Power communication network fault diagnosis method and system based on time constraint graph matching

Abstract

The invention discloses an electric power communication network fault diagnosis method and system based on time constraint graph matching, and the method comprises the steps: generating a to-be-diagnosed electric power communication network data graph based on a topological structure of a to-be-diagnosed electric power communication network; determining the typical type of the power communication network fault, and generating a power communication network fault query graph containing a time constraint relation under different faults based on the topological structure of the to-be-diagnosed power communication network; storing the candidate edges and the partial matching relationship thereof in a preset index structure, and querying an electric power communication network data sub-graph matched with the electric power communication network fault query graph time constraint in the to-be-diagnosed electric power communication network data graph through an incremental matching algorithm according to the generated matching sequence based on the preset index structure; and determining a defect fault result of the to-be-diagnosed power communication network according to the power communication network data sub-graph. According to the invention, the fault diagnosis efficiency and accuracy of the power communication network are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart grid fault diagnosis, and in particular relates to a method and system for power communication network fault diagnosis based on time-constrained graph matching. Background Art

[0002] The power communication network is an important part of the modern power system. Its main function is to provide reliable communication support for the coordination, monitoring and control of power equipment. With the rapid development of smart grids, the power communication network plays an increasingly important role in the operation of power systems. It not only needs to realize basic communication functions, but also needs to support complex tasks such as power dispatching, real-time monitoring and protection control. The realization of these functions depends on the high reliability, low latency and high bandwidth performance of the power communication network. However, the complexity and dynamic nature of the power communication network also make it vulnerable to various defects or failures.

[0003] In actual operation, the power communication network may face a variety of problems, such as communication delay, data packet loss, equipment abnormality, and network topology changes. Communication delay may be caused by network congestion, link quality degradation, or equipment performance problems, which in turn affects the real-time control and protection functions of the power system; data packet loss may be caused by network interference or equipment abnormality, resulting in the obstruction of the transmission of key information; equipment abnormalities such as failures of routers, switches, or optical fiber links may cause network interruptions and affect the overall operation of the system; in addition, due to equipment maintenance, fault isolation, or adjustment of load balancing strategies, the network topology may change, which further increases the complexity of fault diagnosis. These problems will not only reduce the operating efficiency of the power communication network, but may also pose a threat to the stability and safety of the power system. For example, communication delays may cause the protection device to fail to operate in time, thereby causing chain failures or even large-scale power outages. Therefore, it is of great significance to quickly and accurately diagnose defects and faults in the power communication network to ensure the safe operation of the power system.

[0004] Although fault diagnosis plays an important role in power communication networks, this process faces many challenges. First, power communication networks are usually composed of multiple layers, multiple devices, and multiple protocols. The network is large in scale and complex in structure. Faults may occur on devices or links at different levels, which increases the difficulty of diagnosis. Second, the operating state of the power communication network is dynamically changing, and the propagation of faults is real-time and random. Traditional static diagnosis methods are difficult to adapt to this dynamic environment. In addition, some faults may exist in a hidden form, such as performance degradation of some links or intermittent abnormalities of equipment. These problems are difficult to directly detect through traditional monitoring methods. Finally, the amount of data generated in the power communication network is huge and may contain a large amount of noise data. How to extract useful fault information from massive data is an important research issue.

[0005] In order to solve the above difficulties, researchers have proposed a variety of fault diagnosis methods, including rule-based diagnosis, data-driven machine learning methods, and analysis methods based on graph models. However, these methods still have certain limitations when facing complex power communication networks. For example, rule-based methods rely on expert experience and are difficult to adapt to complex dynamic environments; although data-driven methods can learn rules from data, they may have difficulty explaining the root cause of the fault in the absence of prior knowledge. Summary of the invention

[0006] In order to address the deficiencies in the prior art, the present invention provides a method and system for power communication network fault diagnosis based on time-constrained graph matching, which can quickly and accurately identify the root cause and propagation path of the fault in a dynamic environment including multiple communication devices and complex network topology structures, thereby improving the efficiency and accuracy of power communication network fault diagnosis.

[0007] The present invention adopts the following technical solution.

[0008] A first aspect of the present invention provides a method for diagnosing faults in a power communication network based on time-constrained graph matching, comprising: S1: generating a data graph of the power communication network to be diagnosed based on the topological structure of the power communication network to be diagnosed; S2: Determine the typical types of power communication network faults, and generate a power communication network fault query graph containing time constraints under different faults based on the topological structure of the power communication network to be diagnosed; S3: According to the edge labels and time constraint relationships of the power communication network fault query graph, all candidate edges are found from the power communication network data graph to be diagnosed, and the partial matching relationships between the candidate edges are recorded, and the candidate edges and their partial matching relationships are stored in a preset index structure, and based on the preset index structure, an incremental matching algorithm is used to query the power communication network data subgraph that matches the time constraint of the power communication network fault query graph in the power communication network data graph to be diagnosed according to the generated matching order; S4: Determine the defect and fault results of the power communication network to be diagnosed according to the power communication network data subgraph.

