Power distribution network monitoring system, method, equipment and medium

Abstract

The invention relates to the technical field of power system monitoring, in particular to a power distribution network monitoring system, method, device and medium, the system comprises a micro-processing module and a cloud computing center, and the micro-processing module is used for analyzing the operation state of a target node based on power parameters. And when the operation state analysis result is abnormal operation, generating an abnormal operation early warning. And the cloud computing center is used for acquiring the electric power parameters and the operation state analysis result transmitted by the micro-processing module in real time, and storing the electric power parameters and the operation state analysis result to a power distribution network monitoring database. And then, performing multi-dimensional data analysis based on the power distribution network monitoring database, and determining a multi-dimensional analysis result. The micro-processing module is responsible for real-time data processing and simple operation state analysis, and the cloud computing center is responsible for data analysis and deep data mining of a complex scene, so that the power distribution network monitoring requirements in the complex scene can be met, and the response speed and the real-time performance of the power distribution network monitoring system are improved.

Description

Technical Field

[0001] The present application relates to the technical field of power system monitoring, and in particular to a distribution network monitoring system, method, device and medium. Background Art

[0002] With the rapid development of science and technology, the intelligent upgrade of power systems has become an inevitable trend. As an important part of the power system, the stability and reliability of the distribution network are directly related to the safe operation of the entire power grid. In recent years, intelligent distribution monitoring systems have emerged. Intelligent distribution monitoring systems can not only monitor the operating status of distribution facilities in real time, but also prevent faults, improve operation and maintenance efficiency, and ensure safe and stable power supply.

[0003] At present, the common distribution network monitoring solution is to collect grid operation data through sensors and control devices installed at key nodes of the grid, transmit the grid operation data to the cloud computing center, and rely on the cloud computing center to process and analyze a large amount of grid operation data. However, since the cloud computing center is usually far away from the data source, there is a delay in data transmission and processing, which makes the distribution network monitoring have a long response time.

[0004] Therefore, how to improve the response speed and real-time performance of the distribution network monitoring system is an urgent problem to be solved by technical personnel in this field. Summary of the invention

[0005] The purpose of this application is to provide a distribution network monitoring system, method, device and medium to solve at least one of the above technical problems.

[0006] The above invention objectives of the present application are achieved through the following technical solutions: In a first aspect, the present application provides a power distribution network monitoring system, which adopts the following technical solution: A power distribution network monitoring system, comprising: A micro-processing module, used to collect power parameters of target nodes in the power distribution network, perform an operation status analysis on the target nodes based on the power parameters, and determine an operation status analysis result corresponding to the target nodes, wherein the micro-processing module is an intelligent component with real-time data processing capability deployed at each key node of the power distribution network; When the operation status analysis result is abnormal operation, an abnormal operation warning is generated, wherein the abnormal operation warning is used to prompt the power grid maintenance personnel to deal with abnormal conditions in the power distribution network; A cloud computing center, used for acquiring the power parameters and the operating status analysis results transmitted by the microprocessing module in real time, and storing the power parameters and the operating status analysis results in a power distribution network monitoring database; A multi-dimensional data analysis is performed based on the power distribution network monitoring database to determine a multi-dimensional analysis result.

[0007] By adopting the above technical solution, the distribution network monitoring system includes: a microprocessor module and a cloud computing center. The microprocessor module is an intelligent component with real-time data processing capability deployed at each key node of the distribution network, which is used to collect the power parameters of the target node in the distribution network, analyze the operating status of the target node based on the power parameters, and determine the operating status analysis result corresponding to the target node. When the operating status analysis result is an abnormal operation, an abnormal operation warning is generated to prompt the power grid maintenance personnel to deal with the abnormal situation in the distribution network. The cloud computing center is used to obtain the power parameters and operating status analysis results transmitted by the microprocessor module in real time, and store the power parameters and operating status analysis results in the distribution network monitoring database. Then, multi-dimensional data analysis is performed based on the distribution network monitoring database to determine the multi-dimensional analysis results. The microprocessor module in the distribution network monitoring system is responsible for real-time data processing and simple operation status analysis, while the cloud computing center is responsible for data analysis and deep data mining in complex scenarios. This flexible collaborative computing framework enables the edge computing microprocessor module and the cloud computing center to work together seamlessly, which not only gives full play to the efficient response advantages of the microprocessor module, but also leverages the powerful processing capabilities and storage resources of the cloud computing center to cope with distribution network monitoring needs in complex scenarios, thereby improving the response speed and real-time performance of the distribution network monitoring system.

[0008] In a preferred example, the present application may be further configured as follows: when the microprocessing module generates an abnormal operation warning when the operation status analysis result is abnormal operation, it is used to: When the running state analysis result is abnormal operation, a microprocessing module network is obtained, and associated microprocessing modules are screened based on the abnormal microprocessing module and the microprocessing module network to determine a plurality of associated microprocessing modules, wherein the abnormal microprocessing module is a microprocessing module whose running state analysis result is abnormal operation; Performing abnormal area analysis based on the operating status analysis result corresponding to each of the associated micro-processing modules to determine the operating abnormal area; Based on the abnormal operation area, an abnormal operation warning is generated.

[0009] In a preferred example, the present application can be further configured as: a microprocessing module, further used for: Based on the abnormal operation area, determining a multimodal acquisition device, and controlling the multimodal acquisition device to acquire multimodal data corresponding to the abnormal operation area; Perform abnormal equipment analysis based on the multimodal data to obtain equipment location and fault information corresponding to the abnormal equipment; Accordingly, when the microprocessing module generates an operation abnormality warning based on the operation abnormality area, it is used to: The operation abnormality warning is generated based on the operation abnormality area, the equipment location and the fault information.

