Power distribution network frame model construction method and device, computer equipment, storage medium and computer program product

Abstract

The invention relates to a power distribution network frame model construction method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a plurality of first equipment nodes associated with the low-voltage power distribution network and a topological connection relationship of the first equipment nodes; constructing a first power distribution network frame model corresponding to the low-voltage power distribution network according to the topological connection relationship of the first equipment nodes; determining a medium-voltage power distribution network associated with the low-voltage power distribution network, and obtaining a second power distribution network frame model corresponding to the medium-voltage power distribution network and a plurality of second equipment nodes in the second power distribution network frame model; and according to the first attribute information of each first equipment node and the second attribute information of each second equipment node, splicing the first power distribution network frame model and the second power distribution network frame model to obtain a target power distribution network frame model. By adopting the method, the construction accuracy of the power distribution network frame model can be improved.

Description

Technical Field

[0001] The present application relates to the field of power grid technology, and in particular to a method, device, computer equipment, computer-readable storage medium and computer program product for constructing a distribution network grid model. Background Art

[0002] At present, in order to conduct a comprehensive and systematic analysis and evaluation of the power grid, it is crucial to construct a distribution network model.

[0003] In traditional technology, in the process of constructing the distribution network grid model, manual docking and fusion are generally adopted; however, this method has subjective factors and is prone to errors, resulting in low accuracy in the construction of the distribution network grid model. Summary of the invention

[0004] Based on this, it is necessary to provide a distribution network framework model construction method, device, computer equipment, computer readable storage medium and computer program product that can improve the construction accuracy of the distribution network framework model in order to address the above technical problems.

[0005] In a first aspect, the present application provides a method for constructing a distribution network model, comprising:

[0006] Acquire a plurality of first device nodes associated with the low-voltage distribution network, and a topological connection relationship between the first device nodes;

[0007] Constructing a first distribution network grid model corresponding to the low-voltage distribution network according to the topological connection relationship of each of the first device nodes;

[0008] Determine a medium voltage distribution network associated with the low voltage distribution network, and obtain a second distribution network grid model corresponding to the medium voltage distribution network, and a plurality of second device nodes in the second distribution network grid model;

[0009] According to the first attribute information of each of the first device nodes and the second attribute information of each of the second device nodes, the first distribution network framework model and the second distribution network framework model are spliced ​​to obtain a target distribution network framework model.

[0010] In one embodiment, the first attribute information of each of the first device nodes includes first position information and first voltage information of each of the first device nodes, and the second attribute information of each of the second device nodes includes second position information and second voltage information of each of the second device nodes;

[0011] The first distribution network grid model and the second distribution network grid model are spliced ​​according to the first attribute information of each of the first device nodes and the second attribute information of each of the second device nodes to obtain a target distribution network grid model, including:

[0012] According to the first position information and the first voltage information of each of the first device nodes, and the second position information and the second voltage information of each of the second device nodes, a candidate device node combination is determined from each of the first device nodes and each of the second device nodes; each candidate device node combination includes a first device node and a second device node, and the first position information of the included first device node and the second position information of the included second device node meet a first preset condition, and the first voltage information of the included first device node and the second voltage information of the included second device node meet a second preset condition;

[0013] Filtering out a target device node combination that meets a third preset condition from the candidate device node combination;

[0014] According to the target device node combination, the first distribution network framework model and the second distribution network framework model are spliced ​​to obtain a target distribution network framework model.

[0015] In one embodiment, the step of selecting a target device node combination that satisfies a third preset condition from the candidate device node combination includes:

[0016] Determine the target information corresponding to each of the candidate device node combinations according to the first position information and the first voltage information of the first device node in each of the candidate device node combinations, and the second position information and the second voltage information of the second device node in each of the candidate device node combinations;

[0017] From each of the candidate device node combinations, a candidate device node combination with the largest corresponding target information is screened out as the target device node combination.

[0018] In one embodiment, determining the target information corresponding to each candidate device node combination according to the first position information and the first voltage information of the first device node in each candidate device node combination, and the second position information and the second voltage information of the second device node in each candidate device node combination, includes:

[0019] Determine the distance information corresponding to each of the candidate device node combinations according to the first position information of the first device node in each of the candidate device node combinations and the second position information of the second device node in each of the candidate device node combinations;

[0020] Determine voltage difference information corresponding to each candidate device node combination according to first voltage information of a first device node in each candidate device node combination and second voltage information of a second device node in each candidate device node combination;

[0021] The distance information and the voltage difference information corresponding to each of the candidate device node combinations are fused to obtain the target information corresponding to each of the candidate device node combinations.

[0022] In one embodiment, constructing a first distribution network grid model corresponding to the low-voltage distribution network according to the topological connection relationship of each of the first device nodes includes:

[0023] Constructing an initial distribution network grid model corresponding to the low-voltage distribution network according to the topological connection relationship of each of the first device nodes;

[0024] Identifying abnormal information in the initial distribution network grid model; the abnormal information includes abnormal device nodes in the initial distribution network grid model, and topological connection relationships of the abnormal device nodes;

[0025] The abnormal information is deleted from the initial distribution network framework model to obtain a first distribution network framework model corresponding to the low-voltage distribution network.

[0026] In one embodiment, the acquiring of a plurality of first device nodes associated with the low-voltage distribution network and a topological connection relationship of each of the first device nodes includes:

[0027] Obtaining target file information associated with the low voltage distribution network;

[0028] Parsing the target file information to obtain parsed information;

[0029] A plurality of first device nodes associated with the low-voltage distribution network and a topological connection relationship between the first device nodes are extracted from the parsed information.

[0030] In a second aspect, the present application also provides a distribution network grid model construction device, comprising:

[0031] A first information acquisition module, used to acquire a plurality of first device nodes associated with the low-voltage distribution network, and a topological connection relationship between each of the first device nodes;

[0032] An initial model building module, used to build a first distribution network grid model corresponding to the low-voltage distribution network according to the topological connection relationship of each of the first device nodes;

[0033] A second information acquisition module, used to determine a medium voltage distribution network associated with the low voltage distribution network, and obtain a second distribution network grid model corresponding to the medium voltage distribution network, and a plurality of second device nodes in the second distribution network grid model;

[0034] The target model construction module is used to splice the first distribution network framework model and the second distribution network framework model according to the first attribute information of each of the first device nodes and the second attribute information of each of the second device nodes to obtain a target distribution network framework model.

