Virtual power plant operation state global monitoring method and system, terminal and storage medium

Abstract

The invention relates to the technical field of virtual power plants, and particularly provides a virtual power plant operation state global monitoring method and system, a terminal and a storage medium, and the method comprises the steps: collecting the response frequency and average response time of each subordinate virtual power plant to a scheduling instruction in a monitoring period; calculating the weighted sum of the response times and the average response time of the subordinate virtual power plant to obtain a response score; screening out an abnormal subordinate virtual power plant of which the response score cannot reach a response threshold value, and issuing a self-checking task to the abnormal subordinate virtual power plant, so that the abnormal subordinate virtual power plant executes the self-checking task based on a preset self-checking strategy; and receiving self-inspection data returned by the abnormal subordinate virtual power plant, and storing the self-inspection data to a monitoring log. According to the invention, the occupation of global monitoring on computing resources is reduced, and the accuracy of global monitoring is ensured.

Description

Technical Field

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

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

[0003] At present, the deployment architecture of virtual power plants is a hierarchical architecture, that is, one upper virtual power plant and multiple lower virtual power plants. The upper virtual power plant has the authority to monitor, dispatch and manage the lower virtual power plants. Monitoring of 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 virtual power plant needs to consume a lot of computing power to monitor the lower virtual power plants, which seriously affects the processing efficiency of other businesses. Summary of the invention

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

[0005] In a first aspect, the present invention provides a method for globally monitoring the operating status of a virtual power plant, comprising: 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 the monitoring log.

[0006] In an optional implementation, the number of responses and average response time of each subordinate virtual power plant to the dispatching instruction during the monitoring period are collected, 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.

[0007] In an optional embodiment, 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.

[0008] In an optional implementation, configuring a self-checking strategy includes: 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.

[0009] In an optional implementation, receiving self-test data returned by the abnormal subordinate virtual power plant and saving the self-test data to a monitoring log includes: 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.

[0010] In a second aspect, the present invention provides a virtual power plant operation status global monitoring system, comprising: 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; The result saving module is used to receive the self-test data returned by the abnormal subordinate virtual power plant and save the self-test data to the monitoring log.

[0011] In an optional embodiment, 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.

[0012] In an optional embodiment, the system further includes: 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.

[0013] In a third aspect, a terminal is provided, including: processor, memory, wherein: The memory is used to store computer programs. The processor is used to call and run the computer program from the memory, so that the terminal executes the above-mentioned terminal method.

[0014] According to a fourth aspect, a computer storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed on a computer, the computer executes the methods described in the above aspects.

[0015] The beneficial effects of the present invention lie in that the global monitoring method, system, terminal and storage medium for the operating status of a virtual power plant provided by the present invention can evaluate the operating status of each subordinate virtual power plant by monitoring the number of responses and the average response time of each subordinate virtual power plant. This process does not require the capture of monitoring data from the subordinate virtual power plant, thereby saving communication resources and computing resources, and performs fine detection of abnormal subordinate virtual power plants based on the evaluation results, thereby ensuring the accuracy of global monitoring, and the calculation process of the fine detection is executed by the subordinate virtual power plant and will not occupy the computing resources of the upper-level virtual power plant.

[0016] In addition, the invention has a reliable design principle, a simple structure and a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention.

[0019] Figure 2 is a schematic block diagram of a system according to an embodiment of the present invention.

[0020] Figure 3 A schematic diagram of the structure of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0023] The method for globally monitoring the operating status of a virtual power plant provided in an embodiment of the present invention is executed by a computer device, and accordingly, the system for globally monitoring the operating status of a virtual power plant runs in the computer device.

[0024] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention. Figure 1 The execution subject may be a virtual power plant operation status global monitoring system. According to different requirements, the order of the steps in the flow chart may be changed, and some may be omitted.

[0025] like Figure 1 As shown, the method includes: Step 110, collecting the number of responses and average response time of each subordinate virtual power plant to the dispatching instruction during the monitoring period; Step 120, calculating the weighted sum of the number of responses and the average response time of the subordinate virtual power plant to obtain a response score; Step 130, screening out abnormal subordinate virtual power plants whose response scores do not reach the response threshold, issuing 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; Step 140, receiving the self-test data returned by the abnormal subordinate virtual power plant, and saving the self-test data to the monitoring log.

[0026] To facilitate understanding of the present invention, the global monitoring method for the operating status of a virtual power plant provided by the present invention is further described below based on the principle of the global monitoring method for the operating status of a virtual power plant of the present invention and in combination with the process of globally monitoring the operating status of a virtual power plant in an embodiment.

[0027] Specifically, the global monitoring method of the virtual power plant operation status includes: S1. Collect the number of responses and average response time of each subordinate virtual power plant to the dispatching instructions during the monitoring period.

[0028] The dispatching instructions issued 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 are recorded in the operation log; the time difference between the time of receiving the response information and the issuing time is taken as the response time; based on the operation log, the number of responses and the average response time of each subordinate virtual power plant in this monitoring cycle are counted.

