Intelligent power grid data communication optimization method based on edge calculation

Abstract

The invention provides a smart power grid data communication optimization method based on edge calculation, and relates to the technical field of smart power grids, and the method comprises the steps: obtaining an inspection topology network of a target power grid; performing multi-angle communication analysis to obtain K power transmission equipment communication feature tuple sets and L inspection site communication feature tuples; carrying out edge step identification to obtain M edge topology sub-networks; performing balanced configuration of sub-network service resources to obtain M edge computing servers; performing routing inspection by using a routing inspection unmanned aerial vehicle and a routing inspection robot to obtain routing inspection video data of L sites; and distributing the data to M edge computing servers for data preprocessing, and uploading the data to a cloud computing center for routing inspection data analysis to obtain a routing inspection analysis result. According to the invention, the technical problem of low data processing efficiency caused by data transmission delay and overweight network load due to the fact that power grid monitoring generally depends on centralized data processing in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and in particular to a smart grid data communication optimization method based on edge computing. Background Art

[0002] In traditional smart grids, data usually needs to be transmitted from edge devices to the central cloud computing center for centralized processing. This method will result in higher transmission delays, especially in scenarios with high real-time monitoring and response requirements. For example, status monitoring and fault diagnosis of transmission lines require fast feedback, and the high latency in traditional methods may affect the security and stability of the power grid. This centralized processing mode often leads to excessive use of network bandwidth and increased network load. In smart grids, especially when large-scale data is collected and processed, the demand for data transmission increases dramatically, which will lead to reduced transmission efficiency. In addition, bandwidth limitations will affect data transmission speed and reliability, which in turn affects the real-time monitoring and decision-making capabilities of the power grid. Summary of the invention

[0003] This application provides a smart grid data communication optimization method based on edge computing, aiming to solve the technical problem that power grid monitoring in the prior art usually relies on centralized data processing, resulting in data transmission delays and excessive network load, which in turn leads to low data processing efficiency.

[0004] The present application discloses a method for optimizing data communication of a smart grid based on edge computing, the method comprising: obtaining a patrol topology network of a target power grid, wherein the patrol topology network comprises K transmission lines, L patrol sites and a cloud computing center, and the K transmission lines comprise K transmission equipment sets and K transmission paths; performing multi-angle communication analysis on the K transmission equipment sets and the L patrol sites to obtain K transmission equipment communication feature tuple sets and L patrol site communication feature tuples; performing edge jump identification on the patrol topology network based on the K transmission equipment communication feature tuple sets and the L patrol site communication feature tuples to obtain M edge topology sub-networks, wherein the M edge topology sub-networks comprise M edge communication coefficients; performing edge jump identification on the patrol topology network based on the K transmission equipment communication feature tuple sets and the L patrol site communication feature tuples to obtain M edge topology sub-networks, wherein the M edge topology sub-networks comprise M edge communication coefficients; performing edge jump identification on the patrol topology network based on the K transmission equipment communication feature tuple sets and the L patrol site communication feature tuples M edge communication coefficients are used to balance the sub-network service resources of the M edge topology sub-networks to obtain M configured edge computing servers; K inspection drones are used to inspect the K transmission lines according to the K transmission paths respectively to obtain K line inspection video data sets, and L inspection robots are used to inspect the L inspection sites according to preset in-station inspection routes to obtain L site inspection video data; based on the M edge topology sub-networks, the K line inspection video data sets and the L site inspection video data are distributed to M edge computing servers for data preprocessing, and the preprocessed data are uploaded to the cloud computing center for inspection data analysis according to the preset communication protocol to obtain the inspection analysis results of the target power grid.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages: By obtaining the inspection topology network of the target power grid, including the layout of transmission lines, inspection sites and cloud computing centers, a comprehensive understanding of the power grid structure is provided, which lays the foundation for subsequent communication analysis and edge computing optimization, and ensures the accuracy of data collection and processing; multi-angle communication analysis is performed on transmission equipment and inspection sites to obtain a set of communication feature tuples. This multi-dimensional data analysis can identify the communication characteristics of equipment and sites, establish comprehensive communication characteristics, and help optimize data flow and network load; edge jump identification is performed on the inspection topology network based on the communication feature tuple set, the power grid can be divided into multiple edge topology sub-networks, and the edge communication coefficient can be calculated. This method can effectively identify key edge nodes and communication paths in the network, optimize the path selection for data transmission, and improve data processing efficiency and transmission stability; sub-network service resources are balanced based on the edge communication coefficient, and the resource allocation of edge computing servers is optimized, which ensures the balanced use of computing resources and improves The processing capacity and response speed of the server are improved; inspection drones and inspection robots are used to inspect transmission lines and inspection sites to obtain inspection video data. This method can provide real-time equipment status and safety monitoring, and improve the automation level and accuracy of inspections. Drones cover a wide area of ​​the transmission lines, while robots conduct detailed inspections inside the sites, ensuring the comprehensiveness and meticulousness of the data; data preprocessing is performed through edge computing servers, and the processed data is uploaded to the cloud computing center, which optimizes the data processing flow. Edge computing reduces data transmission delays and completes preliminary processing locally, thereby reducing the pressure on the cloud computing center and improving the response speed and data processing efficiency of the overall system; the cloud computing center analyzes the uploaded preprocessed data to generate inspection analysis results for the target power grid. This centralized analysis can integrate data from different sources, provide a comprehensive assessment of the power grid's operating status and potential problems, and improve the scientificity and effectiveness of power grid management.

