Power distribution communication network fault path tracing method and system based on knowledge graph

Abstract

The invention relates to the technical field of power distribution communication networks, and discloses a power distribution communication network fault path tracing method and system based on a knowledge graph, and the method comprises the steps: obtaining the dependency strength of a corresponding communication link according to the communication data of each communication link in the knowledge graph; identifying a fault propagation path in the directed edge set on the basis of a fault propagation rate obtained by calculation of the dependency strength; screening the fault propagation path diagram based on a preset fault propagation termination condition to obtain a first fault propagation path sub-diagram, and determining the degree of each level of node in the first fault propagation path sub-diagram; identifying key nodes in the first fault propagation path subgraph according to the first node importance; and expanding the first fault propagation path sub-graph based on the outgoing side link data to obtain a second fault propagation path sub-graph. According to the invention, key fault nodes and propagation paths can be quickly positioned.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution communication networks, and in particular, to a method and system for tracing the fault path of a distribution communication network based on a knowledge graph. Background Art

[0002] In the construction of smart grids, the stability of distribution communication networks is crucial. With the improvement of the intelligence level of the power grid, the number and types of devices in the distribution communication network are increasing continuously, and the topological relationship between devices is becoming more and more complex. By modeling the topological relationship between devices through a knowledge graph, the hierarchical structure and connection mode of devices can be intuitively displayed, thus facilitating the fault analysis of the distribution communication network. When a key node device fails, the fault information will spread according to the topological structure of the knowledge graph, and the propagation path is affected by the device connection relationship and dependence strength, triggering a fault cascade effect. In this process, it is necessary to dynamically adjust the node importance weight and the graph inference depth. The node importance weight is determined based on the device topological position, the fault transmission path, and the degree of abnormal state, and the graph inference depth depends on the complexity of the fault propagation and the change of the device dependence relationship.

[0003] However, the existing fault tracing technologies have some defects in dynamically adjusting the node importance weight and the graph inference depth. First, they overemphasize the device information at a certain level while ignoring the potential fault sources at other levels. Second, the insufficient graph inference depth leads to the truncation of the fault propagation path, thus unable to fully present the fault cascade effect, seriously affecting the accuracy of fault tracing.

[0004] Therefore, it is urgent to propose an efficient method for tracing the fault path of a distribution communication network to achieve the dynamic balance of the node importance weight and the graph inference depth. Summary of the Invention

[0005] In view of the defects existing in the prior art, the present invention provides a method and system for tracing the fault path of a distribution communication network based on a knowledge graph.

[0006] In a first aspect, an embodiment of the present invention provides a method for tracing the fault path of a distribution communication network based on a knowledge graph, including: Constructing a corresponding knowledge graph based on the operating state and topological structure of the target distribution communication network, and obtaining the dependence relationship strength corresponding to each communication link according to the communication data of each communication link in the knowledge graph; Obtaining the set of directed edges of abnormal nodes in the knowledge graph, and identifying the fault propagation path in the set of directed edges based on the fault propagation rate calculated according to the dependence relationship strength, where the set of directed edges includes the directed edges between the abnormal node and each adjacent node; Traverse the fault propagation path to generate a fault propagation path graph, and filter the fault propagation path graph based on a preset fault propagation termination condition to obtain a first sub - graph of the fault propagation path, and determine the degree of each level of nodes in the first sub - graph of the fault propagation path; Perform fitting prediction based on the degree of each level of nodes in the first sub - graph of the fault propagation path to obtain the first node importance of the corresponding nodes, and identify the key nodes in the first sub - graph of the fault propagation path according to the first node importance; Obtain the out - edge link data of the key nodes in the first sub - graph of the fault propagation path, and expand the first sub - graph of the fault propagation path based on the out - edge link data to obtain a second sub - graph of the fault propagation path.

[0007] Preferably, obtaining the dependency strength of each communication link according to the communication data of each communication link in the knowledge graph includes: Obtain the first operation state data of each communication link in the knowledge graph, and perform weighted calculation based on the first operation state data to obtain the first link score corresponding to the communication link, where the first operation state data includes communication delay, bandwidth utilization rate, and data packet loss rate; Perform weighted correction on the first link score based on the physical distance of each communication link to obtain the second link score corresponding to the communication link; Obtain the service traffic data of each communication link, and perform weighted calculation based on the service traffic data to obtain the service dependency of the corresponding communication link, where the service traffic data includes control instruction flow, measurement data flow, and alarm information flow; Perform weighted fusion on the second link score and the service dependency of each communication link to obtain the dependency strength of the corresponding communication link.

[0008] Preferably, before obtaining the set of directed edges of abnormal nodes in the knowledge graph, it further includes: Obtain the second operation state data of each node in the knowledge graph, and calculate the degree of abnormality of the corresponding node based on the second operation state data, where the second operation state data includes response delay time, load rate, and bandwidth utilization rate; Judge whether the degree of abnormality is greater than a preset abnormality determination threshold, and if so, determine that the corresponding node is an abnormal node.

[0009] Preferably, identifying the fault propagation path in the set of directed edges based on the fault propagation rate calculated from the dependency strength includes: Obtain the dependency strength of the communication link corresponding to each directed edge in the directed edge set, and perform a linear calculation on the dependency strength to obtain the initial fault propagation rate corresponding to the directed edge; Obtain the performance index data of the communication link corresponding to each directed edge, and perform a weighted calculation based on the performance index data to obtain the edge weight coefficient corresponding to the directed edge, where the performance index data includes physical distance, network hop count, and communication delay; Based on the edge weight coefficient of each directed edge, perform a weighted correction on the initial fault propagation rate to obtain the fault propagation rate corresponding to the directed edge; Judge whether the fault propagation rate is greater than the preset propagation rate threshold. If so, mark the corresponding directed edge as a fault propagation path.