[0029] Specifically, within the specified monitoring cycle, the system first automatically records and collects the responses of all subordinate virtual power plants to the superior dispatching instructions. This includes the issuance 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 source of the response (that is, which specific subordinate virtual power plant) and the specific time of the response. Subsequently, the system calculates the time difference (that is, the response time) of each response, and based on these data, calculates the total number of responses and the average response time of each subordinate virtual power plant in the current monitoring cycle. These data provide a key basis for subsequent evaluation and optimization.

[0030] S2. Calculate the weighted sum of the response times and average response time of the lower-level virtual power plants to obtain a response score.

[0031] The weight of the number of responses is a positive number, and the weight of the average response time is a negative number.

[0032] Specifically, in order to comprehensively evaluate the response performance of the lower-level 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). In this way, the system can more comprehensively reflect the comprehensive performance of the power plant in terms of response speed and number of responses.

[0033] S3. 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 pre-set self-inspection strategy.

[0034] The average response score is calculated and set as the response threshold.

[0035] In one embodiment of the present invention, the self-inspection strategy is configured and deployed by the upper-level virtual power plant, including: configuring the self-inspection strategy, the self-inspection strategy including monitoring indicators and a monitoring indicator analysis method based on a clustering algorithm; and deploying the self-inspection strategy to all lower-level virtual power plants.

[0036] Configure the self-check policy, including: Analyze the business types of the lower-level virtual power plants, hierarchically comb 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: in-depth understanding of the specific business undertaken by each power plant, such as power generation scheduling, energy storage management, power market transactions, etc. Subsequently, for each business type, a detailed hierarchical combing of the functional modules is carried out. This process includes identifying and listing all the functional modules involved in the business processing, and building a clear functional module sequence according to their execution order and dependencies in the business process. After the combing is completed, the functional module sequence corresponding to each business type is sorted and written into a special target file for subsequent self-check strategy configuration and data analysis.

[0037] 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.

[0038] 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 response scores lower than the threshold, the system marks them as abnormal and automatically issues self-inspection tasks to these power plants. The self-inspection 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-inspection process, the system will record the operating status, success rate and other information of the functional modules in detail, identify abnormal functional modules through clustering analysis, and generate targeted early warning information based on the location and impact of these modules in the business logic. For identified anomalies, the system will attempt to automatically repair or provide repair suggestions, and record the entire self-inspection process and results in the self-inspection log.

[0039] The steps for the lower-level virtual power plant to perform self-inspection based on the self-inspection strategy include: Parse the function module sequence in the target file. In order to monitor the running status of the function modules in real time, configure a dedicated probe script for each function module in the target file. These scripts can automatically capture each request processed by the function module and the corresponding processing results. Based on these captured data, we further count the success rate of each function 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 function module.

[0040] Next, we use a clustering algorithm to conduct a comprehensive analysis of the success rate and number of requests processed for each functional module. During the clustering process, similar functional modules (close in success rate and number of requests processed) 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 success rate and number of requests processed. When calculating the weighted sum of the centroids, we give a positive weight to the success rate because it represents the performance of the functional module; while the number of requests processed is given a negative weight to balance the situation where high processing volume may be accompanied by low success rate. Clusters with the smallest weighted sum are considered abnormal clusters because they may have performance problems.

[0041] Once an abnormal cluster is identified, we match the abnormal function modules in it with the initially sorted function module sequence. After the match is successful, we combine these abnormal function modules together to form an abnormal module combination. In order to determine the priority and accuracy of the warning, we sort each function module in the abnormal module combination from low to high according to the success rate. If the abnormal function module ranked first happens to be the lowest-level function module (that is, the basic link in the business process), we directly write it into the warning information, because the failure of these basic links often has the greatest impact on the entire business process.

[0042] If the top-ranked abnormal function module is not the bottom-level function module, we need to further analyze the success rate difference between it and the bottom-level function module. If this difference does not exceed the set threshold, it means that although the function module performs poorly, its impact on the bottom-level function module is still within the controllable range. At this time, we are more inclined to write the bottom-level function module into the warning information to prevent potential risks. However, if this difference exceeds the set threshold, it means that the problem of the abnormal function module has had a significant impact on the bottom-level function module. We must write it directly into the warning information and give priority to it.

[0043] Finally, we start the repair process for the functional modules listed in the warning information. The repair operation may include restarting the functional module, updating the software patch, adjusting the configuration parameters, etc. After the repair is completed, we will record the warning information and the entire repair process in detail in the self-check log for subsequent analysis and audit.

[0044] S4. Receive the self-test data returned by the abnormal lower-level virtual power plant, and save the self-test data to the monitoring log.

[0045] Receive self-test data returned by an abnormal subordinate virtual power plant, the self-test data including a self-test log and the real-time load and operating status of aggregated resources; convert the self-test log and the real-time load and operating status of aggregated resources into display data using a visualization plug-in; save the self-test data to a monitoring log; count the subordinate virtual power plants corresponding to the self-test data in the monitoring log, and 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.