[0006] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 A schematic diagram of a smart grid data communication optimization method based on edge computing is provided for an embodiment of the present application; Figure 2 A schematic diagram of the process of constructing K sets of communication feature tuples of power transmission equipment and L sets of communication feature tuples of inspection sites in a smart grid data communication optimization method based on edge computing is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0008] The embodiments of the present application provide a smart grid data communication optimization method based on edge computing, which solves the technical problem that power grid monitoring in the prior art usually relies on centralized data processing, resulting in data transmission delays and excessive network load, which in turn leads to low data processing efficiency.

[0009] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0010] like Figure 1 As shown, an embodiment of the present application provides a smart grid data communication optimization method based on edge computing, the method comprising: An inspection topology network of a target power grid is obtained, wherein the inspection topology network includes K transmission lines, L inspection sites, and a cloud computing center, and the K transmission lines include K transmission equipment sets and K transmission paths.

[0011] First, clarify the physical scope and equipment distribution of the target power grid, including all transmission lines, transmission equipment, and inspection stations. The target power grid can be a regional power grid or a sub-part of a large power system.

[0012] Determine the K transmission lines, L inspection sites and cloud computing center of the target power grid, where K and L are positive integers. The K transmission lines are all the transmission lines that need to be inspected in the target power grid. These lines include not only the actual physical lines, but also all the related transmission equipment, such as transformers, transmission towers, switchgear, etc.; the L inspection sites are the sites in the power grid that need regular or irregular inspections. These sites usually include key nodes such as substations, distribution stations, and control centers; the cloud computing center is connected to the power grid and is responsible for processing and storing data obtained from power grid inspections and providing computing resources for data analysis.

[0013] For the K transmission lines, all transmission equipment contained in each transmission line are determined, including cables, transformers, switchgear, etc. These equipment play a key role in the operation of the power grid. K transmission lines include K sets of transmission equipment; at the same time, the specific path of each transmission line is determined, including information such as the physical layout of the line, the length of the path, the terrain environment passed through, etc. The detailed information of the path is very important for subsequent inspection planning. K transmission lines include K transmission paths.

[0014] The information of K transmission lines, L inspection stations and cloud computing centers is integrated into a complete inspection topology network. This topology network can display all key components of the power grid and their interrelationships, which can be used to plan and execute power grid inspection tasks.

[0015] A multi-angle communication analysis is performed on the K power transmission equipment sets and the L inspection sites to obtain K power transmission equipment communication feature tuple sets and L inspection site communication feature tuple sets.

[0016] For a set of K power transmission devices, their surrounding environmental characteristics, location coordinates, and channel power losses are analyzed respectively to obtain K sets of power transmission device environmental characteristics, K sets of power transmission device location coordinates, and K sets of power transmission device channel power losses. These sets are integrated to obtain K sets of power transmission device communication feature tuples. Each tuple contains the unchangeable three-dimensional communication characteristics of each power transmission device, and these characteristics cannot be changed in practical applications. For each inspection site, its surrounding environmental characteristics, location coordinates, and channel power losses are also analyzed to obtain L inspection site communication feature tuples.

[0017] The inspection topology network is subjected to edge jump identification based on the K sets of communication feature tuples of transmission equipment and the L inspection site communication feature tuples to obtain M edge topology sub-networks, wherein the M edge topology sub-networks include M edge communication coefficients.

[0018] The obtained K sets of communication feature tuples of transmission equipment and L communication feature tuples of inspection sites are used as the basic data for analyzing the inspection topology network. In graph theory, jump recognition involves detecting key turning points or edge positions between nodes in the network. Edge jump recognition is used here to locate points where communication features in the inspection topology network change significantly. These changes mark the division of different regions or subnetworks. According to the results of edge jump recognition, the inspection topology network is divided into M edge topology subnetworks, where M is a positive integer and each subnetwork represents an independent part of the inspection network. The M edge topology subnetworks include M edge communication coefficients, which represent the communication efficiency, stability and other related communication performance indicators within the subnetwork and between the subnetwork and the external network.

[0019] Based on the M edge communication coefficients, sub-network service resources are balanced configured for the M edge topology sub-networks to obtain M configured edge computing servers.

[0020] Using the calculated M edge communication coefficients, evaluate the computing resource requirements of each edge topology subnetwork, including computing power, storage space, network bandwidth, response time, etc. Determine the balanced configuration goals, such as avoiding over-allocation or under-allocation of resources while ensuring that each subnetwork can meet its service requirements.

[0021] Based on the balanced configuration goal, according to the edge communication coefficient, the computing resources are reasonably allocated so that the load of each sub-network is evenly distributed. For example, for sub-networks with higher service requirements, more computing resources are allocated first to ensure their communication quality and service performance. According to the edge communication coefficient and demand assessment results, resources are allocated to M edge topology sub-networks. The resources allocated to each sub-network include computing power, storage, and network bandwidth. According to the resource allocation results, the corresponding edge computing server is deployed for each edge topology sub-network to obtain the configured M edge computing servers. The edge computing servers are responsible for processing computing tasks and data processing within the sub-network, and providing low-latency and high-efficiency computing services.

[0022] By evenly configuring the service resources of the sub-networks, the computing efficiency and stability of the inspection topology network can be effectively improved, ensuring that the edge computing server can operate stably under high load conditions and providing reliable computing support for power grid inspection and maintenance.

[0023] K inspection drones are used to inspect the K transmission lines according to the K transmission paths, respectively, to obtain K line inspection video data sets, and L inspection robots are used to inspect the L inspection sites according to preset in-station inspection routes, to obtain L site inspection video data.

[0024] Each transmission line is assigned a patrol drone. The patrol drone is a flying device used for transmission line inspection. It is equipped with a high-definition camera and can automatically fly along a preset path to obtain video data on the operation and safety of the transmission line. The drone automatically inspects along the transmission path. The drone uses its high-definition camera to record the operation and safety of the transmission line and its equipment in real time, obtain detailed video data of the line, and transmit it to the ground control center in real time to obtain K line inspection video data sets.

