Power distribution network topology identification self-iteration updating method

Abstract

The invention relates to the technical field of power grid topology data updating, in particular to a power distribution network topology identification self-iteration updating method, which comprises the following steps of: acquiring a multi-layer topology structure of a power distribution network, adopting a random walk algorithm and considering the influence of edges and weight information of the edges on walk; performing multi-step migration on nodes in the same-layer power distribution network topological structure in the multi-layer topological structure of the power distribution network, calculating access probabilities among different nodes in a simulation process, and marking node pairs with the access probabilities exceeding a preset access probability threshold as potential associated nodes in the same layer; and analyzing relevance among nodes in the cross-layer power distribution network topological structure according to the same-layer potential relevance nodes, and generating same-layer and cross-layer potential relevance node information. According to the method, the problem that the incidence relation between the nodes in a complex network is difficult to accurately capture is solved by extracting the nodes, the paths and the weight information of the nodes in the multi-layer topological structure of the power distribution network and determining the potential correlation between the nodes in combination with a random walk algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid topology data updating, and in particular to a distribution network topology identification self-iterative updating method. Background Art

[0002] The self-iterative update method for distribution network topology identification can combine real-time data and historical data, dynamically monitor the operating status of the distribution network, and continuously improve the accuracy and robustness of topology identification through iterative optimization process. In addition, it can also automatically adjust the update strategy in the case of abnormal device data or system disturbance to ensure the real-time and reliability of topology data, thereby supporting intelligent scheduling, fault diagnosis and optimized operation of the distribution network.

[0003] Existing technologies have difficulty in fully considering multi-dimensional factors such as path weight, transmission capacity, and load bearing ratio in the node association analysis of multi-layer topology structures, which is not conducive to the accurate identification of potential associations between nodes. For example, in a complex distribution network, low-weight connections may be overly neglected, thus affecting the accuracy of key node analysis. Missing parameters in distribution network data collection are usually simply processed by interpolation methods, resulting in deficiencies in the network topology structure in optimizing data integrity. In a dynamic environment, it is difficult for node weight adjustment to reflect changes in node status signals in real time, which may cause the operating status assessment to be inconsistent with the actual network dynamics. In addition, for the identification and optimization of key nodes, there may also be improper restoration priorities after important node connections are interrupted, resulting in resource allocation and network stability being affected, limiting the optimization capabilities in complex distribution network topology operation scenarios. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a distribution network topology identification self-iterative update method.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: a distribution network topology identification self-iterative update method, comprising the following steps:

[0006] S1: Obtain a multi-layer topological structure of a distribution network, which includes nodes, edges connecting nodes, and edge weight information. A random walk algorithm is used and the influence of edges and edge weight information on the walk is considered. Multi-step walks are performed on nodes in the same-layer distribution network topological structure in the multi-layer topological structure of the distribution network. The access probability between different nodes in the simulation process is calculated. Node pairs whose access probability exceeds a preset access probability threshold are marked as potential associated nodes in the same layer. The association between nodes in the cross-layer distribution network topological structure is analyzed based on the potential associated nodes in the same layer, and the potential associated node information in the same layer and across layers is generated.

[0007] S2: Collect node operating parameters of each layer in the multi-layer topology of the distribution network, the node operating parameters include load current, node voltage and status signal, identify missing or abnormal data of some node operating parameters during the collection process, obtain the node operating parameters with missing marks, integrate the historical trend data of the node operating parameters with the potential associated node information of the same layer and across layers, use Gaussian process regression to predict the node operating parameters with missing marks, obtain node operating parameter prediction data, complete the node operating parameter prediction data to the corresponding node, and generate multi-layer topology data after parameter completion;

[0008] S3: Based on the multi-layer topology data after parameter completion, the transition probability of the state signal in the node operation parameter is calculated through a continuous-time Markov process to obtain the node state change analysis result, and according to the node state change analysis result, the weight information of the node in the multi-layer topology structure of the distribution network is dynamically adjusted to generate the multi-layer topology data after weight adjustment;

[0009] S4: Based on the multi-layer topology data after the weight adjustment, identify the key nodes and non-key nodes in the distribution network topology structure, refer to the potential associated node information in the same layer and across layers, maintain and optimize the connection relationship between the key nodes and potential associated nodes and non-key nodes, and generate a distribution network topology identification self-iterative update result.

[0010] As a further solution of the present invention, the step of obtaining the access probability between different nodes in the calculation simulation process is specifically as follows:

[0011] S111: Based on the multi-layer topological structure of the distribution network, the geographic coordinates and topological connection relationships of the nodes in the multi-layer topological structure of the distribution network are derived through a geographic information system, the edges corresponding to the nodes and the weight information of the edges are obtained, and a network data set of the distribution network is generated;

[0012] S112: Based on the nodes of the same-layer distribution network topology structure in the network data set of the distribution network, set the starting node and the number of walking steps of the random walk, select the walking path according to the edge and the weight information of the edge to complete the simulation, record the access order and the number of visits between different nodes in each walking step, and generate the node access data of the same-layer distribution network topology structure;

[0013] S113: Node access data based on the topology of the same-layer distribution network, using the formula:

[0014]

[0015] In the process of calculating the random walk, the access probability P between node i and node j in the node access data of the same layer distribution network topology structure is ij , generate access probability data between nodes;

[0016] Among them, f ij is the cumulative number of visits from node i to node j in the node access data of the same-layer distribution network topology during the random walk. is the total number of times node i visits all other nodes j' in the node access data of the same-layer distribution network topology during the random walk, and n is the total number of nodes in the current same-layer distribution network topology.

[0017] As a further solution of the present invention, the steps of acquiring the potential associated node information of the same layer and across layers are specifically as follows:

[0018] S121: Compare the access probability data between the nodes with a preset access probability threshold, screen node pairs whose access probability exceeds the access probability threshold, mark these node pairs as potential associated nodes at the same layer, and generate analysis results of potential associated nodes at the same layer;

[0019] S122: Collect node information in the cross-layer distribution network topology structure, obtain access probability data between nodes in the cross-layer distribution network topology structure, refer to the preset cross-layer access probability threshold, mark the cross-layer potential associated nodes, integrate the same-layer potential associated node analysis results, and generate the same-layer and cross-layer potential associated node information.

[0020] As a further solution of the present invention, the step of obtaining the operating parameters of the node with missing mark is specifically:

[0021] S211: collecting node operating parameters of each layer in the multi-layer topology of the distribution network, identifying missing or abnormal data caused by collection delay or sensor failure during the collection process, marking the missing or abnormal data, and generating node operating parameters marked with missing data;

[0022] S212: Based on the node operation parameters marked as missing, mark the missing period according to the missing type and time range of the missing or abnormal data, classify and sort, and generate a missing parameter classification result.

