Power transmission path determination method and device, computer equipment and storage medium

Abstract

The invention relates to a power transmission path determination method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: obtaining a plurality of candidate power transmission paths corresponding to a user, each candidate power transmission path comprising power grid nodes in different power transmission stages, and determining an edge weight between each power grid node in the candidate power transmission paths and a power grid node in the next power transmission stage, on the basis of a plurality of edge weights corresponding to the candidate power transmission paths, path analysis is carried out on the candidate power transmission paths to obtain respective path scores of the candidate power transmission paths, by adopting the method, the edge weights reflect the mutual influence between adjacent power grid nodes, and the path scores of the candidate power transmission paths are obtained. The accuracy of the path score of each candidate power transmission path obtained based on the edge weight is higher, the candidate power transmission path with the highest path score is determined as the target transmission path for the user, and the accuracy of the target transmission path is higher.

Description

Technical Field

[0001] The present application relates to the technical field of power grid management, and in particular to a method, apparatus, computer equipment, storage medium and computer program product for determining a power transmission path. Background Art

[0002] With the development of social economy and the improvement of people's living standards, the demand for electricity is growing, and the scale and complexity of the power system are also increasing. As an important part of the modern power system, the core goal of smart grid is to improve the reliability and efficiency of the power system and enhance the ability to respond to emergencies. In smart grid, the path quality of power transmission path directly affects the stability of the power system.

[0003] In conventional technology, a target transmission path is generally randomly determined among multiple power transmission paths corresponding to electricity consumption, which results in a problem of low accuracy of the determined target transmission path. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for determining a power transmission path that can improve the accuracy of a target transmission path in order to address the above technical problems.

[0005] In a first aspect, the present application provides a method for determining a power transmission path. The method comprises:

[0006] Acquire multiple candidate power transmission paths corresponding to the user; each of the candidate power transmission paths includes grid nodes at different power transmission stages; for each of the candidate power transmission paths, determine the edge weight between each grid node in the candidate power transmission path and the grid node in the next power transmission stage; based on the multiple edge weights corresponding to the candidate power transmission paths, perform path analysis on the candidate power transmission paths to obtain the path score of each candidate power transmission path; determine the candidate power transmission path with the highest path score as the target transmission path for the user.

[0007] In one of the embodiments, determining the edge weight between each grid node in the candidate power transmission path and the grid node of the next power transmission stage includes: obtaining the node failure rate of each grid node in the candidate power transmission path; and determining the edge weight between each grid node in the candidate power transmission path and the grid node of the next power transmission stage based on each of the node failure rates.

[0008] In one of the embodiments, the edge weight between each grid node in the candidate power transmission path and the grid node of the next power transmission stage is determined based on the failure rate of each node, including: obtaining the power transmission spatiotemporal parameters between each grid node in the candidate power transmission path and the grid node of the next power transmission stage, and the node load of each grid node in the candidate power transmission path; determining the load weight configured for each node load, and the spatiotemporal weight configured for the power transmission spatiotemporal parameters; based on the failure rate of each node, the load of each node, the load weight, the power transmission spatiotemporal parameters, and the spatiotemporal weight, determine the edge weight between each grid node in the candidate power transmission path and the grid node of the next power transmission stage.

[0009] In one of the embodiments, the method further includes: supplying power to the user based on the target transmission path; in the case where there is an abnormal node in the target transmission path, determining a backup path that does not contain the abnormal node among the candidate power transmission paths; replacing the target transmission path with the backup path, and supplying power to the user based on the backup path.

[0010] In one of the embodiments, when there are abnormal nodes in the target transmission path, a backup path that does not contain the abnormal node is determined in each of the candidate power transmission paths, including: performing node status monitoring on each grid node in the target transmission path to obtain a node monitoring result of each grid node; when there are grid nodes in the target transmission path whose node monitoring results are abnormal, screening out the backup path with the highest path score and that does not contain the abnormal node in each of the candidate power transmission paths.

[0011] In one of the embodiments, the method further includes: obtaining power parameter information of each power transmission stage in the power grid; and writing each power parameter information into a graph database in the form of a linked list according to the power transmission order and information acquisition timing.

[0012] In a second aspect, the present application also provides a device for determining a power transmission path. The device comprises:

[0013] An acquisition module is used to acquire multiple candidate power transmission paths corresponding to the user; each of the candidate power transmission paths includes grid nodes in different power transmission stages; an edge weight determination module is used to determine, for each of the candidate power transmission paths, the edge weight between each grid node in the candidate power transmission path and the grid node in the next power transmission stage; a path analysis module is used to perform path analysis on the candidate power transmission paths based on the multiple edge weights corresponding to the candidate power transmission paths to obtain the path scores of each of the candidate power transmission paths; a path determination module is used to determine the candidate power transmission path with the highest path score as the target transmission path for the user.

[0014] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0015] Acquire multiple candidate power transmission paths corresponding to the user; each of the candidate power transmission paths includes grid nodes at different power transmission stages; for each of the candidate power transmission paths, determine the edge weight between each grid node in the candidate power transmission path and the grid node in the next power transmission stage; based on the multiple edge weights corresponding to the candidate power transmission paths, perform path analysis on the candidate power transmission paths to obtain the path score of each candidate power transmission path; determine the candidate power transmission path with the highest path score as the target transmission path for the user.

