Parallel power supply path searching method and system based on graph database

Abstract

The invention discloses a parallel power supply path searching method and system based on a graph database, and the method comprises the steps: building a power grid power supply association graph model based on structural model connection files and equipment coordinate information of all transformer substations, power transmission lines, feeder lines, loads and distributed power supplies in a region; updating the power grid power supply association graph model according to the latest minute-level section data based on the power grid power supply association graph model; calculating an optimized power supply path by using mixed integer linear programming based on the updated power grid power supply association graph model; based on the optimized power supply path, if a power grid fault is detected, using a Dijkstra algorithm to quickly find an alternative path bypassing a fault area; based on the alternative path bypassing the fault area, deep analysis is carried out through a station power supply source searching algorithm, and a power supply path is refined; a graph parallel computing technology is utilized to distribute computing tasks to a plurality of nodes for parallel execution, and a path search result is obtained; and determining a power supply path from the source side to the target station according to the path search result and the power supply state, marking the power supply path on the geographic coordinates by using a graph database visualization component, and simultaneously performing minute-level dynamic updating to obtain visual and complete path information. Under the conditions that a large number of distributed power supplies are accessed and the topological structure of the power grid is complex and changeable, rapid and accurate power supply path information can be provided, and valuable support is provided for regulation and control and optimization decision making of a power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power analysis, and in particular to a parallel power supply path search method and system based on a graph database. Background Art

[0002] Power supply path search in power systems is an important task, which is used to determine the power supply path from the power source to the target station. The traditional control and operation system power supply path search method is usually implemented based on a relational database and a graph theory algorithm. However, in large-scale power systems where a large number of distributed power sources are connected and coordinated control of the main distribution network is required, the processing efficiency of the traditional control and operation system is low due to the huge number of nodes and the complex topological structure with changeable power supply directions. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a parallel power supply path search method and system based on a graph database to solve the problem of low processing efficiency of the existing power supply path search method for the control operation system.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a parallel power supply path search method based on a graph database, comprising: creating a power grid power supply association graph model based on a structured model connection file of all substations, transmission lines, feeders, loads, and distributed power sources in a region and device coordinate information;

[0008] Based on the power grid power supply association graph model, the power grid power supply association graph model is updated according to the latest minute-level section data;

[0009] Based on the updated power supply association graph model, the optimized power supply path is calculated using mixed integer linear programming;

[0010] Based on the optimized power supply path, if a grid fault is detected, the Dijkstra algorithm is used to quickly find an alternative path around the fault area;

[0011] Based on alternative paths that bypass the fault area, the power supply source search algorithm of the station is used to conduct in-depth analysis and refine the power supply path;

[0012] Using graph parallel computing technology, the computing tasks are distributed to multiple nodes for parallel execution to obtain path search results;

[0013] According to the path search results and power supply status, the power supply path from the source side to the target station is confirmed, and the power supply path is highlighted and rendered on the geographic coordinates using the graph database visualization component. At the same time, it is dynamically updated at the minute level to obtain intuitive and complete path information.

[0014] As a preferred solution of the parallel power supply path search method based on the graph database described in the present invention, wherein:

[0015] The structured model connection file includes Excel format, XML format, CIM / E format and JSON format.

[0016] As a preferred solution of the parallel power supply path search method based on the graph database described in the present invention, wherein:

[0017] The power grid power supply association graph model includes station nodes, unit nodes, load nodes and transmission line nodes;

[0018] The station node is directly connected to the unit node and load node through edges.

[0019] As a preferred solution of the parallel power supply path search method based on the graph database described in the present invention, wherein:

[0020] The station power supply source search algorithm includes the following steps:

[0021] Start by searching for directly connected power supply nodes and expand to searching for associated power supply nodes;

[0022] Add a "traversed" flag to each new site node traversed. If a site node that has been traversed is encountered, skip the node.