[0023] As a further solution of the present invention, the step of obtaining the multi-layer topological data after parameter completion is specifically as follows:

[0024] S221: Collect historical trend data of node operation parameters, combine potential associated node information in the same layer and across layers, analyze and integrate abnormal points in node operation parameters, and generate node historical operation trends and associated node data;

[0025] S222: referring to the historical operation trend of the node and the associated node data, using the Gaussian process regression method to predict the operation parameters of the nodes with missing markers, and generating node operation parameter prediction data by fitting the node operation parameters and calculating the values ​​of the missing nodes;

[0026] S223: Based on the missing parameter classification results, the node operation parameter prediction data is completed to the corresponding nodes and time positions in the marked missing node operation parameters, the node operation parameters are updated and verified, and the completed node operation parameters are integrated into the multi-layer topology structure of the distribution network to generate multi-layer topology data after parameter completion.

[0027] As a further solution of the present invention, the step of obtaining the node state change analysis result is specifically:

[0028] S311: Based on the multi-layer topology data after parameter completion, the formula is used:

[0029]

[0030] Calculate the state signal transition probability P of node x to node y at time t in the multi-layer topology data after parameter completion xy (t), generate the transition probability analysis results of the state signal between nodes;

[0031] Where N is the total number of nodes in the multi-layer topology data after parameter completion, P xk (t) is the specific state signal transition probability from node x to intermediate node k in the multi-layer topology data after parameter completion, P ky (t+Δt) is the probability of a specific state signal transfer from the intermediate node k to the node y at time t+Δt in the multi-layer topology data after parameter completion, and Δt is the time delay of the state signal transmission between nodes in the multi-layer topology data after parameter completion;

[0032] S312: Based on the transition probability analysis result of the inter-node status signal, the change trend of the node operation status is analyzed in combination with the time series to generate a node status change analysis result.

[0033] As a further solution of the present invention, the step of acquiring the weight-adjusted multi-layer topological data is specifically as follows:

[0034] S321: Collecting initial weights of nodes, adjusting initial weights of nodes based on changes in node transfer probabilities in the node state change analysis results, and generating a weight list of nodes after weight adjustment;

[0035] S322: Based on the weight-adjusted node weight list, the weight-adjusted node weight list is integrated into the multi-layer topology structure of the distribution network, and the weight-adjusted multi-layer topology data is generated by verifying whether the node weight change is in line with the current trend of the node status.

[0036] As a further solution of the present invention, the step of obtaining the self-iterative update result of the distribution network topology identification is specifically as follows:

[0037] S411: Based on the weight-adjusted multi-layer topology data, identify key nodes and non-key nodes according to node weights, combine the potential associated node information of the same layer and across layers, maintain the key nodes and potential associated nodes, and generate optimization results of the key nodes and potential associated nodes;

[0038] S412: Based on the optimization results of the key nodes and potential associated nodes, optimize the non-key nodes, delete redundant paths and re-plan the connection layout with the key nodes, reload the optimized distribution network multi-layer topology structure data, and generate the distribution network topology identification self-iteration update result.

[0039] Compared with the prior art, the advantages and positive effects of the present invention are:

[0040] In the present invention, by extracting the nodes, paths and their weight information in the multi-layer topological structure of the distribution network, and combining the random walk algorithm to clarify the potential correlation between nodes, the problem of the difficulty in accurately capturing the correlation relationship between nodes in a complex network is solved, and accurate data support is provided for further topological optimization. Combining historical trend data with potential correlation information, the missing parameters in data acquisition are predicted and supplemented to ensure the integrity of the topological structure data and solve the parameter fault problem caused by data acquisition delay or equipment abnormality. The node weight is dynamically adjusted using a continuous-time Markov process to achieve accurate quantification and optimization of node state changes, so that the topological data can reflect the dynamic changes of network operation in real time. Combining key node identification with potential correlation node optimization, the stability of the connection between key nodes and important nodes is improved, and redundancy is reduced by optimizing the connection of non-key nodes, thereby improving the overall efficiency and stability of the network. The dynamic adaptability of topological identification, as well as the operating efficiency and fault response capabilities in a complex network environment are significantly improved, and the effect of dynamic update and iterative improvement of the distribution network topology is optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0042] Figure 2 A flow chart showing the access probabilities between different nodes during the calculation simulation process of the present invention;

[0043] Figure 3 A flowchart of the present invention for obtaining information of potential associated nodes in the same layer and across layers;

[0044] Figure 4 A flow chart of the node operation parameters for marking missing nodes of the present invention;

[0045] Figure 5 A flowchart of the present invention for obtaining multi-layer topology data after parameter completion;

[0046] Figure 6 A flowchart of obtaining the node state change analysis result of the present invention;

[0047] Figure 7 A flowchart of obtaining multi-layer topological data after weight adjustment for the present invention;

[0048] Figure 8 The present invention is a flow chart for obtaining the self-iterative update result of distribution network topology identification. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention 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 invention and are not intended to limit the present invention.

[0050] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions 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 orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0051] See also Figure 1 The present invention provides a technical solution: a distribution network topology identification self-iterative update method, comprising the following steps:

[0052] S1: Based on the multi-layer topological structure of the distribution network, extract the nodes, edges and edge weight information of each layer in the multi-layer topological structure of the distribution network, where the node is a specific substation or load equipment in the distribution network, the edge is a path between nodes, such as a power transmission line, and the edge weight information refers to the transmission capacity, fault state or load bearing ratio of the edge. A random walk algorithm is used and the influence of the edge and the edge weight information on the walk is considered. Multi-step walks are performed on the nodes in the same-layer distribution network topological structure in the multi-layer topological structure of the distribution network. For example, during the walk, the edge weight information is used to improve the edge walk priority, ensure that important connections are accessed frequently during the simulation process, simulate the propagation path of information in the network, and calculate the access probability between different nodes during the simulation process. With reference to a preset access probability threshold, the node pairs whose access probability exceeds the access probability threshold are marked as potential associated nodes of the same layer. According to the potential associated nodes of the same layer, the correlation between nodes in the cross-layer distribution network topological structure is analyzed to generate the potential associated node information of the same layer and cross-layer.