[0016] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0017] Acquire multiple candidate power transmission paths corresponding to the user; each of the candidate power transmission paths includes grid nodes at different power transmission stages; for each of the candidate power transmission paths, determine the edge weight between each grid node in the candidate power transmission path and the grid node in the next power transmission stage; based on the multiple edge weights corresponding to the candidate power transmission paths, perform path analysis on the candidate power transmission paths to obtain the path score of each candidate power transmission path; determine the candidate power transmission path with the highest path score as the target transmission path for the user.

[0018] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0019] Acquire multiple candidate power transmission paths corresponding to the user; each of the candidate power transmission paths includes grid nodes at different power transmission stages; for each of the candidate power transmission paths, determine the edge weight between each grid node in the candidate power transmission path and the grid node in the next power transmission stage; based on the multiple edge weights corresponding to the candidate power transmission paths, perform path analysis on the candidate power transmission paths to obtain the path score of each candidate power transmission path; determine the candidate power transmission path with the highest path score as the target transmission path for the user.

[0020] The above-mentioned power transmission path determination method, device, computer equipment, storage medium and computer program product obtain multiple candidate power transmission paths corresponding to the user, each candidate power transmission path includes grid nodes in different power transmission stages, and fully considers the nodes in different power transmission stages, which is conducive to improving the accuracy of subsequent determination of the target transmission path. For each candidate power transmission path, the edge weight between each grid node in the candidate power transmission path and the grid node in the next power transmission stage is determined, and based on the multiple edge weights corresponding to the candidate power transmission path, the candidate power transmission path is analyzed to obtain the path score of each candidate power transmission path. With the above-mentioned method, the edge weight reflects the mutual influence between adjacent grid nodes, and the accuracy of the path score of each candidate power transmission path obtained based on the edge weight is higher. The candidate power transmission path with the highest path score is determined as the target transmission path for the user, and the accuracy of the target transmission path is also higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] FIG1 is a diagram showing an application environment of a method for determining a power transmission path in one embodiment;

[0022] FIG2 is a schematic diagram of a flow chart of a method for determining a power transmission path in one embodiment;

[0023] FIG3 is a schematic diagram of a flow chart of a method for temporal management of power grid data based on graph computing in one embodiment;

[0024] FIG4 is a schematic flow chart of a method for managing power grid data in one embodiment;

[0025] FIG5 is a structural block diagram of a device for determining a power transmission path in one embodiment;

[0026] FIG. 6 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0028] The power transmission path determination method provided in the embodiment of the present application can be applied in the application environment shown in FIG1 . The terminal 102 communicates with the server 104 through a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers.

[0029] The terminal 102 may obtain multiple candidate power transmission paths corresponding to the user from the server 104, each candidate power transmission path including grid nodes at different power transmission stages. The terminal 102 may determine the edge weight between each grid node in the candidate power transmission path and the grid node in the next power transmission stage for each candidate power transmission path. The terminal 102 may perform path analysis on the candidate power transmission paths based on the multiple edge weights corresponding to the candidate power transmission paths to obtain the path scores of each candidate power transmission path. The terminal 102 may determine the candidate power transmission path with the highest path score as the target transmission path for the user. The terminal 102 may send the target transmission path to the server 104.

[0030] The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers.

[0031] In one embodiment, as shown in FIG2 , a method for determining a power transmission path is provided, and the method is described by taking the method applied to the terminal in FIG1 as an example, including:

[0032] S202, obtaining multiple candidate power transmission paths corresponding to the user.

[0033] Among them, users can represent electricity users in the power grid. Grid users refer to units or individuals that consume electricity through the power grid, including power generation companies, main grid direct supply users, etc. In the era of smart grids, the role of users has changed from passive service recipients to important participants who can actively influence the power system. Users not only need to manage their own energy consumption, but can also participate in demand response and power dispatch operations.

[0034] Specifically, the user can indicate the electrical equipment owned by the user, for example, the electrical equipment owned by the user, such as air conditioners, lighting lamps, washing machines, etc.

[0035] The candidate power transmission path may represent the entire power transmission process from the power station to the user's power consumption equipment. Specifically, each candidate power transmission path includes grid nodes at different power transmission stages.

[0036] The power transmission stages can include the power generation stage, the voltage step-up stage, the power transmission stage, the voltage step-down stage and the power distribution stage.

[0037] A grid node can represent a specific power processing device in the power transmission stage. For example, for the power generation stage, a grid node can be a thermal power station, a hydroelectric power plant, a nuclear power plant, etc. These power plants generate electricity through generators. For the step-up stage, a grid node can be a step-up transformer. For the transmission stage, a grid node can be a transmission node of a high-voltage transmission line. After the voltage is increased by a step-up transformer, the electric energy is transmitted through the high-voltage transmission line. These high-voltage transmission lines can be overhead transmission lines or underground transmission lines. Overhead transmission lines are usually used for long-distance transmission, while underground transmission lines are more used for power transmission within cities. For the step-down stage, a grid node can be a step-down transformer. For the distribution stage, a grid node can be a distribution transformer for each high-voltage distribution line and low-voltage distribution line.

[0038] Specifically, the terminal can read the historical node corresponding to the user from the historical power distribution information corresponding to the user. The terminal can determine multiple candidate power transmission lines including each historical node.

[0039] Exemplarily, the terminal can read the historical nodes corresponding to the user (power stations A and B, step-up transformer A, transmission node A, step-down transformer A, distribution transformer A) from the historical power distribution information corresponding to the user, and the terminal can determine multiple candidate power transmission lines containing each historical node (for example, power station A-step-up transformer A-transmission node A-step-down transformer A-distribution transformer A).