[0010] Preferably, traversing the fault propagation path to generate a fault propagation path graph, and screening the fault propagation path graph based on a preset fault propagation termination condition to obtain a first fault propagation path sub-graph, and determining the degree of each level of nodes in the first fault propagation path sub-graph, including: Use the breadth-first search algorithm to traverse the fault propagation path to obtain a fault propagation path graph; Use the depth-first search algorithm to obtain the number of levels of the fault propagation path graph; Based on the total number of nodes and the network maximum diameter of the target distribution communication network, calculate a preset fault propagation depth reflecting the network scale of the target distribution communication network; Based on the number of levels and the preset fault propagation depth, judge whether the fault propagation path graph meets the preset fault propagation termination condition. If so, screen the fault propagation path graph to obtain a first fault propagation path sub-graph, otherwise characterize the fault propagation path graph as the first fault propagation path sub-graph, where the preset fault propagation termination condition includes that the number of levels is greater than the preset fault propagation depth; Count the in-degree and out-degree of each level of nodes in the first fault propagation path sub-graph.

[0011] Preferably, based on the degree of each level of nodes in the first fault propagation path sub-graph, perform a fitting prediction to obtain the first node importance corresponding to the node, and identify the key nodes in the first fault propagation path sub-graph according to the first node importance, including: Perform a weighted calculation on the degree of each level of nodes in the first fault propagation path sub-graph to obtain the node eigenvalue corresponding to the node; Use the random forest algorithm to perform a fitting prediction on the node eigenvalues of each level of nodes in the first fault propagation path sub-graph to obtain the first node importance corresponding to the node; Perform eigenvector centrality evaluation on each hierarchical node in the first fault propagation path subgraph, and characterize the obtained network centrality as the second node importance of the corresponding node; Perform weighted calculation on the first node importance and the second node importance to obtain the node importance of the corresponding node; Determine whether the node importance is higher than a first preset threshold. If so, mark the corresponding node as a key node in the first fault propagation path subgraph.

[0012] Preferably, the method for expanding the first fault propagation path subgraph based on the out-edge link data to obtain a second fault propagation path subgraph includes: Perform weighted calculation based on the out-edge link data to obtain the node coupling coefficient corresponding to each out-edge link, where the out-edge link data includes the communication delay, bandwidth utilization rate, and data packet loss rate of several out-edge links; Use the random forest algorithm to fit and predict the node coupling coefficient corresponding to each out-edge link to obtain the fault propagation influence intensity of the target node in each out-edge link; Determine the fault propagation depth increment value based on the fault propagation influence intensity, and hierarchically expand the first fault propagation path subgraph according to the fault propagation depth increment value to obtain a third fault propagation path subgraph; Obtain the communication link data between the key nodes and each newly added boundary node in the third fault propagation path subgraph, and calculate the fault propagation probability between the key nodes and each newly added boundary node in the third fault propagation path subgraph based on the communication link data; Perform node marking on the third fault propagation path subgraph based on the fault propagation probability to obtain a second fault propagation path subgraph, where the node marking includes marking the newly added boundary nodes corresponding to the fault propagation probability greater than a second preset threshold in the third fault propagation path subgraph.

[0013] In a second aspect, an embodiment of the present invention provides a fault path traceability system for a distribution communication network based on a knowledge graph, including: A dependency strength determination module, configured to construct a corresponding knowledge graph based on the operating status and topological structure of a target distribution communication network, and obtain the dependency strength of the corresponding communication link according to the communication data of each communication link in the knowledge graph; A fault propagation path determination module, configured to obtain a set of directed edges of abnormal nodes in the knowledge graph, and identify a fault propagation path in the set of directed edges based on the fault propagation rate calculated according to the dependency strength, where the set of directed edges includes directed edges between the abnormal nodes and each adjacent node; A node degree determination module, configured to traverse the fault propagation path to generate a fault propagation path graph, and filter the fault propagation path graph based on a preset fault propagation termination condition to obtain a first sub-graph of the fault propagation path, and determine the degree of each level of nodes in the first sub-graph of the fault propagation path; A key node identification module, configured to perform fitting prediction based on the degree of each level of nodes in the first sub-graph of the fault propagation path to obtain the first node importance of the corresponding nodes, and identify key nodes in the first sub-graph of the fault propagation path according to the first node importance; A target fault propagation path determination module, configured to obtain out-edge link data of the key nodes in the first sub-graph of the fault propagation path, and expand the first sub-graph of the fault propagation path based on the out-edge link data to obtain a second sub-graph of the fault propagation path.

[0014] Preferably, the dependency strength determination module includes: A first link score determination unit, configured to obtain first operation state data of each communication link in the knowledge graph, and perform weighted calculation based on the first operation state data to obtain a first link score corresponding to the communication link, where the first operation state data includes communication delay, bandwidth utilization rate, and data packet loss rate; A second link score determination unit, configured to perform weighted correction on the first link score based on the physical distance of each communication link to obtain a second link score corresponding to the communication link; A service dependency determination unit, configured to obtain service traffic data of each communication link, and perform weighted calculation based on the service traffic data to obtain a service dependency corresponding to the communication link, where the service traffic data includes control instruction flow, measurement data flow, and alarm information flow; A weighted fusion unit, configured to perform weighted fusion on the second link score and the service dependency of each communication link to obtain the dependency strength corresponding to the communication link.