[0025] Each inspection site is equipped with an inspection robot. The inspection robot is a ground mobile device used for internal inspections of the site. It is equipped with a high-definition camera and can automatically inspect within the preset route and collect video data on the operating status and safety of the equipment inside the site. According to the structural layout of each inspection site, the inspection route within the site is preset. The inspection route should cover all key equipment and areas inside the site to ensure the comprehensiveness and accuracy of the inspection. The inspection robot automatically inspects the inspection site according to the preset inspection route. Through its camera, it obtains the operating status and safety of the equipment inside the site in real time, collects the site inspection video data, and uploads it to the control center in real time to obtain L site inspection video data.

[0026] Based on the M edge topology sub-networks, the K line inspection video data sets and the L site inspection video data are distributed to M edge computing servers for data preprocessing, and the preprocessed data are uploaded to the cloud computing center for inspection data analysis according to a preset communication protocol to obtain the inspection analysis results of the target power grid.

[0027] Based on M edge topology sub-networks, K line inspection video data sets and L site inspection video data are distributed to M edge computing servers. Each edge computing server corresponds to an edge topology sub-network and is responsible for processing the assigned video data.

[0028] After receiving the distributed data, the edge computing server uses its computing resources to pre-process the data, including video compression, denoising, image enhancement, data format conversion, and preliminary fault detection. Among them, preliminary fault detection can use a preset detection algorithm to perform preliminary analysis on the inspection video data to identify possible equipment failures, line anomalies or safety hazards. The preprocessing aims to reduce the amount of data transmission, improve data quality, and preliminarily identify possible safety hazards or failures.

[0029] The preprocessed data is uploaded to the cloud computing center according to the preset communication protocol. After receiving the preprocessed data from M edge computing servers, the cloud computing center uses advanced analysis algorithms and big data processing tools to conduct in-depth analysis of all inspection video data. The analysis process includes a comprehensive inspection of the equipment operating status, identification of safety hazards, trend prediction, etc., and generates inspection analysis results of the target power grid. The analysis results include equipment health status, risk assessment, fault prediction and other information to support power grid operation and maintenance decisions.

[0030] Furthermore, if Figure 2 As shown, a multi-angle communication analysis is performed on the K power transmission equipment sets and the L inspection sites to obtain K power transmission equipment communication feature tuple sets and L inspection site communication feature tuples, including: A local scene sensor is used to identify the nearby environmental features of the K power transmission equipment sets and the L inspection sites, and obtain K power transmission equipment environmental feature sets and L inspection site features; in combination with the inspection topology network, the K power transmission equipment position coordinate sets, L inspection site position coordinates and cloud computing center position coordinates of the K power transmission equipment sets, L inspection site position coordinates and cloud computing center position coordinates are obtained; based on the channel power loss function, the K power transmission equipment position coordinate sets, L inspection site position coordinates and cloud computing center position coordinates are analyzed for channel power loss, and the K power transmission equipment channel power loss amounts and the L site channel power loss amounts are obtained; based on the K power transmission equipment environmental feature sets, L inspection site features, K power transmission equipment position coordinate sets, L inspection site position coordinates, K power transmission equipment channel power loss amounts and L site channel power loss amounts, the K power transmission equipment communication feature tuple sets and the L inspection site communication feature tuples are constructed.

[0031] Local scene sensors are devices used to perceive environmental information in a specific area, such as high-definition cameras, lidars, and temperature and humidity sensors. Local scene sensors are installed at key locations of K power transmission equipment sets and L inspection stations so that they can cover the environment around the equipment.

[0032] The local scene sensor collects environmental data around the power transmission equipment and inspection sites, including visual images, temperature distribution, humidity level, obstacle location, etc., and uses computer vision and sensor data processing algorithms to extract key environmental features from the collected data. For example, image processing algorithms are used to identify physical features such as surrounding trees, buildings, and terrain changes; temperature sensors are used to record temperature changes near the equipment; and laser radar is used to draw a three-dimensional environmental model around the equipment. After environmental feature extraction, a set of K power transmission equipment environmental features and L inspection site features are formed.

[0033] Use GPS or Beidou navigation system to obtain the precise geographic coordinates of each power transmission equipment and inspection site. For the cloud computing center, also obtain its geographic coordinates. Combine the geographic coordinate data of K power transmission equipment sets into a set to represent the specific locations of these devices in the inspection topology network, and obtain K power transmission equipment location coordinate sets; similarly, combine the geographic coordinate data of L inspection sites into a set to obtain L inspection site location coordinates; similarly, obtain the cloud computing center location coordinates. Integrate the above location coordinates with the inspection topology network to determine the relative position and distance relationship of each device and site in the network, laying the foundation for subsequent inspection tasks and data processing.

[0034] A channel power loss function is set, and the channel power loss is calculated for the K power transmission equipment location coordinate sets and the cloud computing center location coordinates through the channel power loss function to obtain the channel power loss amounts of the K power transmission equipment; the channel power loss is calculated for the L inspection site location coordinates and the cloud computing center location coordinates through the channel power loss function to obtain the channel power loss amounts of the L sites.

[0035] The K sets of transmission equipment environment features, the K sets of transmission equipment location coordinates, and the K transmission equipment channel power loss are integrated to obtain the K sets of transmission equipment communication feature tuples, and the L inspection site features, the L inspection site location coordinates, and the L site channel power loss are integrated to obtain the L inspection site communication feature tuples. The communication feature tuple contains a three-dimensional communication feature that cannot be changed by a transmission equipment or inspection site, including environmental features, location coordinates, and channel power loss, which is used for the communication characteristics of the transmission equipment or inspection site.