[0053] S2: Collect node operating parameters of each layer in the multi-layer topology of the distribution network. The node operating parameters include load current, node voltage and status signal. During the collection process, identify the missing node operating parameters due to data acquisition delay or sensor failure, obtain the node operating parameters with missing tags, integrate the historical trend data of the node operating parameters with the potential associated node information in the same layer and across layers, where the historical trend data of the node operating parameters include the fluctuation pattern of the load current, the time series change of the node voltage and the abnormal record of the status signal, use Gaussian process regression to predict the node operating parameters with missing tags, obtain the node operating parameter prediction data, complete the node operating parameter prediction data to the corresponding node, and generate the multi-layer topology data with parameter completion;

[0054] S3: Based on the multi-layer topological data after parameter completion, the transition probability of the state signal in the node operation parameters is calculated through the continuous-time Markov process, and the evolution process of the node operation parameters in the multi-layer topological structure of the distribution network is analyzed to obtain the node state change analysis results. According to the node state change analysis results, the weight information of the nodes in the multi-layer topological structure of the distribution network is dynamically adjusted. If the transition probability of the state signal is greater, the weight of the node is higher, and the smaller the transition probability of the state signal is, the lower the weight of the node is, and the multi-layer topological data after weight adjustment is generated;

[0055] S4: Based on the multi-layer topology data after weight adjustment, determine the weight of the node to identify the key nodes and non-key nodes in the distribution network topology structure, refer to the potential associated node information in the same layer and across layers, maintain the connection relationship between the key nodes and the potential associated nodes, including giving priority to restoring the key node connection interruption caused by the fault, ensuring that the shortest path between the key node and other important nodes remains unobstructed, maintaining the stability of the network, and optimizing the connection of non-key nodes to reduce redundancy and improve the overall efficiency of the network. Through gradual adjustment and improvement, the self-iterative update result of the distribution network topology identification is generated;

[0056] The information of potential associated nodes in the same layer and across layers includes potential associated node pairs in the same layer, potential associated node pairs across layers and association strength indicators. The multi-layer topology data after parameter completion includes completed node load current data, completed node voltage data and completed status signal data. The multi-layer topology data after weight adjustment includes the probability distribution results of node state changes, dynamically adjusted node weight values ​​and node classification information. The self-iterative update results of distribution network topology identification include the identification results of key nodes, priority connection data of key nodes and optimized connection data of non-key nodes.

[0057] See also Figure 2 ,The steps to obtain the access probabilities between different nodes in the calculation simulation process are as follows:

[0058] S111: Based on the multi-layer topological structure of the distribution network, the geographic coordinates and topological connection relationships of the nodes in the multi-layer topological structure of the distribution network are derived through a geographic information system, the edges corresponding to the nodes and the weight information of the edges are obtained, and a network data set of the distribution network is generated;

[0059] Extracting the node, edge and edge weight information of each layer in the multi-layer topological structure of the distribution network includes the following specific operations: Extraction of node information: Export the geographic coordinates and topological connection relationship of each substation, switch station and load equipment through the GIS system, and extract the equipment name, voltage level, operation number and equipment type of each node in combination with the PMS system. For example, a specific substation node "Substation A" contains basic information such as geographic coordinates (x=123.45, y=67.89), voltage level (35kV), equipment type (main transformer), etc. Extraction of edge information: Use the SCADA system to obtain line connection information in real time, such as marking the 10kV transmission line between "Substation A" and "Load Equipment B" as an edge, and record its specific parameters, including line name, length (e.g. 5.2 kilometers), conductor material (e.g. aluminum alloy conductor), etc., to form topological edge connection data. Extraction of weight information: Transmission capacity: Based on the rated current carrying capacity of the line, the current operating status is monitored through SCADA. For example, the rated current carrying capacity of a line is 200A, and the actual load measured by the SCADA system is 150A. Therefore, the transmission capacity weight is initially set to 150 / 200=0.75; Fault status: Combined with the alarm records of fault monitoring equipment and protection devices, the number of faults of a line in the past year (for example, 3 times) is counted and a weight value is assigned, for example, the weight value of a low fault rate line is 1, and the weight value of a high fault rate line is 0.5; Load carrying ratio: Extract the ratio of the maximum load value of the line to the current rated current carrying capacity from the historical load curve. For example, the load curve shows that the maximum load of a line is 180A, and the load ratio weight is 180 / 200=0.9. Finally, through the above operations, a complete three-layer topology data structure example can be formed, including the "substation A" node, the "load device B" node, and the line edge connecting the two, with additional weight information (transmission capacity = 0.75, fault status = 1, load carrying ratio = 0.9).

[0060] S112: Based on the nodes of the same-layer distribution network topology structure in the network data set of the distribution network, set the starting node and the number of walking steps of the random walk, select the walking path according to the edge and edge weight information to complete the simulation, record the access order and number of visits between different nodes in each walking step, and generate the node access data of the same-layer distribution network topology structure;

[0061] The same-layer topology data is loaded through Cytoscape software, and node and edge information is imported, including node attributes such as node number, node type and operating status, edge attributes such as starting and target nodes, transmission weight, fault state weight and load carrying ratio weight. Then, the starting node is set as "substation A" in the walking algorithm, the number of walking steps is 10, and the priority calculation function of weight influence parameters is enabled. Assuming that there are nodes "substation A", "load equipment B" and "switch equipment C" in the same-layer distribution network topology structure, and the nodes are connected by lines "line AB" and "line BC", the process of obtaining each weight information is as follows: Transmission capacity weight: based on the rated current carrying capacity of the line, extracted through the SCADA system. Taking "line AB" as an example, its rated current carrying capacity is 200A, and the current actual load recorded by the system is 150A. The transmission capacity weight is calculated by the normalization formula, that is, the rated current carrying capacity of the current line is divided by the maximum current carrying capacity of all lines in the network. Assuming that the maximum rated current in the network is 200A, the transmission capacity weight of "Line AB" is 200 / 200=1; the rated current of "Line BC" is 150A, then its transmission capacity weight is 150 / 200=0.75. Fault state weight: The fault frequency and fault duration of the line are statistically analyzed through historical data, and the fault records of the past year are extracted from the power grid equipment management system (such as the PMS system). For example, the cumulative fault duration of "Line AB" in the past year is 4 hours, and the total operating time is 8760 hours. The fault state weight w is calculated. fault for, t fault is the cumulative fault duration, t total is the total operating time, that is, 1-(4 / 8760)≈0.9995. The cumulative failure time recorded by "Line BC" is 20 hours, so its weight is 1-(20 / 8760)≈0.9977. Load bearing ratio weight: calculated in combination with the current load and rated current carrying capacity monitored by the SCADA system. For example, the current load of "Line AB" is 140A, the rated current carrying capacity is 200A, and the load bearing ratio is 140 / 200=0.7; the current load of "Line BC" is 120A, the rated current carrying capacity is 150A, and the load bearing ratio is 120 / 150=0.8. According to the inverse proportional relationship of the load bearing ratio, the load bearing ratio weight w load Calculated as w load =1-r load , r loadis the load-bearing ratio, i.e., "line AB" is 1-0.7=0.3, and "line BC" is 1-0.8=0.2. The random walk starts from the starting node "substation A" and selects the next path based on the comprehensive weight information of the above edges. For example, "line AB" has the highest comprehensive weight due to its high transmission capacity, excellent fault status, and low load bearing ratio, so it is selected as the first step of the walk to visit "load device B". Starting from "load device B", the walk algorithm continues to dynamically calculate the comprehensive weight of "line BC" in the optional path, and concludes that its lower load-bearing ratio weight makes it better than other candidate paths, so it continues to walk to "switch device C". During the entire walk, each step records the access order and number of visits between different nodes, as well as the access frequency of the edges, forming the propagation path "substation A→load device B→switch device C", and saves the number of visits to each edge. For example, "line AB" is visited more frequently, indicating its importance in the topological structure of the same-layer distribution network. After the simulation is completed, Cytoscape generates a visual propagation path diagram and exports the access path and weight impact data.

