subordinate virtual power plant, and save the self - inspection data to the monitoring log, including:

[0025] Receive the self - inspection data returned by the abnormal subordinate virtual power plant, where the self - inspection data includes the self - inspection log and the real - time load and operating status of the aggregated resources;

[0026] Use the visualization plugin to convert the self - inspection log and the real - time load and operating status of the aggregated resources into display data;

[0027] Save the self - inspection data to the monitoring log;

[0028] Count the subordinate virtual power plants corresponding to the self - inspection data in the monitoring log. If the same subordinate virtual power plant appears in adjacent monitoring periods, send the information of the subordinate virtual power plant to the operation and maintenance management terminal.

[0029] In a second aspect, the present invention provides a global monitoring system for the operating state of a virtual power plant, including:

[0030] A response monitoring module, configured to collect the number of responses and the average response time of each subordinate virtual power plant to the dispatching instruction within a monitoring period;

[0031] A response evaluation module, configured to calculate the weighted sum of the number of responses and the average response time of the subordinate virtual power plant to obtain a response score;

[0032] An abnormality screening module, configured to screen out abnormal subordinate virtual power plants whose response scores do not reach the response threshold, and issue a self - inspection task to the abnormal subordinate virtual power plants, so that the abnormal subordinate virtual power plants execute the self - inspection task based on a pre - set self - inspection strategy;

[0033] A result saving module, configured to receive the self - inspection data returned by the abnormal subordinate virtual power plant, and save the self - inspection data to the monitoring log.

[0034] In an optional embodiment, the response monitoring module includes:

[0035] A data recording unit, configured to record the dispatched scheduling instructions and the dispatch time in the operation log, as well as the received response information, the subordinate virtual power plant to which the response information belongs, and the time of receiving the response information;

[0036] An actual calculation unit, configured to use the time difference between the time of receiving the response information and the dispatch time as the response time;

[0037] A data statistics unit, configured to statistically calculate the response times and average response times of each subordinate virtual power plant during the current monitoring period based on the operation log.

[0038] In an optional embodiment, the system further includes:

[0039] A policy configuration module, configured to configure a self-checking policy, where the self-checking policy includes monitoring metrics and a monitoring metric analysis method based on a clustering algorithm;

[0040] A policy deployment module, configured to deploy the self-checking policy to all subordinate virtual power plants.

[0041] In a third aspect, a terminal is provided, including:

[0042] A processor and a memory, where

[0043] The memory is used to store a computer program,

[0044] The processor is configured to call and run the computer program from the memory, so that the terminal executes the method of the above terminal.

[0045] In a fourth aspect, a computer storage medium is provided, where instructions are stored in the computer-readable storage medium, and when the instructions are run on a computer, the computer is made to execute the methods described in the above aspects.

[0046] The beneficial effects of the present invention are as follows. The virtual power plant operation status global monitoring method, system, terminal, and storage medium provided by the present invention can evaluate the operation status of each subordinate virtual power plant by monitoring the response times and average response times of each subordinate virtual power plant. This process does not require fetching monitoring data from the subordinate virtual power plants, thereby saving communication resources and computing resources. Moreover, based on the evaluation results, fine detection is performed on abnormal subordinate virtual power plants, ensuring the accuracy of global monitoring, and the calculation process of fine detection is executed by the subordinate virtual power plants, without occupying the computing resources of the superior virtual power plant.

[0047] In addition, the design principle of the present invention is reliable, the structure is simple, and it has a very broad application prospect. Description of the Drawings

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0049] Figure 1 It is a schematic flowchart of the method according to an embodiment of the present invention.

[0050] Figure 2 It is a schematic block diagram of the system according to an embodiment of the present invention.

[0051] Figure 3 It is a schematic structural diagram of a terminal provided by an embodiment of the present invention. Detailed implementation manners

[0052] In order to enable those skilled in the art of this technology to better understand the technical solutions in the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the description of the present invention in this specification are only for the purpose of describing specific embodiments, and are not intended to limit the present invention.