[0036] Further, based on the channel power loss function, a channel power loss analysis is performed on the K power transmission equipment location coordinate sets, the L inspection site location coordinates, and the cloud computing center location coordinates to obtain the K power transmission equipment channel power loss amounts and the L site channel power loss amounts, including: Construct the channel power loss function, wherein the channel power loss function is: ; in, is the channel power loss, The height from the horizontal plane of the signal transmission device of the inspection drone or inspection robot for inspecting the power transmission equipment or inspection station. is the location coordinates of the power transmission equipment or the inspection station. is the location coordinate of the cloud computing center, , The reference distance When the transmission power is 1w, the receiving power is The transmission loss of the signal transmission device of the inspection drone or inspection robot for inspecting power transmission equipment or inspection stations, The receiving loss of the cloud computing center, Represents the signal transmission wavelength; respectively input the K power transmission equipment location coordinate sets and the cloud computing center location coordinates into the channel power loss function to obtain the channel power loss amounts of the K power transmission equipment; respectively input the L inspection site location coordinates and the cloud computing center location coordinates into the channel power loss function to obtain the channel power loss amounts of the L sites.

[0037] Specifically, the channel power loss function is: ; is the channel power loss, which is a measure of the power loss of the signal from the source to the receiver, that is, the power lost by the signal during transmission from the inspection equipment or site to the cloud computing center. The greater the power loss, the worse the signal quality. This formula calculates the power loss of the signal during transmission. The numerator is As the benchmark value of signal loss, it indicates the signal attenuation at the reference distance; the denominator combines the height factor and the actual transmission distance to adjust this benchmark value, thereby calculating the actual channel power loss, thereby determining the signal propagation performance in different environments and conditions, and providing a basis for optimizing communication networks and adjusting equipment configurations.

[0038] The K transmission equipment location coordinate sets and the cloud computing center location coordinates are respectively input into the channel power loss function, and after calculation, the channel power loss of the K transmission equipment is obtained; the L inspection site location coordinates and the cloud computing center location coordinates are respectively input into the channel power loss function, and after calculation, the channel power loss of the L sites is obtained.

[0039] Furthermore, the local scene sensor is used to identify the nearby environmental features of the K power transmission equipment sets and the L inspection sites, and obtain the K power transmission equipment environmental feature sets and the L inspection site features, including: The video surveillance equipment is used to collect monitoring videos of the K power transmission equipment sets and the L inspection sites, so as to obtain K power transmission equipment monitoring screen sets and L inspection site monitoring screens; the interest recognition unit in the local scene sensor is called to extract the objects of interest from the K power transmission equipment monitoring screen sets and the L inspection site monitoring screens, so as to obtain K power transmission equipment monitoring target sets and L inspection site monitoring targets; the K power transmission equipment monitoring target sets and the L inspection site monitoring targets are used as dynamic semantic recognition objects, and the semantic recognition unit in the local scene sensor is called to perform environmental dynamic feature recognition, so as to obtain the K power transmission equipment environmental feature sets and the L inspection site features.

[0040] According to the geographical environment, equipment layout and monitoring requirements of the power transmission equipment and inspection sites, appropriate video surveillance equipment, such as high-definition cameras, are deployed to ensure that the key areas of the power transmission equipment and inspection sites are covered. After the video surveillance equipment is started, the K power transmission equipment sets and L inspection sites are continuously monitored to obtain K power transmission equipment monitoring screen sets and L inspection site monitoring screens.

[0041] The interest recognition unit in the local scene sensor is mainly used to automatically analyze the monitoring screen and identify the dynamic interference around the equipment, such as whether there is anyone close to the equipment, whether there is a dangerous source around the equipment, etc. The monitoring screen is analyzed using the interest recognition unit, which can use image processing algorithms, deep learning models and other technologies to accurately extract the targets of interest and obtain K sets of transmission equipment monitoring targets and L inspection site monitoring targets.

[0042] The K transmission equipment monitoring target sets and L inspection site monitoring targets are used as dynamic semantic recognition objects, which is the basis for environmental dynamic feature recognition. The semantic recognition unit of the local scene sensor is called, which is responsible for extracting environmental dynamic features from the monitoring video and using deep learning or other advanced image processing technologies to identify changes in the environment to identify dynamic features, including the movement of objects, the approach of people, and changes around equipment.

[0043] Dynamic feature recognition is performed on the monitored target. For example, it is detected whether there is anyone approaching the power transmission equipment, whether there are any dangerous sources (such as flames, smoke) around the equipment, etc. The recognition results are processed into environmental feature data, including the environmental features of each power transmission equipment and the environmental features of each inspection site, such as whether there is dynamic interference. These features provide dynamic information about the environment around the equipment and the site. Each power transmission equipment corresponds to a power transmission equipment environmental feature.

[0044] Furthermore, edge jump identification is performed on the inspection topology network based on the K power transmission equipment communication feature tuple sets and the L inspection site communication feature tuples to obtain M edge topology sub-networks, including: N communication feature tuples are randomly selected from the K transmission equipment communication feature tuple sets and the L inspection site communication feature tuples for edge jump starting point authentication. When the authentication is passed, N edge jump starting points are obtained, wherein N is an integer greater than or equal to 3; edge jump identification is performed in the inspection topology network based on the N edge jump starting points to obtain N first edge topology sub-networks, wherein the N first edge topology sub-networks include N first edge communication coefficients; N updated edge jump starting points are obtained again to perform edge jump identification on the inspection topology network, and after multiple updates and identifications, until the K transmission equipment communication feature tuple sets and the L inspection site communication feature tuples are all divided into edge topology sub-networks, N updated edge topology sub-network sets are obtained; the M edge topology sub-networks and the M edge communication coefficients are obtained according to the N first edge topology sub-networks and the N updated edge topology sub-network sets.

