A power distribution network monitoring system, method, device and medium

Abstract

This application relates to the technical field of power system monitoring, and in particular to a distribution network monitoring system, method, device, and medium. The system includes a microprocessor module and a cloud computing center. The microprocessor module is used to analyze the operational status of target nodes based on power parameters. When the operational status analysis result indicates an operational anomaly, an operational anomaly warning is generated. The cloud computing center is used to acquire the power parameters and operational status analysis results transmitted by the microprocessor module in real time and store them in a 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 is responsible for real-time data processing and simple operational status analysis, while the cloud computing center is responsible for data analysis of complex scenarios and deep data mining, which can meet the distribution network monitoring needs in complex scenarios and improve the response speed and real-time performance of the distribution network monitoring system.

Description

Technical Field

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

[0002] With the rapid development of technology, the intelligent upgrading of power systems has become an inevitable trend. As a crucial component of the power system, the stability and reliability of the distribution network directly affect the safe operation of the entire power grid. In recent years, intelligent distribution monitoring systems have emerged. These 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 a safe and stable power supply.

[0003] Current common power distribution network monitoring solutions rely on sensors and control equipment installed at key nodes of the power grid to collect grid operation data, which is then transmitted to a cloud computing center for processing and analysis. However, because cloud computing centers are typically located far from the data sources, data transmission and processing are delayed, resulting in long response times for power distribution network monitoring.

[0004] Therefore, how to improve the response speed and real-time performance of power distribution network monitoring systems is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

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

[0006] The above-mentioned inventive objective of this application is achieved through the following technical solutions:

[0007] Firstly, this application provides a power distribution network monitoring system, which adopts the following technical solution:

[0008] A power distribution network monitoring system, comprising:

[0009] The microprocessor module is used to collect power parameters of target nodes in the power distribution network, perform operational status analysis on the target nodes based on the power parameters, and determine the operational status analysis results corresponding to the target nodes. The microprocessor module is an intelligent component with real-time data processing capabilities deployed at each key node of the power distribution network.

[0010] When the operation status analysis result is an operation anomaly, an operation anomaly warning is generated, wherein the operation anomaly warning is used to prompt power grid maintenance personnel to handle abnormal situations in the power distribution network;

[0011] The cloud computing center is used to acquire the power parameters and the operation status analysis results transmitted by the microprocessor module in real time, and to store the power parameters and the operation status analysis results in the power distribution network monitoring database.

[0012] Multi-dimensional data analysis is performed based on the power distribution network monitoring database to determine the multi-dimensional analysis results.

[0013] By adopting the above technical solution, the power 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 key nodes of the power distribution network. It collects power parameters of target nodes in the power distribution network, analyzes the operational status of the target nodes based on the power parameters, and determines the corresponding operational status analysis results. When the operational status analysis result indicates an operational anomaly, an operational anomaly warning is generated to prompt power grid maintenance personnel to handle abnormal situations in the power distribution network. The cloud computing center is used to acquire the power parameters and operational status analysis results transmitted by the microprocessor module in real time and store them in the power distribution network monitoring database. Then, multi-dimensional data analysis is performed based on the power distribution network monitoring database to determine the multi-dimensional analysis results. The microprocessor module in this power distribution network monitoring system is responsible for real-time data processing and basic operational status analysis, while the cloud computing center is responsible for data analysis and in-depth data mining for complex scenarios. This flexible collaborative computing framework enables the edge computing microprocessor module and the cloud computing center to work seamlessly together, leveraging the high-efficiency response advantage of the microprocessor module and the powerful processing capabilities and storage resources of the cloud computing center. This allows the system to meet the power distribution network monitoring needs in complex scenarios, improving the response speed and real-time performance of the power distribution network monitoring system.

[0014] In a preferred embodiment, this application can be further configured such that: when the microprocessor module generates a runtime anomaly warning when the runtime status analysis result is a runtime anomaly, it is used to:

[0015] When the operation status analysis result is an operation anomaly, the microprocessor module network is obtained, and the associated microprocessor modules are screened based on the abnormal microprocessor modules and the microprocessor module network to determine multiple associated microprocessor modules, wherein the abnormal microprocessor modules are the microprocessor modules whose operation status analysis result is an operation anomaly.

[0016] Based on the operational status analysis results corresponding to each of the associated microprocessor modules, anomaly region analysis is performed to determine the operational anomaly region.

[0017] Based on the aforementioned abnormal operation area, an abnormal operation warning is generated.

[0018] In a preferred embodiment, this application can be further configured as: a microprocessor module, also used for:

[0019] Based on the abnormal operating area, a multimodal acquisition device is identified, and the multimodal acquisition device is controlled to acquire multimodal data corresponding to the abnormal operating area.

[0020] Based on the multimodal data, abnormal equipment analysis is performed to obtain the equipment location and fault information corresponding to the abnormal equipment;

[0021] Accordingly, when the microprocessor module generates an operational anomaly warning based on the aforementioned operational anomaly region, it is used to:

[0022] The abnormal operation warning is generated based on the abnormal operation area, the device location, and the fault information.