[0015] Preferably, the fault propagation path determination module includes: A linear calculation unit, configured to obtain the dependency strength of each communication link corresponding to each directed edge in the set of directed edges, and perform linear calculation on the dependency strength to obtain an initial fault propagation rate corresponding to the directed edge; A weighted calculation unit is configured to obtain the performance metric data of each of the directed edges corresponding to the communication link, and perform weighted calculation based on the performance metric data to obtain an edge weight coefficient corresponding to the directed edge, where the performance metric data includes physical distance, network hop count, and communication delay; A weighted correction unit is configured to perform weighted correction on the initial fault propagation rate based on the edge weight coefficient of each of the directed edges to obtain a fault propagation rate corresponding to the directed edge; A propagation rate determination unit is configured to determine whether the fault propagation rate is greater than a preset propagation rate threshold, and if so, mark the corresponding directed edge as a fault propagation path.

[0016] Compared with the prior art, the method and system for tracing the fault path of a distribution communication network based on a knowledge graph according to an embodiment of the present invention have the beneficial effects that: by constructing a knowledge graph, the operation status and topological structure information of the distribution communication network can be comprehensively integrated; according to the multi-source data of the communication link, the strength of the dependency relationship can be accurately obtained, and the fault propagation path can be effectively identified; in the analysis of the fault propagation path graph, a first fault propagation path subgraph is screened out based on a preset fault propagation termination condition, and then the key nodes are accurately identified; by utilizing the data of the out-edge link of the key nodes, the subgraph is expanded. The present invention greatly improves the accuracy and comprehensiveness of fault analysis, can quickly locate the key fault nodes and propagation paths, and realizes the dynamic balance of the node importance and the reasoning depth of the knowledge graph. Description of the Drawings

[0017] Figure 1 is a schematic flowchart of a method for tracing the fault path of a distribution communication network based on a knowledge graph according to an embodiment of the present invention; Figure 2 is a schematic flowchart of obtaining the strength of the dependency relationship according to an embodiment of the present invention; Figure 3 is a schematic structural diagram of a system for tracing the fault path of a distribution communication network based on a knowledge graph according to an embodiment of the present invention. Detailed Embodiments

[0018] The following further describes in detail the specific embodiments of the present invention with reference to the drawings and embodiments. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0019] In the description of the present invention, it should be understood that the terms "first" and "second" etc. used in the present invention are used to distinguish different objects, rather than to describe a specific order.

[0020] In the description of the present invention, it should be noted that unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0021] As Figure 1 shown, an embodiment of the present invention provides a method for tracing the fault path of a distribution communication network based on a knowledge graph, including the steps of: S1. Construct a corresponding knowledge graph based on the operating state and topological structure of the target distribution communication network, and obtain the dependence strength of each communication link in the knowledge graph according to the communication data of each communication link; To construct the knowledge graph of the target distribution communication network, it is first necessary to determine the topological structure of the target distribution communication network, then obtain the relevant data of the operating state, and use technologies such as knowledge extraction and knowledge fusion to convert the relevant data into knowledge, and finally display it in an intuitive graphical manner. The communication data of each communication link in the knowledge graph includes first operating state data, physical distance, and traffic data.

[0022] Specifically, as Figure 2 shown, step S1 includes: S101. Obtain the first operating state data of each communication link in the knowledge graph, and perform weighted calculation based on the first operating state data to obtain the first link score of the corresponding communication link; The first operating state data includes communication delay, bandwidth utilization rate, and data packet loss rate. Perform weighted calculation on the communication delay, bandwidth utilization rate, and data packet loss rate to obtain the first link score of the corresponding communication link. This score reflects the operating quality of the communication link. It should be noted that weights are assigned to the communication delay, bandwidth utilization rate, and data packet loss rate respectively based on the service requirements of the target distribution communication network. Considering that there is a differential protection service with high real-time requirements in the target distribution communication network, in this embodiment, the weight coefficient of the communication delay is set to 0.4, the weight coefficient of the bandwidth utilization rate is set to 0.3, and the weight coefficient of the data packet loss rate is set to 0.3.

[0023] S102. Perform weighted correction on the first link score based on the physical distance of each communication link to obtain the second link score of the corresponding communication link; Considering that the physical distances of each communication link vary greatly, the physical distance is converted into a normalization coefficient between 0 and 1, and the first link score is weighted and corrected according to the normalized physical distance to obtain the second link score of the corresponding communication link. By correcting the first link score with the physical distance, it can be found that the farther the link is, the more obvious the correction effect on its score is, which reflects the influence of the physical distance on the reliability of the communication link.

[0024] S103. Obtain the service traffic data of each communication link, and perform weighted calculation based on the service traffic data to obtain the service dependence degree of the corresponding communication link; The service traffic data includes control instruction flow, measurement data flow, and alarm information flow. Specifically, the control instruction flow includes key instructions such as start-stop control of distributed generation equipment and distribution network topology switching control, the measurement data flow includes real-time measurement values such as voltage, current, active power, and reactive power, and the alarm information flow includes operation events such as equipment fault alarms and over-limit alarms.

[0025] Perform weighted calculation on the control instruction flow, measurement data flow, and alarm information flow to obtain the service dependence degree of the corresponding communication link. It should be noted that considering the importance of different types of service traffic data to the safe and stable operation of the target distribution communication network, in this embodiment, the weight coefficient of the control instruction flow is set to 0.4, the weight coefficient of the measurement data flow is set to 0.3, and the weight coefficient of the alarm information flow is set to 0.3.