[0040] S204 , for each candidate power transmission path, determining an edge weight between each grid node in the candidate power transmission path and a grid node in the next power transmission stage.

[0041] The next power transmission stage may represent a downstream transmission stage in the candidate power transmission path. For example, the next power transmission stage of the power generation stage may be a boosting stage.

[0042] The edge weight can represent the transmission time, transmission distance, etc. between the grid node and the grid node of the next power transmission stage. The edge weight can also represent the node failure rate. The edge weight can also represent the influence of the grid node and the grid node of the next power transmission stage on the candidate power transmission path. The larger the edge weight, the shorter the transmission time and transmission distance between the two grid nodes, and the higher the node failure rate.

[0043] Specifically, the terminal may determine, for each candidate power transmission path, an edge weight between each grid node in the candidate power transmission path and a grid node in the next power transmission stage.

[0044] In one embodiment, the terminal may determine the edge weight between each grid node in the candidate power transmission path and the grid node in the next power transmission stage according to the respective call frequencies of each grid node in the candidate power transmission path. For example, for two adjacent grid nodes, the terminal may obtain the respective call frequencies of the two grid nodes. The terminal may determine the edge weight between each grid node in the candidate power transmission path and the grid node in the next power transmission stage according to the average of the two call frequencies.

[0045] In one embodiment, the terminal may obtain the node failure rate of each grid node in the candidate power transmission path, and determine the edge weight between each grid node in the candidate power transmission path and the grid node in the next power transmission stage based on the node failure rate.

[0046] In one embodiment, the terminal can obtain the power transmission spatiotemporal parameters between each grid node in the candidate power transmission path and the grid node of the next power transmission stage, and the node load of each grid node in the candidate power transmission path. The terminal can determine the load weight configured for each node load, and the spatiotemporal weight configured for the power transmission spatiotemporal parameters. The terminal can determine the edge weight between each grid node in the candidate power transmission path and the grid node of the next power transmission stage based on the failure rate of each node, the load of each node, the load weight, the power transmission spatiotemporal parameters, and the spatiotemporal weight.

[0047] S206: Based on the multiple edge weights corresponding to the candidate power transmission paths, path analysis is performed on the candidate power transmission paths to obtain path scores for each candidate power transmission path.

[0048] Therein, each grid node and the grid node of the next power transmission stage may correspond to an edge weight. It can be understood that when a candidate power transmission path includes 5 grid nodes, the candidate power transmission path may correspond to 4 edge weights.

[0049] The path score can reflect the power transmission efficiency and power transmission reliability of each candidate power transmission path.

[0050] There are many ways to analyze the path. Specifically, for each candidate power transmission path, the terminal can take the average of multiple edge weights corresponding to the candidate power transmission path. The terminal can use the inverse ratio of the average as the path score of the candidate power transmission path.

[0051] The terminal may also obtain the user's power demand tendency. The power demand tendency may be a power transmission stability tendency or a power transmission efficiency tendency. The terminal may increase the edge weights that match the power demand tendency to obtain each new edge weight. The terminal may average each new edge weight. The terminal may use the inverse ratio of the average as the path score of the candidate power transmission path.

[0052] Exemplarily, when the power demand tendency is a stable power transmission tendency, the terminal can increase the edge weight corresponding to the failure rate exceeding the preset threshold to obtain a new edge weight. The terminal can take the average of each new edge weight. The terminal can use the inverse ratio of the average as the path score of the candidate power transmission path.

[0053] When the power demand tendency is the power transmission efficiency tendency, the terminal can increase the edge weight corresponding to the transmission time exceeding the preset threshold to obtain a new edge weight. The terminal can take the average of the new edge weights. The terminal can use the inverse ratio of the average as the path score of the candidate power transmission path.

[0054] S208, determining the candidate power transmission path with the highest path score as the target transmission path for the user.

[0055] Among them, the target transmission path can be considered as the candidate power transmission path with the highest power transmission efficiency and the strongest power transmission reliability among all candidate power transmission paths.

[0056] Specifically, when an abnormality occurs in the target transmission path, the terminal may select a candidate power transmission path having a score second only to the target transmission path as a new target transmission path.

[0057] In one embodiment, the terminal can supply power to the user based on the target transmission path. If there is an abnormal node in the target transmission path, the terminal can determine an alternative path that does not contain the abnormal node among the candidate power transmission paths. The terminal can replace the target transmission path with the alternative path and supply power to the user based on the alternative path.

[0058] In one embodiment, the terminal can perform node status monitoring on each grid node in the target transmission path to obtain node monitoring results of each grid node. When there is a grid node with an abnormal node monitoring result in the target transmission path, the terminal can select a backup path with the highest path score and no abnormal node from among the candidate power transmission paths.

[0059] In the above-mentioned power transmission path determination method, multiple candidate power transmission paths corresponding to the user are obtained, and each candidate power transmission path includes grid nodes in different power transmission stages. Fully considering the nodes in different power transmission stages is conducive to improving the accuracy of the subsequent determination of the target transmission path. For each candidate power transmission path, the edge weight between each grid node in the candidate power transmission path and the grid node in the next power transmission stage is determined. Based on the multiple edge weights corresponding to the candidate power transmission path, the candidate power transmission path is analyzed to obtain the path score of each candidate power transmission path. With the above-mentioned method, the edge weight reflects the mutual influence between adjacent grid nodes. The accuracy of the path score of each candidate power transmission path obtained based on the edge weight is higher. The candidate power transmission path with the highest path score is determined as the target transmission path for the user, and the accuracy of the target transmission path is also higher.