[0062] S113: Node access data based on the topology of the same-layer distribution network, using the formula:

[0063]

[0064] In the process of calculating the random walk, the access probability P between node i and node j in the node access data of the same layer distribution network topology structure is ij , generate access probability data between nodes;

[0065] Among them, P ij Used to quantify the strength of the association between two nodes, f ij It is the cumulative number of visits from node i to node j in the node access data of the same-layer distribution network topology during the random walk process. It is directly obtained by recording the access path of each step in the simulation process. It is the total number of times that node i visits all other nodes j' in the node access data of the same-layer distribution network topology during the random walk, which is obtained by accumulating all the access times of node i during the simulated walk. n is the total number of nodes in the current same-layer distribution network topology, which is obtained by directly importing the node list through the distribution network management system (such as GIS or PMS system) and counting the number of nodes.

[0066] Assuming the simulation of random walk process, for substation A1, The number of visits from substation A1 to load device B is 70. The number of visits from substation A1 to switchgear C is 20, and the total number of visits is:

[0067]

[0068] For substation A2, The number of visits from substation A2 to load device B is 40. The number of visits from substation A2 to switchgear C is 30, and the total number of visits is:

[0069]

[0070] For substation A1, The access probability from substation A1 to load device B is:

[0071]

[0072] Access probability from substation A1 to switchgear C:

[0073]

[0074] For substation A2, The access probability from substation A2 to load device B is:

[0075]

[0076] Access probability from substation A2 to switchgear C:

[0077]

[0078] The results show that the access probability from substation A1 to load device B is 77.8%, the access probability from substation A1 to switch device C is 22.2%, the access probability from substation A2 to load device B is 57.1%, and the access probability from substation A2 to switch device C is 42.9%.

[0079] See also Figure 3 ,The specific steps for obtaining the potential associated node information in the same layer and across layers are:

[0080] S121: Compare the access probability data between nodes with a preset access probability threshold, screen out node pairs whose access probability exceeds the access probability threshold, mark these node pairs as potential associated nodes at the same layer, and generate analysis results of potential associated nodes at the same layer;

[0081] Referring to the preset access probability threshold, the node pairs whose access probability exceeds the access probability threshold are marked as potential associated nodes of the same layer. By analyzing the calculated four access probabilities, the access probability threshold is set to 60% (0.6). The calculation results are as follows: the access probability from substation A1 to load device B is 77.8%, which exceeds the access probability threshold and is marked as a potential associated node of the same layer; the access probability from substation A1 to switch device C is 22.2%, which is lower than the access probability threshold and is not marked as a potential associated node of the same layer; the access probability from substation A2 to load device B is 57.1%, which is slightly lower than the access probability threshold, so it is not marked as a potential associated node of the same layer; the access probability from substation A2 to switch device C is 42.9%, which is also lower than the access probability threshold and is also not marked as a potential associated node of the same layer. Based on the above analysis, the potential associated nodes of the same layer are finally determined to be substation A1 and load device B.

[0082] S122: Collect node information in the cross-layer distribution network topology structure, obtain access probability data between nodes in the cross-layer distribution network topology structure, refer to a preset cross-layer access probability threshold, mark cross-layer potential associated nodes, integrate the same-layer potential associated node analysis results, and generate the same-layer and cross-layer potential associated node information;

[0083] According to the potential associated nodes in the same layer, the association between nodes in the cross-layer distribution network topology is analyzed to generate the potential associated node information of the same layer and cross-layer. The cross-layer association analysis uses the same calculation method as the same layer association analysis, that is, the potential association between nodes is evaluated by calculating the access probability between cross-layer nodes. The specific operation is as follows: First, the potential associated node pair of the same layer (substation A1 and load device B) is used as the starting point of the cross-layer analysis, and the node connection data in the cross-layer distribution network topology is collected, including the connection relationship between substation A1 and other layer devices (such as user terminal U1), load device B and other layer devices (such as transformer T1). The node access data in the cross-layer connection path is extracted through the online monitoring system, including load flow, node status information and transmission performance parameters. The calculation process of the cross-layer access probability is the same as the calculation process of the same layer access probability. For example, the number of visits from load device B to user terminal U1 is 50 times, the number of visits from load device B to transformer T1 is 20 times, and the total number of visits is 70 times. According to the calculation, the access probability of load device B to user terminal U1 is 71.4%, and the access probability of load device B to transformer T1 is 28.6%. If the preset cross-layer access probability threshold is 70%, the node pairs with access probability exceeding the threshold are marked as cross-layer potential associated nodes. Therefore, load device B and user terminal U1 are marked as cross-layer potential associated nodes, and finally generate the same-layer potential associated node (substation A1 and load device B) and cross-layer potential associated node (load device B and user terminal U1) information.

[0084] See also Figure 4 , the specific steps for obtaining the running parameters of the node with missing mark are:

[0085] S211: collecting node operating parameters of each layer in the multi-layer topology of the distribution network, identifying missing or abnormal data caused by collection delay or sensor failure during the collection process, marking the missing or abnormal data, and generating node operating parameters marked with missing data;