[0023] In a preferred embodiment, this application can be further configured such that: when the cloud computing model performs multi-dimensional data analysis based on the power distribution network monitoring database and determines the multi-dimensional analysis results, it is used to:

[0024] Based on the power distribution network monitoring database, potential fault analysis is performed to determine the results of the potential fault analysis.

[0025] Based on the power distribution network monitoring database, load trend analysis is performed to determine the load trend analysis results.

[0026] Based on the power distribution network monitoring database, energy efficiency optimization analysis is performed to determine the results of the energy efficiency optimization analysis.

[0027] By combining the results of the potential fault analysis, the load trend analysis, and the energy efficiency optimization analysis, a multi-dimensional analysis result is obtained.

[0028] In a preferred embodiment, this application can be further configured such that: when the cloud computing center performs the action of storing the power parameters and the operating status analysis results to the power distribution network monitoring database, it is used for:

[0029] Data is encrypted based on the power parameters and the operating status analysis results to obtain encrypted data;

[0030] Obtain the blockchain network, filter 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;

[0031] The blockchain network is controlled to perform data verification and consensus on the encrypted data, and the verification and consensus results are determined.

[0032] 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 power distribution network monitoring database.

[0033] In a preferred embodiment, this application can be further configured as: a microprocessor module, also used for:

[0034] The transmission distance is calculated based on the target node and the cloud computing center to determine the data transmission distance.

[0035] Obtain data transmission requirements, select a communication method based on the data transmission distance and the data transmission requirements, and determine the data communication method.

[0036] According to the data communication method described above, the power parameters and the operating status analysis results are transmitted to the cloud computing center.

[0037] Secondly, this application provides a method for monitoring power distribution networks, which adopts the following technical solution:

[0038] A method for monitoring a power distribution network, comprising:

[0039] Collect power parameters of target nodes in the power distribution network, perform operational status analysis on the target nodes based on the power parameters, and determine the operational status analysis results corresponding to the target nodes;

[0040] When the operation status analysis result is an operation anomaly, an operation anomaly warning is generated, wherein the operation anomaly warning is used to prompt power grid maintenance personnel to handle abnormal situations in the power distribution network;

[0041] The power parameters and the operation status analysis results are acquired in real time and stored in the power distribution network monitoring database.

[0042] Multi-dimensional data analysis is performed based on the power distribution network monitoring database to determine the multi-dimensional analysis results.

[0043] Thirdly, this application provides an electronic device that adopts the following technical solution:

[0044] At least one processor;

[0045] Memory;

[0046] At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: perform the above-described power distribution network monitoring method.

[0047] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0048] A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the power distribution network monitoring method described above.

[0049] In summary, this application includes at least one of the following beneficial technical effects:

[0050] The power distribution network monitoring system comprises a microprocessor module and a cloud computing center. The microprocessor module, an intelligent component with real-time data processing capabilities, is deployed at key nodes in the power distribution network. It collects power parameters from target nodes, analyzes their operational status based on these parameters, and determines the corresponding operational status analysis results. When the operational status analysis result indicates an anomaly, an anomaly warning is generated to alert power grid maintenance personnel to address the abnormal situation in the power distribution network. The cloud computing center acquires the power parameters and operational status analysis results transmitted by the microprocessor module in real time and stores them in the power distribution network monitoring database. Then, multi-dimensional data analysis is performed based on the database to determine the multi-dimensional analysis results. The microprocessor module in this power distribution network monitoring system is responsible for real-time data processing and basic operational status analysis, while the cloud computing center is responsible for data analysis and in-depth data mining for complex scenarios. This flexible collaborative computing framework enables the edge computing microprocessor module and the cloud computing center to work seamlessly together, leveraging the high-efficiency response advantage of the microprocessor module and the powerful processing capabilities and storage resources of the cloud computing center. This allows the system to meet the power distribution network monitoring needs in complex scenarios, improving the response speed and real-time performance of the power distribution network monitoring system.

[0051] When the operational status analysis indicates an operational anomaly, the microprocessor module network is acquired, and related microprocessor modules are filtered based on the abnormal microprocessor module and the microprocessor module network to identify multiple related microprocessor modules. Then, based on the operational status analysis results corresponding to each related microprocessor module, anomaly region analysis is performed to determine the operational anomaly region. Finally, based on the operational anomaly region, an operational anomaly warning is generated. Through the related microprocessor module filtering and anomaly region analysis operations, the fault location time is significantly shortened, and the located operational anomaly regions are recorded in the operational anomaly warning, enabling power grid maintenance personnel to quickly find the problem and take corresponding repair measures, thereby improving the response speed and stability of the distribution network. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating a power distribution network monitoring system according to one embodiment of this application;

[0053] Figure 2 This is a schematic diagram of the structure of a power distribution network monitoring method according to one embodiment of this application;

[0054] Figure 3 This is a schematic diagram of the structure of an electronic device according to one embodiment of this application. Detailed Implementation

[0055] The following combination Figures 1 to 3 This application will be described in further detail.

[0056] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they are within the scope of this application.