[0045] From the K sets of communication feature tuples of transmission equipment and the L sets of communication feature tuples of inspection sites, N communication feature tuples are randomly extracted, where N is an integer greater than or equal to 3, to verify whether the extracted N communication feature tuples can be used as valid edge jump starting points. The edge jump starting point refers to the initial node selected in the edge computing network. These nodes serve as the starting point for data transmission and processing in the network. The verification standard can be based on factors such as the stability of communication characteristics, the acceptable range of channel power loss, and the safety of environmental characteristics. Only feature tuples that meet these standards can pass the authentication. After the authentication is passed, N edge jump starting points are obtained.

[0046] Edge jump identification refers to determining the optimal path and structure of edge computing in the inspection topology network based on the selected N edge jump starting points by analyzing the communication characteristics between each starting point and other nodes. Specifically, the N edge jump starting points are used to analyze the communication paths with other nodes in the inspection topology network, mainly analyzing factors such as the channel power loss, device location, and environmental characteristics in the communication feature tuple to identify stable and efficient communication paths. According to the results of the path analysis, the inspection topology network is divided into several subnetworks, each of which is centered on an edge jump starting point to form independent N first edge topology subnetworks. Each first edge topology subnetwork is a subnetwork with an edge jump starting point as the core node, including other nodes directly or indirectly connected to the core node. These subnetworks will undertake data processing tasks in the local area. The communication paths in each subnetwork are analyzed, the communication efficiency of each path is calculated, and the communication coefficient of the subnetwork is obtained, which is used to quantify the communication performance of the first edge topology subnetwork.

[0047] According to the previous identification result, a new jump starting point is selected based on the communication feature tuple of the uncovered area. The updated jump starting point is used to perform edge jump identification on the inspection topology network again. This process is iterated continuously until all communication feature tuples of the entire inspection topology network are divided into edge topology sub-networks, and finally a set of N updated edge topology sub-networks is formed, each sub-network in the set covers a specific communication feature tuple.

[0048] The N first edge topology subnetworks and the N updated edge topology subnetworks are integrated to eventually generate M edge topology subnetworks. Each edge topology subnetwork is independent and has efficient communication capabilities, which can maximize the efficiency in inspection tasks. Each edge topology subnetwork corresponds to an edge communication coefficient, which represents key parameters such as communication quality, channel stability, etc. between devices or sites within the network.

[0049] Further, including: According to the coordinate positions of the N edge jump starting points, neighborhood identification is performed in the inspection topology network according to a preset identification bandwidth to obtain N edge jump starting point neighborhoods, wherein each edge jump starting point neighborhood includes multiple transmission equipment communication feature tuples and multiple inspection site communication feature tuples; the N edge jump starting points and the corresponding N edge jump starting point neighborhoods are similarly identified using a cosine similarity calculation formula, and the N edge jump starting point neighborhoods are cleaned according to the identification results to obtain N cleaned edge jump starting point neighborhoods; the N first edge topology sub-networks are constructed according to the N cleaned edge jump starting point neighborhoods, wherein the N first edge topology sub-networks include N first transmission equipment communication feature tuple sets and N inspection site communication feature tuple sets; based on the N first transmission equipment communication feature tuple sets and the N inspection site communication feature tuple sets, communication coefficients are concentratedly analyzed to obtain N first edge communication coefficients.

[0050] The preset identification bandwidth refers to the range of communication feature tuples covered from an edge jump starting point in the inspection topology network, which is similar to the radius in a multidimensional space. The size of the bandwidth determines the number of transmission equipment and inspection sites that can be included in the same neighborhood. The preset identification bandwidth is determined based on the scale of the inspection topology network, the equipment distribution density, and the similarity of communication characteristics. A larger bandwidth covers a wider area but may introduce more heterogeneous communication feature tuples; a smaller bandwidth focuses on more compact equipment and sites with similar communication characteristics.

[0051] Using the coordinate positions of the N edge jump starting points obtained previously, the specific position of each jump starting point in the inspection topology network is clarified. Starting from each edge jump starting point, the neighborhood range is identified with the preset identification bandwidth as the radius. Within this range, multiple transmission equipment communication feature tuples and inspection site communication feature tuples close to the starting point are included. According to the identification results, N edge jump starting point neighborhoods are formed.

[0052] For each edge jump starting point, the cosine similarity calculation formula is used to calculate the cosine similarity between it and all the communication feature tuples in its neighborhood to obtain a set of similarity values. The closer the similarity value is to 1, the higher the similarity of the two vectors and the closer the communication features are. According to the actual application requirements, a similarity threshold is set, and the communication feature tuples with similarity values ​​lower than the threshold are marked as low-similarity tuples, while those above the threshold are marked as high-similarity tuples. Cleaning is performed based on the marking results. Specifically, in each neighborhood, the communication feature tuples marked as low-similarity tuples are removed. These tuples may introduce communication interference or instability. According to the cleaning results, N cleaned edge jump starting point neighborhoods are obtained. The cleaned edge jump starting point neighborhood retains the communication feature tuples that are most similar to the edge jump starting point, and the communication between these tuples is more stable.