[0035] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0036] Acquire a plurality of first device nodes associated with the low-voltage distribution network, and a topological connection relationship between the first device nodes;

[0037] Constructing a first distribution network grid model corresponding to the low-voltage distribution network according to the topological connection relationship of each of the first device nodes;

[0038] Determine a medium voltage distribution network associated with the low voltage distribution network, and obtain a second distribution network grid model corresponding to the medium voltage distribution network, and a plurality of second device nodes in the second distribution network grid model;

[0039] According to the first attribute information of each of the first device nodes and the second attribute information of each of the second device nodes, the first distribution network framework model and the second distribution network framework model are spliced ​​to obtain a target distribution network framework model.

[0040] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0041] Acquire a plurality of first device nodes associated with the low-voltage distribution network, and a topological connection relationship between the first device nodes;

[0042] Constructing a first distribution network grid model corresponding to the low-voltage distribution network according to the topological connection relationship of each of the first device nodes;

[0043] Determine a medium voltage distribution network associated with the low voltage distribution network, and obtain a second distribution network grid model corresponding to the medium voltage distribution network, and a plurality of second device nodes in the second distribution network grid model;

[0044] According to the first attribute information of each of the first device nodes and the second attribute information of each of the second device nodes, the first distribution network framework model and the second distribution network framework model are spliced ​​to obtain a target distribution network framework model.

[0045] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0046] Acquire a plurality of first device nodes associated with the low-voltage distribution network, and a topological connection relationship between the first device nodes;

[0047] Constructing a first distribution network grid model corresponding to the low-voltage distribution network according to the topological connection relationship of each of the first device nodes;

[0048] Determine a medium voltage distribution network associated with the low voltage distribution network, and obtain a second distribution network grid model corresponding to the medium voltage distribution network, and a plurality of second device nodes in the second distribution network grid model;

[0049] According to the first attribute information of each of the first device nodes and the second attribute information of each of the second device nodes, the first distribution network framework model and the second distribution network framework model are spliced ​​to obtain a target distribution network framework model.

[0050] The above-mentioned distribution network grid model construction method, device, computer equipment, storage medium and computer program product first obtain multiple first device nodes associated with the low-voltage distribution network, and the topological connection relationship of each first device node, and then construct a first distribution network grid model corresponding to the low-voltage distribution network according to the topological connection relationship of each first device node, then determine the medium-voltage distribution network associated with the low-voltage distribution network, and obtain a second distribution network grid model corresponding to the medium-voltage distribution network, and multiple second device nodes in the second distribution network grid model. Finally, according to the first attribute information of each first device node and the second attribute information of each second device node, the first distribution network grid model and the second distribution network grid model are spliced ​​to obtain the target distribution network grid model. In this way, in the process of constructing the distribution network framework model, by respectively obtaining relevant information of the low-voltage distribution network and the medium-voltage distribution network, and based on the topological connection relationship and attribute information of the device nodes, the distribution network framework model corresponding to the low-voltage distribution network and the distribution network framework model corresponding to the medium-voltage distribution network are spliced, so that the entire distribution network framework model can be constructed more comprehensively and accurately, which is conducive to improving the construction accuracy of the distribution network framework model; moreover, the entire process does not require human intervention, avoiding the subjective factors and prone to errors in the way of manual docking and fusion, resulting in the defect of low construction accuracy of the distribution network framework model, thereby improving the construction accuracy of the distribution network framework model. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 A schematic diagram of a flow chart of a method for constructing a distribution network grid model in one embodiment;

[0053] Figure 2 A schematic flow chart of steps for constructing a target power distribution network grid model in one embodiment;

[0054] Figure 3 A schematic diagram of a flow chart of a method for constructing a distribution network model in another embodiment;

[0055] Figure 4 A flowchart of the steps of a method for generating a low-voltage distribution network intelligent grid based on the CIM standard in one embodiment;

[0056] Figure 5 A structural block diagram of a device for constructing a distribution network model in one embodiment;

[0057] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0058] 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.

[0059] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0060] In an exemplary embodiment, Figure 1As shown, a method for constructing a distribution network grid model is provided. This embodiment uses the method applied to a server as an example for illustration; it can be understood that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be but is not limited to various personal computers, laptops, smart phones and tablets; the server can be implemented as an independent server or a server cluster composed of multiple servers, such as a network server. In this embodiment, the method includes the following steps:

[0061] Step S101: Acquire a plurality of first device nodes associated with a low-voltage distribution network, and a topological connection relationship between the first device nodes.

[0062] Among them, the low-voltage distribution network refers to the distribution network that directly supplies power to users in the power system, and its voltage level is usually between 220 volts and 380 volts.

[0063] The first device node refers to a device node in the low-voltage distribution network.

[0064] The topological connection relationship is used to characterize the connection relationship and topological information between device nodes, such as the connection mode and connection type between device nodes.

[0065] Exemplarily, the server obtains CIM (Common Information Model) file information of the low-voltage distribution network from a database in response to a service interface call instruction; then, the server extracts multiple device nodes associated with the low-voltage distribution network from the CIM file information of the low-voltage distribution network as first device nodes; then, the server extracts the topological connection relationship of each first device node from the CIM file information of the low-voltage distribution network.

[0066] Step S102: constructing a first distribution network framework model corresponding to the low-voltage distribution network according to the topological connection relationship of each first device node.

[0067] Among them, the first distribution network framework model refers to the distribution network framework model corresponding to the low-voltage distribution network.

[0068] Exemplarily, the server combines each first device node according to the topological connection relationship of each first device node to obtain a combined first device node; then, the server constructs a distribution network grid model corresponding to the low-voltage distribution network according to the combined first device nodes as the first distribution network grid model.