[0057] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. It should be noted that in the optional embodiments of this application, the object information and other related data involved require the permission or consent of the object when the embodiments of this application are applied to specific products or technologies, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. That is to say, if the embodiments of this application involve data related to the object, it needs to be obtained with the authorization and consent of the object, the authorization and consent of the relevant departments, and in compliance with the relevant laws, regulations, and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information requires the consent of the individual. If sensitive information is involved, the separate consent of the information subject is required, and the embodiments also need to be implemented with the authorization and consent of the object.

[0058] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0059] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0060] This application provides a power distribution network monitoring system, including 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 key nodes of the power distribution network. It collects power parameters of target nodes in the power distribution network, analyzes the operational status of the target nodes based on the power parameters, and determines the corresponding operational status analysis results. When the operational status analysis result indicates an operational anomaly, an operational anomaly warning is generated to prompt power grid maintenance personnel to handle abnormal situations in the power distribution network. The cloud computing center 102 is used to acquire the power parameters and operational status analysis results transmitted by the microprocessor module 101 in real time and store them in a power distribution network monitoring database. Then, multi-dimensional data analysis is performed based on the power distribution network monitoring database to determine the multi-dimensional analysis results. In this power distribution network monitoring system, the microprocessor module 101 is responsible for real-time data processing and simple operational status analysis, while the cloud computing center 102 is responsible for data analysis and in-depth data mining in complex scenarios. This flexible collaborative computing framework enables the edge computing microprocessor module 101 and the cloud computing center 102 to work seamlessly together. It leverages the high-efficiency response advantage of the microprocessor module 101 and the powerful processing capabilities and storage resources of the cloud computing center 102 to meet the power distribution network monitoring needs in complex scenarios, thereby improving the response speed and real-time performance of the power distribution network monitoring system.

[0061] A power distribution network monitoring system, such as Figure 1 As shown, the system includes a microprocessor module 101 and a cloud computing center 102, wherein:

[0062] The microprocessor module 101 is used to collect the power parameters of the target nodes in the power distribution network, perform operational status analysis on the target nodes based on the power parameters, and determine the operational status analysis results corresponding to the target nodes. The microprocessor module 101 is an intelligent component with real-time data processing capabilities deployed at each key node of the power distribution network.

[0063] When the operation status analysis result is an operation anomaly, an operation anomaly warning is generated. The operation anomaly warning is used to prompt power grid maintenance personnel to handle abnormal situations in the distribution network.

[0064] In this embodiment of the application, the microprocessor module 101 is an intelligent component with real-time data processing capabilities deployed at each key node of the power distribution network. The microprocessor module 101 is a high-performance, low-power microprocessor responsible for real-time data processing and simple operation status analysis. Furthermore, the microprocessor 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 for communicating with other microprocessor modules 101.

[0065] The specific implementation process of the microprocessor module 101 is as follows: The microprocessor module 101 is equipped with a data acquisition program that acquires the power parameters of target nodes in the power distribution network in real time through the sensor interface within the microprocessor module 101. These power parameters include, but are not limited to, current, voltage, power factor, and temperature. The target nodes to be monitored are typically key equipment in the power distribution network, such as transformers, circuit breakers, and important lines. Then, the acquired power parameters are preprocessed, including but not limited to data cleaning, filtering, and outlier detection, to ensure the accuracy and reliability of the data. A pre-defined operational status assessment model is used to evaluate the status of the processed power parameters. This operational status assessment model is based on machine learning and deep learning technologies, and achieves an accurate assessment of the current status of the target node through the analysis and learning of historical data. When the operational status analysis result indicates normal operation, it indicates that the target node in the power distribution network is currently in normal operating condition. No further special operations are required; continuous monitoring of the target node's power parameters is sufficient to ensure timely detection of potential changes or anomalies. When the operation status analysis result indicates an operational anomaly, an operational anomaly warning is generated. This warning helps power grid maintenance personnel handle abnormal situations in the distribution network. There are various methods for generating operational anomaly warnings, and this application embodiment does not limit this. In one feasible method, when the operation status analysis result indicates an operational anomaly, the microprocessor module 101 network is acquired, and based on the abnormal microprocessor module 101 and the microprocessor module 101 network, associated microprocessor modules 101 are filtered to determine multiple associated microprocessor modules 101. The abnormal microprocessor modules 101 are those whose operation status analysis result indicates an operational anomaly. An anomaly region analysis is performed based on the operation status analysis result corresponding to each associated microprocessor module 101 to determine the operational anomaly region. Based on the operational anomaly region, an operational anomaly warning is generated.

[0066] The cloud computing center 102 is used to acquire 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 power distribution network monitoring database.

[0067] Multi-dimensional data analysis is performed based on the power distribution network monitoring database to determine the results of the multi-dimensional analysis.

[0068] In this embodiment of the 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. Its large-scale data storage capability provides rich data support for power distribution network monitoring. Furthermore, the cloud computing center 102 can perform multi-dimensional analysis on the large amount of stored data, which helps to reveal the operating rules and potential problems of the power distribution network and provides support for power grid management and decision-making.