[0053] Based on N cleaning edge jump starting point neighborhoods, communication links between nodes are constructed, each link corresponds to the communication relationship between the transmission equipment communication feature tuple and the inspection site communication feature tuple, and N first edge topology sub-networks are constructed according to the determined nodes and communication links. The topological structure of each sub-network reflects the communication path and association relationship between the nodes in the edge jump starting point neighborhood. In the constructed first edge topology sub-network, corresponding communication feature sets are generated, including N first transmission equipment communication feature tuple sets and N inspection site communication feature tuple sets. These sets contain effective communication feature information in the sub-network to support subsequent data transmission and processing.

[0054] A feature vector is constructed based on a set of N first transmission equipment communication feature tuples and a set of N inspection station communication feature tuples. The extracted feature vectors are used for centralized analysis. The communication coefficient of each edge topology subnetwork is calculated according to the analysis results to obtain N first edge communication coefficients. The communication coefficient is a key indicator reflecting factors such as communication efficiency, channel loss, and interference degree between nodes in the subnetwork, and is used to evaluate and optimize network performance.

[0055] Furthermore, based on the N first transmission equipment communication feature tuple sets and the N inspection site communication feature tuple sets, a centralized analysis of communication coefficients is performed to obtain N first edge communication coefficients, including: According to preset weights, the communication characteristics of the N first power transmission equipment communication feature tuple sets and the N inspection site communication feature tuple sets are weightedly calculated to obtain N first power transmission equipment communication coefficient sets and N first inspection site communication coefficient sets; N concentrated analysis starting points are randomly selected from the N first power transmission equipment communication coefficient sets, and the N first power transmission equipment communication coefficient sets are iterated according to a preset iteration step to obtain N iterated first power transmission equipment communication coefficients; it is determined whether the concentration difference between the N iterated first power transmission equipment communication coefficients and the N concentrated analysis starting points meets the preset set Moderate difference, if not, then update the N iterative first power transmission device communication coefficients to the N iterative centralized analysis starting points, and continue to iterate according to the preset iteration step; if so, stop the iteration, and use the N iterative first power transmission device communication coefficients as the N first target power transmission device communication coefficients; perform centralized analysis of the communication coefficients of the N first inspection site communication coefficients to obtain the N first target inspection site communication coefficients; perform weighted calculation on the N first target power transmission device communication coefficients and the N first target inspection site communication coefficients according to the preset weights to obtain the N first edge communication coefficients.

[0056] According to the requirements of the actual application scenario and the importance of the communication features, the weights of each feature in each feature tuple are set. These weights can be determined through historical data analysis or experimental results. For example, in power transmission equipment communication, the channel power loss is more important than the device location coordinates, so its weight is higher, and the sum of the weights of each feature is 1.

[0057] According to the preset weights, each feature in each communication feature tuple is multiplied by the corresponding weight value, and then the weighted feature values ​​are added to obtain the communication coefficient of a single feature tuple. The above weighted calculation is performed on all tuples in the N first power transmission device communication feature tuple sets and the N inspection site communication feature tuple sets to obtain N first power transmission device communication coefficient sets and N first inspection site communication coefficient sets, respectively.

[0058] According to the calculated N sets of communication coefficients of the first transmission equipment, N starting points are randomly selected as the initial positions for centralized analysis. According to the actual application requirements and the changing rules of the equipment communication characteristics, the preset iteration step is set. The iteration step refers to the amplitude of the communication coefficient adjustment in each iteration. When the step is large, the iteration speed is fast, but the accuracy may be insufficient; when the step is small, the iteration accuracy is high, but more iterations may be required.

[0059] Taking N randomly selected concentrated analysis starting points as initial points, in the first round of iteration, these starting points are adjusted according to the preset iteration step size, and new communication coefficients are calculated. The coefficients in the N first transmission device communication coefficient sets are adjusted for multiple iterations. In each iteration, the updated communication coefficients are used as input for the next iteration, and these coefficients are gradually optimized until the preset convergence conditions are met or the maximum number of iterations is reached, and the final N iterative first transmission device communication coefficients are obtained.

[0060] The concentration difference between the N iterative first transmission device communication coefficients and the N concentrated analysis starting points is calculated. The concentration difference is the difference between the communication coefficient updated each time during the iteration and the initial concentrated analysis starting point, which can be obtained by calculating the difference between each iterative communication coefficient and its corresponding starting point coefficient.

[0061] A preset concentration difference is set to determine whether the iteration has converged. It can be set based on the needs of the application scenario and the characteristics of the algorithm. The calculated concentration difference is compared with the preset concentration difference. If the calculated difference is greater than the preset concentration difference, it means that the current iteration result has not converged. At this time, the communication coefficient of the first transmission device of N iterations is updated to the starting point of the N iterations of concentrated analysis, and the next round of iterations is continued according to the preset iteration step.

[0062] If the difference is less than or equal to the preset concentration difference, the iteration process has converged and the iteration can be stopped. At this time, the current N iterative first transmission device communication coefficients are used as the final N first target transmission device communication coefficients as the basis for subsequent analysis and decision-making.

[0063] The same analysis method as that for the power transmission equipment is used to analyze and obtain the communication coefficients of the N first target inspection sites, which will not be repeated here for the sake of brevity.

[0064] According to the actual application requirements and network optimization strategies, preset weights are set. The weights can reflect the influence of different communication coefficients on the edge communication performance. According to the preset weights, the communication coefficients of the N first target transmission equipment and the communication coefficients of the N first target inspection sites are weightedly calculated to obtain the final N first edge communication coefficients.

[0065] Further, including: The operation status of the M edge computing servers is monitored to obtain M operation coefficients; when the M operation coefficients are greater than or equal to preset operation coefficients, an early warning instruction is obtained, and the early warning instruction is sent to the cloud computing center for alarm.