[0069] Step S103, determining a medium voltage distribution network associated with the low voltage distribution network, and obtaining a second distribution network grid model corresponding to the medium voltage distribution network, and a plurality of second device nodes in the second distribution network grid model.

[0070] Among them, the medium-voltage distribution network refers to the distribution network between the transmission network and the low-voltage distribution network in the power system, and its voltage level is usually between 10 kV and 35 kV.

[0071] The second distribution network framework model refers to the distribution network framework model corresponding to the medium voltage distribution network.

[0072] The second device node refers to a device node in the medium voltage distribution network.

[0073] Exemplarily, the server selects a medium-voltage distribution network that needs to be spliced ​​with a low-voltage distribution network from multiple medium-voltage distribution networks as a medium-voltage distribution network associated with the low-voltage distribution network; then, the server obtains a distribution network grid model corresponding to the medium-voltage distribution network as a second distribution network grid model; then, the server obtains multiple device nodes in the second distribution network grid model as second device nodes.

[0074] Step S104, according to the first attribute information of each first device node and the second attribute information of each second device node, the first distribution network framework model and the second distribution network framework model are spliced ​​to obtain a target distribution network framework model.

[0075] The first attribute information refers to the attribute information of the first device node, including the location information and voltage information of the first device node.

[0076] The second attribute information refers to the attribute information of the second device node, including the location information and voltage information of the second device node.

[0077] Among them, splicing processing refers to the corresponding processing process of combining two distribution network grid models.

[0078] The target distribution network grid model refers to a distribution network grid model obtained by splicing the first distribution network grid model and the second distribution network grid model.

[0079] Exemplarily, the server converts the second distribution network framework model according to the format and data structure of the first distribution network framework model, and obtains a second distribution network framework model with the same format and data structure as the first distribution network framework model as the converted second distribution network framework model; then, the server obtains the first attribute information of each first device node in the first distribution network framework model, and the second attribute information of each second device node in the converted second distribution network framework model, and splices the first distribution network framework model and the converted second distribution network framework model to obtain the spliced ​​distribution network framework model as the target distribution network framework model.

[0080] In the above-mentioned distribution network grid model construction method, a plurality of first device nodes associated with the low-voltage distribution network and the topological connection relationship of each first device node are first obtained, and then a first distribution network grid model corresponding to the low-voltage distribution network is constructed according to the topological connection relationship of each first device node. Next, a medium-voltage distribution network associated with the low-voltage distribution network is determined, and a second distribution network grid model corresponding to the medium-voltage distribution network and a plurality of second device nodes in the second distribution network grid model are obtained. Finally, according to the first attribute information of each first device node and the second attribute information of each second device node, the first distribution network grid model and the second distribution network grid model are spliced ​​to obtain a target distribution network grid model. In this way, in the process of constructing the distribution network framework model, by respectively obtaining relevant information of the low-voltage distribution network and the medium-voltage distribution network, and based on the topological connection relationship and attribute information of the device nodes, the distribution network framework model corresponding to the low-voltage distribution network and the distribution network framework model corresponding to the medium-voltage distribution network are spliced, so that the entire distribution network framework model can be constructed more comprehensively and accurately, which is conducive to improving the construction accuracy of the distribution network framework model; moreover, the entire process does not require human intervention, avoiding the subjective factors and prone to errors in the way of manual docking and fusion, resulting in the defect of low construction accuracy of the distribution network framework model, thereby improving the construction accuracy of the distribution network framework model.

[0081] In an exemplary embodiment, the first attribute information of each first device node includes first position information and first voltage information of each first device node, and the second attribute information of each second device node includes second position information and second voltage information of each second device node.

[0082] So, if Figure 2 As shown, the above step S104, according to the first attribute information of each first device node and the second attribute information of each second device node, splicing the first distribution network grid model and the second distribution network grid model to obtain the target distribution network grid model, specifically includes the following steps:

[0083] Step S201, based on the first position information and the first voltage information of each first device node, and the second position information and the second voltage information of each second device node, determine a candidate device node combination from each first device node and each second device node; each candidate device node combination includes a first device node and a second device node, and the first position information of the included first device node and the second position information of the included second device node meet a first preset condition, and the first voltage information of the included first device node and the second voltage information of the included second device node meet a second preset condition.

[0084] Step S202: Filter out a target device node combination that meets a third preset condition from the candidate device node combination.

[0085] Step S203, according to the target device node combination, the first distribution network framework model and the second distribution network framework model are spliced ​​to obtain the target distribution network framework model.

[0086] The first location information refers to the location information of the first device node, and the location information refers to the physical installation location of the device corresponding to the device node.

[0087] The first voltage information refers to the voltage information of the first device node, and the voltage information refers to the voltage level corresponding to the device node.

[0088] The second location information refers to the location information of the second device node.

[0089] The second voltage information refers to the voltage information of the second device node.

[0090] The candidate device node combination is used to represent an initial combination between a first device node and a second device node.

[0091] The first preset condition refers to a screening condition for judging the first location information and the second location information, such as a distance threshold. It should be noted that the first preset condition may be determined according to circumstances.

[0092] The second preset condition refers to a screening condition for judging the first voltage information and the second voltage information, such as a voltage difference threshold. It should be noted that the second preset condition may be determined according to the circumstances.

[0093] The third preset condition refers to a screening condition for judging the candidate device node combination. It should be noted that the third preset condition may be determined according to the circumstances.

[0094] The target device node combination refers to a candidate device node combination that meets the third preset condition.

[0095] Exemplarily, the server selects a first device node and a second device node that both meet the first preset condition and the second preset condition from each first device node and each second device node based on the first location information and the first voltage information of each first device node and the second location information and the second voltage information of each second device node, and combines the first device node and the second device node to obtain a candidate device node combination; then, the server selects a device node combination that meets the third preset condition from the candidate device node combination as the target device node combination; then, the server splices the first distribution network grid model and the second distribution network grid model based on the target device node combination to obtain the spliced ​​distribution network grid model as the target distribution network grid model.