[0010] In a preferred example, the present application may be further configured as follows: when the cloud computing model performs the multi-dimensional data analysis based on the power distribution network monitoring database and determines the multi-dimensional analysis results, it is used to: Performing potential fault analysis based on the power distribution network monitoring database and determining potential fault analysis results; Performing load trend analysis based on the power distribution network monitoring database to determine a load trend analysis result; Performing energy efficiency optimization analysis based on the power distribution network monitoring database to determine energy efficiency optimization analysis results; The potential fault analysis results, the load trend analysis results and the energy efficiency optimization analysis results are integrated to obtain a multi-dimensional analysis result.

[0011] In a preferred example, the present application may be further configured as follows: when the cloud computing center performs the step of storing the power parameters and the operation status analysis results in the power distribution network monitoring database, it is used to: Performing data encryption based on the power parameter and the operating status analysis result to obtain encrypted data; Obtaining a blockchain network, screening storage nodes based on the encrypted data and the blockchain network, determining a target node, and uploading the encrypted data to the target node in the blockchain network; Controlling the blockchain network to perform data verification and consensus on the encrypted data, and determining a verification consensus result; When the verification consensus result is passed, it is determined that the encrypted data is successfully uploaded to the target node, and the encrypted data is synchronously stored in the distribution network monitoring database.

[0012] In a preferred example, the present application can be further configured as: a microprocessing module, further used for: Performing transmission distance calculation based on the target node and the cloud computing center to determine the data transmission distance; Acquire data transmission requirements, select a communication mode based on the data transmission distance and the data transmission requirements, and determine a data communication mode; According to the data communication method, the power parameters and the operating status analysis results are transmitted to the cloud computing center.

[0013] In a second aspect, the present application provides a distribution network monitoring method, which adopts the following technical solution: A method for monitoring a power distribution network, comprising: Collecting power parameters of a target node in a power distribution network, performing an operation status analysis on the target node based on the power parameters, and determining an operation status analysis result corresponding to the target node; When the operation status analysis result is abnormal operation, an abnormal operation warning is generated, wherein the abnormal operation warning is used to prompt the power grid maintenance personnel to deal with abnormal conditions in the power distribution network; Acquire the power parameters and the operating status analysis results in real time, and store the power parameters and the operating status analysis results in a power distribution network monitoring database; A multi-dimensional data analysis is performed based on the power distribution network monitoring database to determine a multi-dimensional analysis result.

[0014] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: at least one processor; Memory; At least one application, wherein the at least one application is stored in the memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the above-mentioned power distribution network monitoring method.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program, which, when executed in a computer, causes the computer to execute the above-mentioned power distribution network monitoring method.

[0016] In summary, the present application includes at least one of the following beneficial technical effects: The distribution network monitoring system includes: a microprocessor module and a cloud computing center. The microprocessor module is an intelligent component with real-time data processing capabilities deployed at each key node of the distribution network. It is used to collect the power parameters of the target nodes in the distribution network, analyze the operating status of the target nodes based on the power parameters, and determine the operating status analysis results corresponding to the target nodes. When the operating status analysis result is an abnormal operation, an abnormal operation warning is generated to prompt the power grid maintenance personnel to deal with abnormal conditions in the distribution network. The cloud computing center is used to obtain the power parameters and operating status analysis results transmitted by the microprocessor module in real time, and store the power parameters and operating status analysis results in the distribution network monitoring database. Then, multi-dimensional data analysis is performed based on the distribution network monitoring database to determine the multi-dimensional analysis results. The microprocessor module in the distribution network monitoring system is responsible for real-time data processing and simple operation status analysis, while the cloud computing center is responsible for data analysis and deep data mining in complex scenarios. This flexible collaborative computing framework enables the edge computing microprocessor module and the cloud computing center to work together seamlessly, which not only gives full play to the efficient response advantages of the microprocessor module, but also leverages the powerful processing capabilities and storage resources of the cloud computing center to cope with distribution network monitoring needs in complex scenarios, thereby improving the response speed and real-time performance of the distribution network monitoring system.

[0017] When the result of the operation status analysis is abnormal operation, the microprocessor module network is obtained, and the associated microprocessor modules are screened based on the abnormal microprocessor module and the microprocessor module network to determine multiple associated microprocessor modules. Then, based on the operation status analysis result corresponding to each associated microprocessor module, an abnormal area analysis is performed to determine the abnormal operation area. Finally, based on the abnormal operation area, an abnormal operation warning is generated. Through the associated microprocessor module screening and abnormal area analysis operations, the time for fault location is greatly shortened, and the located abnormal operation area is recorded in the abnormal operation warning, so that the power grid maintenance personnel can find the problem more quickly and take corresponding measures to repair it, thereby improving the response speed and stability of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of a power distribution network monitoring system according to one embodiment of the present application; Figure 2 It is a structural schematic diagram of a power distribution network monitoring method according to one embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device according to one embodiment of the present application. DETAILED DESCRIPTION

[0019] The following combination Figures 1 to 3 This application is described in further detail.

[0020] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, a person skilled in the art may make non-creative modifications to the present embodiment as needed, but such modifications are protected by the patent law as long as they are within the scope of the present application.