[0066] The operation coefficient is the quantitative value of the operation status and performance indicators of each edge computing server. Specifically, through hardware monitoring tools and sensors, CPU utilization, memory usage, disk read and write speed and other indicators are collected, and the collected monitoring data is standardized to ensure the comparability of various indicators. According to the weights of different indicators, the data of each indicator are weighted and summed to obtain M operation coefficients. The weights can be adjusted according to system requirements and performance importance.

[0067] Define the maximum allowable performance index value of the edge computing server under normal operating conditions, set a preset operating coefficient, compare the operating coefficient of each edge computing server with the preset operating coefficient, and determine whether it exceeds the preset threshold. When the operating coefficient is greater than or equal to the preset operating coefficient, trigger an early warning, generate corresponding early warning instructions, and send the early warning instructions to the cloud computing center for alarm to ensure stable and efficient operation of the system.

[0068] In summary, the smart grid data communication optimization method based on edge computing provided by the embodiment of the present application has the following technical effects: By obtaining the inspection topology network of the target power grid, including the layout of transmission lines, inspection sites and cloud computing centers, a comprehensive understanding of the power grid structure is provided, which lays the foundation for subsequent communication analysis and edge computing optimization, and ensures the accuracy of data collection and processing; multi-angle communication analysis is performed on transmission equipment and inspection sites to obtain a set of communication feature tuples. This multi-dimensional data analysis can identify the communication characteristics of equipment and sites, establish comprehensive communication characteristics, and help optimize data flow and network load; edge jump identification is performed on the inspection topology network based on the communication feature tuple set, the power grid can be divided into multiple edge topology sub-networks, and the edge communication coefficient can be calculated. This method can effectively identify key edge nodes and communication paths in the network, optimize the path selection for data transmission, and improve data processing efficiency and transmission stability; sub-network service resources are balanced based on the edge communication coefficient, and the resource allocation of edge computing servers is optimized, which ensures the balanced use of computing resources and improves The processing capacity and response speed of the server are improved; inspection drones and inspection robots are used to inspect transmission lines and inspection sites to obtain inspection video data. This method can provide real-time equipment status and safety monitoring, and improve the automation level and accuracy of inspections. Drones cover a wide area of ​​the transmission lines, while robots conduct detailed inspections inside the sites, ensuring the comprehensiveness and meticulousness of the data; data preprocessing is performed through edge computing servers, and the processed data is uploaded to the cloud computing center, which optimizes the data processing flow. Edge computing reduces data transmission delays and completes preliminary processing locally, thereby reducing the pressure on the cloud computing center and improving the response speed and data processing efficiency of the overall system; the cloud computing center analyzes the uploaded preprocessed data to generate inspection analysis results for the target power grid. This centralized analysis can integrate data from different sources, provide a comprehensive assessment of the power grid's operating status and potential problems, and improve the scientificity and effectiveness of power grid management.

[0069] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A smart grid data communication optimization method based on edge computing, characterized in that: The method comprises: Obtaining an inspection topology network of a target power grid, wherein the inspection topology network includes K transmission lines, L inspection sites, and a cloud computing center, and the K transmission lines include K transmission equipment sets and K transmission paths; Perform multi-angle communication analysis on the K power transmission equipment sets and the L inspection sites to obtain K power transmission equipment communication feature tuple sets and L inspection site communication feature tuples; Based on the K transmission equipment communication feature tuple sets and the L inspection site communication feature tuples, edge jump identification is performed on the inspection topology network to obtain M edge topology sub-networks, wherein the M edge topology sub-networks include M edge communication coefficients; Based on the M edge communication coefficients, the M edge topology sub-networks are configured with sub-network service resources in a balanced manner to obtain M configured edge computing servers; Using K inspection drones to inspect the K transmission lines according to the K transmission paths, respectively, to obtain K line inspection video data sets, and using L inspection robots to inspect the L inspection sites according to preset in-station inspection routes, to obtain L site inspection video data; Based on the M edge topology sub-networks, the K line inspection video data sets and the L site inspection video data are distributed to M edge computing servers for data preprocessing, and the preprocessed data are uploaded to the cloud computing center for inspection data analysis according to a preset communication protocol to obtain the inspection analysis results of the target power grid.

2. A smart grid data communication optimization method based on edge computing as claimed in claim 1, characterized in that: Performing multi-angle communication analysis on the K power transmission equipment sets and the L inspection sites to obtain K power transmission equipment communication feature tuple sets and L inspection site communication feature tuples, including: Using a local scene sensor to identify the surrounding environmental features of the K power transmission equipment sets and the L inspection sites, to obtain K power transmission equipment environmental feature sets and L inspection site features; In combination with the inspection topology network, obtain the K power transmission equipment sets, the L inspection sites, and the K power transmission equipment location coordinate sets, the L inspection site location coordinates, and the cloud computing center location coordinates of the cloud computing center; Based on the channel power loss function, a channel power loss analysis is performed on the K power transmission equipment location coordinate sets, the L inspection site location coordinates and the cloud computing center location coordinates to obtain the K power transmission equipment channel power loss amounts and the L site channel power loss amounts; The K power transmission equipment communication feature tuple sets and the L inspection site communication feature tuples are constructed based on the K power transmission equipment environmental feature sets, the L inspection site features, the K power transmission equipment location coordinate sets, the L inspection site location coordinates, the K power transmission equipment channel power loss amounts and the L site channel power loss amounts.