[0054] The global monitoring method for the operation status of the virtual power plant provided by the embodiment of the present invention is executed by a computer device. Correspondingly, the global monitoring system for the operation status of the virtual power plant runs in the computer device.

[0055] Figure 1 It is a schematic flowchart of the method according to an embodiment of the present invention. Among them, Figure 1 The execution subject can be a global monitoring system for the operation status of a virtual power plant. According to different requirements, the order of the steps in this flowchart can be changed, and some can be omitted.

[0056] Such as Figure 1 shown, the method includes:

[0057] Step 110, collecting the number of responses and the average response time of each subordinate virtual power plant to the dispatching instruction within the monitoring period;

[0058] Step 120: Calculate the weighted sum of the response times and average response times of the subordinate virtual power plants to obtain a response score.

[0059] Step 130: Screen out the abnormal subordinate virtual power plants whose response scores do not reach the response threshold, and issue self-check tasks to the abnormal subordinate virtual power plants so that the abnormal subordinate virtual power plants execute the self-check tasks based on the pre-set self-check strategies.

[0060] Step 140: Receive the self-check data returned by the abnormal subordinate virtual power plants and save the self-check data to the monitoring log.

[0061] For the convenience of understanding the present invention, the principle of the global monitoring method for the operating state of the virtual power plant of the present invention is described below, in combination with the process of globally monitoring the operating state of the virtual power plant in the embodiments, to further describe the global monitoring method for the operating state of the virtual power plant provided by the present invention.

[0062] Specifically, the global monitoring method for the operating state of the virtual power plant includes:

[0063] S1: Collect the response times and average response times of each subordinate virtual power plant to the dispatching instructions during the monitoring period.

[0064] Record the issued dispatching instructions and the issuing time, as well as the received response information, the subordinate virtual power plant to which the response information belongs, and the time of receiving the response information in the operation log; use the time difference between the time of receiving the response information and the issuing time as the response time; based on the operation log, count the response times and average response times of each subordinate virtual power plant during the current monitoring period.

[0065] Specifically, within the specified monitoring period, the system first automatically records and collects the response situations of all subordinate virtual power plants to the superior dispatching instructions. This includes the issuing time and content of each dispatching instruction, as well as the response information returned by each subordinate virtual power plant after receiving the instruction, the response source (i.e., which specific subordinate virtual power plant), and the specific time of the response. Subsequently, the system calculates the time difference of each response (i.e., the response time), and based on these data, counts the total response times and average response times of each subordinate virtual power plant during the current monitoring period. These data provide key bases for subsequent evaluation and optimization.

[0066] S2: Calculate the weighted sum of the response times and average response times of the subordinate virtual power plants to obtain a response score.

[0067] The weight of the response time is a positive number, and the weight of the average response time is a negative number.

[0068] Specifically, to comprehensively evaluate the response performance of subordinate virtual power plants, the system uses a weighted sum method to calculate the response score of each power plant. Specifically, the number of responses is given a positive weight because it represents the activity and execution efficiency of the power plant; while the average response time, as a key indicator to measure the response speed, is given a negative weight (that is, the longer the response time, the smaller the contribution to the score). Through this method, the system can more comprehensively reflect the comprehensive performance of the power plant in terms of response speed and the number of responses.

[0069] S3. Screen out abnormal subordinate virtual power plants whose response scores do not reach the response threshold, and issue self-check tasks to the abnormal subordinate virtual power plants, so that the abnormal subordinate virtual power plants execute the self-check tasks based on the pre-set self-check strategies.

[0070] Calculate the average response score and set the average response score as the response threshold.

[0071] In an embodiment of the present invention, the self-check strategy is configured and deployed by the superior virtual power plant, including: configuring the self-check strategy, the self-check strategy includes monitoring indicators, a monitoring indicator analysis method based on a clustering algorithm; deploying the self-check strategy to all subordinate virtual power plants.