[0023] As a preferred solution of the parallel power supply path search method based on the graph database described in the present invention, wherein:

[0024] The step of searching for a directly connected power supply node comprises the following steps:

[0025] Loading a power grid power supply association graph model including station nodes, unit nodes, load nodes and transmission line nodes, wherein the load nodes include normal load nodes and negative load nodes;

[0026] Determine the target station node for searching the power supply path;

[0027] Establish a direct connection with the target station through the graph topology of "station node-unit node" and "station node-load node" to determine the direct power supply node;

[0028] The searching for the associated power supply node comprises the following steps:

[0029] Through the graph association structure of "station node-transmission line node" in the power grid power supply association graph model;

[0030] Find all line nodes with negative transmission power, and further search for other station node sets S associated with the target station s assoc (s);

[0031] Determine whether there are any associated and untraversed stations in the associated station;

[0032] If it exists, continue traversing through the graph association structure of "station node-transmission line node";

[0033] If it does not exist, the record is returned to the unit collection.

[0034] As a preferred solution of the parallel power supply path search method based on the graph database described in the present invention, wherein:

[0035] The graph parallel computing technology comprises the following steps:

[0036] Initialize the parallel computing environment: Use the distributed computing framework to start multiple computing nodes, and load the shard data of the power grid power supply association graph model on each computing node;

[0037] Parallel execution of path search: For each target site node, it is executed in parallel on each computing node;

[0038] Result collection and integration: Each computing node sends the power supply path search results of the target site it is responsible for to the coordination node, and the coordination node collects and integrates the results of all computing nodes.

[0039] As a preferred solution of the parallel power supply path search method based on the graph database described in the present invention, wherein:

[0040] The updating of the power grid power supply association graph model comprises the following steps:

[0041] Collect the latest status data of all power grid components in real time every minute, including current, voltage, and power;

[0042] Clean and preprocess the collected data;

[0043] Load the current power grid power supply association graph model G = (V, E);

[0044] For each edge e = (i, j) ∈ E in the model, update its attributes according to the latest data, especially the impedance Z ij and the maximum transmission capacity C ij, use the following formula to update the real-time weight w of the edge ij :

[0045] w ij =Z ij +α·(C ij -P ij ) 2

[0046] Among them, P ij is the real-time transmission power of edge (i, j), and α is the preset adjustment coefficient;

[0047] If P ij >C ij , it means the line is overloaded and in a fault state;

[0048] For the detected fault edge, its weight w ij Set to a maximum value to isolate the fault area in the graph model;

[0049] All nodes participating in parallel computing synchronize the updated graph model G′;

[0050] Verify that the updated model accurately reflects the current state of the grid by comparing it with real-time data;

[0051] If the model does not match the real-time data, the model update step is re-executed;

[0052] If the model matches the real-time data, the process ends.

[0053] In a second aspect, the present invention provides a parallel power supply path search system based on a graph database, comprising:

[0054] The power grid power supply association graph model building module is used to create a power grid power supply association graph model based on the structured model connection files and equipment coordinate information of all substations, transmission lines, feeders, loads, and distributed power sources in the region;

[0055] A power grid power supply association graph model updating module is used to update the power grid power supply association graph model based on the power grid power supply association graph model and the latest minute-level section data;

[0056] A power supply path optimization module is used to calculate the optimized power supply path based on the updated power supply association graph model of the power grid and using mixed integer linear programming;

[0057] A fault recovery path search module is used to quickly find an alternative path around the fault area using the Dijkstra algorithm based on the optimized power supply path if a power grid fault is detected;

[0058] The station power supply source search module is used to perform in-depth analysis and refine the power supply path based on alternative paths that bypass the fault area through the station power supply source search algorithm;

[0059] The parallel computing module is used to distribute the computing tasks to multiple nodes for parallel execution using graph parallel computing technology to obtain path search results;

[0060] The power supply path visualization rendering and update module is used to confirm the power supply path from the source side to the target station based on the path search results and power supply status, and use the graph database visualization component to highlight the power supply path on the geographic coordinates, while performing dynamic updates at the minute level to obtain intuitive and complete path information.

[0061] In a third aspect, the present invention provides a computing device, comprising:

[0062] Memory, used to store programs;

[0063] A processor is used to execute the computer executable instructions, which, when executed by the processor, implement the steps of the parallel power supply path search method based on the graph database.

[0064] In a fourth aspect, the present invention provides a computer-readable storage medium, comprising: when the program is executed by a processor, the steps of implementing the parallel power supply path search method based on a graph database are implemented.