[0096] For example, the server can obtain the target distribution network model through the following formula:

[0097] , formula (1)

[0098] in, represents the target distribution network model; represents the second distribution network grid model; represents the first distribution network grid model; Indicates the target device node combination; F indicates the merging function, which indicates that Will and Merge into process.

[0099] In this embodiment, by comprehensively considering the location information and voltage information of the device nodes and setting multiple preset conditions for screening and combination, it is possible to more accurately select a suitable device node combination and splice the distribution network grid model, which is beneficial to improving the construction accuracy of the target distribution network grid model.

[0100] In an exemplary embodiment, a target device node combination that meets a third preset condition is screened out from the candidate device node combinations, specifically including the following contents: based on the first position information and the first voltage information of the first device node in each candidate device node combination, and the second position information and the second voltage information of the second device node in each candidate device node combination, the target information corresponding to each candidate device node combination is determined; from each candidate device node combination, the candidate device node combination with the largest corresponding target information is screened out as the target device node combination.

[0101] The target information refers to the scoring information corresponding to each candidate device node combination, which is used to represent the matching degree between the first device node and the second device node in each candidate device node combination.

[0102] Exemplarily, the server obtains the correspondence between the first position information, the first voltage information, the second position information, the second voltage information and the target information as the target correspondence; then, the server determines the target information corresponding to each candidate device node combination based on the target correspondence, as well as the first position information and the first voltage information of the first device node in each candidate device node combination, and the second position information and the second voltage information of the second device node in each candidate device node combination; then, the server sorts the target information corresponding to each candidate device node combination to obtain the target information corresponding to each sorted candidate device node combination, and based on the target information corresponding to each sorted candidate device node combination, screens out the candidate device node combination with the largest corresponding target information from each candidate device node combination, and uses the candidate device node combination as the target device node combination.

[0103] In this embodiment, the target information is determined by comprehensively evaluating the location and voltage information of the device nodes, and the candidate device node combination with the largest target information is screened out as the target device node combination, which helps to build a distribution network model with better performance and higher efficiency, thereby reducing potential failure risks and enhancing the stability and reliability of power grid operation.

[0104] In an exemplary embodiment, target information corresponding to each candidate device node combination is determined based on the first position information and the first voltage information of the first device node in each candidate device node combination, and the second position information and the second voltage information of the second device node in each candidate device node combination, specifically including the following contents: distance information corresponding to each candidate device node combination is determined based on the first position information of the first device node in each candidate device node combination, and the second position information of the second device node in each candidate device node combination; voltage difference information corresponding to each candidate device node combination is determined based on the first voltage information of the first device node in each candidate device node combination, and the second voltage information of the second device node in each candidate device node combination; distance information and voltage difference information corresponding to each candidate device node combination are fused to obtain target information corresponding to each candidate device node combination.

[0105] The distance information is used to represent the difference between the first position information and the second position information, and is generally represented by d. It should be noted that the distance information is obtained based on CIS (Composite Interface Structure) analysis.

[0106] The voltage difference information is used to represent the difference between the first voltage information and the second voltage information, and is generally used express.

[0107] The fusion processing may refer to weighted summation processing.

[0108] Exemplarily, the server obtains the correspondence between the first location information, the second location information and the distance information as the first correspondence; then, the server determines the distance information corresponding to each candidate device node combination based on the first correspondence, the first location information of the first device node in each candidate device node combination, and the second location information of the second device node in each candidate device node combination; for example, the server performs difference processing on the first location information of the first device node in each candidate device node combination and the second location information of the second device node in each candidate device node combination based on the first correspondence to obtain the distance information corresponding to each candidate device node combination; then, the server obtains the correspondence between the first voltage information, the second voltage information and the voltage difference information to obtain the second correspondence; then, the server obtains the correspondence between the first voltage information, the second voltage information and the voltage difference information based on the second correspondence, the first voltage information of the first device node in each candidate device node combination, and the second location information of the second device node in each candidate device node combination. The server uses the second voltage information of the second device node to determine the voltage difference information corresponding to each candidate device node combination; for example, the server performs difference processing on the first voltage information of the first device node in each candidate device node combination and the second voltage information of the second device node in each candidate device node combination according to the second corresponding relationship, and obtains the voltage difference information corresponding to each candidate device node combination; then, obtains the first weight of the distance information corresponding to each candidate device node combination and the second weight of the voltage difference information corresponding to each candidate device node combination; then, the server performs fusion processing on the distance information and voltage difference information corresponding to each candidate device node combination according to the first weight and the second weight, and obtains the target information corresponding to each candidate device node combination; for example, the server performs weighted sum processing on the distance information and voltage difference information corresponding to each candidate device node combination according to the first weight and the second weight, and obtains the target information corresponding to each candidate device node combination.

[0109] For example, the server can obtain target information through the following formula:

[0110] , formula (2)

[0111] in, Represents the target information of the i-th pair of potential splicing points; and represents the weight coefficient of distance proximity and voltage level matching; d represents the distance between two potential splicing points; Indicates the difference in voltage levels between two potential splice points.

[0112] In this embodiment, the target information is obtained by fusing the distance information and the voltage difference information, which is equivalent to comprehensively considering multiple factors to determine more accurate target information, which is beneficial to improving the accuracy of determining the target information corresponding to each candidate device node combination, and provides a data basis for the subsequent screening of more suitable candidate device node combinations.

[0113] In an exemplary embodiment, a first distribution network grid model corresponding to the low-voltage distribution network is constructed according to the topological connection relationship of each first device node, which specifically includes the following contents: according to the topological connection relationship of each first device node, an initial distribution network grid model corresponding to the low-voltage distribution network is constructed; abnormal information in the initial distribution network grid model is identified; the abnormal information includes abnormal device nodes in the initial distribution network grid model, and the topological connection relationship of the abnormal device nodes; the abnormal information is deleted from the initial distribution network grid model to obtain the first distribution network grid model corresponding to the low-voltage distribution network.