[0021] In order to make the purpose, technical scheme and advantages of the embodiment of the present application clearer, the technical scheme in the embodiment of the present application will be clearly and completely described in conjunction with the drawings in the embodiment of the present application. Obviously, the described embodiment is a part of the embodiment of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application. It should be noted that in the optional embodiments of the present application, the object information and other related data involved, when the embodiments in the present application are applied to specific products or technologies, need to obtain the permission or consent of the object, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions. In other words, if the data related to the object is involved in the embodiment of the present application, it needs to be obtained through the authorization and consent of the object, the authorization and consent of the relevant departments, and in accordance with the relevant laws, regulations and standards of the country and region. If personal information is involved in the embodiment, the acquisition of all personal information needs to obtain the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained, and the embodiment also needs to be implemented with the authorization and consent of the object.

[0022] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0023] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0024] The embodiment of the present application provides a distribution network monitoring system, including: a microprocessor module 101 and a cloud computing center 102, wherein the microprocessor module 101 is an intelligent component with real-time data processing capability deployed at each key node of the distribution network, and is used to collect power parameters of target nodes in the distribution network, perform operating status analysis on the target nodes based on the power parameters, and determine the operating status analysis results corresponding to the target nodes. When the operating status analysis result is an abnormal operation, an abnormal operation warning is generated to prompt the power grid maintenance personnel to handle abnormal conditions in the distribution network. The cloud computing center 102 is used to obtain the power parameters and operating status analysis results transmitted by the microprocessor module 101 in real time, and store the power parameters and operating status analysis results in the distribution network monitoring database. Then, multi-dimensional data analysis is performed based on the distribution network monitoring database to determine the multi-dimensional analysis results. The microprocessor module 101 in the distribution network monitoring system is responsible for real-time data processing and simple operation status analysis, while the cloud computing center 102 is responsible for data analysis and deep data mining in complex scenarios. The flexible collaborative computing framework enables the edge computing microprocessor module 101 and the cloud computing center 102 to work together seamlessly, which not only brings out the efficient response advantage of the microprocessor module 101, but also leverages the powerful processing power and storage resources of the cloud computing center 102 to cope with the distribution network monitoring needs in complex scenarios, thereby improving the response speed and real-time performance of the distribution network monitoring system.

[0025] A distribution network monitoring system, such as Figure 1 As shown, the system includes a microprocessing module 101 and a cloud computing center 102, wherein: The micro-processing module 101 is used to collect power parameters of target nodes in the power distribution network, perform operation status analysis on the target nodes based on the power parameters, and determine the operation status analysis results corresponding to the target nodes, wherein the micro-processing module 101 is an intelligent component with real-time data processing capability deployed at each key node of the power distribution network; When the operation status analysis result is abnormal operation, an abnormal operation warning is generated, wherein the abnormal operation warning is used to prompt the power grid maintenance personnel to deal with abnormal conditions in the distribution network; For the embodiment of the present application, the microprocessing module 101 is an intelligent component with real-time data processing capability deployed at each key node of the power distribution network. The microprocessing module 101 is a high-performance, low-power microprocessor responsible for real-time data processing and simple operation status analysis. In addition, the microprocessing module 101 includes wired and wireless communication modules for uploading the collected power parameters and operation status analysis results to the cloud computing center 102, or communicating with other microprocessing modules 101.

[0026] The specific implementation process for the microprocessing module 101 is as follows: a data acquisition program is set in the microprocessing module 101, and the power parameters of the target node in the distribution network are obtained in real time through the sensor interface in the microprocessing module 101, wherein the power parameters include but are not limited to: current, voltage, power factor, temperature, etc. The target node to be monitored is usually a key device in the distribution network, such as a transformer, a circuit breaker, an important line, etc. Then, the collected power parameters are preprocessed, and the preprocessing includes but is not limited to: data cleaning, filtering, abnormal value detection, etc., to ensure the accuracy and reliability of the data. The state of the processed power parameters is evaluated using a pre-set operation status evaluation model. The operation status evaluation model is based on machine learning, deep learning and other technologies, and accurately evaluates the current state of the target node through the analysis and learning of historical data. When the operation status analysis result is normal operation, it indicates that the target node in the distribution network is currently in a normal operating state, and there is no need to perform other special operations. The power parameters of the target node are continuously monitored to ensure that potential changes or abnormal conditions are discovered in time. When the result of the operation status analysis is abnormal operation, an abnormal operation warning is generated, and the abnormal operation warning is convenient for prompting the power grid maintenance personnel to deal with abnormal conditions in the power distribution network. There are many ways to generate abnormal operation warnings, and the embodiments of the present application are no longer limited. In one achievable method, when the result of the operation status analysis is abnormal operation, the microprocessing module 101 network is obtained, and the associated microprocessing module 101 is screened based on the abnormal microprocessing module 101 and the microprocessing module 101 network, and multiple associated microprocessing modules 101 are determined, wherein the abnormal microprocessing module 101 is a microprocessing module 101 whose operation status analysis result is abnormal operation; based on the operation status analysis result corresponding to each associated microprocessing module 101, an abnormal area analysis is performed to determine the abnormal operation area; based on the abnormal operation area, an abnormal operation warning is generated.

[0027] The cloud computing center 102 is used to obtain the power parameters and operation status analysis results transmitted by the microprocessing module 101 in real time, and store the power parameters and operation status analysis results in the distribution network monitoring database; Perform multi-dimensional data analysis based on the distribution network monitoring database to determine the multi-dimensional analysis results.

[0028] For the embodiment of the present application, the microprocessor module 101 transmits the power parameters and operating status analysis results to the cloud computing center 102 in real time via wired or wireless means. The cloud computing center 102 has powerful data processing and data storage capabilities. The large-scale data storage capacity provides rich data support for distribution network monitoring. Moreover, the cloud computing center 102 can perform multi-dimensional analysis on the large amount of stored data, which helps to reveal the operating laws and potential problems of the distribution network and provide support for power grid management and decision-making.