[0069] The specific implementation process for cloud computing center 102 is as follows: Cloud computing center 102 is equipped with a dedicated data receiving module for receiving data packets transmitted by microprocessor module 101. After receiving the data packet composed of power parameters and operating status analysis results, the data receiving module verifies the data packet. Verification dimensions include, but are not limited to, format verification and data range verification to ensure the integrity and accuracy of the data in the data packet. The power distribution network monitoring database is the core component of cloud computing center 102, used to store power parameters and operating status analysis results. The database design needs to consider the data structure, indexing, and storage methods to optimize query efficiency and data consistency. Simultaneously, cloud computing center 102 also needs to regularly monitor and optimize the performance of the power distribution network monitoring database to ensure its efficient operation. To improve the accuracy of data stored in the power distribution network monitoring database, the following technical solution can be followed when storing data: Data is encrypted based on power parameters and operating status analysis results to obtain encrypted data; a blockchain network is acquired, and storage nodes are screened based on the encrypted data and the blockchain network to determine the target node, and the encrypted data is uploaded to the target node in the blockchain network; 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 successful, the encrypted data is determined to have been successfully uploaded to the target node, and the encrypted data is synchronously stored in the power distribution network monitoring database.

[0070] The cloud computing center 102 possesses powerful data processing capabilities, responsible for data analysis and in-depth data mining in complex scenarios. Therefore, it performs multi-dimensional data analysis based on the power distribution network monitoring database to determine the results. This multi-dimensional data analysis includes, but is not limited to, potential fault analysis, load change trend analysis, user behavior pattern analysis, and energy efficiency optimization analysis. Through multi-dimensional data analysis, real-time monitoring and intelligent scheduling of the power distribution network can be achieved, improving the automation level of the power grid and promoting its development towards greater intelligence and automation. In this power distribution network monitoring system, the microprocessor module 101 is responsible for real-time data processing and simple operational status analysis, while the cloud computing center 102 is responsible for data analysis and in-depth data mining in complex scenarios. This flexible collaborative computing framework enables seamless cooperation between the edge computing microprocessor module 101 and the cloud computing center 102. It leverages the high-efficiency response advantage of the microprocessor module 101 while utilizing the powerful processing capabilities and storage resources of the cloud computing center 102 to meet the monitoring needs of the power distribution network in complex scenarios, improving the response speed and real-time performance of the power distribution network monitoring system.

[0071] As can be seen, in this embodiment, the power 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 power distribution network. It is used to collect power parameters of target nodes in the power distribution network, perform operational status analysis on the target nodes based on the power parameters, and determine the operational status analysis results corresponding to the target nodes. When the operational status analysis result indicates an operational anomaly, an operational anomaly warning is generated to prompt power grid maintenance personnel to handle abnormal situations in the power distribution network. The cloud computing center 102 is used to acquire the power parameters and operational status analysis results transmitted by the microprocessor module 101 in real time and store the power parameters and operational status analysis results in the power distribution network monitoring database. Then, multi-dimensional data analysis is performed based on the power distribution network monitoring database to determine the multi-dimensional analysis results. In this power distribution network monitoring system, the microprocessor module 101 is responsible for real-time data processing and simple operational status analysis, while the cloud computing center 102 is responsible for data analysis and in-depth data mining in complex scenarios. This flexible collaborative computing framework enables the edge computing microprocessor module 101 and the cloud computing center 102 to work seamlessly together. It leverages the high-efficiency response advantage of the microprocessor module 101 and the powerful processing capabilities and storage resources of the cloud computing center 102 to meet the power distribution network monitoring needs in complex scenarios, thereby improving the response speed and real-time performance of the power distribution network monitoring system.

[0072] Furthermore, to improve the response speed and stability of the power distribution network, in this embodiment, when the microprocessor module 101 generates an operational anomaly warning when the operational status analysis result indicates an operational anomaly, it is used to:

[0073] When the operation status analysis result is an operation anomaly, the microprocessor module 101 network is obtained, and the associated microprocessor modules 101 are screened based on the abnormal microprocessor module 101 and the microprocessor module 101 network to determine multiple associated microprocessor modules 101, wherein the abnormal microprocessor module 101 is the microprocessor module 101 whose operation status analysis result is an operation anomaly.

[0074] Based on the operational status analysis results of each associated microprocessor module 101, anomaly region analysis is performed to determine the operational anomaly region.

[0075] Based on the abnormal operation area, generate an abnormal operation warning.

[0076] In the embodiments of this application, when generating an operational anomaly warning, the time for fault location is greatly shortened by filtering and analyzing abnormal areas through the associated microprocessor module 101, and the located operational anomaly areas are recorded in the operational anomaly warning, so that 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 power distribution network.

[0077] Specifically, when the operational status analysis result indicates an operational anomaly, it signifies that the power distribution equipment at the target node is malfunctioning. Therefore, the microprocessor module 101 network is acquired. This network is typically presented as an association graph, and its topology represents the physical connections or logical dependencies between the microprocessor modules 101. Thus, based on the abnormal microprocessor module 101 and the microprocessor module 101 network, associated microprocessor modules 101 are screened to identify multiple associated microprocessor modules 101. The abnormal microprocessor module 101 is defined as the microprocessor module 101 whose operational status analysis result indicates an operational anomaly, and the associated microprocessor modules 101 are those that have a physical connection, data exchange, or functional dependency with the abnormal microprocessor module 101. That is, during the associated microprocessor module 101 screening, the network searches for microprocessor modules 101 that are directly or indirectly connected to the abnormal microprocessor module 101.