[0026] S104. Perform weighted fusion on the second link score and service dependence degree of each communication link to obtain the dependence relationship strength of the corresponding communication link.

[0027] The dependence relationship strength of the corresponding communication link obtained after weighted fusion can be used to evaluate the importance of the corresponding communication link in the entire target distribution communication network. This quantitative evaluation method provides an important reference for the optimal configuration and fault prevention of the target distribution communication network. It should be noted that in the weighted fusion process, the weight coefficients given to the second link score and service dependence degree are the same.

[0028] S2. Obtain the set of directed edges of abnormal nodes in the knowledge graph, and identify the fault propagation paths in the set of directed edges based on the fault propagation rate calculated from the dependence relationship strength; Before obtaining the set of directed edges of abnormal nodes in the knowledge graph, it also includes the steps of: 1) Obtain the second operating state data of each node in the knowledge graph, and calculate the abnormality degree of the corresponding node based on the second operating state data; Compare the second operating state data with the preset normal operating state data, and calculate the deviation ratio between the two to obtain the abnormality degree of the corresponding node. Specifically, the second operating state data includes response delay time, load rate, and bandwidth utilization rate, and the preset normal operating state data includes a response delay time of 5 milliseconds, a load rate of 40%, and a bandwidth utilization rate of 30%.

[0029] 2) Determine whether the abnormality degree is greater than the preset abnormality determination threshold. If so, determine that the corresponding node is an abnormal node.

[0030] After identifying the abnormal node, use the graph traversal algorithm to find the adjacent nodes of the abnormal node in the knowledge graph, so as to obtain the set of directed edges. The set of directed edges includes the directed edges between the abnormal node and each adjacent node. Specifically, step S2 includes: 1) Obtain the dependency strength of each communication link corresponding to the directed edges in the set of directed edges, and perform a linear calculation on the dependency strength to obtain the initial fault propagation rate of the corresponding directed edge; There is a certain positive correlation between the dependency strength and the initial fault propagation rate. The greater the dependency strength, the greater the initial fault propagation rate, because a tight dependency relationship makes it easier for abnormalities to spread between nodes.

[0031] Specifically, use the preset linear function to calculate the initial fault propagation path rate of the corresponding directed edge. Further, the parameters in the preset linear function can be estimated through regression analysis using the data such as the dependency strength and fault propagation rate collected in the target distribution communication network.

[0032] 2) Obtain the performance index data of each communication link corresponding to the directed edges, and perform a weighted calculation based on the performance index data to obtain the edge weight coefficient of the corresponding directed edge; The performance index data includes physical distance, network hop count, and communication delay. Standardize the physical distance, network hop count, and communication delay of each communication link corresponding to the directed edges respectively, and perform a weighted calculation on the standardized physical distance, network hop count, and communication delay to obtain the edge weight coefficient of the corresponding directed edge. The edge weight coefficient can comprehensively reflect the performance and quality of the communication link, providing a reference basis for network resource allocation. It should be noted that the weight coefficients of the physical distance, network hop count, and communication delay are determined according to the actual situation of the target distribution communication network. Considering that there are differential protection services with high real-time requirements in the target distribution communication network, the weight coefficient of the communication delay can be set larger.

[0033] 3) Perform a weighted correction on the initial fault propagation rate based on the edge weight coefficient of each directed edge to obtain the fault propagation rate of the corresponding directed edge; 4) Determine whether the fault propagation rate is greater than the preset propagation rate threshold. If so, mark the corresponding directed edge as the fault propagation path.

[0034] In this embodiment, the preset propagation rate threshold is 0.06. If there is a fault propagation rate greater than 0.06, mark the corresponding directed edge as the fault propagation path. It should be noted that the preset propagation rate threshold in this embodiment can be adjusted according to the actual situation. If the marking requirement for the fault propagation path is relatively high, the preset propagation rate threshold can be appropriately increased; otherwise, it can be appropriately decreased.

[0035] S3. Traverse the fault propagation path to generate a fault propagation path graph, and based on the preset fault propagation termination condition, screen the fault propagation path graph to obtain the first fault propagation path sub-graph, and determine the degree of each hierarchical node in the first fault propagation path sub-graph; Specifically, step S3 includes: 1) Use the breadth-first search algorithm to traverse the fault propagation path to obtain the fault propagation path graph; The fault propagation path graph includes, but is not limited to, three levels: the control layer, the bay layer, and the execution layer. It intuitively shows the diffusion trend of the abnormal state in the target distribution communication network, highlights the high-risk propagation paths, and provides an important basis for fault isolation and risk control.

[0036] 2) Use the depth-first search algorithm to obtain the number of levels of the fault propagation path graph; The fault propagation path graph includes, but is not limited to, three levels: the control layer, the bay layer, and the execution layer.

[0037] 3) Based on the total number of nodes in the target distribution communication network and the network maximum diameter, calculate the preset fault propagation depth reflecting the network scale of the target distribution communication network; The total number of nodes reflects the overall scale of the target distribution communication network, while the network maximum diameter reflects the maximum distance between nodes in the target distribution communication network, that is, the maximum range where the fault may spread.

[0038] Specifically, after normalizing the total number of nodes and the network maximum diameter respectively, use the following formula to calculate the preset fault propagation depth: Among them, represents the preset fault propagation depth, represents the proportionality constant, which is used to adjust the final calculation result to conform to the actual network situation, and represent the weight coefficients, represents the normalized total number of nodes, represents the normalized network maximum diameter.