[0060] In one embodiment, determining the edge weight between each grid node in the candidate power transmission path and the grid node of the next power transmission stage includes: obtaining the node failure rate of each grid node in the candidate power transmission path, and determining the edge weight between each grid node in the candidate power transmission path and the grid node of the next power transmission stage based on the failure rate of each node.

[0061] Among them, the node failure rate can represent the probability of a grid node failing within a certain period of time.

[0062] The failure rate of power grid nodes is affected by many factors, including natural environment, equipment aging, external force damage, etc. Specifically, the node failure rate can be the probability of failure of multiple transmission nodes in the transmission stage.

[0063] Specifically, the terminal may determine the edge weight between each grid node in the candidate power transmission path and the grid node in the next power transmission stage based on the failure rate of each node.

[0064] In one embodiment, the terminal may determine the node failure rate of each grid node in the candidate power transmission path and the grid node in the next power transmission stage, and may perform statistics on the node failure rate to obtain the edge weight.

[0065] In one embodiment, the terminal may average the failure rates of the two nodes to obtain the edge weight. A node with a high failure rate means that the reliability of the node is low, and vice versa. Averaging the failure rates of the two nodes can comprehensively reflect the connection reliability between the two nodes. If the failure rates of the two nodes are both high, the connection reliability between them is low; conversely, if the failure rates of the two nodes are both low, the connection reliability between them is high. Taking the average can balance the impact of the two nodes on the connection reliability and ensure that the impact of the two nodes is taken into account.

[0066] In one of the embodiments, the terminal may take the inverse ratio of the mean of the failure rates of the two nodes as the path score of the candidate power transmission path.

[0067] In this embodiment, the node failure rate of each grid node in the candidate power transmission path is obtained, and based on the failure rate of each node, the edge weight between each grid node in the candidate power transmission path and the grid node of the next power transmission stage is determined. The edge weight reflects the node failure rate of the grid node, thereby providing a basis for subsequently determining the target transmission path for power transmission reliability.

[0068] In one embodiment, based on the failure rate of each node, the edge weight between each grid node in the candidate power transmission path and the grid node of the next power transmission stage is determined, including: obtaining the power transmission spatiotemporal parameters between each grid node in the candidate power transmission path and the grid node of the next power transmission stage, and the node load of each grid node in the candidate power transmission path, determining the load weight configured for each node load, and the spatiotemporal weight configured for the power transmission spatiotemporal parameters, and determining the edge weight between each grid node in the candidate power transmission path and the grid node of the next power transmission stage based on the failure rate of each node, the load of each node, the load weight, the power transmission spatiotemporal parameters, and the spatiotemporal weight.

[0069] The power transmission spatiotemporal parameters may include a power transmission time parameter and a power transmission space parameter. The power transmission time parameter may be the power transmission time between nodes, and the power transmission space parameter may be the power transmission distance between nodes or the connection distance between nodes.

[0070] Node load can represent the workload of the node. Specifically, node load can represent the amount of power demand currently processed by the node. This can include but is not limited to: Power consumption: For nodes where user terminals or power-consuming devices are located, workload refers to the amount of power these devices are using. Transmission load: For intermediate nodes such as substations or transmission lines, workload refers to the amount of current transmitted through the node. Power generation: For power station nodes, workload refers to the power output it generates.

[0071] The spatiotemporal weight may be a weight coefficient configured for the spatiotemporal parameters of power transmission. Specifically, the spatiotemporal weight may include a time weight coefficient configured for the power transmission time and a distance weight coefficient configured for the power transmission distance.

[0072] The load weight may be a weight coefficient configured for the node load.

[0073] Exemplarily, the calculation formula of the edge weight between each grid node in the candidate power transmission path and the grid node in the next power transmission stage is as follows:

[0074]

[0075] in, It can represent the edge weight between node i and node j, It can represent the failure rate between node i and node j, It can represent the power transmission time between node i and node j, It can represent the power transmission distance between node i and node j, It can represent the load between node i and node j. It can represent the time weight coefficient, It can represent the distance weight coefficient, Can represent the load weight factor.

[0076] Among them, T can represent the maximum transmission time, D can represent the maximum connection distance, L can represent the maximum load, and α, β, and γ can represent adjustment coefficients respectively.

[0077] In this embodiment, the spatiotemporal parameters of power transmission between each grid node in the candidate power transmission path and the grid node of the next power transmission stage, as well as the node load of each grid node in the candidate power transmission path are obtained, and the load weight configured for each node load and the spatiotemporal weight configured for the power transmission spatiotemporal parameters are determined. Based on the failure rate of each node, the load of each node, the load weight, the spatiotemporal parameters of power transmission, and the spatiotemporal weight, the edge weight between each grid node in the candidate power transmission path and the grid node of the next power transmission stage is determined. The edge weight reflects the node failure rate, power transmission time, and power transmission distance, thereby providing a basis for subsequently determining the target transmission path for power transmission reliability and power transmission efficiency.

[0078] In one embodiment, the method for determining a power transmission path further includes: supplying power to a user based on a target transmission path, and when an abnormal node exists in the target transmission path, determining a backup path that does not contain the abnormal node among the candidate power transmission paths, replacing the target transmission path with the backup path, and supplying power to the user based on the backup path.

[0079] The target transmission path reflects the direction of the current flow received by the electric device corresponding to the user. Specifically, the current flows from the grid node in the power generation stage of the target transmission path to the electric device of the user.