[0086] Collect the node operating parameters of each layer in the multi-layer topology of the distribution network. The node operating parameters include load current, node voltage and status signal. In the collection process, combined with the actual scenario of self-iterative update of the distribution network topology identification, the specific operation is as follows: Extract the operating parameters of each node from the SCADA system. For example, in a 10kV distribution line, the load current sampling data of the substation "main substation A" is [80A, 85A, 82A], and the node voltage sampling value is [9.7kV, 9.8kV, 9.9kV]. Its status signal reflects the status of the device switch (such as "on / off"). At the same time, the load current data collected from the load device "residential load device B" should be [30A, 28A, 32A], the node voltage is [9.6kV, 9.5kV, 9.7kV], and the status signal indicates the operating status of the device (such as "normal" or "alarm"). Confirm the integrity and accuracy of these data by checking them one by one. During the data collection process, check whether there is data missing or abnormality caused by acquisition delay or sensor failure. For example, the data extracted from the substation "main transformer station A" found that the load current record was missing in a certain period, and the data was displayed as empty; the node voltage data collected from the load device "residential load device B" suddenly jumped to 12kV at a certain moment, which was inconsistent with the normal distribution voltage range (10±0.5kV), and it was initially judged to be an abnormality caused by sensor failure. Mark the data anomalies and missing conditions. For example, in the load current record of the substation "main transformer station A", it was found that there was a 5-minute sampling delay in a certain period, and this part of the data was marked as "delay missing"; in the voltage record of the load device "residential load device B", the record of 12kV that exceeded the normal range was marked as "abnormal missing". The missing data was further verified in combination with the historical operation data of the node. For example, by retrieving the load current fluctuation pattern of the load device "residential load device B" in the past month, it was found that its normal load range in the same time period was [25A, 35A], thereby verifying the impact range of the current missing data on the overall operation parameter analysis. Finally, through the above process, the load current delay data of the substation "main substation A", the missing current record and abnormal voltage record of the load equipment "residential load equipment B" are clarified.

[0087] S212: Based on the marked missing node operation parameters, mark the missing period according to the missing type and time range of the missing or abnormal data, classify and sort, and generate a missing parameter classification result;

[0088] Integrate the missing parameters and obtain the operating parameters of the nodes marked as missing. The specific classification and sorting process is as follows: Classification of missing data types: Classify and sort according to the type of missing data. For example, the load current data missing due to collection delay in the substation "main transformer station A" is classified as "delay missing" and the voltage abnormality record caused by sensor failure in the load equipment "residential load equipment B" is classified as "abnormal missing". Classification of missing data range: Mark the time range of the missing data in combination with the monitoring timeline. For example, the missing load current data in "main transformer station A" is recorded from 8:00 to 8:05 in the morning of a certain day, and the abnormal voltage record time in "residential load equipment B" is 2:15 in the afternoon.

[0089] See also Figure 5 , the steps for obtaining multi-layer topology data after parameter completion are as follows:

[0090] S221: Collect historical trend data of node operation parameters, combine potential associated node information in the same layer and across layers, analyze and integrate abnormal points in node operation parameters, and generate node historical operation trends and associated node data;

[0091] The historical trend data of node operation parameters and the potential associated node information of the same layer and cross-layer are integrated. The specific process is as follows: For the historical trend data analysis of node operation parameters, load current fluctuation mode: Taking a load device as an example, the load current data of the past 30 days is extracted. The fluctuation range during the peak period of the day is [80A, 100A], and the low period at night is [50A, 70A]. By analyzing the abnormal peak value of the load current, it is found that there is an abnormal record of more than 110A during the day on a certain date. This period overlaps with the high access probability of the cross-layer potential associated node "user terminal", which indicates that attention should be paid to the state change of this node. Time series change of node voltage: The voltage time series data of the substation node is extracted, and it is found that the fluctuation range during the morning peak period is [9.6kV, 9.8kV], and it remains at [9.7kV, 9.9kV] at night. The abnormal fluctuation of a jump to 10.2kV overlaps with the high load current record time point of the potential associated node "load device" at the same layer, and further analysis is needed to analyze the possible source of voltage fluctuation. Abnormal records of status signals: Analysis of the status signals of the load equipment revealed that it recorded “overload alarms” multiple times during the high-load operation period of the past week. These alarms highly overlapped with the time points when the load current reached its peak, suggesting that the equipment may need further inspection. For the integration of same-layer and cross-layer potential associated node information, same-layer potential associated nodes: Combined with the analysis results of the same-layer potential associated nodes, for example, the access probability between a substation and a load device is 77.8%, which is marked as a same-layer potential associated node. When analyzing the voltage time series of the substation, focus on the potential impact of the load device on the voltage fluctuation of the substation. Cross-layer potential associated nodes: Combined with the analysis results of the cross-layer potential associated nodes, for example, the access probability between a load device and a user terminal is 71.4%, which is marked as a cross-layer potential associated node. When analyzing the load current fluctuation of the load device, the high access probability time period is associated with the load status of the user terminal for analysis.

[0092] S222: referring to the historical operation trend of the node and the data of the associated nodes, using the Gaussian process regression method, predicting the operation parameters of the nodes with missing markers, and generating node operation parameter prediction data by fitting the node operation parameters and calculating the values ​​of the missing nodes;

[0093] The Gaussian process regression method is used to predict the operating parameters of nodes marked as missing. The specific operations are as follows: Prepare reference data for prediction. For example, for the 5-minute period marked as missing in the load current record of the substation "main substation A", extract the data points of normal operation before and after [80A, 85A, 82A] as reference; for the voltage record marked as abnormal missing in the load equipment "residential load equipment B", extract the normal voltage values ​​of the adjacent time periods [9.6kV, 9.5kV, 9.7kV]. The regression process includes fitting data trends: by using MATLAB tools, the input normal operation data is sorted into a time series, and the upper and lower time points of the missing period are used as boundary points (for example, the load current before the missing point is 80A, and the load current after the missing point is 85A). Call the Gaussian process regression tool to fit a continuous trend curve and infer the change trajectory of the missing point. Infer the value of the missing point: Calculate the possible parameter value of the missing point by the rate of change of the fitting curve. For example, for a 5-minute missing period, it is inferred based on the time axis that the current may be between 83A and 84A. Extended multidimensional regression: When predicting voltage records, combined with the correlation trend of load current and voltage, the fluctuation range of load current [30A, 32A] and voltage change [9.6kV, 9.7kV] are input, and the predicted voltage value (such as 9.8kV) is generated through the Gaussian process regression tool. Prediction verification: The generated predicted value is verified, and the predicted load current [84A] is compared with the normal range [80A, 100A] in the historical trend data, and the predicted voltage value [9.8kV] is compared with the normal voltage range [9.5kV, 10kV] in the adjacent time period to confirm its rationality and ensure that the predicted value is within a reasonable range. Finally, the operating parameter prediction data of the missing nodes is generated through MATLAB to provide reliable data support for completing the missing records.