[0039] 4) Determine whether the fault propagation path graph meets the preset fault propagation termination condition based on the number of levels and the preset fault propagation depth. If so, screen the fault propagation path graph to obtain the first fault propagation path sub-graph; otherwise, characterize the fault propagation path graph as the first fault propagation path sub-graph. The preset fault propagation termination condition includes that the number of levels of the fault propagation path graph is greater than the preset fault propagation depth. When the fault propagation path graph meets the preset fault propagation termination condition, truncate the propagation path at the level equal to the preset fault propagation depth in the fault propagation path graph to obtain the first fault propagation path sub-graph; when the fault propagation path graph does not meet the preset fault propagation termination condition, characterize the fault propagation path graph as the first fault propagation path sub-graph.

[0040] 5) Count the in-degree and out-degree of each level node in the first fault propagation path sub-graph.

[0041] Count the number of links pointing to each level node in the first fault propagation path sub-graph as the in-degree of the corresponding level node, and count the number of links starting from each level node in the first fault propagation path sub-graph as the out-degree of the corresponding level node.

[0042] S4. Perform fitting prediction based on the degree of each level node in the first fault propagation path sub-graph to obtain the first node importance of the corresponding node, and identify the key nodes in the first fault propagation path sub-graph according to the first node importance. Specifically, step S4 includes: 1) Perform weighted calculation on the degree of each level node in the first fault propagation path sub-graph to obtain the node feature value of the corresponding node. Assign weight coefficients to the in-degree and out-degree of each level node in the first fault propagation path sub-graph respectively, and perform weighted calculation on the in-degree and out-degree based on the weight coefficients to obtain the node feature value of the corresponding node. Considering that the weight coefficients of the in-degree and out-degree are the same, in this embodiment, the weight coefficient of the in-degree is set to 0.3, and the weight coefficient of the out-degree is set to 0.3.

[0043] 2) Use the random forest algorithm to perform fitting prediction on the node feature values of each level node in the first fault propagation path sub-graph to obtain the first node importance of the corresponding node. Use the random forest algorithm to realize the fitting of the relationship between the node feature value and the node importance, and predict the node feature values of each level node in the first fault propagation path sub-graph according to this fitting relationship to obtain the first node importance of the corresponding node.

[0044] 3) Evaluate the eigenvector centrality of each level node in the first fault propagation path sub-graph, and characterize the obtained network centrality as the second node importance of the corresponding node. The eigenvector centrality algorithm is used to calculate the network centrality of each level of nodes in the first fault propagation path subgraph, and the network centrality is characterized as the second node importance of the corresponding nodes.

[0045] 4) Perform weighted calculation on the first node importance and the second node importance to obtain the node importance of the corresponding nodes; In the weighted calculation process, the weight coefficients given to the first node importance and the second node importance are the same.

[0046] 5) Determine whether the node importance is higher than the first preset threshold. If so, mark the corresponding node as a key node in the first fault propagation path subgraph.

[0047] The key node is a node in the first fault propagation path subgraph whose node importance is higher than the first preset threshold. The first preset threshold in this embodiment is 0.75. If there is a node importance higher than 0.75, the corresponding node is marked as a key node. It should be noted that the first preset threshold in this embodiment can be adjusted according to the actual situation. If the marking requirement for key nodes is high, the first preset threshold can be appropriately increased, and vice versa, it can be appropriately decreased.

[0048] S5. Obtain the out-edge link data of the key nodes in the first fault propagation path subgraph, and expand the first fault propagation path subgraph based on the out-edge link data to obtain the second fault propagation path subgraph.

[0049] The out-edge link data includes the communication delay, bandwidth utilization rate, and data packet loss rate of several out-edge links. Specifically, after obtaining the out-edge link data, step S5 includes: 1) Perform weighted calculation based on the out-edge link data to obtain the node-to-node coupling coefficient corresponding to each out-edge link; Perform standardization processing on the communication delay of each out-edge link in the out-edge link data, and assign weights to the standardized communication delay, bandwidth utilization rate, and data packet loss rate respectively. Finally, perform weighted calculation on the standardized communication delay, bandwidth utilization rate, and data packet loss rate based on the weights to obtain the node-to-node coupling coefficient corresponding to each out-edge link.

[0050] Specifically, the following formula is used to calculate the node-to-node coupling coefficient: Among them, represents the node-to-node coupling coefficient, , and represent weights, represents the bandwidth utilization rate, represents the standardized communication delay, represents the data packet loss rate. It should be noted that the higher the bandwidth utilization rate, the more frequent the communication between nodes, and the higher the coupling degree; the smaller the communication delay, the more timely the interaction between nodes, and the higher the coupling degree. Therefore, is used to represent the positive impact of communication delay on the coupling coefficient; the lower the data packet loss rate, the higher the reliability of data transmission, and the higher the coupling degree between nodes. Therefore, is used to reflect the effect of data packet loss rate on the coupling coefficient.

[0051] 2) Use the random forest algorithm to fit and predict the coupling coefficient between nodes corresponding to each outgoing link, and obtain the fault propagation influence intensity of the target node in each outgoing link; Use the random forest algorithm to fit the relationship between the coupling coefficient between nodes and the fault propagation influence intensity, and predict the coupling coefficient between nodes corresponding to each outgoing link based on this fitting relationship to obtain the fault propagation influence intensity of the corresponding target node.