[0029] The specific implementation process for the cloud computing center 102 is as follows: the cloud computing center 102 is provided with a dedicated data receiving module for receiving the data packet transmitted by the microprocessing module 101, and after receiving the data packet consisting of the power parameters and the operating status analysis results, the data receiving module verifies the data packet, and the verification dimensions include but are not limited to: format verification, data range verification, etc., to ensure the integrity and accuracy of the data in the data packet. The distribution network monitoring database is the core part of the cloud computing center 102, which is used to store the power parameters and operating status analysis results. The database design needs to consider the structure, index, storage method, etc. of the data to optimize the query efficiency and data consistency; at the same time, the cloud computing center 102 also needs to regularly monitor and optimize the performance of the distribution network monitoring database to ensure its efficient operation. In order to improve the accuracy of data stored in the distribution network monitoring database, when executing data storage, the following technical solutions can be followed: encrypt data based on power parameters and operating status analysis results to obtain encrypted data; obtain the blockchain network, screen storage nodes based on the encrypted data and the blockchain network, determine the target node, and upload the encrypted data to the target node in the blockchain network; control the blockchain network to perform data verification and consensus on the encrypted data, and determine the verification consensus result; when the verification consensus result is passed, it is determined that the encrypted data has been successfully uploaded to the target node, and the encrypted data is synchronously stored in the distribution network monitoring database.

[0030] The cloud computing center 102 has a strong data processing capability and is responsible for data analysis and deep data mining of complex scenarios. Therefore, multi-dimensional data analysis is performed based on the distribution network monitoring database to determine the multi-dimensional analysis results, wherein the multi-dimensional data analysis includes but is not limited to: potential fault analysis, load change trend analysis, user behavior pattern analysis, energy efficiency optimization analysis, etc. Through multi-dimensional data analysis, real-time monitoring and intelligent scheduling of the distribution network can be achieved, the automation level of the power grid can be improved, and the power grid can be promoted to develop in a more intelligent and automated direction. The micro-processing module 101 in the distribution network monitoring system is responsible for real-time data processing and simple operation status analysis, while the cloud computing center 102 is responsible for data analysis and deep data mining of complex scenarios. The flexible collaborative computing framework enables the micro-processing module 101 of the edge computing and the cloud computing center 102 to work seamlessly, which not only gives play to the efficient response advantage of the micro-processing module 101, but also relies on the powerful processing power and storage resources of the cloud computing center 102 to cope with the distribution network monitoring needs in complex scenarios, and improves the response speed and real-time performance of the distribution network monitoring system.

[0031] It can be seen that in the embodiment of the present application, the distribution network monitoring system includes: a microprocessor module 101 and a cloud computing center 102. The microprocessor module 101 is an intelligent component with real-time data processing capabilities deployed at each key node of the distribution network, and is used to collect the power parameters of the target node in the distribution network, and perform an operation status analysis on the target node based on the power parameters, and determine the operation status analysis result corresponding to the target node. When the operation status analysis result is an operation abnormality, an operation abnormality warning is generated to prompt the power grid maintenance personnel to handle the abnormal situation in the distribution network. The cloud computing center 102 is used to obtain the power parameters and operation status analysis results transmitted by the microprocessor module 101 in real time, and store the power parameters and operation status analysis results in the distribution network monitoring database. Then, multi-dimensional data analysis is performed based on the distribution network monitoring database to determine the multi-dimensional analysis results. The microprocessor module 101 in the distribution network monitoring system is responsible for real-time data processing and simple operation status analysis, while the cloud computing center 102 is responsible for data analysis and deep data mining in complex scenarios. The flexible collaborative computing framework enables the edge computing microprocessor module 101 and the cloud computing center 102 to work together seamlessly, which not only brings out the efficient response advantage of the microprocessor module 101, but also leverages the powerful processing power and storage resources of the cloud computing center 102 to cope with the distribution network monitoring needs in complex scenarios, thereby improving the response speed and real-time performance of the distribution network monitoring system.

[0032] Further, in order to improve the response speed and stability of the power distribution network, in the embodiment of the present application, when the operation status analysis result is abnormal operation, the microprocessing module 101 generates an abnormal operation warning, which is used to: When the running state analysis result is abnormal operation, the network of the microprocessing module 101 is obtained, and the associated microprocessing module 101 is screened based on the abnormal microprocessing module 101 and the network of the microprocessing module 101 to determine a plurality of associated microprocessing modules 101, wherein the abnormal microprocessing module 101 is the microprocessing module 101 whose running state analysis result is abnormal operation; Perform abnormal area analysis based on the operation status analysis results corresponding to each associated micro-processing module 101 to determine the operation abnormal area; Generate an operation abnormality warning based on the operation abnormality area.

[0033] For the embodiment of the present application, when generating an abnormal operation warning, the time for fault location is greatly shortened by associating the microprocessor module 101 with the screening and abnormal area analysis operations, and the located abnormal operation area is recorded in the abnormal operation warning, so that the power grid maintenance personnel can find the problem more quickly and take corresponding measures to repair it, thereby improving the response speed and stability of the distribution network.