[0065] Beneficial effects of the present invention: The present invention ensures the real-time and accuracy of power supply path search by updating the power supply association graph model in real time, and can timely reflect changes in power grid structure and load fluctuations. The application of mixed integer linear programming (MILP) enables the selection of power supply paths to achieve the lowest cost while meeting all load requirements, thereby improving the economic benefits of power system operation. The integration of the Dijkstra algorithm enables alternative paths to be quickly found after a power grid fault is detected, reducing the time and scope of power outages caused by the fault. The use of graph parallel computing technology greatly improves the speed of power supply path search, especially when dealing with large-scale power grids, it can significantly shorten the calculation time and improve the overall system performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0067] Figure 1A basic flow chart of a parallel power supply path search method based on a graph database provided by an embodiment of the present invention;

[0068] Figure 2 A framework flow chart of a parallel power supply path search method based on a graph database provided by an embodiment of the present invention;

[0069] Figure 3 A power grid power supply association graph model and a topology association structure diagram of a parallel power supply path search method based on a graph database are provided as an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0071] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0072] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0073] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0074] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0075] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0076] Example 1

[0077] Reference Figure 1-3 , is an embodiment of the present invention, and provides a parallel power supply path search method based on a graph database, such as Figure 1 , as shown in 2, comprising the following steps:

[0078] S1: Create a power grid power supply association diagram model based on the structured model connection files and equipment coordinate information of all substations, transmission lines, feeders, loads, and distributed power sources in the region;

[0079] In an embodiment of the present application, a structured model connection file of all substations, transmission lines, feeders, loads, and distributed power sources in a certain area is imported, and the format is not limited to Excel format, XML format, CIM / E format, JSON format, etc., and the equipment contains coordinate information. The topological data includes all equipment in the substation, transmission lines, feeders, loads, and distributed power sources, such as main transformers, busbars, switch connection devices, line ends, etc. The structured model connection file is updated as the actual grid connection relationship changes. The measurement data needs to collect minute-level section data for real-time update. This step is the basis for the entire power supply path search.

[0080] In the embodiments of the present application, Figure 3 As shown in the figure, based on the full topological model data of the power grid, the power grid power supply association graph model is simplified and constructed. The power grid power supply association graph model only retains the station nodes (Node: Substation), unit nodes (Node: Unit), load nodes (Node: Load) and transmission line nodes (Node: Line) in the full topological model data. The station nodes are directly associated with the unit nodes or load nodes through edges (Edge: sub_unit or sub_load). Simplify the number of search devices and improve efficiency.

[0081] In the embodiment of the present application, the information of all substations, transmission lines, feeders, loads and distributed power sources is extracted from the imported structured model connection file to construct V and E. Let G = (V, E) be a power grid power supply association graph model, where V is a node set and E is an edge set.

[0082] S2: Based on the power grid power supply association graph model, the power grid power supply association graph model is updated according to the latest minute-level section data;

[0083] It should be noted that after the power grid power supply association graph model is constructed, the attributes of the edges in the graph model, such as the impedance or transmission capacity of the line, need to be updated according to the latest minute-level section data to ensure that the model always reflects the actual state of the power grid. This step is crucial because it directly affects the effectiveness of the next MILP optimization and Dijkstra algorithm.

[0084] In an embodiment of the present application, updating the power grid power supply association graph model includes the following steps:

[0085] S2.1: Data collection: Collect the latest status data of all substations, transmission lines, feeders, loads, and distributed power sources from the power grid monitoring system in real time, including but not limited to line current, voltage, power, equipment status, etc., once every minute;

[0086] S2.2: Data preprocessing: Clean and preprocess the collected data to ensure the completeness and accuracy of the data and remove outliers and missing data;

[0087] S2.3: Model update: Load the current power grid power supply association diagram model G = (V, E).

[0088] For each edge e = (i, j) ∈ E in the model, update its attributes according to the latest data, especially the impedance Z ij and the maximum transmission capacity C ij , use the following formula to update the real-time weight w of the edge ij :

[0089] w ij =Z ij +α·(C ij -P ij ) 2

[0090] Among them, P ij is the real-time transmission power of edge (i, j), and α is a preset adjustment coefficient used to balance the impact of impedance and transmission power on weight.