[0094] S223: Based on the missing parameter classification result, the node operation parameter prediction data is completed to the corresponding node and time position in the marked missing node operation parameters, the node operation parameters are updated and verified, and the completed node operation parameters are integrated into the multi-layer topology structure of the distribution network to generate multi-layer topology data after parameter completion;

[0095] The node operation parameter prediction data is completed to the corresponding node to generate the multi-layer topology data after parameter completion. The specific operations are as follows: According to the generated prediction data, the prediction value is inserted into the corresponding node and time position. For example, for the missing point of the load current record of the substation "main substation A", the predicted 84A is filled into the missing 5-minute period; for the abnormal voltage record of the load device "residential load device B", the predicted 9.8kV replaces the original abnormal value. The completed operation data is merged with the existing complete data set to update the operation parameters of each node. For example, in the data table of the substation node, the completed continuous load current time series [80A, 84A, 85A] is re-recorded; in the data table of the load device node, the voltage time series is updated to [9.6kV, 9.8kV, 9.7kV], and at the same time, it is ensured that all node operation data are arranged in order according to the time axis. Based on the completed node operation data, the multi-layer topology of the distribution network is reloaded. For example, the operating parameters of substation nodes and load device nodes are synchronized to the same-layer connection of the topology structure; the cross-layer connection between load devices and cross-layer devices (such as user terminals) is also readjusted through the updated operating data to ensure the integrity and accuracy of the overall topology. Check the completed data to verify whether the updated node parameters are consistent with the trend of the original data. For example, check whether the load current sequence of "main substation A" remains within the historical fluctuation range [80A, 100A], and confirm that the voltage sequence of "residential load device B" remains within the normal range [9.5kV, 10kV] to ensure the rationality and consistency of the completed data. Finally, complete the completion of all node operating parameters, and integrate the completed data into the multi-layer topology of the distribution network.

[0096] See also Figure 6 , the specific steps for obtaining the node status change analysis results are:

[0097] S311: Based on the multi-layer topology data after parameter completion, the formula is used:

[0098]

[0099] Calculate the state signal transition probability P of node x to node y at time t in the multi-layer topology data after parameter completion xy (t), generate the transition probability analysis results of the state signal between nodes;

[0100] Among them, P xy (t) is used to represent the transfer trend of the state signal at the current moment, N is the total number of nodes in the multi-layer topology data after parameter completion, and is used to integrate the impact of all paths, P xk(t) is the transfer probability of a specific state signal from node x to the intermediate node k in the multi-layer topology data after parameter completion, such as the transfer probability after the load current missing is completed, which is obtained by counting the historical records of the state signal from node x to the intermediate node k. For example, the load current transfer record of the substation node is extracted from the multi-layer topology data after completion, and the ratio of the transfer number to the total transmission number is calculated:

[0101]

[0102] For example, if the signal transfer record shows that there are 20 transfers from node x (main transformer station) to intermediate node k (load equipment), and a total of 40 transfers, then P xk (t) = 20 / 40 = 0.5;

[0103] P ky (t+Δt) is the specific state signal transfer probability of the intermediate node k transferring to the node y at time t+Δt in the multi-layer topology data after parameter completion, such as predicting the transfer probability after voltage fluctuation completion. The signal transfer delay record from the intermediate node k to the node y is extracted from the completed topology data, for example, the voltage abnormality response signal record from the load device, that is, the intermediate node k to the user terminal, that is, the node y, is combined with the delay time to calculate the transfer probability after delay:

[0104]

[0105] For example, the voltage signal from the load device, that is, the intermediate node k, to the user terminal, that is, the node y, is delayed and transferred 30 times, and the total record is 50, then P ky (t+Δt)=30 / 50=0.6;

[0106] Δt is the time delay of the state signal transmission between nodes in the multi-layer topology data after parameter completion. It is calculated by the timestamp recorded in the device operation log in combination with specific operation characteristics such as current delay or device response time. For example, the response delay from the intermediate node k to the node y is recorded as [1.8, 2.0, 2.2] minutes, and the average value is taken as Δt = 2 minutes.

[0107] For the load current and voltage conversion of node A→B→C, it is assumed that the load current record of node A (main transformer station) is extracted from the multi-layer topology data after parameter completion, and the load current transfer record from A to the intermediate node B (load device) is extracted. The number of transfers is 20 times, and the total record is 40 times. Calculate:

[0108]

[0109] Extract the voltage response record from the intermediate node B to the node C (user terminal). Delay time Δt = 2 minutes (the average value is obtained from multiple response delay records, for example: [1.8, 2.0, 2.2] minutes), the number of signal responses after delay is 30, and the total number of records is 50. Calculate:

[0110]

[0111] Substitute into the formula for calculation: According to the formula:

[0112] P AC (8:00)=P AB (8:00) P BC (8:02)

[0113] P AC (8:00) = 0.5 0.6 = 0.3

[0114] The results show that, at the initial time t=8:00, the signal transfer probability from node A (main transformer station) to node C (user terminal) is 30%, and the delay time Δt=2 minutes.

[0115] S312: Based on the transition probability analysis result of the state signal between nodes, the change trend of the node operation state is analyzed in combination with the time series to generate a node state change analysis result;

[0116] The node transition probability provides key data for analyzing the evolution of node operating parameters in the multi-layer topology of the distribution network. For example, by comparing the transition probability of 30% for the node path A→B→C and the assumed transition probability of 45% for the path B→C→D, the difference in the state change trend between nodes can be analyzed. By classifying and sorting the operating data of node C, it is found that as an intermediate node, the fluctuation range of the load current in the two paths is [84A, 90A], but in the path B→C→D, the delay time is small and the voltage fluctuation range is relatively stable at [9.6kV, 9.8kV]. In the comparison process, through time series analysis, it is found that the high transition probability of path B→C→D corresponds to the high number of visits and stable operating state of load device D, while the lower transition probability of path A→B→C shows that the load fluctuation of node A is large, which may affect the overall transmission efficiency. Finally, combined with the transition probability distribution of all paths, it is confirmed that path B→C→D is critical in the overall topology, while path A→B→C needs to be further optimized. Through these analyses, node state change analysis results covering the entire topology are generated, providing a basis for operational adjustments of the multi-layer topology.