[0072] Configuring the self-check strategy includes:

[0073] Analyze the business types of subordinate virtual power plants, conduct hierarchical sorting on the functional modules involved in the business types to obtain the functional module sequence of each business type, and write the functional module sequence corresponding to each business type into the target file; specifically include: deeply understand the specific business undertaken by each power plant, such as power generation scheduling, energy storage management, power market trading, etc. Subsequently, for each business type, conduct a detailed hierarchical sorting of the functional modules. This process includes identifying and listing all functional modules participating in the business processing, and constructing a clear functional module sequence according to their execution order and dependency relationship in the business process. After completion of the sorting, organize the functional module sequence corresponding to each business type and write it into a special target file for subsequent self-check strategy configuration and data analysis.

[0074] By configuring probe scripts for the functional modules in the target file, obtain the requests processed by the functional modules and the processing results, and then calculate the success rate based on the requests processed by the functional modules and the processing results;

[0075] Cluster the success rates and request processing quantities of each functional module, and calculate the weighted sum of the success rate and request processing quantity of the centroid of each cluster, where the weight of the request processing quantity is negative and the weight of the success rate is positive, and screen out the abnormal cluster with the smallest weighted sum;

[0076] Match the abnormal functional modules included in the abnormal clustering clusters with the functional module sequence, and output the successfully matched abnormal functional modules as abnormal module combinations;

[0077] Sort the abnormal functional modules in the abnormal module combination in ascending order of success rate. If the abnormal functional module at the top of the sorting is the bottom-layer functional module, write the bottom-layer functional module into the warning information; if the abnormal functional module at the top of the sorting is not the bottom-layer functional module, calculate the success rate difference between the abnormal functional module at the top of the sorting and the bottom-layer functional module. If the success rate difference does not exceed the set threshold, write the corresponding bottom-layer functional module into the warning information; if the abnormal functional module at the top of the sorting is not the bottom-layer functional module and the success rate difference exceeds the set threshold, write the abnormal functional module at the top of the sorting into the warning information;

[0078] Repair the functional modules in the warning information, and write the warning information and repair operations into the self-check log.

[0079] Specifically, the system first calculates the average response score of all subordinate virtual power plants and sets this value as the response threshold to identify power plants with abnormal performance. For power plants with a response score lower than this threshold, the system marks them as abnormal and automatically issues self-check tasks to these power plants. The self-check strategy is uniformly configured and deployed by the superior virtual power plant, including setting monitoring indicators, using clustering algorithms to analyze monitoring data, etc. During the self-check process, the system will record in detail information such as the running status and success rate of functional modules, identify abnormal functional modules through clustering analysis, and generate targeted warning information based on the positions and influence degrees of these modules in the business logic. For the identified abnormalities, the system will attempt to automatically repair or provide repair suggestions, and record the entire self-check process and results in the self-check log.

[0080] The steps for the subordinate virtual power plant to perform self-check based on the self-check strategy include:

[0081] Parse the functional module sequence in the target file. To monitor the running status of functional modules in real time, dedicated probe scripts are configured for each functional module in the target file. These scripts can automatically capture each request processed by the functional module and the corresponding processing results. Based on the captured data, we further calculate the success rate of each functional module, that is, the ratio of the number of successfully processed requests to the total number of requests. This indicator directly reflects the stability and reliability of the functional module.

[0082] Next, we use a clustering algorithm to comprehensively analyze the success rate and request processing volume of each functional module. During the clustering process, similar functional modules (close in terms of success rate and request processing volume) are grouped into a cluster. Then, we calculate the centroid of each cluster, which is the weighted average of all functional modules in the cluster in terms of the success rate and request processing volume dimensions. When calculating the weighted sum of the centroid, we assign a positive weight to the success rate because it represents the performance of the functional module; while the request processing volume is assigned a negative weight to balance the situation of high processing volume but possibly accompanied by a low success rate. The cluster with the smallest weighted sum is regarded as an abnormal cluster because they may have performance problems.

[0083] Once the abnormal clusters are identified, we match the abnormal functional modules in them with the initially sorted sequence of functional modules. After successful matching, we combine these abnormal functional modules together to form an abnormal module combination. To determine the priority and accuracy of the warning, we sort each functional module in the abnormal module combination from the lowest to the highest success rate. If the abnormal functional module ranked at the top is exactly the bottom-level functional module (i.e., the basic link in the business process), we directly write it into the warning message because the failures of these basic links often have the greatest impact on the entire business process.