[0034] Specifically, when the result of the operation status analysis is abnormal operation, it indicates that the power distribution equipment at the target node has an abnormal operation. Therefore, a microprocessing module 101 network is obtained. The microprocessing module 101 network is usually presented in the form of an association relationship diagram. The topological structure of the microprocessing module 101 network represents the physical connection or logical dependency between the microprocessing modules 101. Therefore, based on the abnormal microprocessing module 101 and the microprocessing module 101 network, the associated microprocessing module 101 is screened, and multiple associated microprocessing modules 101 are determined, wherein the abnormal microprocessing module 101 is the microprocessing module 101 whose operation status analysis result is abnormal operation, and the associated microprocessing module 101 is the microprocessing module 101 that has a physical connection, data exchange or functional dependency with the abnormal microprocessing module 101. That is, when performing the screening of the associated microprocessing module 101, the microprocessing module 101 network is searched for the microprocessing module 101 that is directly or indirectly connected to the abnormal microprocessing module 101.

[0035] There are many ways to screen the associated microprocessor module 101, which are not limited in the embodiments of the present application. In one achievable method, a network analysis tool is used to perform a topological structure analysis on the microprocessor module 101 network, and based on the position of the abnormal microprocessor module 101 in the network, other microprocessor modules 101 directly connected to the abnormal microprocessor module 101 are identified and recorded as the associated microprocessor module 101. Then, a data association analysis tool is used to analyze the data exchange records between the abnormal microprocessor module 101 and other microprocessor modules 101, and other microprocessor modules 101 with data exchange are recorded as the associated microprocessor modules 101, and it is determined whether there are abnormalities or inconsistencies in the data exchange process, such as data loss, delay, error, etc. Further, a functional dependency analysis is performed using the microprocessor module 101 network, and the functional dependency relationship between the abnormal module and other modules is analyzed, and the microprocessor modules 101 with functional dependency relationship with the abnormal microprocessor module 101 are determined and recorded as the associated microprocessor modules 101.

[0036] Then, based on the operation status analysis results corresponding to each associated micro-processing module 101, the abnormal operation status is screened, the abnormal associated micro-processing module 101 is determined, and the abnormal operation area is determined based on the micro-processing module 101 network, the abnormal micro-processing module 101 and the abnormal associated micro-processing module 101. The abnormal operation area may be an area with abnormal operation composed of one, two or more key nodes. Finally, based on the abnormal operation area, an abnormal operation warning is generated.

[0037] It can be seen that in the embodiment of the present application, when the result of the operation status analysis is abnormal operation, the network of microprocessor modules 101 is obtained, and the associated microprocessor modules 101 are screened based on the abnormal microprocessor modules 101 and the network of microprocessor modules 101 to determine multiple associated microprocessor modules 101. Then, based on the operation status analysis result corresponding to each associated microprocessor module 101, an abnormal area analysis is performed to determine the abnormal operation area, and finally, based on the abnormal operation area, an abnormal operation warning is generated. Through the screening of the associated microprocessor modules 101 and the abnormal area analysis operation, the time for fault location is greatly shortened, and the located abnormal operation area is recorded in the abnormal operation warning, so that the power grid maintenance personnel can find the problem more quickly and take corresponding measures to repair it, thereby improving the response speed and stability of the distribution network.

[0038] Furthermore, in order to improve the efficiency of fault diagnosis, in the embodiment of the present application, the microprocessing module 101 is also used for: Based on the abnormal operation area, determine the multimodal acquisition device, and control the multimodal acquisition device to collect multimodal data corresponding to the abnormal operation area; Analyze abnormal equipment based on multimodal data to obtain the equipment location and fault information corresponding to the abnormal equipment; Accordingly, when the microprocessing module 101 generates an abnormal operation warning based on the abnormal operation area, it is used to: Generate an operation abnormality warning based on the operation abnormality area, equipment location and fault information.

[0039] For the embodiment of the present application, a multimodal acquisition device is also provided at each key node in the power distribution network to collect multimodal data of the power distribution network, wherein the multimodal data includes but is not limited to: visual image information and sound spectrum data, visual image information, such as line photos, substation video streams, supplementary sound spectrum data, such as high-voltage equipment discharge sound. Multimodal data can fully reflect the operating status and fault characteristics of the equipment, help to quickly identify the location and fault type of the equipment with abnormal operation in the abnormal operation area, help to reduce the delay and loss of fault processing caused by misjudgment or missed judgment, and improve the efficiency of fault diagnosis.

[0040] Specifically, the microprocessing module 101 pre-stores the installation position and data collection range of each multimodal acquisition device. Therefore, based on the abnormal operation area, the data collection range is matched with each multimodal acquisition device to determine the multimodal acquisition device that can cover the abnormal operation area. The multimodal acquisition device can be one, two or more. Then, the microprocessing module 101 sends a data collection instruction to the multimodal acquisition device through remote communication to control the multimodal acquisition device to start data collection for the abnormal operation area, and transmits the collected multimodal data to the microprocessing module 101 in real time, wherein the multimodal data includes but is not limited to: visual image information and sound spectrum data. Furthermore, abnormal equipment analysis is performed based on the multimodal data to obtain the equipment location and fault information corresponding to the abnormal equipment. There are many ways to implement abnormal equipment analysis, which are not limited in the embodiments of the present application. In one feasible method, the shape, color, texture and other features of the equipment are extracted from the visual image information in the multimodal data; the frequency, amplitude, timbre and other sound features are extracted from the sound spectrum data in the multimodal data; the features extracted from the visual image information and the sound spectrum data are fused using the feature-level fusion method to form a more comprehensive feature vector. Then, anomaly detection and fault diagnosis are performed based on the fused feature vector to determine the device location and fault information (including: fault type, fault cause) corresponding to the abnormal device. Finally, an abnormal operation warning is generated based on the abnormal operation area, device location and fault information.