[0117] See also Figure 7 , the steps for obtaining the multi-layer topological data after weight adjustment are as follows:

[0118] S321: Collecting initial weights of nodes, adjusting initial weights of nodes based on changes in node transfer probabilities in node state change analysis results, and generating a weight list of nodes after weight adjustment;

[0119] Taking the signal transfer probability from the main transformer station node A to the user terminal node C as an example, at the initial time t = 8:00, the signal transfer probability is calculated to be 30%. After the delay time Δt = 2 minutes, the signal transfer data is counted again, and the transfer probability is 60%. Assume that the transfer probability data of other paths are as follows: the initial transfer probability from node B (load equipment) to node D (distribution switch) is 40%, and the transfer probability is 50% after the delay Δt = 2 minutes; the initial transfer probability from node E (backup equipment) to node F (load equipment) is 20%, and the transfer probability after the delay is 25%. Through these transfer probability data, the dynamic operation status of the node is analyzed. In the process of weight adjustment, the initial weight is assumed to be 0.5 for node A, 0.3 for node B, and 0.2 for node E. According to the change of transfer probability, the weight value of the node is adjusted: for node A, its transfer probability increases from 30% to 60%, indicating that the signal propagation increases significantly, and the weight needs to be adjusted appropriately. The calculated weight is adjusted to 0.7; the transfer probability of node B increases from 40% to 50%, the change is relatively gentle, and the weight adjustment is small, from 0.3 to 0.35; the transfer probability of node E increases from 20% to 25%, the change is the lowest, and the weight is only slightly adjusted to 0.22. The specific adjustment process is as follows: Combined with the initial weight of each node and the change ratio of the transfer probability, the weight adjustment rule is defined: weight re-value = initial weight + transfer probability increment × adjustment factor. Among them, the adjustment factor can be set according to the importance of the node in the distribution network. For example, for the main substation node A, as a key device, the change of its signal transfer directly affects the operation stability of the entire distribution network, so a higher adjustment factor of 0.6 is set; for nodes B and E, as load devices and backup devices respectively, their signal changes have limited impact on the overall system, so the adjustment factor is set to a lower value of 0.3. The specific calculation is as follows: For node A, the transfer probability increment is 60%-30%=30%, and its weight is calculated as 0.5+0.3×0.6=0.68. The transfer probability increment of node B is 50%-40%=10%, and the weight is calculated as 0.3+0.1×0.3=0.33. The transfer probability increment of node E is 25%-20%=5%, and the weight is calculated as 0.2+0.05×0.3=0.215. Finally, through the above analysis and weight adjustment process, the weight of node A is significantly improved, indicating that it has a higher importance in signal propagation, while the weights of nodes B and E change less, reflecting their lower impact on the overall operation of the network.

[0120] S322: Based on the weight-adjusted node weight list, the weight-adjusted node weight list is integrated into the multi-layer topology structure of the distribution network, and the weight-adjusted multi-layer topology data is generated by verifying whether the node weight change conforms to the current trend of the node state;

[0121] The dynamically adjusted node weights are integrated into the multi-layer topology of the distribution network. The specific operations are as follows: the adjusted weight values ​​are combined with the node connection relationship data, and the connection parameters between it and other nodes (such as B and C) are reloaded; and they are synchronized to the topological relationship between it and other nodes. After integrating the data, the updated multi-layer topological data is checked based on the time series to verify whether the change of node weights conforms to the trend of the probability distribution of node state changes. For example, in the state change probability at time t = 8:00 and t + Δt = 8:02, the state signal transfer probability of node A increases from 30% to 68%. Combined with the updated weight data, it shows that the increase in the weight of node A in this time period is consistent with the significant growth trend of the state signal transfer probability; the state signal transfer probability of node B remains at 20% in the same time period, verifying that the adjustment of its weight value of 0.33 conforms to the probability distribution trend of this stable state. Finally, a sample check is performed on the data after the weights of other nodes are updated. For example, the state change probability record of node E is compared with its updated weight of 0.215, and it is found that it is at a lower weight level, which is consistent with the characteristic that the state signal of the node fluctuates less during this period. Finally, multi-layer topological data containing dynamic weight adjustment results is generated to provide support for subsequent analysis and application.

[0122] See also Figure 8 ,The specific steps for obtaining the self-iterative update results of distribution network topology identification are:

[0123] S411: Based on the weight-adjusted multi-layer topology data, identify key nodes and non-key nodes according to the node weights, combine the potential associated node information in the same layer and across layers, maintain the key nodes and potential associated nodes, and generate optimization results of the key nodes and potential associated nodes;

[0124] First, node weights are extracted to identify key nodes. For example, the weight of the substation node is 0.85, the weight of the load device node is 0.65, and the weight of the user terminal node is 0.45. The substation is determined as a key node and the load device is a secondary key node based on the weight size. At the same time, the potential associated node information of the same layer and across layers is referred to. In the process of maintaining the connection relationship between key nodes and potential associated nodes, for example, it is detected that the connection between the substation and the load device is interrupted due to a line fault, and the backup line is preferentially retrieved to restore the connection. By replanning the path, the backup line connecting the substation node to the load device node is loaded as the main path. The path selection is based on the principle of shortest path priority, and the connection status of potential associated nodes across layers is ensured. For example, the connection integrity between the load device and the user terminal is verified. If a transmission abnormality is found in the connection, the network bandwidth is reallocated to prioritize the connection between the key node and the potential associated node. Finally, the maintenance of the key nodes and potential associated nodes is completed to ensure the overall stability of the multi-layer topology structure of the distribution network.

[0125] S412: Based on the optimization results of the key nodes and the potential associated nodes, optimize the non-key nodes, delete the redundant paths and re-plan the connection layout with the key nodes, reload the optimized distribution network multi-layer topology structure data, and generate the distribution network topology identification self-iteration update result;

[0126] For non-critical nodes with lower weights (such as user terminal nodes with a weight of 0.45), optimization connection operations are carried out. By analyzing the connection redundancy of non-critical nodes, for example, there are three parallel paths between the user terminal node and multiple load device nodes. The transmission status shows that the load rate of two paths is 30%, and the load rate of the other path is 90%, which is significant redundancy. Gradually adjust the connection of non-critical nodes, merge the load flow of the two 30% load paths into a single path by redistributing the load, and close the other redundant path. At the same time, optimize the connection layout of non-critical nodes and other nodes. For example, replan the path between the user terminal node and the secondary critical node (such as the load device node with a weight of 0.65), and give priority to the path with the best transmission performance. After the adjustment is completed, combined with the maintenance results of the key nodes, reload the updated multi-layer topology data, verify the optimized network load distribution and operation status, ensure that the connection redundancy of non-critical nodes is reduced, improve the overall operation efficiency of the network, and generate the self-iterative update results of the distribution network topology identification.