[0078] There are various methods for filtering associated microprocessor modules 101, and this application embodiment does not limit the methods. In one feasible method, a network analysis tool is used to perform topology analysis on the network of microprocessor modules 101. Based on the location of the abnormal microprocessor module 101 in the network, other microprocessor modules 101 directly connected to the abnormal microprocessor module 101 are identified and denoted as associated microprocessor modules 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 denoted as associated microprocessor modules 101. It is also determined whether there are any anomalies or inconsistencies in the data exchange process, such as data loss, delay, or errors. Furthermore, a functional dependency analysis is performed on the network of microprocessor modules 101 to analyze the functional dependencies between the abnormal module and other modules, and the microprocessor modules 101 with functional dependencies on the abnormal microprocessor module 101 are identified and denoted as associated microprocessor modules 101.

[0079] Furthermore, based on the operational status analysis results corresponding to each associated microprocessor module 101, operational anomaly status is screened to identify associated microprocessor modules 101 with anomalies. Then, based on the microprocessor module 101 network, the abnormal microprocessor modules 101, and the abnormal associated microprocessor modules 101, a topological connection is established to determine operational anomaly regions. These operational anomaly regions may be areas containing one, two, or more key nodes. Finally, based on these operational anomaly regions, an operational anomaly warning is generated.

[0080] As can be seen, in this embodiment, when the operational status analysis result indicates an operational anomaly, the microprocessor module 101 network is acquired, and based on the abnormal microprocessor module 101 and the microprocessor module 101 network, associated microprocessor modules 101 are filtered to identify multiple associated microprocessor modules 101. Then, based on the operational status analysis result corresponding to each associated microprocessor module 101, anomaly region analysis is performed to determine the operational anomaly region. Finally, based on the operational anomaly region, an operational anomaly warning is generated. Through the associated microprocessor module 101 filtering and anomaly region analysis operations, the fault location time is greatly shortened, and the located operational anomaly region is recorded in the operational anomaly warning, so that 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.

[0081] Furthermore, to improve the efficiency of fault diagnosis, in this embodiment of the application, the microprocessor module 101 is also used for:

[0082] Based on the abnormal operation area, the multimodal acquisition device is identified, and the multimodal acquisition device is controlled to collect the multimodal data corresponding to the abnormal operation area;

[0083] Anomaly analysis is performed based on multimodal data to obtain the location and fault information of the anomaly.

[0084] Accordingly, when the microprocessor module 101 generates a runtime exception warning based on the runtime exception region, it is used for:

[0085] Based on the abnormal operating area, equipment location, and fault information, an abnormal operating warning is generated.

[0086] In this embodiment of the application, multimodal acquisition devices are also installed at key nodes in the power distribution network to collect multimodal data of the power distribution network. This multimodal data includes, but is not limited to, visual image information and sound spectrum data. Visual image information includes, for example, line photographs and substation video streams. Supplementary sound spectrum data includes, for example, discharge sounds from high-voltage equipment. Multimodal data can comprehensively reflect the operating status and fault characteristics of equipment, helping to quickly identify the location and fault type of abnormal equipment in areas of abnormal operation. This helps reduce delays and losses in fault handling caused by misjudgment or omission, and improves the efficiency of fault diagnosis.

[0087] Specifically, the microprocessor module 101 pre-stores the installation location and data acquisition range of each multimodal acquisition device. Therefore, based on the abnormal operating area and the data acquisition range matching of each multimodal acquisition device, a multimodal acquisition device capable of covering the abnormal operating area is determined. This multimodal acquisition device can be one, two, or more. Then, the microprocessor module 101 sends data acquisition commands to the multimodal acquisition devices via remote communication to control the multimodal acquisition devices to start acquiring data from the abnormal operating area and transmit the acquired multimodal data to the microprocessor module 101 in real time. The multimodal data includes, but is not limited to, visual image information and sound spectrum data. Furthermore, based on the multimodal data, abnormal device analysis is performed to obtain the device location and fault information corresponding to the abnormal device. There are various ways to implement abnormal device analysis, and this embodiment does not limit the specific implementation. In one feasible approach, features such as shape, color, and texture of the device are extracted from visual image information in multimodal data; sound features such as frequency, amplitude, and timbre are extracted from sound spectrum data in multimodal data; a feature-level fusion method is used to fuse the features extracted from visual image information and sound spectrum data 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 and cause) corresponding to the abnormal device. Finally, an operational anomaly warning is generated based on the abnormal operating area, device location, and fault information.