[0084] If the abnormal functional module ranked at the top is not the bottom-level functional module, we need to further analyze the difference in success rate between it and the bottom-level functional module. If this difference does not exceed the set threshold, it means that although the functional module performs poorly, its impact on the bottom-level functional module is still within the controllable range. At this time, we are more inclined to write the bottom-level functional module into the warning message to prevent potential risks. However, if this difference exceeds the set threshold, it means that the problem of this abnormal functional module has had a significant impact on the bottom-level functional module, and we must write it directly into the warning message and give it priority treatment.

[0085] Finally, for the functional modules listed in the warning message, we initiate a repair process. The repair operations may include restarting the functional module, updating software patches, adjusting configuration parameters, etc. After the repair is completed, we record the warning message and the entire repair process in detail in the self-check log for subsequent analysis and auditing.

[0086] S4. Receive the self-check data returned by the abnormal subordinate virtual power plant and save the self-check data to the monitoring log.

[0087] Receive the self - inspection data returned by the abnormal lower - level virtual power plant. The self - inspection data includes the self - inspection log and the real - time load and operating status of the aggregated resources. Use a visualization plugin to convert the self - inspection log and the real - time load and operating status of the aggregated resources into display data. Save the self - inspection data to the monitoring log. Count the lower - level virtual power plants corresponding to the self - inspection data in the monitoring log. If the same lower - level virtual power plant appears in adjacent monitoring cycles, send the information of the lower - level virtual power plant to the operation and maintenance management terminal.

[0088] Specifically, when receiving the self - inspection data returned by the abnormal lower - level virtual power plant, first ensure the integrity and accuracy of the data. These self - inspection data not only contain a detailed self - inspection log, but also cover key data such as the real - time load information and operating status of the aggregated resources. The self - inspection log details the problems found during the self - inspection, the repair measures taken, and the repair results, which is an important basis for evaluating the health status and performance of the power plant. The real - time load and operating status of the aggregated resources directly reflect the current operating efficiency and stability of the power plant.

[0089] To display these data more intuitively, use advanced visualization plugin technology. These plugins can intelligently analyze the self - inspection log and real - time data and convert them into easy - to - understand display forms such as charts and dashboards. Operation and maintenance personnel can quickly grasp the current status of the power plant through simple interface operations, including which functional modules have problems, whether the resource load is too high, and whether the operating status is stable. This visual presentation method greatly improves the efficiency and accuracy of data analysis.

[0090] While converting the self - inspection data into display data, these data will also be saved to a dedicated monitoring log. The monitoring log is an important database for recording the operation history and performance of the power plant. It contains all the self - inspection data and operating status information of the power plant in each monitoring cycle. By regularly analyzing the monitoring log, operation and maintenance personnel can timely discover potential problems in the operation of the power plant and take corresponding preventive measures.

[0091] In addition, it also has intelligent statistics and warning functions. When counting the self - inspection data in the monitoring log, special attention will be paid to those lower - level virtual power plants that continuously have problems. If a power plant is marked as abnormal in multiple adjacent monitoring cycles, it is considered that the power plant has relatively serious operation problems and needs to be highly concerned. At this time, the information of the power plant will be automatically sent to the operation and maintenance management terminal to remind the operation and maintenance personnel to immediately conduct further inspections and processing.

[0092] The operation and maintenance management terminal is an important platform for operation and maintenance personnel to interact with. When receiving the abnormal power plant information sent, the operation and maintenance personnel can respond quickly, view the detailed self-check logs and real-time data through the terminal, and understand the specific problems of the power plant. At the same time, they can also use the tools provided by the terminal for remote operations, such as restarting function modules, adjusting resource allocation, etc., to quickly restore the normal operation of the power plant. This efficient operation and maintenance response mechanism ensures the stability and reliability of the power plant and provides a strong guarantee for the overall operation of the power grid.