[0114] Among them, the initial distribution network grid model is used to characterize the preliminary grid model sketch corresponding to the low-voltage distribution network.

[0115] The abnormal information is used to characterize the circulating device nodes and isolated device nodes in the initial distribution network model, as well as the topological connection relationships corresponding to the circulating device nodes and the topological connection relationships corresponding to the isolated device nodes.

[0116] Exemplarily, the server combines each first device node according to the topological connection relationship of each first device node to obtain the combined first device node as the initial distribution network grid model corresponding to the low-voltage distribution network; then, the server identifies the circulation device nodes and isolated device nodes in the initial distribution network grid model, as well as the topological connection relationship corresponding to the circulation device nodes and the topological connection relationship corresponding to the isolated device nodes, all of which are used as abnormal information in the initial distribution network grid model; then, the server deletes the abnormal information from the initial distribution network grid model to obtain the processed initial distribution network grid model as the first distribution network grid model corresponding to the low-voltage distribution network.

[0117] For example, based on the topological connection relationship, the server models the low-voltage distribution network as a graph as a preliminary grid model sketch, where the nodes in the graph represent device nodes, and the edges in the graph represent the connection relationship between device nodes; then, the server assigns an unvisited state to each device node in the grid model sketch, and selects the power supply of the grid as the initial starting point; then, the server marks the initial starting point as being accessed and performs a DFS (Depth-First Search) traversal; then, for each accessed device node, the server traverses all unvisited adjacent device nodes. And repeat this process recursively; after completing the traversal of the adjacent nodes of a device node, the server marks the device node as visited; if a device node marked as being visited or visited is encountered, it indicates that there is a loop, and the server records the loop and performs subsequent traversals; after completing all traversals, the server obtains the optimized grid model diagram, and processes the loops and isolated parts in the optimized grid model diagram to obtain a simplified grid model diagram; then, the server compares the simplified grid model diagram with the original data for integrity verification, and adjusts the grid model diagram based on the verification results to obtain a grid topology structure model.

[0118] In this embodiment, by identifying and deleting abnormal information in the distribution network grid model, factors that may cause model errors or inaccuracies can be removed, thereby significantly improving the accuracy of the first distribution network grid model corresponding to the low-voltage distribution network, providing a more reliable basis for subsequent model construction.

[0119] In an exemplary embodiment, multiple first device nodes associated with a low-voltage distribution network and a topological connection relationship between each first device node are obtained, specifically including the following contents: obtaining target file information associated with the low-voltage distribution network; parsing the target file information to obtain parsed information; and extracting multiple first device nodes associated with the low-voltage distribution network and a topological connection relationship between each first device node from the parsed information.

[0120] The target file information refers to the CIM file information.

[0121] Among them, parsing processing refers to the process of disassembling and analyzing the target file information.

[0122] The parsed information refers to the target file information after parsing.

[0123] Exemplarily, in response to a service interface call instruction, the server obtains CIM file information of the low-voltage distribution network from a database as target file information associated with the low-voltage distribution network; then, the server parses the target file information to obtain the parsed target file information as parsed information; then, the server extracts multiple device nodes associated with the low-voltage distribution network from the parsed information as first device nodes; then, the server extracts the topological connection relationship of each first device node from the parsed information.

[0124] In this embodiment, by quickly acquiring and parsing the target file information associated with the low-voltage distribution network, it is helpful to improve the efficiency of information acquisition; moreover, the entire process does not require human intervention, which greatly reduces the time and energy of manual data processing, thereby improving the efficiency of information acquisition.

[0125] In an exemplary embodiment, Figure 3 As shown, another distribution network model construction method is provided, and the method is applied to a server for example, including the following steps:

[0126] Step S301, obtaining target file information associated with the low-voltage distribution network; parsing the target file information to obtain parsed information; extracting from the parsed information a plurality of first device nodes associated with the low-voltage distribution network and a topological connection relationship between the first device nodes.

[0127] Step S302: constructing an initial distribution network framework model corresponding to the low-voltage distribution network according to the topological connection relationship of each first device node.

[0128] Step S303, identifying abnormal information in the initial distribution network framework model; the abnormal information includes abnormal device nodes in the initial distribution network framework model, and the topological connection relationship of the abnormal device nodes.

[0129] Step S304, deleting abnormal information from the initial distribution network framework model to obtain a first distribution network framework model corresponding to the low-voltage distribution network.

[0130] Step S305, determining a medium voltage distribution network associated with the low voltage distribution network, and obtaining a second distribution network grid model corresponding to the medium voltage distribution network, and a plurality of second device nodes in the second distribution network grid model.

[0131] Step S306, determining a candidate device node combination from each first device node and each second device node based on the first position information and the first voltage information of each first device node, and the second position information and the second voltage information of each second device node; each candidate device node combination includes a first device node and a second device node, and the first position information of the included first device node and the second position information of the included second device node meet a first preset condition, and the first voltage information of the included first device node and the second voltage information of the included second device node meet a second preset condition.

[0132] Step S307 , determining distance information corresponding to each candidate device node combination according to the first position information of the first device node in each candidate device node combination and the second position information of the second device node in each candidate device node combination.

[0133] Step S308 , determining voltage difference information corresponding to each candidate device node combination according to the first voltage information of the first device node in each candidate device node combination and the second voltage information of the second device node in each candidate device node combination.

[0134] Step S309 , fusing the distance information and the voltage difference information corresponding to each candidate device node combination to obtain the target information corresponding to each candidate device node combination.

[0135] Step S310 , selecting a candidate device node combination with the largest corresponding target information from each candidate device node combination as a target device node combination.

[0136] Step S311, according to the target device node combination, the first distribution network framework model and the second distribution network framework model are spliced ​​to obtain the target distribution network framework model.