[0088] As can be seen, in this embodiment, a multimodal acquisition device is determined based on the abnormal operating area, and the multimodal acquisition device is controlled to collect multimodal data corresponding to the abnormal operating area. Then, abnormal equipment analysis is performed based on the multimodal data to obtain the equipment location and fault information corresponding to the abnormal equipment. Multimodal data can comprehensively reflect the operating status and fault characteristics of the equipment, which helps to quickly identify the location and fault type of abnormal equipment in the abnormal operating area, helps to reduce the delay and loss in fault handling caused by misjudgment or omission, and improves the efficiency of fault diagnosis.

[0089] Furthermore, in order to promptly detect abnormal data and potential fault points in the power distribution network and avoid situations of power surplus or shortage, in this embodiment of the application, the cloud computing model, when performing multi-dimensional data analysis based on the power distribution network monitoring database and determining the multi-dimensional analysis results, is used for:

[0090] Based on the power distribution network monitoring database, potential fault analysis is performed to determine the results of the potential fault analysis.

[0091] Based on the power distribution network monitoring database, load trend analysis is performed to determine the results of the load trend analysis.

[0092] By combining the results of potential fault analysis and load trend analysis, multi-dimensional analysis results are obtained.

[0093] In this embodiment of the application, to promptly detect abnormal data and potential fault points in the distribution network, potential fault analysis is performed based on the distribution network monitoring database. The analysis results help power grid maintenance personnel take preventative measures to reduce power outage time and economic losses caused by faults. Therefore, a fault prediction model is pre-stored in the cloud computing model. This model is constructed based on historical fault data and expert experience, reflecting the correlation between power parameters, operating status, and potential faults. When the fault prediction model detects a potential fault in the distribution network, data mining and machine learning techniques are used to uncover patterns and correlations behind the power parameter and operating status analysis results, discovering potential fault information. Fault location technology is then used to determine the specific location, cause, and type of the potential fault. By integrating the potential fault information, location, cause, and type, the potential fault analysis results are obtained.

[0094] To enable power grid operators to rationally schedule power production and transmission, optimize resource allocation, and avoid power surpluses or shortages, load trend analysis is used to predict future load changes in the power grid, providing data support for balancing supply and demand in the electricity market. Therefore, appropriate load trend analysis methods are selected, such as time-series analysis or machine learning methods. A load trend prediction model is constructed based on the selected method. This model is obtained through continuous training and optimization using a large amount of historical data, enabling rapid and accurate prediction of load usage. Furthermore, load trend analysis is conducted based on the distribution network monitoring database and the load trend prediction model to determine the results. These results provide the electricity demand for each region over a future period, aiding in the optimization and upgrading of the distribution network and improving power supply reliability and economy. Finally, by integrating the potential fault analysis results and the load trend analysis results, a multi-dimensional analysis is obtained.

[0095] Of course, behavioral pattern analysis can also be performed based on the power grid monitoring database to determine the results. Simultaneously, energy efficiency optimization analysis can be conducted based on the same database to determine the results. Behavioral pattern analysis can reveal the operational patterns of various devices, users, or systems within the power grid. By studying these patterns, power grid operation strategies can be optimized, improving grid efficiency and security. Energy efficiency optimization analysis aims to identify energy efficiency bottlenecks in the power grid and propose improvement measures. Implementing these measures can significantly reduce power grid energy consumption and emissions, promoting the achievement of energy conservation and emission reduction goals.

[0096] As can be seen, in this embodiment, in order to promptly detect 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 results. To enable grid operators to rationally 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 results. Finally, by combining the potential fault analysis results and the load trend analysis results, a multi-dimensional analysis result is obtained.

[0097] Furthermore, to improve data security and reliability and effectively prevent the uploading of false or invalid data, in this embodiment of the application, the cloud computing center 102, when storing the analysis results of power parameters and operating status to the power distribution network monitoring database, is used for:

[0098] Data is encrypted based on the analysis results of power parameters and operating status to obtain encrypted data;

[0099] Obtain the blockchain network, filter storage nodes based on encrypted data and the blockchain network, determine the target node, and upload the encrypted data to the target node in the blockchain network;

[0100] Control the blockchain network to perform data verification and consensus on encrypted data, and determine the verification and consensus results;

[0101] When the consensus verification result is successful, it is determined that the encrypted data has been successfully uploaded to the target node, and the encrypted data is synchronously stored in the power distribution network monitoring database.

[0102] In this embodiment of the application, to effectively prevent unauthorized access or tampering of data during transmission and storage, encryption algorithms are used to encrypt the power parameters and operating status analysis results, resulting in encrypted data that ensures the confidentiality and integrity of the data. The encryption algorithms include, but are not limited to, symmetric encryption algorithms (such as AES, DES, etc.) and asymmetric encryption algorithms (such as RSA, ECC, etc.). In a blockchain network, storage nodes are responsible for storing data. To ensure data security and reliability, suitable storage nodes need to be selected. Therefore, storage node selection is performed based on encrypted data and the blockchain network to determine target nodes. The selection process can consider factors such as storage capacity, computing power, online time, and reputation. Through evaluation of multiple factors, the most suitable storage node is selected as the target node. Then, the encrypted data is uploaded to the selected target node using the API or tools provided by the blockchain network. Blockchain technology has decentralized, distributed storage, and tamper-proof characteristics. Storing encrypted data in a blockchain network can further improve data security and reliability. Even if a node fails or is attacked, other nodes can still maintain data integrity and consistency. Furthermore, the blockchain network is controlled to perform data verification and consensus on the encrypted data, determining the verification consensus result, which includes pass and fail. The data verification mechanism in the blockchain network ensures that the uploaded data is authentic and valid. Through the consensus algorithm, 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 or invalid data. When the verification consensus result is pass, it is determined that the encrypted data has been successfully uploaded to the target node and is synchronously stored in the power distribution network monitoring database. When the verification consensus result is fail, a data reception failure command is sent to the microprocessor module 101 so that the microprocessor module 101 can resend the power parameters and operating status analysis results.