[0080] An abnormal node may represent an abnormal grid node. For example, the node failure rate of a grid node exceeds a preset failure rate. For another example, the transmission distance of a grid node exceeds a preset distance. For another example, the transmission time of a grid node exceeds a preset duration.

[0081] The backup path may be a candidate power transmission path that does not include an abnormal node.

[0082] Specifically, the terminal may determine multiple alternative power transmission paths that do not contain abnormal nodes among the candidate power transmission paths. The terminal may randomly determine an alternative power transmission path as the standby path among the alternative power transmission paths. The standby path replaces the target transmission path, and supplies power to the user based on the standby path.

[0083] In one of the embodiments, the terminal may determine the candidate power transmission path with the highest score among the candidate power transmission paths as the backup path.

[0084] In this embodiment, power is supplied to users based on the target transmission path. When an abnormal node exists in the target transmission path, a backup path that does not contain the abnormal node is determined among the candidate power transmission paths, and the target transmission path is replaced by the backup path. When an abnormal node exists in the target transmission path, the backup path is quickly determined, and power is supplied to users based on the backup path, which is beneficial to improving the power supply quality.

[0085] In one embodiment, when there are abnormal nodes in the target transmission path, a backup path that does not contain the abnormal node is determined in each candidate power transmission path, including: performing node status monitoring on each power grid node in the target transmission path to obtain a node monitoring result for each power grid node; when there are power grid nodes with abnormal node monitoring results in the target transmission path, a backup path with the highest path score and that does not contain the abnormal node is screened in each candidate power transmission path.

[0086] Among them, node status monitoring can be to monitor the node failure rate, power transmission distance, power transmission time, and power load corresponding to the power grid node.

[0087] The node monitoring result can represent the monitoring result obtained by monitoring the node failure rate, power transmission distance, power transmission time, and power load corresponding to the power grid node. It can be understood that if any of the monitoring results of the node failure rate, power transmission distance, power transmission time, and power load is abnormal, it can be said that the node monitoring result of the power grid node is abnormal. If the monitoring results of the node failure rate, power transmission distance, power transmission time, and power load are all normal, it can be said that the node monitoring result of the power grid node is normal.

[0088] Specifically, the terminal can perform node status monitoring on each grid node in the target transmission path to obtain the node failure rate, power transmission distance, power transmission time, and power load of the grid node. When all four sub-monitoring results are normal, the terminal can determine that the node monitoring result of the grid node is normal. When there is an abnormality in the four sub-monitoring results, the terminal can determine that the node monitoring result of the grid node is abnormal.

[0089] In this embodiment, node status monitoring is performed on each grid node in the target transmission path to obtain node monitoring results of each grid node. When there are grid nodes with abnormal node monitoring results in the target transmission path, among the candidate power transmission paths, a backup path with the highest path score and no abnormal nodes is screened. When there are abnormal nodes in the target transmission path, the backup path is quickly determined, and power is supplied to users based on the backup path, which is beneficial to improving the power supply quality.

[0090] In one embodiment, the method for determining the power transmission path further includes: obtaining power parameter information of each power transmission stage in the power grid, and writing each power parameter information into a graph database in the form of a linked list according to the power transmission order and information acquisition sequence.

[0091] Among them, the power parameter information can represent real-time and historical data corresponding to the power transmission stage.

[0092] Real-time data refers to data collected at the current moment or in the recent period. These data reflect the real-time status of the power grid, including but not limited to: Real-time output power of power stations: indicates the actual power generated by power stations at a certain moment. Real-time load status of substations: shows the power load currently being processed by substations. Real-time current strength of lines: reflects the current currently passing through a transmission line. The status of switching equipment: such as whether the circuit breaker is closed, etc., represents the real-time operating status of key equipment in the power grid.

[0093] Historical data is a record of a period of time in the past. It provides a basis for analyzing trends, pattern recognition and predicting the future, including but not limited to: Power consumption records for the past year: including power consumption in different time periods such as peak hours and off-peak hours. Fault records: record the time, location, type and repair process of faults in the power grid. Maintenance records: include the time of regular equipment inspections, problems found and measures taken. Environmental factor data: data such as temperature, humidity and other factors that affect the operation of the power grid over time.

[0094] The power transmission sequence may represent the sequence between the power transmission stages. For example, the power transmission sequence may be represented as a power generation stage, a voltage step-up stage, a power transmission stage, a voltage step-down stage, and a power distribution stage.

[0095] The information acquisition sequence may indicate the order in which each piece of information is generated.

[0096] Specifically, the terminal can write each power parameter information into the graph database in the form of a linked list according to the power transmission order and information acquisition timing.

[0097] Exemplarily, the terminal can store all acquired power grid data (real-time data and historical data) in a graph database to ensure data structuring and efficient access. Specifically, the terminal can use a graph database client to establish a connection with the graph database, define node attributes, such as node ID, node type such as power station, substation, distribution station, etc., node status such as online / offline, normal / fault, etc., and define edge attributes, such as the source node ID, target node ID, edge type such as high-voltage transmission line, low-voltage distribution line, etc.

[0098] Specifically, the terminal can pre-process the power grid data, where the pre-processing of real-time and historical data includes but is not limited to: Data cleaning: remove or correct erroneous data records, such as missing value filling, outlier detection and processing. Format unification: convert data from different sources into a consistent format for subsequent processing. Timestamp standardization: ensure that the timestamps of all data use the same time standard (such as UTC) and have a uniform accuracy (seconds / milliseconds). Deduplication: remove duplicate records, especially when multiple systems collect the same data point at the same time. Aggregation: aggregate data in time as needed, such as from minute-level data to hour-level or daily-level data.