[0137] In the above-mentioned distribution network framework model construction method, in the process of distribution network framework model construction, by respectively obtaining relevant information of the low-voltage distribution network and the medium-voltage distribution network, and based on the topological connection relationship and attribute information of the device nodes, the distribution network framework model corresponding to the low-voltage distribution network and the distribution network framework model corresponding to the medium-voltage distribution network are spliced, so that the entire distribution network framework model can be constructed more comprehensively and accurately, which is conducive to improving the construction accuracy of the distribution network framework model; moreover, the entire process does not require human intervention, avoiding the subjective factors and prone to errors in the way of manual docking and fusion, resulting in the defect of low construction accuracy of the distribution network framework model, thereby improving the construction accuracy of the distribution network framework model.

[0138] In an exemplary embodiment, in order to more clearly illustrate the distribution network grid model construction method provided in the embodiment of the present application, the distribution network grid model construction method is specifically described below with a specific embodiment. Figure 4 As shown, the present application also provides a method for generating an intelligent grid of a low-voltage distribution network based on the CIM standard. In the process of constructing a distribution network grid model, a plurality of first device nodes associated with the low-voltage distribution network and the topological connection relationship of each first device node are first obtained. Then, according to the topological connection relationship of each first device node, a first distribution network grid model corresponding to the low-voltage distribution network is constructed. Next, a medium-voltage distribution network associated with the low-voltage distribution network is determined, and a second distribution network grid model corresponding to the medium-voltage distribution network and a plurality of second device nodes in the second distribution network grid model are obtained. Finally, according to the first attribute information of each first device node and the second attribute information of each second device node, the first distribution network grid model and the second distribution network grid model are spliced ​​to obtain a target distribution network grid model. Specifically, it includes the following contents:

[0139] Step 1: Based on the service interface call, obtain the CIM file information of the low-voltage distribution network;

[0140] Step 2: Parse the acquired CIM file information to extract basic equipment ledger attributes and topological connection relationships;

[0141] Equipment ledger attributes include equipment ID (identity), name, type, installation location, rated parameters, etc.; the topological connection relationship defines the connection method between devices.

[0142] Step 3: Automatically construct a grid topology model of the low-voltage distribution network based on the topological connection relationship;

[0143] Step 3 includes the following steps:

[0144] Step 3.1: Model the low-voltage distribution network as a graph based on the topological connection relationship to obtain a preliminary grid model sketch, where the nodes in the graph represent devices and connection points, and the edges represent the connection relationship between devices;

[0145] Step 3.2: Based on DFS, traverse the grid model sketch, build the actual topological structure, and obtain the optimized grid model diagram;

[0146] Step 3.2 includes the following steps:

[0147] Step 3.21: Assign an unvisited state to each node in the grid model sketch and select the power source of the grid as the initial starting point;

[0148] Step 3.22: Start from the starting node and mark it as being visited to perform DFS traversal; for each visited node, traverse all unvisited adjacent nodes and repeat this process recursively; after completing the traversal of the adjacent nodes of a node, mark the node as visited; if a node marked as being visited or visited is encountered, it indicates that there is a cycle, record the cycle and perform subsequent traversal.

[0149] Step 3.3: Process the loops and isolated parts in the optimized grid model diagram to obtain a simplified grid model diagram;

[0150] Step 3.4: Compare the simplified grid model diagram with the original data to verify the integrity, and adjust the grid model diagram based on the verification results to obtain the grid topology structure model.

[0151] Step 4: Obtain the existing medium-voltage distribution network grid model construction results, and splice the medium-voltage distribution network grid model and the grid topology structure model of the low-voltage distribution network based on the intelligent splicing algorithm to obtain the medium- and low-voltage distribution network grid model;

[0152] Step 4 includes the following steps:

[0153] Step 4.1: Obtain the existing medium-voltage distribution network grid model construction results, and convert the medium-voltage distribution network grid model into a format and data structure that is the same as the grid topology model of the low-voltage distribution network;

[0154] Step 4.2: Analyze the grid model of the medium voltage distribution network and the grid topology model of the low voltage distribution network, and identify the equipment or connection points that can be used as splicing points;

[0155] Step 4.2 includes the following steps:

[0156] Step 4.21: extracting equipment location, voltage level and connection information data from the medium voltage distribution network grid model and the low voltage distribution network grid topology structure model;

[0157] Step 4.22: Based on the extracted data information, identify devices that are physically close and have compatible voltage levels as potential splicing points;

[0158] Step 4.23: Assign a score to each pair of potential splicing points based on the proximity of the location and the matching of the voltage levels;

[0159] The specific steps of step 4.23 are as follows:

[0160] Based on CIS analysis, the distance between two potential splicing points is obtained; and the voltage level difference between the two potential splicing points is calculated;

[0161] Each pair of splice points is scored based on the distance between the two potential splice points and the difference in voltage levels between the two potential splice points:

[0162] ;

[0163] Where: and represents the weight coefficient of distance proximity and voltage level matching; d represents the distance between two potential splicing points; Indicates the difference in voltage levels between two potential splicing points; Represents the score of the i-th pair of potential splicing points.

[0164] Step 4.24: Sort the scores of all splicing point pairs, and select the splicing point pair with the highest score as the splicing point of the medium voltage distribution network grid model and the grid topology structure model of the low voltage distribution network.

[0165] Step 4.3: Using an intelligent splicing algorithm, based on the information of the splicing point pairs, the medium voltage distribution network grid model and the low voltage distribution network grid topology structure model are merged into a coherent medium and low voltage distribution network grid model;

[0166] The intelligent splicing algorithm in step 4.3 is as follows:

[0167] ;

[0168] Where: It represents the grid model of the combined medium and low voltage distribution network; Represents a medium voltage distribution network grid model; A grid topology model representing a low voltage distribution network; represents the selected splicing point pair; F represents the merging function, which represents the Will and Merge into process.

[0169] The process of merging through the merge function is as follows:

[0170] Identify specific locations and connection information of the splicing point pairs in the medium voltage distribution network grid model and the low voltage distribution network grid topology structure model;

[0171] In the grid model of the medium voltage distribution network and the grid topology structure model of the low voltage distribution network, the connection points or devices bounded by the identified splicing point pairs are logically merged;

[0172] After the merger, the connection relationship in the medium and low voltage distribution network grid model is adjusted to ensure the consistency of electrical parameters and the correctness of physical connections.