[0127] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A distribution network topology identification self-iterative update method, characterized in that: The following steps are involved: S1: Obtain the multi-layer topological structure of the distribution network, which includes nodes, edges connecting nodes, and edge weight information. A random walk algorithm is used and the influence of edges and edge weight information on the walk is considered. Multi-step walks are performed on the nodes of the same-layer distribution network topological structure in the multi-layer topological structure of the distribution network. The access probability between different nodes in the simulation process is calculated, and the node pairs whose access probability exceeds a preset access probability threshold are marked as potential associated nodes of the same layer. The association between nodes in the cross-layer distribution network topological structure is analyzed based on the potential associated nodes of the same layer, and the potential associated node information of the same layer and cross-layer is generated. S2: Collect node operating parameters of each layer in the multi-layer topology of the distribution network, the node operating parameters include load current, node voltage and status signal, identify missing or abnormal data of some node operating parameters during the collection process, obtain the node operating parameters with missing marks, integrate the historical trend data of the node operating parameters with the potential associated node information of the same layer and across layers, use Gaussian process regression to predict the node operating parameters with missing marks, obtain node operating parameter prediction data, complete the node operating parameter prediction data to the corresponding node, and generate multi-layer topology data after parameter completion; S3: Based on the multi-layer topology data after parameter completion, the transition probability of the state signal in the node operation parameter is calculated through a continuous-time Markov process to obtain the node state change analysis result, and according to the node state change analysis result, the weight information of the node in the multi-layer topology structure of the distribution network is dynamically adjusted to generate the multi-layer topology data after weight adjustment; S4: Based on the multi-layer topology data after the weight adjustment, identify the key nodes and non-key nodes in the distribution network topology structure, refer to the potential associated node information in the same layer and across layers, maintain and optimize the connection relationship between the key nodes and potential associated nodes and non-key nodes, and generate a distribution network topology identification self-iterative update result.

2. A distribution network topology identification self-iterative update method according to claim 1, characterized in that: The steps for obtaining the access probabilities between different nodes during the calculation simulation are specifically as follows: S111: Based on the multi-layer topological structure of the distribution network, the geographic coordinates and topological connection relationships of the nodes in the multi-layer topological structure of the distribution network are derived through a geographic information system, the edges corresponding to the nodes and the weight information of the edges are obtained, and a network data set of the distribution network is generated; S112: Based on the nodes of the same-layer distribution network topology structure in the network data set of the distribution network, set the starting node and the number of walking steps of the random walk, select the walking path according to the edge and the weight information of the edge to complete the simulation, record the access order and the number of visits between different nodes in each walking step, and generate the node access data of the same-layer distribution network topology structure; S113: Node access data based on the topology of the same-layer distribution network, using the formula: In the process of calculating the random walk, the access probability P between node i and node j in the node access data of the same layer distribution network topology structure is ij , generate access probability data between nodes; Among them, f ij is the cumulative number of visits from node i to node j in the node access data of the same-layer distribution network topology during the random walk. is the total number of times node i visits all other nodes j' in the node access data of the same-layer distribution network topology during the random walk, and n is the total number of nodes in the current same-layer distribution network topology.

3. A distribution network topology identification self-iterative update method according to claim 2, characterized in that: The steps for obtaining the potential associated node information of the same layer and across layers are specifically as follows: S121: Compare the access probability data between the nodes with a preset access probability threshold, screen node pairs whose access probability exceeds the access probability threshold, mark these node pairs as potential associated nodes at the same layer, and generate analysis results of potential associated nodes at the same layer; S122: Collect node information in the cross-layer distribution network topology structure, obtain access probability data between nodes in the cross-layer distribution network topology structure, refer to the preset cross-layer access probability threshold, mark the cross-layer potential associated nodes, integrate the same-layer potential associated node analysis results, and generate the same-layer and cross-layer potential associated node information.

4. A distribution network topology identification self-iterative update method according to claim 1, characterized in that: The steps for obtaining the running parameters of the node with missing mark are specifically as follows: S211: collecting node operating parameters of each layer in the multi-layer topology of the distribution network, identifying missing or abnormal data caused by collection delay or sensor failure during the collection process, marking the missing or abnormal data, and generating node operating parameters marked with missing data; S212: Based on the node operation parameters marked as missing, mark the missing period according to the missing type and time range of the missing or abnormal data, classify and sort, and generate a missing parameter classification result.

5. A distribution network topology identification self-iterative update method according to claim 4, characterized in that: The steps for obtaining the multi-layer topology data after parameter completion are specifically as follows: S221: Collect historical trend data of node operation parameters, combine potential associated node information in the same layer and across layers, analyze and integrate abnormal points in node operation parameters, and generate node historical operation trends and associated node data; S222: referring to the historical operation trend of the node and the associated node data, using the Gaussian process regression method to predict the operation parameters of the nodes with missing markers, and generating node operation parameter prediction data by fitting the node operation parameters and calculating the values ​​of the missing nodes; S223: Based on the missing parameter classification results, the node operation parameter prediction data is completed to the corresponding nodes and time positions in the marked missing node operation parameters, the node operation parameters are updated and verified, and the completed node operation parameters are integrated into the multi-layer topology structure of the distribution network to generate multi-layer topology data after parameter completion.

6. A distribution network topology identification self-iterative update method according to claim 1, characterized in that: The steps for obtaining the node state change analysis result are specifically as follows: S311: Based on the multi-layer topology data after parameter completion, the formula is used: Calculate the state signal transition probability P of node x to node y at time t in the multi-layer topology data after parameter completion xy (t), generate the transition probability analysis results of the state signal between nodes; Where N is the total number of nodes in the multi-layer topology data after parameter completion, P xk (t) is the specific state signal transition probability from node x to intermediate node k in the multi-layer topology data after parameter completion, P ky (t+Δt) is the probability of a specific state signal transfer from the intermediate node k to the node y at time t+Δt in the multi-layer topology data after parameter completion, and Δt is the time delay of the state signal transmission between nodes in the multi-layer topology data after parameter completion; S312: Based on the transition probability analysis result of the inter-node status signal, the change trend of the node operation status is analyzed in combination with the time series to generate a node status change analysis result.

7. A distribution network topology identification self-iterative update method according to claim 1, characterized in that: The steps for obtaining the weight-adjusted multi-layer topological data are specifically as follows: S321: Collecting initial weights of nodes, adjusting initial weights of nodes based on changes in node transfer probabilities in the node state change analysis results, and generating a weight list of nodes after weight adjustment; S322: Based on the weight-adjusted node weight list, the weight-adjusted node weight list is integrated into the multi-layer topology structure of the distribution network, and the weight-adjusted multi-layer topology data is generated by verifying whether the node weight change is in line with the current trend of the node status.

8. A distribution network topology identification self-iterative update method according to claim 1, characterized in that: The steps for obtaining the self-iterative update result of the distribution network topology identification are specifically as follows: S411: Based on the weight-adjusted multi-layer topology data, identify key nodes and non-key nodes according to node weights, combine the potential associated node information of the same layer and across layers, maintain the key nodes and potential associated nodes, and generate optimization results of the key nodes and potential associated nodes; S412: Based on the optimization results of the key nodes and potential associated nodes, optimize the non-key nodes, delete redundant paths and re-plan the connection layout with the key nodes, reload the optimized distribution network multi-layer topology structure data, and generate the distribution network topology identification self-iteration update result.

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