[0099] Specifically, the terminal can also write the pre-processed real-time data and historical data into the graph database in the form of a linked list according to the power transmission order and information acquisition sequence. The data stored in the graph database is in the form of node data and edge data.

[0100] On this basis, the node data and edge data can be further preprocessed.

[0101] The preprocessing of node data includes but is not limited to: Attribute extraction: Extract key attributes of nodes from raw data, such as ID, type, status, etc. Geocoding: If the node location information is in text form, it may be necessary to convert it into longitude and latitude coordinates. Normalization / standardization: For numerical attributes (such as capacity), perform appropriate scaling to ensure that it is within a reasonable range. Categorical encoding: For categorical attributes (such as node type), apply encoding techniques (such as one-hot encoding) to facilitate use by machine learning models.

[0102] Preprocessing of edge data includes but is not limited to: Connection relationship confirmation: Verify that each edge is indeed connected to the correct source node and target node. Attribute filling: Supplement necessary attribute information for each edge, such as transmission capacity, length, etc. Impedance calculation: Calculate the resistance or other electrical parameters of each edge based on factors such as material properties and line length. State initialization: Set the initial state of each edge (normal / under repair / faulty) according to the latest maintenance report. Import the preprocessed node data into the graph database to ensure that each node has a unique identifier. Import the preprocessed edge data into the graph database to ensure that each edge connects the correct source node and target node. Establish relationships between nodes in the graph database to ensure the correctness of the graph structure. Create indexes for key attributes of nodes (such as node ID, node type) to improve query performance. Create indexes for key attributes of edges (such as source node ID and target node ID of edges) to improve query performance.

[0103] After preprocessing the node data and edge data in the graph database, the data in the graph database can be versioned and the data status at each time point can be recorded to ensure the historical traceability and consistency of the data.

[0104] Specifically, each node and edge has a version list that records its status at different time points. Each node in the version list contains a version number, timestamp, data content, and a pointer to the next version. The data content is further divided into node data content and edge data content.

[0105] Specifically, for a node, the data content may include but is not limited to the following information: Node ID: unique identifier. Node type: such as power station, substation, user terminal, etc. Node status: such as online / offline, normal / faulty, etc. Geographic coordinates: indicates the precise location of the node. Capacity: describes the maximum power load that the node can handle. Voltage level: indicates the voltage level at which the node operates.

[0106] For edges, the data content will also include its related attributes, but not limited to the following information: The source node ID and target node ID of the edge: define which two nodes this edge connects. Edge type: such as high-voltage transmission lines, low-voltage distribution lines, etc. Transmission capacity: the maximum amount of electricity allowed to pass through the line. Length: physical distance. Resistance value: the resistance characteristics of the line material. When the data changes, a new version node is created and added to the version list. The new version node contains the latest data content and timestamp, as well as a pointer to the old version node. A query interface is provided to obtain the data version at a specific time point based on the timestamp or version number. When querying, start from the latest version and traverse backward along the version list until a version that meets the conditions is found.

[0107] Specifically, the terminal may directly obtain multiple candidate power transmission paths from the graph database. The terminal may also obtain the edge weight between each grid node in the candidate power transmission path and the grid node in the next power transmission stage from the graph database.

[0108] In this embodiment, the power parameter information of each power transmission stage in the power grid is obtained, and the power parameter information is written into the graph database in the form of a linked list according to the power transmission order and the information acquisition sequence, and the candidate power transmission path and edge weight are obtained from the graph database. Invalid calculations are reduced and the efficiency of graph calculations is improved. When processing large-scale data, the query performance is significantly improved and the query time is reduced. Transmission time, connection distance, and node load are dynamic factors considered by the present invention, so that the node weight can reflect the changes in the node status in a timely manner, enhance the consistency of the data, especially in a high-concurrency environment, and can better ensure the real-time and accuracy of the data.

[0109] In one embodiment, as shown in FIG3 , a method for temporal management of power grid data based on graph computing is provided, including:

[0110] S301, obtaining power parameter information of each power transmission stage in the power grid.

[0111] S302, writing each power parameter information into the graph database in the form of a linked list according to the power transmission order and information acquisition sequence.

[0112] Among them, as shown in Figure 4, the flow chart of the method for managing power grid data includes step 1, obtaining power grid data; step 2, storing the power grid data in a graph database; step 3, performing version management on the data in the graph database to obtain version data to record the data status at each time point; and calculating the node weight according to the version data to manage the power grid data.

[0113] S303, obtaining multiple candidate power transmission paths corresponding to the user, each candidate power transmission path including power grid nodes in different power transmission stages.

[0114] S304: For each candidate power transmission path, obtain a node failure rate of each power grid node in the candidate power transmission path.

[0115] S305, obtaining the spatiotemporal parameters of power transmission between each grid node in the candidate power transmission path and the grid node in the next power transmission stage, and the node load of each grid node in the candidate power transmission path.

[0116] S306, determining the load weight configured for each node load and the spatiotemporal weight configured for the spatiotemporal parameters of power transmission.

[0117] S307, based on the failure rate of each node, the load of each node, the load weight, the spatiotemporal parameters of power transmission, and the spatiotemporal weight, determine the edge weight between each grid node in the candidate power transmission path and the grid node of the next power transmission stage.