[0041] It can be seen that in the embodiment of the present application, based on the abnormal operation area, a multimodal acquisition device is determined, and the multimodal acquisition device is controlled to collect multimodal data corresponding to the abnormal operation area. Then, the abnormal equipment is analyzed based on the multimodal data to obtain the equipment location and fault information corresponding to the abnormal equipment. Multimodal data can fully reflect the operating status and fault characteristics of the equipment, help to quickly identify the location and fault type of the equipment with abnormal operation in the abnormal operation area, help to reduce fault processing delays and losses caused by misjudgment or missed judgment, and improve the efficiency of fault diagnosis.

[0042] Furthermore, in order to timely discover abnormal data and potential fault points in the distribution network and avoid power surplus or shortage, in the embodiment of the present application, the cloud computing model performs multi-dimensional data analysis based on the distribution network monitoring database and determines the multi-dimensional analysis results, which is used to: Conduct potential fault analysis based on the power distribution network monitoring database and determine the potential fault analysis results; Perform load trend analysis based on the distribution network monitoring database and determine the load trend analysis results; The potential fault analysis results and load trend analysis results are combined to obtain multi-dimensional analysis results.

[0043] For the embodiment of the present application, in order to timely discover abnormal data and potential fault points in the distribution network, potential fault analysis is performed based on the distribution network monitoring database to determine the potential fault analysis results, which helps the power grid maintenance personnel to take measures in advance to prevent the occurrence of faults and reduce the power outage time and economic losses caused by faults. Therefore, a fault prediction model is pre-stored in the cloud computing model. The fault prediction model is constructed based on historical fault data and expert experience. The fault prediction model can reflect the correlation between power parameters, operating status and potential faults. When the fault prediction model detects that there is a potential fault in the distribution network, data mining and machine learning techniques are used to mine the laws and associations behind the power parameters and operating status analysis results, and potential fault information is discovered. Through fault location technology, the specific location of the potential fault point, the potential fault cause and the potential fault type are determined, and the potential fault information, potential fault location, potential fault cause and potential fault type are integrated to obtain the potential fault analysis results.

[0044] In order for power grid operators to reasonably arrange power production and transmission, optimize resource allocation, and avoid power surplus or shortage, load trend analysis is used to predict the load changes of the power grid in the future, providing data support for the balance of supply and demand in the power market. Therefore, a suitable load trend analysis method is selected, such as a time series-based analysis method or a machine learning-based method, and a load trend prediction model is constructed according to the selected load trend analysis method. The load trend prediction model is obtained by continuously training and tuning the model based on a large amount of historical data, and can quickly and accurately predict the load usage status. Then, load trend analysis is performed based on the distribution network monitoring database and the load trend prediction model to determine the load trend analysis results. The load trend analysis results give the power demand of each region in the future, which is helpful for optimizing the design and transformation of the distribution network and improving the reliability and economy of power supply. Finally, the potential fault analysis results and load trend analysis results are combined to obtain multi-dimensional analysis results.

[0045] Of course, we can also conduct behavior pattern analysis based on the power network monitoring database to determine the behavior pattern analysis results. At the same time, we can conduct energy efficiency optimization analysis based on the power network monitoring database to determine the energy efficiency optimization analysis results. Behavior pattern analysis can reveal the operating rules of various devices, users or systems in the power grid. By studying the operating rules, we can optimize the operation strategy of the power grid and improve the operation efficiency and safety of the power grid. Energy efficiency optimization analysis aims to find out the energy efficiency bottlenecks in the power grid and propose improvement measures. By implementing these measures, we can significantly reduce the energy consumption and emissions of the power grid and promote the realization of energy conservation and emission reduction goals.

[0046] It can be seen that in the embodiment of the present application, in order to be able to promptly discover abnormal data and potential fault points in the distribution network, potential fault analysis is performed based on the distribution network monitoring database to determine the potential fault analysis results. In order for power grid operators to reasonably arrange power production and transmission, optimize resource allocation, and avoid power surplus or shortage, load trend analysis is performed based on the distribution network monitoring database to determine the load trend analysis results. Finally, the potential fault analysis results and load trend analysis results are combined to obtain multi-dimensional analysis results.

[0047] Furthermore, in order to improve the security and reliability of data and effectively prevent the uploading of false data or invalid data, in the embodiment of the present application, the cloud computing center 102, when executing the storage of the power parameter and operation status analysis results to the distribution network monitoring database, is used to: Data encryption is performed based on power parameters and operating status analysis results to obtain encrypted data; Obtain the blockchain network, screen the storage nodes based on the encrypted data and the blockchain network, determine the target node, and upload the encrypted data to the target node in the blockchain network; Control the blockchain network to verify and reach consensus on encrypted data and determine the verification consensus results; When the verification consensus result is passed, it is determined that the encrypted data is successfully uploaded to the target node, and the encrypted data is synchronously stored in the distribution network monitoring database.