[0103] As can be seen, in this embodiment, data is encrypted based on power parameters and operating status analysis results to obtain encrypted data. Then, a blockchain network is acquired, and storage nodes are screened based on the encrypted data and the blockchain network to determine the target node. The encrypted data is then uploaded to the target node in the blockchain network. Blockchain technology has the characteristics of decentralization, distributed storage, and tamper-proofing. Storing encrypted data in the blockchain network can further improve data security and reliability. Even if a node fails or is attacked, other nodes can still maintain the integrity and consistency of the data. Furthermore, 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 successful, it is determined that the encrypted data has been successfully uploaded to the target node and is synchronously stored in the power distribution network monitoring database. The data verification mechanism in the blockchain network can ensure that the uploaded data is authentic and valid. Through the consensus algorithm, 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 or invalid data.

[0104] 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 this embodiment of the application, the microprocessor module 101 is also used for:

[0105] The transmission distance is calculated based on the target node and cloud computing center 102 to determine the data transmission distance.

[0106] Obtain data transmission requirements, select communication methods based on data transmission distance and requirements, and determine the data communication method.

[0107] According to the data communication method, the power parameters and operating status analysis results are transmitted to the cloud computing center 102.

[0108] In this embodiment, 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 between the target node and the cloud computing center 102, a suitable distance calculation method is selected to calculate the transmission distance between the target node and the cloud computing center 102, thus determining 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 is 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 is required, for example, using a path planning algorithm in a geographic information system (GIS) to calculate a more accurate transmission distance. Then, the data transmission requirements are obtained. These data transmission requirements are pre-set factors that need to be considered for 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. Furthermore, based on data transmission distance and requirements, a communication method is selected to determine the data communication method. Specifically, the applicability of different communication methods (such as wired communication, wireless communication, and satellite communication) is evaluated according to data transmission requirements (e.g., real-time performance, security) and transmission distance. The costs (including equipment purchase, installation, and maintenance costs) and performance (e.g., transmission speed, stability, and reliability) of different communication methods are compared. The technological maturity of the communication methods is assessed to ensure that the selected method is technically feasible and compatible with existing systems or equipment. The final data communication method is determined by integrating the results of these various evaluations. Finally, according to the data communication method, the power parameters and operational status analysis results are transmitted to the cloud computing center 102. By selecting a suitable data communication method, the failure rate and packet loss rate during data transmission are reduced, the reliability and stability of data transmission are improved, and data transmission costs are reduced.

[0109] As can be seen, in this embodiment, 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 a communication method is selected based on the data transmission distance and the data transmission requirements to determine the data communication method. Subsequently, according to the data communication method, the power parameters and operating status analysis results are transmitted to the cloud computing center 102. By selecting an appropriate data communication method, the failure rate and packet loss rate during data transmission are reduced, the reliability and stability of data transmission are improved, and data transmission costs are reduced.

[0110] The above embodiments describe a power distribution network monitoring system from the perspective of the device. The following embodiments describe a power distribution network monitoring method from the perspective of the process flow. For details, please refer to the following embodiments.

[0111] This application provides a power distribution network monitoring method, executed by an electronic device, such as... Figure 2 As shown, the method includes steps S201, S202, S203, and S204, wherein:

[0112] Step S201: Collect the power parameters of the target nodes in the power distribution network, perform operational status analysis on the target nodes based on the power parameters, and determine the operational status analysis results corresponding to the target nodes;

[0113] Step S202: When the operation status analysis result is an operation anomaly, an operation anomaly warning is generated. The operation anomaly warning is used to prompt power grid maintenance personnel to handle abnormal situations in the distribution network.

[0114] Step S203: Acquire power parameters and operating status analysis results in real time, and store the power parameters and operating status analysis results in the power distribution network monitoring database;

[0115] Step S204: Perform multi-dimensional data analysis based on the power distribution network monitoring database to determine the multi-dimensional analysis results.

[0116] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the power distribution network monitoring method described above can be referred to the corresponding process in the aforementioned method embodiments, and will not be repeated here.

[0117] This application provides an electronic device, such as... Figure 3 As shown, Figure 3 The illustrated electronic device 300 includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of this electronic device 300 does not constitute a limitation on the embodiments of this application.

[0118] 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 can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0119] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0120] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0121] The memory 303 is used to store application code that executes the solution of this application, and its 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 content shown in the foregoing method embodiments.