[0118] After obtaining the edge weight, the edge weight can be further optimized.

[0119] The optimized edge weights reduce invalid calculations and improve the efficiency of graph computing. When processing large-scale data, query performance is significantly improved and query time is reduced.

[0120]

[0121] In the formula, represents the optimized edge weight between the i-th node and the j-th node, represents the edge weight between the i-th node and the j-th node, represents the transmission time between the i-th node and the j-th node, Represents the connection distance between the i-th node and the j-th node.

[0122] Specifically, the terminal can generate a target transmission path for the user based on the optimized edge weights and graph calculation algorithm.

[0123] Among them, graph computing algorithms may include: shortest path algorithm, community discovery algorithm and centrality analysis algorithm.

[0124] In the shortest path algorithm (such as Dijkstra algorithm, A* algorithm), edge weight directly affects the path selection. By optimizing the edge weights calculated by initial weight, transmission time and connection distance, the quality of the path can be more accurately evaluated and the optimal path can be selected. For example, in a smart grid, the optimal path from the power station to the user is selected to ensure the efficiency and reliability of power transmission.

[0125] In community discovery algorithms (such as Louvain algorithm, Label Propagation algorithm), edge weights can be used to evaluate the similarity and connection strength between nodes. With optimized edge weights, the community structure in the power grid can be discovered more accurately, helping to optimize the partitioning and management of the power grid. For example, in smart grids, the power demand patterns in different regions are discovered to optimize power distribution and scheduling. For example, in smart grids, key substations and transmission lines are identified and prioritized for maintenance and upgrades.

[0126] Furthermore, the reliability of the power grid is evaluated through the failure rate information in the edge weights. Nodes and paths with higher failure rates need to be paid special attention to, maintained, and optimized. The transmission efficiency of the power grid is evaluated through the transmission time information in the edge weights. Paths with longer transmission times need to be optimized, and similarly, paths with longer transmission distances also need to be optimized to improve the speed and efficiency of power transmission. The resource allocation of the power grid is evaluated through the load information in the edge weights. Nodes and paths with higher loads need to be optimized to avoid overload.

[0127] S308: Based on the multiple edge weights corresponding to the candidate power transmission paths, path analysis is performed on the candidate power transmission paths to obtain path scores for each candidate power transmission path.

[0128] S309: Determine the candidate power transmission path with the highest path score as the target transmission path for the user.

[0129] S310, providing power to the user based on the target transmission path.

[0130] S311, performing node status monitoring on each power grid node in the target transmission path to obtain node monitoring results of each power grid node.

[0131] S312, when there is a power grid node with an abnormal node monitoring result in the target transmission path, a backup path with the highest path score and no abnormal node is selected from among the candidate power transmission paths.

[0132] S313: Replace the target transmission path with the backup path, and provide power to the user based on the backup path.

[0133] In this embodiment, multiple candidate power transmission paths corresponding to the user are obtained, and each candidate power transmission path includes grid nodes at different power transmission stages. Fully considering the nodes at different power transmission stages is conducive to improving the accuracy of the subsequent determination of the target transmission path. For each candidate power transmission path, the edge weight between each grid node in the candidate power transmission path and the grid node of the next power transmission stage is determined. Based on the multiple edge weights corresponding to the candidate power transmission path, the candidate power transmission path is analyzed to obtain the path score of each candidate power transmission path. With the above method, the edge weight reflects the mutual influence between adjacent grid nodes. The accuracy of the path score of each candidate power transmission path obtained based on the edge weight is higher. The candidate power transmission path with the highest path score is determined as the target transmission path for the user, and the accuracy of the target transmission path is also higher.

[0134] The above method can also bring the following beneficial effects:

[0135] (1) The node failure rate is used as the initial weight, and the initial weight is optimized based on the transmission time, connection distance, and node load to obtain the optimized weight, which reduces invalid calculations and improves the efficiency of graph calculations. When processing large-scale data, the query performance is significantly improved and the query time is reduced. Transmission time, connection distance, and node load are dynamic factors considered by the present invention, so that the node weight can reflect the changes in the node status in a timely manner, enhance the consistency of the data, and especially in a high-concurrency environment, can better ensure the real-time and accuracy of the data;

[0136] (2) Effectively optimize the initial weight, transmission time and connection distance, calculate the node weight, and apply it to graph computing, manage power grid data, and evaluate the reliability, efficiency and resource allocation of the power grid. This provides strong support for the efficient operation and management of smart grids.

[0137] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0138] Based on the same inventive concept, the embodiment of the present application also provides a power transmission path determination device for implementing the power transmission path determination method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more power transmission path determination device embodiments provided below can refer to the limitations of the power transmission path determination method above, and will not be repeated here.

[0139] In one embodiment, as shown in FIG5 , a power transmission path determination device is provided, comprising: an acquisition module 501 , an edge weight determination module 502 , a path analysis module 503 and a path determination module 504 , wherein:

[0140] An acquisition module 501 is used to acquire a plurality of candidate power transmission paths corresponding to the user; each candidate power transmission path includes power grid nodes at different power transmission stages;

[0141] An edge weight determination module 502 is used to determine, for each candidate power transmission path, an edge weight between each grid node in the candidate power transmission path and a grid node in the next power transmission stage;

[0142] A path analysis module 503 is used to perform path analysis on the candidate power transmission paths based on multiple edge weights corresponding to the candidate power transmission paths, and obtain a path score for each candidate power transmission path;

[0143] The path determination module 504 is configured to determine the candidate power transmission path with the highest path score as the target transmission path for the user.