[0093] In some embodiments, the virtual power plant operation status global monitoring system may include multiple functional modules composed of computer program segments. The computer programs of each program segment in the virtual power plant operation status global monitoring system can be stored in the memory of the computer device and executed by at least one processor to perform the functions of virtual power plant operation status global monitoring (see Figure 1 for description).

[0094] In this embodiment, according to the functions it performs, the virtual power plant operation status global monitoring system can be divided into multiple functional modules, as Figure 2 shown. The functional modules of system 200 may include: a response monitoring module 210, a response evaluation module 220, an anomaly screening module 230, and a result saving module 240. The module referred to in the present invention means a series of computer program segments that can be executed by at least one processor and can complete fixed functions, and are stored in the memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0095] The response monitoring module is used to collect the number of responses and the average response time of each subordinate virtual power plant to the dispatching instruction during the monitoring period;

[0096] The response evaluation module is used to calculate the weighted sum of the number of responses and the average response time of the subordinate virtual power plant to obtain a response score;

[0097] The anomaly screening module is used to screen out the abnormal subordinate virtual power plants whose response scores do not reach the response threshold, and issue a self-check task to the abnormal subordinate virtual power plants, so that the abnormal subordinate virtual power plants execute the self-check task based on the pre-set self-check strategy;

[0098] The result saving module is used to receive the self-check data returned by the abnormal subordinate virtual power plants and save the self-check data to the monitoring log.

[0099] Optionally, as an embodiment of the present invention, the response monitoring module includes:

[0100] A data recording unit for recording the dispatched scheduling instructions and dispatch time in the operation log, as well as the received response information, the subordinate virtual power plant to which the response information belongs, and the time of receiving the response information;

[0101] An actual calculation unit for taking the time difference between the time of receiving the response information and the dispatch time as the response time;

[0102] A data statistics unit for statistically calculating the response times and average response times of each subordinate virtual power plant during the current monitoring period based on the operation log.

[0103] Optionally, as an embodiment of the present invention, the system further includes:

[0104] A policy configuration module for configuring a self-check policy, where the self-check policy includes monitoring metrics and a monitoring metric analysis method based on a clustering algorithm;

[0105] A policy deployment module for deploying the self-check policy to all subordinate virtual power plants.

[0106] Figure 3 A schematic structural diagram of a terminal 300 provided by an embodiment of the present invention. The terminal 300 can be used to execute the global monitoring method for the operating status of a virtual power plant provided by an embodiment of the present invention.

[0107] Among them, the terminal 300 may include: a processor 310, a memory 320, and a communication unit 330. These components communicate through one or more buses. Those skilled in the art can understand that the structure of the server shown in the figure does not constitute a limitation to the present invention. It can be a bus structure, a star structure, and may also include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0108] Among them, the memory 320 can be used to store the execution instructions of the processor 310. The memory 320 can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk. When the execution instructions in the memory 320 are executed by the processor 310, the terminal 300 can execute some or all of the steps in the above method embodiments.

[0109] The processor 310 is the control center of the storage terminal, connecting various parts of the entire electronic terminal through various interfaces and circuits. By running or executing software programs and / or modules stored in the memory 320, and by calling data stored in the memory, it executes various functions of the electronic terminal and / or processes data. The processor may be composed of an integrated circuit (IC), for example, it may be composed of a single packaged IC, or it may be composed of multiple packaged ICs with the same or different functions connected together. For example, the processor 310 may only include a central processing unit (CPU). In the embodiments of the present invention, the CPU may be a single arithmetic core or may include multiple arithmetic cores.

[0110] The communication unit 330 is used to establish a communication channel so that the storage terminal can communicate with other terminals. It receives user data sent by other terminals or sends user data to other terminals.

[0111] The present invention also provides a computer storage medium. The computer storage medium can store a program which, when executed, can include some or all of the steps in the embodiments provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), or the like.

[0112] Therefore, the present invention can evaluate the operating status of each subordinate virtual power plant by monitoring the response times and average response times of each subordinate virtual power plant. This process does not require fetching monitoring data from the subordinate virtual power plants, thus saving communication resources and computing resources. And based on the evaluation results, fine detection is performed on the abnormal subordinate virtual power plants, ensuring the accuracy of global monitoring. Moreover, the calculation process of the fine detection is executed by the subordinate virtual power plants and does not occupy the computing resources of the superior virtual power plant. The technical effects achievable in this embodiment can be seen from the description above and will not be elaborated here.