[0048] For the embodiment of the present application, in order to effectively prevent data from being accessed or tampered by unauthorized users during transmission and storage, the power parameters and operating status analysis results are encrypted using an encryption algorithm to obtain encrypted data to ensure the confidentiality and integrity of the data, wherein the encryption algorithm includes but is not limited to: symmetric encryption algorithms (such as AES, DES, etc.) and asymmetric encryption algorithms (such as RSA, ECC, etc.). In the blockchain network, the storage node is responsible for storing data. In order to ensure the security and reliability of the data, it is necessary to screen suitable storage nodes. Therefore, storage node screening is performed based on encrypted data and blockchain network to determine the target node. In the storage node screening, the node can be screened from the dimensions of storage capacity, computing power, online time, reputation, etc. Through the evaluation of multiple factors, the most suitable storage node is selected as the target node. Then, the encrypted data is uploaded to the screened target node using the API or tool provided by the blockchain network. Blockchain technology has the characteristics of decentralization, distributed storage and tamper-proof. Storing encrypted data in the blockchain network can further improve the security and reliability of the data. Even if a node fails or is attacked, other nodes can still maintain the integrity and consistency of the data. Then, the blockchain network is controlled to perform data verification and consensus on the encrypted data, and the verification consensus result is determined, wherein the verification consensus result includes: pass and fail. The data verification mechanism in the blockchain network can ensure that the uploaded data is true and valid, and through the consensus algorithm, the nodes in the blockchain network can verify the uploaded data. Only verified data can be stored on the blockchain. The data verification and consensus mechanism can effectively prevent the uploading of false data or invalid data. When the verification consensus result is pass, it is determined that the encrypted data is successfully uploaded to the target node, and the encrypted data is synchronously stored in the distribution network monitoring database. When the verification consensus result is fail, a data reception failure instruction is sent to the microprocessing module 101, so that the microprocessing module 101 can resend the power parameters and operating status analysis results.

[0049] It can be seen that in the embodiment of the present application, data encryption is performed based on the power parameters and the analysis results of the operating status to obtain encrypted data, and then the blockchain network is obtained, the storage node is screened based on the encrypted data and the blockchain network, the target node is determined, and the encrypted data is uploaded to the target node in the blockchain network. Blockchain technology has the characteristics of decentralization, distributed storage and tamper-proof. Storing encrypted data in the blockchain network can further improve the security and reliability of the data. Even if a node fails or is attacked, other nodes can still maintain the integrity and consistency of the data. Then, the blockchain network is controlled to perform data verification and consensus on the encrypted data to determine the verification consensus result. When the verification consensus result is passed, it is determined that the encrypted data is successfully uploaded to the target node, and the encrypted data is synchronously stored in the distribution network monitoring database. The data verification mechanism in the blockchain network can ensure that the uploaded data is true and valid, and through the consensus algorithm, the nodes in the blockchain network can verify the uploaded data. Only verified data can be stored on the blockchain. The data verification and consensus mechanism can effectively prevent the upload of false data or invalid data.

[0050] Furthermore, in order to reduce the failure rate and packet loss rate during data transmission and improve the reliability and stability of data transmission, in the embodiment of the present application, the microprocessing module 101 is also used to: Perform transmission distance calculation based on the target node and the cloud computing center 102 to determine the data transmission distance; Obtain data transmission requirements, select a communication method based on the data transmission distance and data transmission requirements, and determine the data communication method; The power parameters and operation status analysis results are transmitted to the cloud computing center 102 in accordance with the data communication method.

[0051] For the embodiment of the present application, the geographical location information corresponding to the target node and the cloud computing center 102 is obtained. If the target node and the cloud computing center 102 use different coordinate systems, they are converted into a unified coordinate system to facilitate distance calculation. Based on the terrain conditions between the target node and the cloud computing center 102, a suitable distance calculation method is selected to perform the transmission distance calculation between the target node and the cloud computing center 102 to determine the data transmission distance. That is, if the terrain between the target node and the cloud computing center 102 is relatively flat and there are no significant obstacles, a straight-line distance calculation method can be used; if there are complex terrain or obstacles (such as mountains, buildings, etc.) between the target node and the cloud computing center 102, a more complex distance calculation method needs to be used, for example, using a path planning algorithm in a geographic information system (GIS) to calculate a more accurate transmission distance. Then, the data transmission requirement is obtained, which is a factor that needs to be considered in the pre-set data transmission from the target node to the cloud computing center 102, including but not limited to: real-time requirements, security requirements, and communication cost requirements. Then, the communication mode is selected based on the data transmission distance and data transmission requirements, and the data communication mode is determined, that is, according to the data transmission requirements (for example, real-time, security, etc.) and transmission distance, the applicability of different communication modes (such as wired communication, wireless communication, satellite communication, etc.) is evaluated; the cost (including equipment purchase, installation, operation and maintenance costs) and performance (such as transmission speed, stability, reliability, etc.) of different communication modes are compared; the technical maturity of the communication mode is evaluated to ensure that the selected mode is technically feasible and compatible with the existing system or equipment; the data communication mode is determined by combining various evaluation results. Finally, according to the data communication mode, the power parameters and operation status analysis results are transmitted to the cloud computing center 102. By selecting a suitable data communication mode, the failure rate and packet loss rate in the data transmission process are reduced, the reliability and stability of data transmission are improved, and it is helpful to reduce the data transmission cost.

[0052] It can be seen that in the embodiment of the present application, the transmission distance is calculated based on the target node and the cloud computing center 102 to determine the data transmission distance. Then, the data transmission requirements are obtained, and the communication mode is selected based on the data transmission distance and the data transmission requirements to determine the data communication mode. Then, according to the data communication mode, the power parameters and the operating status analysis results are transmitted to the cloud computing center 102. By selecting a suitable data communication mode, the failure rate and packet loss rate in the data transmission process are reduced, the reliability and stability of data transmission are improved, and it helps to reduce the data transmission cost.

[0053] The above embodiment introduces a power distribution network monitoring system from the perspective of an apparatus, and the following embodiment introduces a power distribution network monitoring method from the perspective of a method flow. For details, please refer to the following embodiment.