[0173] An exemplary technical solution for logical merging is as follows:

[0174] Fusion of data structures:

[0175] The basic elements such as nodes and lines in the medium-voltage distribution network grid model and the low-voltage distribution network grid topology structure model are merged into a unified data structure to form a merged medium- and low-voltage distribution network grid model.

[0176] During the merging process, the uniqueness of elements such as nodes and lines is maintained to avoid duplication.

[0177] Matching of topological relationships:

[0178] Based on the selected highest scoring splicing points, their specific locations and connection information in the medium voltage network and low voltage network are identified.

[0179] Along the splicing points, trace the topological relationship of the medium voltage network and the low voltage network respectively, and find the nodes and lines that need to be logically merged.

[0180] Logical merge operation:

[0181] For the identified nodes and lines that need to be merged, perform the following operations:

[0182] Merge nodes: Merge node properties, such as node type, voltage level, etc.

[0183] Merge lines: Merge line properties, such as line length, resistance impedance, etc.

[0184] Establishing a new topological connection: At the splicing point, a new topological connection between the medium voltage network and the low voltage network is established.

[0185] Parameter adjustment and verification:

[0186] After the merger, the electrical parameters of the entire medium and low voltage distribution network grid model are adjusted to ensure the consistency of parameters such as voltage, current, and power of each node and line.

[0187] Check the physical connectivity of the medium and low voltage distribution network grid model to ensure the correctness of the entire network structure.

[0188] Perform power flow calculations to verify the reliability and stability of the merged network.

[0189] Step 4.4: Check the integrity and accuracy of the spliced ​​medium and low voltage distribution network grid model. If the integrity and accuracy check passes, the generated medium and low voltage distribution network grid model is obtained. If the integrity and accuracy check fails, adjustments are made based on the reasons for the verification failure.

[0190] Step 5: Visualize the medium and low voltage distribution network model based on visualization tools.

[0191] The present invention obtains CIM file information by calling a service interface, and extracts equipment ledger attributes and topological relationships, so that a complete grid topology model of a low-voltage distribution network can be quickly constructed, thereby improving the efficiency of low-voltage network modeling; and based on an intelligent algorithm, automatic fusion of medium-voltage and low-voltage networks is achieved. Compared with the traditional method that relies on manual docking, the present invention can automatically identify the differences between medium-voltage and low-voltage networks, and intelligently merge them according to the optimal splicing point pairs, thereby greatly improving the accuracy of medium- and low-voltage network integration and avoiding errors that are prone to occur in manual operations; finally, by visually displaying the grid model of the medium- and low-voltage distribution network, it is beneficial for power workers and operation and maintenance personnel to better understand and master the topological structure and equipment information of the entire distribution network, and provide strong support for distribution network planning, operation and maintenance, and the like.

[0192] In the above-mentioned embodiment, during the process of constructing the distribution network framework model, by respectively obtaining relevant information of the low-voltage distribution network and the medium-voltage distribution network, and based on the topological connection relationship and attribute information of the device nodes, the distribution network framework model corresponding to the low-voltage distribution network and the distribution network framework model corresponding to the medium-voltage distribution network are spliced, so that the entire distribution network framework model can be constructed more comprehensively and accurately, which is conducive to improving the construction accuracy of the distribution network framework model; moreover, the entire process does not require human intervention, avoiding the subjective factors and prone to errors in the form of manual docking and fusion, resulting in the defect of low construction accuracy of the distribution network framework model, thereby improving the construction accuracy of the distribution network framework model.

[0193] 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.

[0194] Based on the same inventive concept, the embodiment of the present application also provides a distribution network grid model construction device for implementing the distribution network grid model construction method involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method, so the specific limitations in one or more distribution network grid model construction device embodiments provided below can refer to the limitations of the distribution network grid model construction method above, and will not be repeated here.

[0195] In an exemplary embodiment, Figure 5 As shown, a distribution network grid model construction device is provided, comprising: a first information acquisition module 501, an initial model construction module 502, a second information acquisition module 503 and a target model construction module 504, wherein:

[0196] The first information acquisition module 501 is used to acquire a plurality of first device nodes associated with the low-voltage distribution network, and a topological connection relationship between the first device nodes.

[0197] The initial model building module 502 is used to build a first distribution network framework model corresponding to the low-voltage distribution network according to the topological connection relationship of each first device node.

[0198] The second information acquisition module 503 is used to determine the medium voltage distribution network associated with the low voltage distribution network, and obtain a second distribution network grid model corresponding to the medium voltage distribution network, and a plurality of second device nodes in the second distribution network grid model.

[0199] The target model building module 504 is used to splice the first distribution network framework model and the second distribution network framework model according to the first attribute information of each first device node and the second attribute information of each second device node to obtain a target distribution network framework model.

[0200] In an exemplary embodiment, the target model construction module 504 is also used to determine a candidate device node combination from each first device node and each second device node based on the first position information and the first voltage information of each first device node, and the second position information and the second voltage information of each second device node; each candidate device node combination includes a first device node and a second device node, and the first position information of the included first device node and the second position information of the included second device node meet the first preset condition, and the first voltage information of the included first device node and the second voltage information of the included second device node meet the second preset condition; from the candidate device node combination, a target device node combination that meets the third preset condition is screened out; according to the target device node combination, the first distribution network grid model and the second distribution network grid model are spliced ​​to obtain the target distribution network grid model.

[0201] In an exemplary embodiment, the target model construction module 504 is also used to determine the target information corresponding to each candidate device node combination based on the first position information and the first voltage information of the first device node in each candidate device node combination, and the second position information and the second voltage information of the second device node in each candidate device node combination; from each candidate device node combination, select the candidate device node combination with the largest corresponding target information as the target device node combination.

[0202] In an exemplary embodiment, the target model construction module 504 is also used to determine the distance information corresponding to each candidate device node combination based on the first position information of the first device node in each candidate device node combination and the second position information of the second device node in each candidate device node combination; determine the voltage difference information corresponding to each candidate device node combination based on the first voltage information of the first device node in each candidate device node combination and the second voltage information of the second device node in each candidate device node combination; and fuse the distance information and voltage difference information corresponding to each candidate device node combination to obtain the target information corresponding to each candidate device node combination.