3. A smart grid data communication optimization method based on edge computing as claimed in claim 2, characterized in that: Based on the channel power loss function, a channel power loss analysis is performed on the K power transmission equipment location coordinate sets, the L inspection site location coordinates and the cloud computing center location coordinates to obtain the K power transmission equipment channel power loss amounts and the L site channel power loss amounts, including: Construct the channel power loss function, wherein the channel power loss function is: ； in, is the channel power loss, The height from the horizontal plane of the signal transmission device of the inspection drone or inspection robot for inspecting the power transmission equipment or inspection station. is the location coordinates of the power transmission equipment or the inspection station. is the location coordinate of the cloud computing center, , The reference distance When the transmission power is 1w, the receiving power is The transmission loss of the signal transmission device of the inspection drone or inspection robot for inspecting power transmission equipment or inspection stations, The receiving loss of the cloud computing center, Indicates the signal transmission wavelength; Inputting the K power transmission equipment location coordinate sets and the cloud computing center location coordinates into the channel power loss function respectively to obtain the channel power loss amounts of the K power transmission equipment; The position coordinates of the L inspection sites and the position coordinates of the cloud computing center are respectively input into the channel power loss function to obtain the channel power loss amounts of the L sites.

4. A smart grid data communication optimization method based on edge computing as claimed in claim 2, characterized in that: The local scene sensor is used to identify the nearby environmental features of the K power transmission equipment sets and the L inspection sites, and obtain the K power transmission equipment environmental feature sets and the L inspection site features, including: Using video monitoring equipment to collect monitoring videos of the K power transmission equipment sets and the L inspection sites, to obtain K power transmission equipment monitoring screen sets and L inspection site monitoring screens; Calling the interest identification unit in the local scene sensor to extract the interested targets from the K power transmission equipment monitoring screen sets and the L inspection site monitoring screens, and obtaining the K power transmission equipment monitoring target sets and the L inspection site monitoring targets; The K power transmission equipment monitoring target sets and the L inspection site monitoring targets are taken as dynamic semantic recognition objects, and the semantic recognition unit in the local scene sensor is called to perform environmental dynamic feature recognition to obtain the K power transmission equipment environmental feature sets and the L inspection site features.

5. The method for optimizing smart grid data communication based on edge computing according to claim 1, characterized in that: Based on the K power transmission equipment communication feature tuple sets and the L inspection site communication feature tuples, edge jump identification is performed on the inspection topology network to obtain M edge topology sub-networks, including: Randomly extract N communication feature tuples from the K power transmission equipment communication feature tuple sets and the L inspection site communication feature tuples to perform edge jump starting point authentication, and when the authentication passes, obtain N edge jump starting points, where N is an integer greater than or equal to 3; Based on the N edge jump starting points, edge jump identification is performed in the inspection topology network to obtain N first edge topology sub-networks, wherein the N first edge topology sub-networks include N first edge communication coefficients; Obtaining N updated edge jump starting points again to perform edge jump identification on the inspection topology network, and performing multiple update identifications until the K transmission equipment communication feature tuple sets and the L inspection site communication feature tuple sets are all divided into edge topology sub-networks, thereby obtaining N updated edge topology sub-network sets; The M edge topology subnetworks and the M edge communication coefficients are obtained according to the N first edge topology subnetworks and the N updated edge topology subnetwork sets.

6. A smart grid data communication optimization method based on edge computing as claimed in claim 5, characterized in that: include: According to the coordinate positions of the N edge jump starting points, neighborhood identification is performed in the inspection topology network according to a preset identification bandwidth to obtain N edge jump starting point neighborhoods, wherein each edge jump starting point neighborhood includes a plurality of transmission equipment communication feature tuples and a plurality of inspection site communication feature tuples; Using a cosine similarity calculation formula, similarity recognition is performed on N edge step starting points and corresponding N edge step starting point neighborhoods, and the N edge step starting point neighborhoods are cleaned according to the recognition result to obtain N cleaned edge step starting point neighborhoods; Constructing the N first edge topology sub-networks according to the N cleaning edge jump starting point neighborhoods, wherein the N first edge topology sub-networks include N first power transmission equipment communication feature tuple sets and N inspection site communication feature tuple sets; Based on the N first power transmission equipment communication feature tuple sets and the N inspection site communication feature tuple sets, a centralized analysis of communication coefficients is performed to obtain N first edge communication coefficients.

7. A smart grid data communication optimization method based on edge computing as claimed in claim 6, characterized in that: Based on the N first power transmission equipment communication feature tuple sets and the N inspection site communication feature tuple sets, a centralized analysis of communication coefficients is performed to obtain N first edge communication coefficients, including: Performing weighted calculation of communication characteristics on the N first power transmission device communication feature tuple sets and the N inspection site communication feature tuple sets according to preset weights, to obtain N first power transmission device communication coefficient sets and N first inspection site communication coefficient sets; Randomly extract N centralized analysis starting points from the N first power transmission device communication coefficient sets, iterate the N first power transmission device communication coefficient sets according to a preset iteration step length, and obtain N iterated first power transmission device communication coefficients; Determine whether the concentration difference between the N iterative first power transmission device communication coefficients and the N concentrated analysis starting points meets a preset concentration difference, and if not, update the N iterative first power transmission device communication coefficients to the N iterative concentrated analysis starting points, and continue to iterate according to a preset iteration step size; If yes, stop the iteration and use the N iterative first power transmission device communication coefficients as N first target power transmission device communication coefficients; Performing centralized communication coefficient analysis on the communication coefficient sets of the N first inspection sites to obtain communication coefficients of the N first target inspection sites; The N first target power transmission equipment communication coefficients and the N first target inspection site communication coefficients are weightedly calculated according to preset weights to obtain the N first edge communication coefficients.

8. The method for optimizing smart grid data communication based on edge computing according to claim 1, characterized in that: include: Performing operation status monitoring on the M edge computing servers to obtain M operation coefficients; When the M operating coefficients are greater than or equal to the preset operating coefficients, an early warning instruction is obtained and the early warning instruction is sent to the cloud computing center for alarm.

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