[0052] 3) Determine the fault propagation depth increment value based on the fault propagation influence intensity, and hierarchically expand the first fault propagation path subgraph according to the fault propagation depth increment value to obtain the third fault propagation path subgraph; Count the number of target nodes corresponding to the fault propagation influence intensity greater than the third preset threshold, and characterize it as the fault propagation depth increment value. Hierarchically expand the first fault propagation path subgraph through depth-first search according to the fault propagation depth increment value to obtain the third fault propagation path subgraph.

[0053] 4) Obtain the communication link data between the key nodes and each newly added boundary node in the third fault propagation path subgraph, and calculate the fault propagation probability between the key nodes and each newly added boundary node in the third fault propagation path subgraph based on the communication link data; Use the association rule mining algorithm based on the communication link data to calculate the fault propagation probability between the key nodes and each newly added boundary node in the third fault propagation path subgraph.

[0054] 5) Perform node marking on the third fault propagation path subgraph based on the fault propagation probability to obtain the second fault propagation path subgraph.

[0055] Mark the newly added boundary nodes corresponding to the fault propagation probability greater than the second preset threshold in the third fault propagation path subgraph to obtain the second fault propagation path subgraph.

[0056] In an embodiment of the present invention, a method for tracing the fault path of a distribution communication network based on a knowledge graph can comprehensively integrate the operation status and topological structure information of the distribution communication network by constructing a knowledge graph; according to the multi-source data of communication links, the strength of the dependence relationship can be accurately obtained, and the fault propagation path can be effectively identified; in the analysis of the fault propagation path graph, a first sub-graph of the fault propagation path is filtered out based on a preset fault propagation termination condition, and then the key nodes are accurately identified; by utilizing the data of the outgoing links of the key nodes, the sub-graph is expanded. The present invention greatly improves the accuracy and comprehensiveness of fault analysis, can quickly locate the key fault nodes and propagation paths, and realizes the dynamic balance of the node importance and the reasoning depth of the knowledge graph.

[0057] Based on the above method for tracing the fault path of a distribution communication network based on a knowledge graph, as Figure 3 shown, an embodiment of the present invention provides a system for tracing the fault path of a distribution communication network based on a knowledge graph, including: A dependence relationship strength determination module 1, configured to construct a corresponding knowledge graph based on the operation status and topological structure of the target distribution communication network, and obtain the dependence relationship strength of each communication link in the knowledge graph according to the communication data of each communication link; Specifically, the dependence relationship strength determination module includes: A first link score determination unit, configured to obtain the first operation status data of each communication link in the knowledge graph, and perform weighted calculation based on the first operation status data to obtain the first link score of the corresponding communication link, where the first operation status data includes communication delay, bandwidth utilization rate, and data packet loss rate; A second link score determination unit, configured to perform weighted correction on the first link score based on the physical distance of each communication link to obtain the second link score of the corresponding communication link; A service dependence degree determination unit, configured to obtain the service traffic data of each communication link, and perform weighted calculation based on the service traffic data to obtain the service dependence degree of the corresponding communication link, where the service traffic data includes control instruction flow, measurement data flow, and alarm information flow; A weighted fusion unit, configured to perform weighted fusion on the second link score and the service dependence degree of each communication link to obtain the dependence relationship strength of the corresponding communication link.

[0058] A fault propagation path determination module 2, configured to obtain the set of directed edges of abnormal nodes in the knowledge graph, and identify the fault propagation path in the set of directed edges based on the fault propagation rate calculated according to the dependence relationship strength, where the set of directed edges includes the directed edges between abnormal nodes and each adjacent node; Specifically, the fault propagation path determination module includes: A linear calculation unit, configured to obtain the dependency strength of the communication link corresponding to each directed edge in the directed edge set, and perform a linear calculation on the dependency strength to obtain the initial fault propagation rate of the corresponding directed edge; A weighted calculation unit, configured to obtain the performance index data of the communication link corresponding to each directed edge, and perform a weighted calculation based on the performance index data to obtain the edge weight coefficient of the corresponding directed edge, where the performance index data includes physical distance, network hop count, and communication delay; A weighted correction unit, configured to perform weighted correction on the initial fault propagation rate based on the edge weight coefficient of each directed edge to obtain the fault propagation rate of the corresponding directed edge; A propagation rate judgment unit, configured to judge whether the fault propagation rate is greater than a preset propagation rate threshold, and if so, mark the corresponding directed edge as a fault propagation path.

[0059] A node degree determination module 3, configured to traverse the fault propagation path to generate a fault propagation path graph, and screen the fault propagation path graph based on a preset fault propagation termination condition to obtain a first fault propagation path sub-graph, and determine the degree of each level node in the first fault propagation path sub-graph; A key node identification module 4, configured to perform fitting prediction based on the degree of each level node in the first fault propagation path sub-graph to obtain the first node importance of the corresponding node, and identify the key nodes in the first fault propagation path sub-graph according to the first node importance; A target fault propagation path determination module 5, configured to obtain the out-edge link data of the key nodes in the first fault propagation path sub-graph, and expand the first fault propagation path sub-graph based on the out-edge link data to obtain a second fault propagation path sub-graph.

[0060] It should be noted that each module in the above-mentioned fault path tracing system for a distribution communication network based on a knowledge graph can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor in the computer device in hardware form or be independent of it, or can be stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to the above-mentioned modules. For the specific limitations of a fault path tracing system for a distribution communication network based on a knowledge graph, refer to the limitations of a fault path tracing method for a distribution communication network based on a knowledge graph in the above text. The two have the same functions and effects, and will not be elaborated here.