[0091] S2.4: Fault detection and isolation: Use real-time data to detect fault conditions in lines or equipment. ij >C ij When , it indicates that the line is overloaded and may be in a fault state;

[0092] For the detected fault edge, its weight w ij Set to a maximum value to isolate the fault area in the graph model;

[0093] S2.5: State synchronization and verification: Ensure that all nodes participating in parallel computing synchronize the updated graph model G′, and use a two-phase commit protocol to ensure data consistency;

[0094] Verify that the updated model accurately reflects the current state of the grid, compare it with real-time data, and make fine-tuning if necessary;

[0095] S2.6: Feedback and iteration: If there is a significant difference between the model and the real-time data, feedback corrections are made and the model update step is re-executed;

[0096] The accuracy and effectiveness of the model are evaluated regularly, and the model parameter α is adjusted according to the evaluation results.

[0097] It should be noted that by setting the weight of the faulty edge to a maximum value instead of directly removing it from the edge set, the existence of the faulty edge can be retained, even if its weight is set to a maximum value, so that the integrity of the graph model is maintained and the topological structure of the graph is not destroyed. This may be very important for subsequent analysis or historical data review.

[0098] It should be noted that setting the weight of the fault edge to a maximum value allows for a quick return to normal after the fault is repaired without rebuilding or modifying the graph model. Once the fault is repaired, the weight of the edge can be simply adjusted back to normal without the need for an additional graph reconstruction process. Setting the weight of the fault edge to a maximum value is a more flexible, efficient, and easier to implement solution, especially for power system environments that require real-time response and are highly dynamic.

[0099] S3: Based on the updated power supply association graph model, the optimized power supply path is calculated using mixed integer linear programming;

[0100] It should be noted that based on the real-time updated grid power supply association graph model, mixed integer linear programming (MILP) is used to find the power supply path with the lowest total cost while meeting all load requirements. This optimization process relies on the updated model data in S2 to ensure that the calculated path is optimal.

[0101] In the embodiment of the present application, the mixed integer linear programming (MILP) is expressed as:

[0102]

[0103] Among them, w ij is the cost or impedance of edge (i,j), x ij is the flow on edge (i,j), y ij is a binary variable indicating whether edge (i, j) is in the power supply path, u ij is the maximum capacity of edge (i,j), d iis the load demand of node i, s i is the power generation of node i.

[0104] S4: Based on the optimized power supply path, if a grid fault is detected, the Dijkstra algorithm is used to quickly find an alternative path that bypasses the fault area;

[0105] It should be noted that after the MILP optimization is completed, if any fault is detected in the power grid, the Dijkstra algorithm will be enabled to quickly find an alternative path around the fault area. This step is a complement to the previous optimization step, ensuring that the power supply path can be quickly adjusted even in the event of a fault, ensuring the continued stable operation of the power grid.

[0106] In the embodiment of the present application, the Dijkstra algorithm includes the following steps:

[0107] S4.1: Initialization: Mark the distances of all nodes as infinity, except the starting node whose distance is 0.

[0108] S4.2: Select the current node: Select the node with the smallest distance from the nodes that have not been visited as the current node.

[0109] S4.3: Update neighbor nodes: Check all unvisited neighbor nodes of the current node, calculate the distance from the start node to the neighbor node, and update the distance value.

[0110] S4.4: Mark the current node as visited.

[0111] S4.5: Repeat steps 2-4 until all nodes have been visited or the target node is found.

[0112] S5: Based on alternative paths that bypass the fault area, the power supply source search algorithm of the station is used to conduct in-depth analysis and refine the power supply path;

[0113] It should be noted that after the optimization and fault recovery paths are determined, we conduct in-depth analysis through the station power supply source search algorithm. The algorithm starts from the power supply path information obtained from S3 and S4, and further refines the power supply path to ensure that the power supply needs of each station can be met.

[0114] In the embodiment of the present application, there are three types of nodes that supply power to the station, namely, unit nodes, negative load nodes, and line nodes with negative transmission power. Among them, unit nodes and negative load nodes are directly connected power supply nodes; line nodes with negative transmission power are associated power supply nodes.