[0046] Specifically, when receiving the self-test data returned by the abnormal lower-level virtual power plant, the integrity and accuracy of the data are ensured first. These self-test data not only include detailed self-test logs, but also cover key data such as real-time load information and operating status of aggregated resources. The self-test log records in detail the problems found during the self-test process, 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.

[0047] In order to display these data more intuitively, advanced visualization plug-in technology is used. These plug-ins can intelligently parse self-check logs 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.

[0048] While converting the self-test data into display data, the data is also saved in a special monitoring log. The monitoring log is an important database that records the operation history and performance of the power plant. It contains all the self-test data and operation status information of the power plant in each monitoring cycle. By regularly analyzing the monitoring log, operation and maintenance personnel can promptly discover potential problems in the operation of the power plant and take corresponding preventive measures.

[0049] In addition, it also has intelligent statistics and early warning functions. When counting the self-inspection data in the monitoring log, special attention will be paid to the lower-level virtual power plants that have continuous problems. If a power plant is marked as abnormal in multiple adjacent monitoring cycles, it will be considered that the power plant has serious operating problems and needs to be paid close attention. 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 conduct further inspection and processing immediately.

[0050] The operation and maintenance management terminal is an important platform for operation and maintenance personnel to interact with the system. When receiving abnormal power plant information, the operation and maintenance personnel can respond quickly and view detailed self-test logs and real-time data through the terminal to understand the specific problems of the power plant. At the same time, they can also use the tools provided by the terminal to perform remote operations, such as restarting functional 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 plant.

[0051] In some embodiments, the virtual power plant operating 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 operating status global monitoring system may be stored in a memory of a computer device and executed by at least one processor to perform (see Figure 1 Description) Function of global monitoring of the operating status of the virtual power plant.

[0052] In this embodiment, the virtual power plant operation status global monitoring system can be divided into multiple functional modules according to the functions it performs, such as Figure 2 As shown. The functional modules of the system 200 may include: a response monitoring module 210, a response evaluation module 220, an abnormal screening module 230 and a result saving module 240. The module referred to in the present invention refers to a series of computer program segments that can be executed by at least one processor and can complete fixed functions, which are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0053] 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; The result saving module is used to receive the self-test data returned by the abnormal subordinate virtual power plant and save the self-test data to the monitoring log.

[0054] Optionally, as an embodiment of the present invention, 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.

[0055] Optionally, as an embodiment of the present invention, the system further includes: 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.

[0056] Figure 3 A structural schematic diagram of a terminal 300 provided in an embodiment of the present invention, wherein the terminal 300 can be used to execute the method for globally monitoring the operating status of a virtual power plant provided in an embodiment of the present invention.

[0057] The terminal 300 may include: a processor 310, a memory 320 and a communication unit 330. These components communicate via one or more buses. Those skilled in the art will appreciate that the server structure shown in the figure does not limit the present invention, and it may be a bus structure or a star structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0058] The memory 320 can be used to store the execution instructions of the processor 310, and 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 perform some or all of the steps in the following method embodiments.

[0059] The processor 310 is the control center of the storage terminal, and uses various interfaces and lines to connect various parts of the entire electronic terminal. It runs or executes software programs and / or modules stored in the memory 320, and calls data stored in the memory to perform various functions of the electronic terminal and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 310 can only include a central processing unit (CPU). In the embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.

[0060] The communication unit 330 is used to establish a communication channel so that the storage terminal can communicate with other terminals, receive user data sent by other terminals or send user data to other terminals.

[0061] The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and when the program is executed, the program may include some or all of the steps in each embodiment provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0062] Therefore, the present invention can evaluate the operating status of each subordinate virtual power plant by monitoring the number of responses and the average response time of each subordinate virtual power plant. This process does not need to capture monitoring data from the subordinate virtual power plant, thereby saving communication resources and computing resources, and based on the evaluation results, the abnormal subordinate virtual power plants are finely detected to ensure the accuracy of global monitoring, and the calculation process of fine detection is executed by the subordinate virtual power plant and will not occupy the computing resources of the upper-level virtual power plant. The technical effects that can be achieved by this embodiment can be found in the description above and will not be repeated here.

[0063] 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 this understanding, the technical solution in the embodiments of the present invention, in essence or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes, including several instructions for enabling 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 each embodiment of the present invention.

[0064] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

[0065] In the 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 only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or modules, which can be electrical, mechanical or other forms.

[0066] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0067] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0068] Although the present invention has been described in detail by referring to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person skilled in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, and they shall be within the scope of protection 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 preset self-inspection strategy; Receive self-test data returned by the abnormal subordinate virtual power plant, and save the self-test data to the monitoring 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 3, characterized in that 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.

5. The method according to claim 4, 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.

6. 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; The result saving module is used to receive the self-test data returned by the abnormal subordinate virtual power plant and save the self-test data to the monitoring log.

7. The system according to claim 6, 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.

8. The system according to claim 6, 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.

9. 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 5 when executing the virtual power plant operating status global monitoring program.

10. 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 5 are implemented.

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