[0113] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solutions in the embodiments of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disc, etc., various media that can store program codes, including several instructions to enable a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0114] For the same or similar parts among the various embodiments in this specification, reference can be made to each other. In particular, for the terminal embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the descriptions in the method embodiments.

[0115] In several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there can be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of systems or modules can be in electrical, mechanical, or other forms.

[0116] The modules described as separate components may or may not be physically separated. The components displayed as modules may or may not be physical modules, that is, they can be located in one place, or they can be distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0117] In addition, in each embodiment of the present invention, the various functional modules can be integrated in a processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0118] Although the present invention has been described in detail by referring to the accompanying drawings and in conjunction with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, those of ordinary skill in the art can make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions should all be within the scope of the present invention / Any person skilled in the art within the technical scope disclosed by the present invention can easily conceive of changes or substitutions, which should all be covered by the protection scope of the present invention.

Claims

1. A method for global monitoring of the operating status of a virtual power plant, characterized in that: include: Collect the number of responses and average response time of each subordinate virtual power plant to the dispatching instructions during the monitoring period; Calculate the weighted sum of the response times and average response time of the subordinate virtual power plants to obtain the response score; Screen out abnormal subordinate virtual power plants whose response scores do not reach a response threshold, and issue self-inspection tasks to the abnormal subordinate virtual power plants, so that the abnormal subordinate virtual power plants perform the self-inspection tasks based on a pre-set self-inspection strategy; Receive self-test data returned by the abnormal subordinate virtual power plant, and save the self-test data to a monitoring log; Configure the self-check policy, including: Analyze the business types of the lower-level virtual power plants, hierarchically sort out the functional modules involved in the business types to obtain the functional module sequence of each business type, and write the functional module sequence corresponding to each business type into the target file; By configuring a probe script for the function module in the target file, the request processed by the function module and the processing result are obtained, and then the success rate is calculated based on the request processed by the function module and the processing result; Cluster the success rate and request processing quantity of each functional module, and calculate the weighted sum of the success rate and request processing quantity of the centroid of each cluster, where the weight of the request processing quantity is a negative number and the weight of the success rate is a positive number, and select the abnormal cluster with the smallest weighted sum; Matching the abnormal function modules contained in the abnormal cluster with the function module sequence, and outputting the successfully matched abnormal function modules as abnormal module combinations; The abnormal function modules in the abnormal module combination are sorted from low to high according to the success rate. If the abnormal function module ranked first is the bottom-level function module, the bottom-level function module is written into the warning information; if the abnormal function module ranked first is not the bottom-level function module, the success rate difference between the abnormal function module ranked first and the bottom-level function module is calculated. If the success rate difference does not exceed the set threshold, the corresponding bottom-level function module is written into the warning information; if the abnormal function module ranked first is not the bottom-level function module and the success rate difference exceeds the set threshold, the abnormal function module ranked first is written into the warning information; The functional modules in the warning information are repaired, and the warning information and the repair operation are written into the self-check log.

2. The method according to claim 1, characterized in that Collect the number of responses and average response time of each subordinate virtual power plant to the dispatching instructions during the monitoring period, including: Record the dispatch instructions issued and the issuing time in the operation log, as well as the received response information, the lower-level virtual power plant to which the response information belongs, and the time when the response information was received; The time difference between the time of receiving the response information and the time of sending the response information is used as the response time; The number of responses and average response time of each subordinate virtual power plant in this monitoring cycle are counted based on the operation log.

3. The method according to claim 1, characterized in that The method further comprises: Configure a self-checking strategy, wherein the self-checking strategy includes monitoring indicators and a monitoring indicator analysis method based on a clustering algorithm; The self-checking strategy is deployed to all subordinate virtual power plants.