[0115] In the embodiment of the present application, the station power supply source search algorithm includes the following steps:

[0116] Start by searching for directly connected power supply nodes and expand to searching for associated power supply nodes;

[0117] Add a "traversed" flag to each new station node traversed. If a station node that has been traversed is encountered, skip the node;

[0118] In an embodiment of the present application, searching for a directly connected power supply node includes the following steps:

[0119] Loading a power grid power supply association graph model including station nodes, unit nodes, load nodes and transmission line nodes, wherein the load nodes include normal load nodes and negative load nodes;

[0120] Determine the target station node for searching the power supply path;

[0121] Establish a direct connection with the target station through the graph topology of "station node-unit node" and "station node-load node" to determine the direct power supply node;

[0122] For the target station node s, the set of directly connected power supply nodes S is determined by querying the edge sets E of "station node-unit node" and "station node-load node" in the graph model. direct (s);

[0123] S direct (s)={u|(s,u)∈E∧u∈U}∪{l|(s,l)∈E∧l∈L}

[0124] Among them, U represents the set of all unit nodes, and L represents the set of all load nodes (including normal load nodes and negative load nodes).

[0125] In an embodiment of the present application, searching for an associated power supply node includes the following steps:

[0126] Through the graph association structure of "station node-transmission line node" in the power grid power supply association graph model;

[0127] Find all line nodes with negative transmission power, and further search for other station node sets S associated with the target station s assoc (s);

[0128] Determine whether there are any associated and untraversed stations in the associated station;

[0129] If it exists, continue traversing through the graph association structure of "station node-transmission line node";

[0130] If it does not exist, the record is returned to the unit collection.

[0131] In the embodiment of the present application, the set of other station nodes S associated with the target station s assoc (s) is expressed as:

[0132]

[0133] Where p(l) represents the transmission power of line l.

[0134] S6: Using graph parallel computing technology, the computing tasks are distributed to multiple nodes for parallel execution to obtain path search results;

[0135] It should be noted that in order to speed up the search process in the station power supply source search algorithm, we use graph parallel computing technology to distribute the computing tasks to multiple nodes for parallel execution. This step improves the efficiency of the power supply path search in S5 and ensures that the power supply path can be quickly determined even in large-scale power grids.

[0136] In the embodiment of the present application, the graph parallel computing technology includes the following steps:

[0137] Initialize the parallel computing environment: Use a distributed computing framework (such as Apache Spark, Apache Flink, etc.) to start multiple computing nodes, and load the shard data of the power grid power supply association graph model on each computing node;

[0138] Parallel execution of path search: For each target site node, it is executed in parallel on each computing node;

[0139] Result collection and integration: Each computing node sends the power supply path search results of the target site it is responsible for to the coordination node, and the coordination node collects and integrates the results of all computing nodes.

[0140] Let P = {p 1 ,p 2 ,...,p n} is the set of computing nodes, D={d 1 ,d 2 ,...,d m} is the shard data set of the power grid power supply association graph model, R i is the computing node p i Initialize the parallel computing environment, assign D to P, and each computing node p i Execute the power supply path search algorithm in parallel to generate search result R i Finally, the coordination node collects and integrates all R i , get the complete power supply path information.

[0141] In an embodiment of the present application, the path search is performed in parallel, including the following steps:

[0142] Search for directly connected power supply nodes: Use the topological relationship of "station-unit" and "station-load" in the graph model to find the unit nodes and load nodes directly connected to the station.

[0143] Search for associated power supply nodes: Use the topological relationship of "station node-transmission line node" in the graph model to find line nodes with negative transmission power (i.e. power input). Through these line nodes, further search for other station nodes associated with the station. For the newly discovered associated station nodes, continue to recursively search for their associated nodes until all possible power supply paths are found.

[0144] Avoid duplicate calculations: Add a "traversed" mark to each traversed site node to avoid duplicate calculations.

[0145] Let B = {b v |v∈V} is a binary array, where b v Indicates whether the node v has been traversed.

[0146] During the traversal process, set b for each new site node v v =1 (traversed), if b is encountered v = 1, then skip the node.

[0147] S7: According to the path search results and power supply status, confirm the power supply path from the source side to the target station, and use the graph database visualization component to highlight the power supply path on the geographic coordinates, while performing dynamic updates at the minute level to obtain intuitive and complete path information.