[0122] Electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Servers can also be included. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0123] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0124] This application provides a computer program product including a computer program that, when executed by a processor, implements the methods described in any of the above embodiments.

[0125] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0126] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A power distribution network monitoring system, characterized in that, include: Microprocessor modules and cloud computing centers, including: A microprocessor module is used to collect power parameters of target nodes in the power distribution network, analyze the operating status of the target nodes based on the power parameters, and determine the corresponding operating status analysis results of the target nodes. The microprocessor module is an intelligent component with real-time data processing capabilities deployed at each key node of the power distribution network. The microprocessor module is equipped with a data acquisition program. The sensor interface in the microprocessor module acquires the power parameters of the target nodes in the power distribution network in real time, including current, voltage, power factor, and temperature. The target nodes to be monitored are key equipment in the power distribution network. The collected power parameters are preprocessed, and the status of the processed power parameters is evaluated using a pre-set operating status evaluation model. When the operation status analysis result indicates an operation anomaly, the microprocessor module network is acquired, and related microprocessor modules are filtered based on the abnormal microprocessor module and the microprocessor module network to identify multiple related microprocessor modules; based on the operation status analysis result corresponding to each related microprocessor module, anomaly region analysis is performed to identify the operation anomaly region; based on the operation anomaly region, an operation anomaly warning is generated. The microprocessor module is also configured to: determine the multimodal acquisition device based on the abnormal operating area, and control the multimodal acquisition device to acquire multimodal data corresponding to the abnormal operating area; perform abnormal equipment analysis based on the multimodal data to obtain the equipment location and fault information corresponding to the abnormal equipment; Accordingly, when the microprocessor module generates an operational anomaly warning based on the aforementioned operational anomaly region, it is used to: The abnormal operation warning is generated based on the abnormal operation area, the device location, and the fault information; A cloud computing center is used to acquire the power parameters and operating status analysis results transmitted by the microprocessor module in real time, and to store the power parameters and operating status analysis results in a power distribution network monitoring database, including: Data is encrypted based on the power parameters and the operating status analysis results to obtain encrypted data; Obtain the blockchain network, filter 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; The blockchain network is controlled to perform data verification and consensus on the encrypted data, and the verification and consensus results are determined. 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 power distribution network monitoring database. Based on the aforementioned power distribution network monitoring database, multi-dimensional data analysis is performed to determine the multi-dimensional analysis results, including: Based on the power distribution network monitoring database, potential fault analysis is performed to determine the results of the potential fault analysis. Based on the power distribution network monitoring database, load trend analysis is performed to determine the load trend analysis results. By combining the results of the potential fault analysis and the load trend analysis, a multi-dimensional analysis result is obtained.

2. The power distribution network monitoring system according to claim 1, characterized in that, The microprocessor module is also used for: The transmission distance is calculated based on the target node and the cloud computing center to determine the data transmission distance. Obtain data transmission requirements, select a communication method based on the data transmission distance and the data transmission requirements, and determine the data communication method. According to the data communication method described above, the power parameters and the operating status analysis results are transmitted to the cloud computing center.

3. A method for monitoring a power distribution network, characterized in that, include: Collect power parameters of target nodes in the power distribution network, perform operational status analysis on the target nodes based on the power parameters, and determine the operational status analysis results corresponding to the target nodes; When the operation status analysis result is an operation anomaly, the microprocessor module network is obtained, and the associated microprocessor modules are screened based on the abnormal microprocessor modules and the microprocessor module network to determine multiple associated microprocessor modules; Based on the operational status analysis results corresponding to each of the associated microprocessor modules, anomaly region analysis is performed to determine the operational anomaly region. Based on the aforementioned abnormal operation area, an abnormal operation warning is generated; It also includes: determining a multimodal acquisition device based on the abnormal operation area, and controlling the multimodal acquisition device to acquire multimodal data corresponding to the abnormal operation area; Based on the multimodal data, abnormal equipment analysis is performed to obtain the equipment location and fault information corresponding to the abnormal equipment; Real-time acquisition of the power parameters and the operational status analysis results, and storage of the power parameters and the operational status analysis results in the power distribution network monitoring database, including: Data is encrypted based on the power parameters and the operating status analysis results to obtain encrypted data; Obtain the blockchain network, filter 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; The blockchain network is controlled to perform data verification and consensus on the encrypted data, and the verification and consensus results are determined. 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 power distribution network monitoring database. Based on the aforementioned power distribution network monitoring database, multi-dimensional data analysis is performed to determine the multi-dimensional analysis results, including: Based on the power distribution network monitoring database, potential fault analysis is performed to determine the results of the potential fault analysis. Based on the power distribution network monitoring database, load trend analysis is performed to determine the load trend analysis results. By combining the results of the potential fault analysis and the load trend analysis, a multi-dimensional analysis result is obtained.

4. 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 memory and configured to be executed by at least one processor, the at least one application being configured to: perform the power distribution network monitoring method of claim 3.

5. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed in a computer, causes the computer to perform the power distribution network monitoring method of claim 3.

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