[0054] The present application embodiment provides a power distribution network monitoring method, which is executed by an electronic device, such as Figure 2 As shown, the method includes step S201, step S202, step S203 and step S204, wherein: Step S201: collecting power parameters of target nodes in the power distribution network, performing operating status analysis on the target nodes based on the power parameters, and determining operating status analysis results corresponding to the target nodes; Step S202: when the operation status analysis result is abnormal operation, an abnormal operation warning is generated, wherein the abnormal operation warning is used to prompt the power grid maintenance personnel to handle the abnormal situation in the distribution network; Step S203: acquiring power parameters and operating status analysis results in real time, and storing the power parameters and operating status analysis results in a distribution network monitoring database; Step S204: Perform multi-dimensional data analysis based on the power distribution network monitoring database to determine the multi-dimensional analysis results.

[0055] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the power distribution network monitoring method described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0056] An electronic device is provided in an embodiment of the present application, such as Figure 3 As shown, Figure 3 The electronic device 300 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0057] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0058] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but it does not mean that there is only one bus or only one type of bus.

[0059] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0060] The memory 303 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the contents shown in the above method embodiment.

[0061] The electronic devices include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs, desktop computers, etc. It can also be a server, etc. Figure 3 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0062] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0063] An embodiment of the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the method in any of the above embodiments is implemented.

[0064] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0065] The above are only some implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A power distribution network monitoring system, characterized in that: include: Microprocessing module and cloud computing center, including: A micro-processing module, used to collect power parameters of target nodes in the power distribution network, perform an operation status analysis on the target nodes based on the power parameters, and determine an operation status analysis result corresponding to the target nodes, wherein the micro-processing module is an intelligent component with real-time data processing capability deployed at each key node of the power distribution network; When the operation status analysis result is abnormal operation, an abnormal operation warning is generated, wherein the abnormal operation warning is used to prompt the power grid maintenance personnel to deal with abnormal conditions in the power distribution network; A cloud computing center, used for acquiring the power parameters and the operating status analysis results transmitted by the microprocessing module in real time, and storing the power parameters and the operating status analysis results in a power distribution network monitoring database; A multi-dimensional data analysis is performed based on the power distribution network monitoring database to determine a multi-dimensional analysis result.

2. The power distribution network monitoring system according to claim 1, characterized in that: When the microprocessing module generates an abnormal operation warning when the operation status analysis result is abnormal operation, it is used to: When the running state analysis result is abnormal operation, a microprocessing module network is obtained, and associated microprocessing modules are screened based on the abnormal microprocessing module and the microprocessing module network to determine a plurality of associated microprocessing modules, wherein the abnormal microprocessing module is a microprocessing module whose running state analysis result is abnormal operation; Performing abnormal area analysis based on the operating status analysis result corresponding to each of the associated micro-processing modules to determine the operating abnormal area; Based on the abnormal operation area, an abnormal operation warning is generated.

3. The power distribution network monitoring system according to claim 2, characterized in that: Microprocessor module, also used for: Based on the abnormal operation area, determining a multimodal acquisition device, and controlling the multimodal acquisition device to acquire multimodal data corresponding to the abnormal operation area; Perform abnormal equipment analysis based on the multimodal data to obtain equipment location and fault information corresponding to the abnormal equipment; Accordingly, when the microprocessing module generates an operation abnormality warning based on the operation abnormality area, it is used to: The operation abnormality warning is generated based on the operation abnormality area, the equipment location and the fault information.

4. The power distribution network monitoring system according to claim 1, characterized in that: When performing the multi-dimensional data analysis based on the power distribution network monitoring database and determining the multi-dimensional analysis results, the cloud computing model is used to: Performing potential fault analysis based on the power distribution network monitoring database and determining potential fault analysis results; Performing load trend analysis based on the power distribution network monitoring database to determine a load trend analysis result; The potential fault analysis results and the load trend analysis results are combined to obtain a multi-dimensional analysis result.

5. The power distribution network monitoring system according to claim 1, characterized in that: When the cloud computing center executes the step of storing the power parameters and the operating status analysis results in the power distribution network monitoring database, it is used to: Performing data encryption based on the power parameter and the operating status analysis result to obtain encrypted data; Obtaining a blockchain network, screening storage nodes based on the encrypted data and the blockchain network, determining a target node, and uploading the encrypted data to the target node in the blockchain network; Controlling the blockchain network to perform data verification and consensus on the encrypted data, and determining a verification consensus result; When the verification consensus result is passed, it is determined that the encrypted data is successfully uploaded to the target node, and the encrypted data is synchronously stored in the distribution network monitoring database.

6. The power distribution network monitoring system according to claim 1, characterized in that: Microprocessor module, also used for: Performing transmission distance calculation based on the target node and the cloud computing center to determine the data transmission distance; Acquire data transmission requirements, select a communication mode based on the data transmission distance and the data transmission requirements, and determine a data communication mode; According to the data communication method, the power parameters and the operating status analysis results are transmitted to the cloud computing center.

7. A method for monitoring a power distribution network, characterized in that: include: Collecting power parameters of a target node in a power distribution network, performing an operation status analysis on the target node based on the power parameters, and determining an operation status analysis result corresponding to the target node; When the operation status analysis result is abnormal operation, an abnormal operation warning is generated, wherein the abnormal operation warning is used to prompt the power grid maintenance personnel to deal with abnormal conditions in the power distribution network; Acquire the power parameters and the operating status analysis results in real time, and store the power parameters and the operating status analysis results in a power distribution network monitoring database; A multi-dimensional data analysis is performed based on the power distribution network monitoring database to determine a multi-dimensional analysis result.

8. An electronic device, characterized in that: include: at least one processor; Memory; At least one application, wherein the at least one application is stored in the memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the power distribution network monitoring method according to claim 7.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the power distribution network monitoring method described in claim 7.

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