[0148] This embodiment also provides a parallel power supply path search system based on a graph database, including:

[0149] The power grid power supply association graph model building module is used to create a power grid power supply association graph model based on the structured model connection files and equipment coordinate information of all substations, transmission lines, feeders, loads, and distributed power sources in the region;

[0150] A power grid power supply association graph model updating module is used to update the power grid power supply association graph model based on the power grid power supply association graph model and the latest minute-level section data;

[0151] A power supply path optimization module is used to calculate the optimized power supply path based on the updated power supply association graph model of the power grid and using mixed integer linear programming;

[0152] A fault recovery path search module is used to quickly find an alternative path around the fault area using the Dijkstra algorithm based on the optimized power supply path if a power grid fault is detected;

[0153] The station power supply source search module is used to perform in-depth analysis and refine the power supply path based on alternative paths that bypass the fault area through the station power supply source search algorithm;

[0154] The parallel computing module is used to distribute the computing tasks to multiple nodes for parallel execution using graph parallel computing technology to obtain path search results;

[0155] The power supply path visualization rendering and update module is used to confirm the power supply path from the source side to the target station based on the path search results and power supply status, and use the graph database visualization component to highlight the power supply path on the geographic coordinates, while performing dynamic updates at the minute level to obtain intuitive and complete path information.

[0156] Furthermore, it also includes:

[0157] Memory, used to store programs;

[0158] A processor is used to load the program to execute the parallel power supply path search method based on the graph database.

[0159] This embodiment also provides a computer-readable storage medium storing a program, and when the program is executed by a processor, the parallel power supply path search method based on the graph database is implemented.

[0160] The storage medium proposed in this embodiment and the parallel power supply path search method based on the graph database proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0161] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0162] Example 2

[0163] Referring to Table 1, an embodiment of the present invention provides a parallel power supply path search method based on a graph database. In order to verify its beneficial effects, comparison results of two solutions are provided.

[0164] The technical solution of the existing method is: execute the power supply path search algorithm in the relational database, record the response time and resource consumption of each search, and test the performance under different data update frequencies.

[0165] Table 1 Comparison table

[0166]

[0167]

[0168] As can be seen from Table 1, the present invention is superior to existing methods in data processing efficiency, concurrency, real-time, complex network processing, distributed power supply integration, power supply path visualization, avoiding repeated calculations and resource consumption. The present invention performs in-depth analysis through the site power supply source search algorithm to obtain complete power supply path information. This enables the present invention to have higher concurrent processing capabilities and better horizontal scalability, can run in a multi-server environment, is suitable for processing large-scale data sets and complex network structures, supports real-time data updates, and can promptly reflect changes in the power grid status.

[0169] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A parallel power supply path search method based on a graph database, characterized in that: include: Create a power grid power supply association diagram model based on the structured model connection files and equipment coordinate information of all substations, transmission lines, feeders, loads, and distributed power sources in the region; Based on the power grid power supply association graph model, the power grid power supply association graph model is updated according to the latest minute-level section data; Based on the updated power supply association graph model, the optimized power supply path is calculated using mixed integer linear programming; Based on the optimized power supply path, if a grid fault is detected, the Dijkstra algorithm is used to quickly find an alternative path around the fault area; Based on alternative paths that bypass the fault area, the power supply source search algorithm of the station is used to conduct in-depth analysis and refine the power supply path; Using graph parallel computing technology, the computing tasks are distributed to multiple nodes for parallel execution to obtain path search results; According to the path search results and power supply status, the power supply path from the source side to the target station is confirmed, and the power supply path is highlighted on the geographic coordinates using the graph database visualization component. At the same time, dynamic updates are performed at the minute level to obtain intuitive and complete path information.

2. The parallel power supply path search method based on graph database according to claim 1, characterized in that: The structured model connection file includes Excel format, XML format, CIM / E format and JSON format.

3. The parallel power supply path search method based on graph database according to claim 1 or 2, characterized in that: The power grid power supply association graph model includes station nodes, unit nodes, load nodes and transmission line nodes; The station node is directly connected to the unit node and load node through edges.