[0144] In one embodiment, the edge weight determination module 502 is also used to obtain the node failure rate of each grid node in the candidate power transmission path; based on the failure rate of each node, determine the edge weight between each grid node in the candidate power transmission path and the grid node of the next power transmission stage.

[0145] In one of the embodiments, the edge weight determination module 502 is also used to obtain the power transmission spatiotemporal parameters between each grid node in the candidate power transmission path and the grid node of the next power transmission stage, and the node load of each grid node in the candidate power transmission path; determine the load weight configured for each node load, and the spatiotemporal weight configured for the power transmission spatiotemporal parameters; based on the failure rate of each node, the load of each node, the load weight, the power transmission spatiotemporal parameters, and the spatiotemporal weight, determine the edge weight between each grid node in the candidate power transmission path and the grid node of the next power transmission stage.

[0146] In one of the embodiments, the power transmission path determination device also includes: a path switching module, which is used to provide power to the user based on the target transmission path; when there is an abnormal node in the target transmission path, determine a backup path that does not contain the abnormal node among the candidate power transmission paths; replace the target transmission path with the backup path, and provide power to the user based on the backup path.

[0147] In one of the embodiments, the path switching module is also used to monitor the node status of each power grid node in the target transmission path to obtain the node monitoring results of each power grid node; when there are power grid nodes with abnormal node monitoring results in the target transmission path, among the candidate power transmission paths, a backup path with the highest path score and without abnormal nodes is screened.

[0148] In one of the embodiments, the power transmission path determination device also includes: a graph database writing module, which is used to obtain the power parameter information of each power transmission stage in the power grid; and write each power parameter information into the graph database in the form of a linked list according to the power transmission order and information acquisition timing.

[0149] Each module in the above-mentioned power transmission path determination device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0150] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG6. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as candidate power transmission paths and edge weights. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for determining a power transmission path is implemented.

[0151] Those skilled in the art will understand that the structure shown in FIG. 6 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0152] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the above method steps are implemented when the processor executes the computer program.

[0153] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the above method steps are implemented.

[0154] In one embodiment, a computer program product is provided, comprising a computer program, which implements the above method steps when executed by a processor.

[0155] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0156] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0157] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining a power transmission path, characterized in that: The method comprises: Acquire multiple candidate power transmission paths corresponding to the user; each of the candidate power transmission paths includes power grid nodes at different power transmission stages; For each of the candidate power transmission paths, determining an edge weight between each grid node in the candidate power transmission path and a grid node in a next power transmission stage; Based on a plurality of edge weights corresponding to the candidate power transmission paths, performing path analysis on the candidate power transmission paths to obtain a path score for each of the candidate power transmission paths; The candidate power transmission path with the highest path score is determined as the target transmission path for the user.

2. The method according to claim 1, characterized in that The determining of the edge weight between each grid node in the candidate power transmission path and the grid node in the next power transmission stage comprises: Obtaining a node failure rate of each grid node in the candidate power transmission path; Based on the failure rate of each node, an edge weight between each grid node in the candidate power transmission path and a grid node in the next power transmission stage is determined.

3. The method according to claim 2, characterized in that The step of determining the edge weight between each grid node in the candidate power transmission path and the grid node in the next power transmission stage based on the failure rate of each node comprises: Acquire the spatiotemporal parameters of power transmission between each grid node in the candidate power transmission path and the grid node in the next power transmission stage, and the node load of each grid node in the candidate power transmission path; Determining a load weight for each of the node load configurations and a spatiotemporal weight for the power transmission spatiotemporal parameter configuration; Based on the failure rate of each node, the load of each node, the load weight, the power transmission spatiotemporal parameter, and the spatiotemporal weight, the edge weight between each grid node in the candidate power transmission path and the grid node of the next power transmission stage is determined.

4. The method according to claim 1, characterized in that: The method further comprises: Based on the target transmission path, supplying power to the user; In the case where an abnormal node exists in the target transmission path, determining a backup path that does not include the abnormal node in each of the candidate power transmission paths; The target transmission path is replaced by the backup path, and power is supplied to the user based on the backup path.

5. The method according to claim 4, characterized in that In the case where an abnormal node exists in the target transmission path, determining a backup path that does not include the abnormal node in each of the candidate power transmission paths includes: Performing node status monitoring on each power grid node in the target transmission path to obtain node monitoring results of each power grid node; When there is a power grid node whose node monitoring result is abnormal in the target transmission path, a backup path with the highest path score and no abnormal node is selected from among the candidate power transmission paths.

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: Obtaining power parameter information of each power transmission stage in the power grid; According to the power transmission order and information acquisition sequence, each power parameter information is written into the graph database in the form of a linked list.

7. A device for determining a power transmission path, characterized in that: The device comprises: An acquisition module, used to acquire a plurality of candidate power transmission paths corresponding to the user; each of the candidate power transmission paths includes power grid nodes at different power transmission stages; An edge weight determination module, configured to determine, for each of the candidate power transmission paths, an edge weight between each grid node in the candidate power transmission path and a grid node in the next power transmission stage; A path analysis module, configured to perform path analysis on the candidate power transmission paths based on a plurality of edge weights corresponding to the candidate power transmission paths, and obtain a path score for each of the candidate power transmission paths; The path determination module is used to determine the candidate power transmission path with the highest path score as the target transmission path for the user.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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