4. The method according to claim 1, characterized in that: Receiving self-test data returned by the abnormal subordinate virtual power plant and saving the self-test data to the monitoring log, including: Receiving self-test data returned by an abnormal subordinate virtual power plant, wherein the self-test data includes a self-test log and real-time load and operation status of aggregated resources; Using a visualization plug-in to convert the self-check log and the real-time load and operation status of the aggregated resources into display data; Saving the self-test data to a monitoring log; The subordinate virtual power plants corresponding to the self-test data in the monitoring log are counted. If the same subordinate virtual power plants appear in adjacent monitoring cycles, the information of the subordinate virtual power plants is sent to the operation and maintenance management terminal.

5. A global monitoring system for the operation status of a virtual power plant, characterized in that: include: The response monitoring module is used to collect the number of responses and average response time of each subordinate virtual power plant to the dispatching instructions during the monitoring period; A response evaluation module is used to calculate the weighted sum of the response times and average response time of the subordinate virtual power plants to obtain a response score; An abnormal screening module is used to screen out abnormal subordinate virtual power plants whose response scores do not meet the response threshold, and issue self-inspection tasks to the abnormal subordinate virtual power plants so that the abnormal subordinate virtual power plants perform the self-inspection tasks based on a preset self-inspection strategy; A result saving module, used for receiving the self-test data returned by the abnormal subordinate virtual power plant, and saving the self-test data to the monitoring log; Configure the self-check policy, including: Analyze the business types of the lower-level virtual power plants, hierarchically sort out the functional modules involved in the business types to obtain the functional module sequence of each business type, and write the functional module sequence corresponding to each business type into the target file; By configuring a probe script for the function module in the target file, the request processed by the function module and the processing result are obtained, and then the success rate is calculated based on the request processed by the function module and the processing result; Cluster the success rate and request processing quantity of each functional module, and calculate the weighted sum of the success rate and request processing quantity of the centroid of each cluster, where the weight of the request processing quantity is a negative number and the weight of the success rate is a positive number, and select the abnormal cluster with the smallest weighted sum; Matching the abnormal function modules contained in the abnormal cluster with the function module sequence, and outputting the successfully matched abnormal function modules as abnormal module combinations; The abnormal function modules in the abnormal module combination are sorted from low to high according to the success rate. If the abnormal function module ranked first is the bottom-level function module, the bottom-level function module is written into the warning information; if the abnormal function module ranked first is not the bottom-level function module, the success rate difference between the abnormal function module ranked first and the bottom-level function module is calculated. If the success rate difference does not exceed the set threshold, the corresponding bottom-level function module is written into the warning information; if the abnormal function module ranked first is not the bottom-level function module and the success rate difference exceeds the set threshold, the abnormal function module ranked first is written into the warning information; The functional modules in the warning information are repaired, and the warning information and the repair operation are written into the self-check log.

6. The system according to claim 5, characterized in that The response monitoring module includes: A data recording unit, used to record in the operation log the dispatching instructions issued and the issuing time, as well as the received response information, the lower-level virtual power plant to which the response information belongs, and the time when the response information was received; An actual calculation unit, used for taking the time difference between the time of receiving the response information and the sending time as the response time; The data statistics unit is used to count the number of responses and average response time of each subordinate virtual power plant in the current monitoring cycle based on the operation log.

7. The system according to claim 5, characterized in that The system further comprises: A strategy configuration module, used to configure a self-check strategy, wherein the self-check strategy includes monitoring indicators and a monitoring indicator analysis method based on a clustering algorithm; A strategy deployment module is used to deploy the self-check strategy to all subordinate virtual power plants.

8. A terminal, characterized in that: include: A memory, used for storing a global monitoring program for the operation status of a virtual power plant; A processor is used to implement the steps of the virtual power plant operating status global monitoring method as described in any one of claims 1 to 4 when executing the virtual power plant operating status global monitoring program.

9. A computer-readable storage medium storing a computer program, characterized in that: The readable storage medium stores a global monitoring program for the operating status of a virtual power plant. When the global monitoring program for the operating status of a virtual power plant is executed by a processor, the steps of the global monitoring method for the operating status of a virtual power plant as described in any one of claims 1 to 4 are implemented.

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