4. The parallel power supply path search method based on graph database according to claim 3, characterized in that: The station power supply source search algorithm includes the following steps: Start by searching for directly connected power supply nodes and expand to searching for associated power supply nodes; Add a "traversed" flag to each new site node traversed. If a site node that has been traversed is encountered, skip the node.

5. The parallel power supply path search method based on graph database according to claim 4, characterized in that: The step of searching for a directly connected power supply node comprises the following steps: Loading a power grid power supply association graph model including station nodes, unit nodes, load nodes and transmission line nodes, wherein the load nodes include normal load nodes and negative load nodes; Determine the target station node for searching the power supply path; Establish a direct connection with the target station through the graph topology of "station node-unit node" and "station node-load node" to determine the direct power supply node; The searching for the associated power supply node comprises the following steps: Through the graph association structure of "station node-transmission line node" in the power grid power supply association graph model; Find all line nodes with negative transmission power, and further search for other station node sets S associated with the target station s assoc (s); Determine whether there are any associated and untraversed stations in the associated station; If it exists, continue traversing through the graph association structure of "station node-transmission line node"; If it does not exist, the record is returned to the unit collection.

6. The parallel power supply path search method based on graph database according to claim 5, characterized in that: The graph parallel computing technology comprises the following steps: Initialize the parallel computing environment: Use the distributed computing framework to start multiple computing nodes, and load the shard data of the power grid power supply association graph model on each computing node; Parallel execution of path search: For each target site node, it is executed in parallel on each computing node; Result collection and integration: Each computing node sends the power supply path search results of the target site it is responsible for to the coordination node, and the coordination node collects and integrates the results of all computing nodes.

7. The parallel power supply path search method based on graph database according to claim 6, characterized in that: The updating of the power grid power supply association graph model comprises the following steps: Collect the latest status data of all power grid components in real time every minute, including current, voltage, and power; Clean and preprocess the collected data; Load the current power grid power supply association graph model G = (V, E); For each edge e = (i, j) ∈ E in the model, update its attributes according to the latest data, especially the impedance Z ij and the maximum transmission capacity C ij , use the following formula to update the real-time weight w of the edge ij : w ij =z ij +α·(C ij -P ij ) 2 Among them, P ij is the real-time transmission power of edge (i, j), and α is the preset adjustment coefficient; If P ij >C ij , it means the line is overloaded and in a fault state; For the detected fault edge, its weight wi j设 is a maximum value, isolating the fault area in the graph model; All nodes participating in parallel computing synchronize the updated graph model G′; Verify that the updated model accurately reflects the current state of the grid by comparing it with real-time data; If the model does not match the real-time data, the model update step is re-executed; If the model matches the real-time data, the process ends.

8. A system based on the parallel power supply path search method based on the graph database according to claim 1, characterized in that: The power grid power supply association graph model building module is used to create a power grid power supply association graph model based on the structured model connection files and equipment coordinate information of all substations, transmission lines, feeders, loads, and distributed power sources in the region; A power grid power supply association graph model updating module is used to update the power grid power supply association graph model based on the power grid power supply association graph model and the latest minute-level section data; A power supply path optimization module is used to calculate the optimized power supply path based on the updated power supply association graph model of the power grid and using mixed integer linear programming; A fault recovery path search module is used to quickly find an alternative path around the fault area using the Dijkstra algorithm based on the optimized power supply path if a power grid fault is detected; The station power supply source search module is used to perform in-depth analysis and refine the power supply path based on alternative paths that bypass the fault area through the station power supply source search algorithm; The parallel computing module is used to distribute the computing tasks to multiple nodes for parallel execution using graph parallel computing technology to obtain path search results; The power supply path visualization rendering and update module is used to confirm the power supply path from the source side to the target station based on the path search results and power supply status, and use the graph database visualization component to highlight the power supply path on the geographic coordinates, while performing dynamic updates at the minute level to obtain intuitive and complete path information.

9. An electronic device, characterized in that: include: Memory, used to store programs; A processor, used to load the program to execute the steps of the parallel power supply path search method based on a graph database as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a program, characterized in that: When the program is executed by a processor, the steps of the parallel power supply path search method based on a graph database as described in any one of claims 1 to 7 are implemented.

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