[0061] In summary, in the embodiment of the present invention, a method and system for tracing the fault path of a distribution communication network based on a knowledge graph can comprehensively integrate the operation status and topological structure information of the distribution communication network by constructing the knowledge graph; according to the multiple data of the communication link, the dependence strength can be accurately obtained, and the fault propagation path can be effectively identified; in the analysis of the fault propagation path graph, the first fault propagation path subgraph is screened based on the preset fault propagation termination condition, and then the key nodes are accurately identified; by using the out-edge link data of the key nodes, the subgraph expansion is realized. The present invention greatly improves the accuracy and comprehensiveness of fault analysis, can quickly locate the key fault nodes and propagation paths, and realizes the dynamic balance of the node importance and the inference depth of the knowledge graph.

[0062] Each embodiment in this specification is described in a progressive manner. For the parts that are the same or similar in each embodiment, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. It should be noted that the above technical features of the embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the above technical features in the embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered that the scope described in this specification.

[0063] The above is only the preferred embodiment of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and replacements can be made, and these improvements and replacements should also be regarded as the protection scope of the present invention.

Claims

1. A method for tracing fault paths in a distribution communication network based on knowledge graph, characterized in that: include: Constructing a corresponding knowledge graph based on the operating status and topological structure of the target power distribution communication network, and obtaining the dependency strength of the corresponding communication link according to the communication data of each communication link in the knowledge graph; Obtaining a directed edge set of the abnormal node in the knowledge graph, and identifying a fault propagation path in the directed edge set based on the fault propagation rate calculated based on the dependency strength, wherein the directed edge set includes a directed edge between the abnormal node and each adjacent node; Traversing the fault propagation path to generate a fault propagation path graph, and filtering the fault propagation path graph based on a preset fault propagation termination condition to obtain a first fault propagation path subgraph, and determining the degree of each level node in the first fault propagation path subgraph; Based on the degree of each level node in the first fault propagation path subgraph, fitting prediction is performed to obtain the first node importance of the corresponding node, and the key node in the first fault propagation path subgraph is identified according to the first node importance; The outbound link data of the key node in the first fault propagation path subgraph is obtained, and the first fault propagation path subgraph is expanded based on the outbound link data to obtain a second fault propagation path subgraph.

2. The method for tracing the fault path of a power distribution communication network according to claim 1, characterized in that: The step of obtaining the dependency strength of each communication link in the knowledge graph according to the communication data of each communication link includes: Acquire first operating status data of each communication link in the knowledge graph, and perform weighted calculation based on the first operating status data to obtain a first link score corresponding to the communication link, wherein the first operating status data includes communication delay, bandwidth utilization, and data packet loss rate; Performing weighted correction on the first link score based on the physical distance of each of the communication links to obtain a second link score corresponding to the communication link; Acquire service flow data of each of the communication links, and perform weighted calculation based on the service flow data to obtain the service dependency of the corresponding communication link, wherein the service flow data includes a control instruction flow, a measurement data flow, and an alarm information flow; The second link score and the service dependency of each of the communication links are weightedly integrated to obtain the dependency strength of the corresponding communication link.

3. The method for tracing the fault path of a power distribution communication network according to claim 1, characterized in that: Before obtaining the directed edge set of the abnormal node in the knowledge graph, the method further includes: Acquire second operating status data of each node in the knowledge graph, and calculate the abnormality degree of the corresponding node based on the second operating status data, wherein the second operating status data includes response delay time, load rate and bandwidth utilization; It is determined whether the abnormality level is greater than a preset abnormality determination threshold, and if so, the corresponding node is determined to be an abnormal node.

4. The method for tracing the fault path of a power distribution communication network according to claim 1, characterized in that: The step of identifying a fault propagation path in the directed edge set based on the fault propagation rate calculated based on the dependency strength includes: Obtaining the dependency strength of the communication link corresponding to each directed edge in the directed edge set, and performing linear calculation on the dependency strength to obtain an initial fault propagation rate corresponding to the directed edge; Acquire performance indicator data of each directed edge corresponding to the communication link, and perform weighted calculation based on the performance indicator data to obtain an edge weight coefficient corresponding to the directed edge, wherein the performance indicator data includes physical distance, network hop count and communication delay; Performing weighted correction on the initial fault propagation rate based on the edge weight coefficient of each directed edge to obtain the fault propagation rate corresponding to the directed edge; It is determined whether the fault propagation rate is greater than a preset propagation rate threshold, and if so, the corresponding directed edge is marked as a fault propagation path.

5. The method for tracing the fault path of a power distribution communication network according to claim 1, characterized in that: The traversing the fault propagation path to generate a fault propagation path graph, screening the fault propagation path graph based on a preset fault propagation termination condition to obtain a first fault propagation path subgraph, and determining the degree of each level node in the first fault propagation path subgraph include: Using a breadth-first search algorithm to traverse the fault propagation path to obtain a fault propagation path graph; Using a depth-first search algorithm to obtain the number of levels of the fault propagation path graph; Based on the total number of nodes and the maximum network diameter of the target power distribution communication network, calculate a preset fault propagation depth reflecting the network scale of the target power distribution communication network; Based on the number of levels and the preset fault propagation depth, it is judged whether the fault propagation path graph satisfies the preset fault propagation termination condition; if so, the fault propagation path graph is screened to obtain a first fault propagation path subgraph; otherwise, the fault propagation path graph is characterized as the first fault propagation path subgraph, wherein the preset fault propagation termination condition includes that the number of levels is greater than the preset fault propagation depth; The in-degree and out-degree of each level node in the first fault propagation path subgraph are counted.