[0203] In an exemplary embodiment, the initial model construction module 502 is also used to construct an initial distribution network grid model corresponding to the low-voltage distribution network based on the topological connection relationship of each first device node; identify abnormal information in the initial distribution network grid model; the abnormal information includes abnormal device nodes in the initial distribution network grid model, and the topological connection relationship of the abnormal device nodes; delete the abnormal information from the initial distribution network grid model to obtain the first distribution network grid model corresponding to the low-voltage distribution network.

[0204] In an exemplary embodiment, the first information acquisition module 501 is also used to obtain target file information associated with the low-voltage distribution network; parse the target file information to obtain parsed information; and extract multiple first device nodes associated with the low-voltage distribution network and the topological connection relationship between each first device node from the parsed information.

[0205] Each module in the above-mentioned distribution network grid model construction device can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0206] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 6As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, 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. Among them, 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 device nodes and topological connection relationships. 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 constructing a distribution network grid model is implemented.

[0207] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the 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 those shown in the figure, or combine certain components, or have a different arrangement of components.

[0208] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0209] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0210] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0211] 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.

[0212] 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.

[0213] 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 construed as limiting the scope of the present application. It should be noted that, for a person of ordinary skill in the art, several modifications 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 constructing a distribution network model, characterized in that: The method comprises: Acquire a plurality of first device nodes associated with the low-voltage distribution network, and a topological connection relationship between the first device nodes; Constructing a first distribution network grid model corresponding to the low-voltage distribution network according to the topological connection relationship of each of the first device nodes; Determine a medium voltage distribution network associated with the low voltage distribution network, and obtain a second distribution network grid model corresponding to the medium voltage distribution network, and a plurality of second device nodes in the second distribution network grid model; According to the first attribute information of each of the first device nodes and the second attribute information of each of the second device nodes, the first distribution network framework model and the second distribution network framework model are spliced ​​to obtain a target distribution network framework model.

2. The method according to claim 1, characterized in that The first attribute information of each of the first device nodes includes first position information and first voltage information of each of the first device nodes, and the second attribute information of each of the second device nodes includes second position information and second voltage information of each of the second device nodes; The first distribution network grid model and the second distribution network grid model are spliced ​​according to the first attribute information of each of the first device nodes and the second attribute information of each of the second device nodes to obtain a target distribution network grid model, including: According to the first position information and the first voltage information of each of the first device nodes, and the second position information and the second voltage information of each of the second device nodes, a candidate device node combination is determined from each of the first device nodes and each of the second device nodes; each candidate device node combination includes a first device node and a second device node, and the first position information of the included first device node and the second position information of the included second device node meet a first preset condition, and the first voltage information of the included first device node and the second voltage information of the included second device node meet a second preset condition; Filtering out a target device node combination that meets a third preset condition from the candidate device node combination; According to the target device node combination, the first distribution network framework model and the second distribution network framework model are spliced ​​to obtain a target distribution network framework model.

3. The method according to claim 2, characterized in that The step of selecting a target device node combination that meets a third preset condition from the candidate device node combination includes: Determine the target information corresponding to each of the candidate device node combinations according to the first position information and the first voltage information of the first device node in each of the candidate device node combinations, and the second position information and the second voltage information of the second device node in each of the candidate device node combinations; From each of the candidate device node combinations, a candidate device node combination with the largest corresponding target information is screened out as the target device node combination.

4. The method according to claim 3, characterized in that: Determining target information corresponding to each candidate device node combination according to first position information and first voltage information of the first device node in each candidate device node combination, and second position information and second voltage information of the second device node in each candidate device node combination, includes: Determine the distance information corresponding to each of the candidate device node combinations according to the first position information of the first device node in each of the candidate device node combinations and the second position information of the second device node in each of the candidate device node combinations; Determine voltage difference information corresponding to each candidate device node combination according to first voltage information of a first device node in each candidate device node combination and second voltage information of a second device node in each candidate device node combination; The distance information and the voltage difference information corresponding to each of the candidate device node combinations are fused to obtain the target information corresponding to each of the candidate device node combinations.

5. The method according to claim 1, characterized in that The step of constructing a first distribution network grid model corresponding to the low-voltage distribution network according to the topological connection relationship of each of the first device nodes includes: Constructing an initial distribution network grid model corresponding to the low-voltage distribution network according to the topological connection relationship of each of the first device nodes; Identifying abnormal information in the initial distribution network grid model; the abnormal information includes abnormal device nodes in the initial distribution network grid model, and topological connection relationships of the abnormal device nodes; The abnormal information is deleted from the initial distribution network framework model to obtain a first distribution network framework model corresponding to the low-voltage distribution network.

6. The method according to any one of claims 1 to 5, characterized in that The acquiring of a plurality of first device nodes associated with the low-voltage distribution network and a topological connection relationship of each of the first device nodes includes: Obtaining target file information associated with the low voltage distribution network; Parsing the target file information to obtain parsed information; A plurality of first device nodes associated with the low-voltage distribution network and a topological connection relationship between the first device nodes are extracted from the parsed information.

7. A distribution network grid model construction device, characterized in that: The device comprises: A first information acquisition module, used to acquire a plurality of first device nodes associated with the low-voltage distribution network, and a topological connection relationship between each of the first device nodes; An initial model building module, used to build a first distribution network grid model corresponding to the low-voltage distribution network according to the topological connection relationship of each of the first device nodes; A second information acquisition module, used to determine a medium voltage distribution network associated with the low voltage distribution network, and obtain a second distribution network grid model corresponding to the medium voltage distribution network, and a plurality of second device nodes in the second distribution network grid model; The target model construction module is used to splice the first distribution network framework model and the second distribution network framework model according to the first attribute information of each of the first device nodes and the second attribute information of each of the second device nodes to obtain a target distribution network framework model.

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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