6. The method for tracing the fault path of a power distribution communication network according to claim 1, characterized in that: The fitting prediction based on the degree of each level node in the first fault propagation path subgraph obtains the first node importance of the corresponding node, and identifies the key node in the first fault propagation path subgraph according to the first node importance, including: Performing weighted calculation on the degree of each level node in the first fault propagation path subgraph to obtain a node characteristic value of the corresponding node; Using a random forest algorithm to perform fitting prediction on the node characteristic values ​​of each level of nodes in the first fault propagation path subgraph, to obtain a first node importance of the corresponding node; Performing feature vector centrality evaluation on each level node in the first fault propagation path subgraph, and representing the obtained network centrality as the second node importance of the corresponding node; Performing weighted calculation on the first node importance and the second node importance to obtain the node importance of the corresponding node; It is determined whether the node importance is higher than a first preset threshold value, and if so, the corresponding node is marked as a key node in the first fault propagation path subgraph.

7. The method for tracing the fault path of a power distribution communication network according to claim 1, characterized in that: The step of expanding the first fault propagation path subgraph based on the outgoing link data to obtain a second fault propagation path subgraph includes: Performing weighted calculation based on the outbound link data to obtain an inter-node coupling coefficient corresponding to each outbound link, wherein the outbound link data includes communication delay, bandwidth utilization and data packet loss rate of several outbound links; A random forest algorithm is used to fit and predict the inter-node coupling coefficient corresponding to each outbound link to obtain the fault propagation influence intensity of the target node in each outbound link; determining a fault propagation depth increment value based on the fault propagation impact intensity, and hierarchically expanding the first fault propagation path subgraph according to the fault propagation depth increment value to obtain a third fault propagation path subgraph; Acquire communication link data between the key node and each newly added boundary node in the third fault propagation path subgraph, and calculate the fault propagation probability between the key node and each newly added boundary node in the third fault propagation path subgraph based on the communication link data; The nodes of the third fault propagation path subgraph are marked based on the fault propagation probability to obtain a second fault propagation path subgraph, wherein the node marking includes marking the newly added boundary nodes corresponding to the fault propagation probability greater than a second preset threshold in the third fault propagation path subgraph.

8. A knowledge graph-based fault path tracing system for distribution communication network, characterized in that: include: A dependency strength determination module, used to construct a corresponding knowledge graph based on the operating status and topological structure of the target power distribution communication network, and obtain the dependency strength of the corresponding communication link according to the communication data of each communication link in the knowledge graph; a fault propagation path determination module, configured to obtain a directed edge set of an abnormal node in the knowledge graph, and identify a fault propagation path in the directed edge set based on the fault propagation rate calculated based on the dependency strength, wherein the directed edge set includes a directed edge between the abnormal node and each adjacent node; A node degree determination module, used to traverse the fault propagation path to generate a fault propagation path graph, and filter the fault propagation path graph based on a preset fault propagation termination condition to obtain a first fault propagation path subgraph, and determine the degree of each level node in the first fault propagation path subgraph; A key node identification module, configured to obtain a first node importance of a corresponding node by performing fitting prediction based on the degree of each level node in the first fault propagation path subgraph, and identify a key node in the first fault propagation path subgraph according to the first node importance; A target fault propagation path determination module is used to obtain the outbound link data of the key node in the first fault propagation path subgraph, and expand the first fault propagation path subgraph based on the outbound link data to obtain a second fault propagation path subgraph.

9. The distribution communication network fault path tracing system according to claim 8, characterized in that: The dependency strength determination module includes: a first link score determination unit, configured to obtain first operating status data of each communication link in the knowledge graph, and perform weighted calculation based on the first operating status data to obtain a first link score corresponding to the communication link, wherein the first operating status data includes communication delay, bandwidth utilization, and data packet loss rate; A second link score determination unit, configured to perform weighted correction on the first link score based on the physical distance of each of the communication links to obtain a second link score corresponding to the communication link; A service dependency determination unit, configured to obtain service flow data of each of the communication links, and perform weighted calculation based on the service flow data to obtain the service dependency of the corresponding communication link, wherein the service flow data includes a control instruction flow, a measurement data flow, and an alarm information flow; The weighted fusion unit is used to perform weighted fusion on the second link score and the service dependency of each of the communication links to obtain the dependency strength of the corresponding communication link.

10. The distribution communication network fault path tracing system according to claim 8, characterized in that: The fault propagation path determination module includes: A linear calculation unit, used to obtain the dependency strength of the communication link corresponding to each directed edge in the directed edge set, and perform linear calculation on the dependency strength to obtain an initial fault propagation rate corresponding to the directed edge; A weighted calculation unit, used to obtain performance indicator data of each directed edge corresponding to the communication link, and perform weighted calculation based on the performance indicator data to obtain an edge weight coefficient corresponding to the directed edge, wherein the performance indicator data includes physical distance, network hop count and communication delay; A weighted correction unit, configured to perform weighted correction on the initial fault propagation rate based on the edge weight coefficient of each directed edge to obtain a fault propagation rate corresponding to the directed edge; The propagation rate judgment unit is used to judge whether the fault propagation rate is greater than a preset propagation rate threshold, and if so, mark the corresponding directed edge as a fault propagation path.

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