[0009] Optionally, in S3, the preset index structure includes node groups and links, each node group includes edges in the power communication network fault query graph and all candidate edges in the power communication network data graph to be diagnosed that match the edges in the power communication network fault query graph, and the links represent the connection relationship between the candidate edges in the power communication network data graph to be diagnosed, and the connection relationship satisfies the structural constraints and time order constraints in the power communication network fault query graph.

[0010] Optionally, multiple candidate edges with the same neighbor structure are merged into one node in a preset index structure.

[0011] Optionally, in S3, when time-constrained matching is performed using an incremental matching algorithm, when an edge in the power communication network data graph to be diagnosed is updated, a corresponding update operation is performed in a preset index structure by searching for a partial match related to the edge update.

[0012] Optionally, in S3, the matching order is generated including: Calculate the number of connected nodes for each node in the preset index structure; Sort the partially matched relationships according to the number of connected nodes for each node; Generate a matching order based on the sorting results.

[0013] Optionally, in S3, searching the power communication network data subgraph that matches the time constraint of the power communication network fault query graph in the power communication network data graph to be diagnosed by using an incremental matching algorithm according to the generated matching order based on the preset index structure includes: Recursively searching for candidate edges that partially match each edge in the power communication network fault query graph in a preset index structure according to a matching order through an incremental matching algorithm, and adding the found candidate edges to a dynamic matching set; If the dynamic matching set corresponding to the first candidate edge found does not contain all the edges in the power communication network fault query graph, then the first candidate edge found is deleted from the dynamic matching set, and the second candidate edge partially matched with the corresponding edge in the power communication network fault query graph is traced back to continue the time constraint matching; If the dynamic matching set contains all the edges in the electric power communication network fault query graph, the recursion is stopped to obtain the electric power communication network data subgraph that matches the time constraint of the electric power communication network fault query graph.

[0014] Optionally, in S4, determining the defect and fault result of the power communication network to be diagnosed according to the power communication network data subgraph includes: respectively calculating the matching degree between a plurality of power communication network data subgraphs and the query graph; Selecting the electric power communication network data subgraph corresponding to the maximum matching degree; Defect and fault results of the power communication network to be diagnosed are determined from the selected power communication network data sub-graph.

[0015] Optionally, respectively calculating the matching degree between a plurality of power communication network data subgraphs and the query graph includes: Calculate the structural matching degree and time matching degree of multiple power communication network data subgraphs and the query graph respectively; The structural matching degree and the temporal matching degree are weightedly summed to obtain the matching degree between multiple power communication network data subgraphs and the query graph.

[0016] A second aspect of the present invention provides a power communication network fault diagnosis system, the system comprising: A first generating module, configured to generate a data graph of the power communication network to be diagnosed based on the topological structure of the power communication network to be diagnosed; The second generation module is used to determine the typical type of power communication network faults, and based on the topological structure of the power communication network to be diagnosed, generate a power communication network fault query graph containing time constraint relationships under different faults; A time constraint graph matching module is used to find all candidate edges from the power communication network data graph to be diagnosed according to the edge labels and time constraint relationships of the power communication network fault query graph, and record the partial matching relationships between the candidate edges, and store the candidate edges and their partial matching relationships in a preset index structure, and query the power communication network data subgraph that matches the time constraint of the power communication network fault query graph in the power communication network data graph to be diagnosed according to the generated matching order through an incremental matching algorithm based on the preset index structure; The determination module is used to determine the defect and fault results of the power communication network to be diagnosed according to the power communication network data sub-graph.

[0017] The third aspect of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is loaded into the processor, the method for diagnosing faults in a power communication network based on time-constrained graph matching is implemented.

[0018] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned method for diagnosing faults in a power communication network based on time-constrained graph matching.

[0019] Compared with the prior art, the beneficial effects of the present invention include at least: The present invention proposes a fault diagnosis method for power communication network based on time-constrained graph matching. By modeling the fault propagation process as a dynamic time sequence process, combining the network topology structure with the graph matching algorithm, the fault root cause and propagation path are accurately identified. Unlike traditional deep learning methods, this method does not rely on a large amount of manually labeled data, thus avoiding the time-consuming and labor-intensive problem of manual labeling. At the same time, by introducing time constraints, the timing characteristics of fault propagation are fully utilized, a series of timing constraint relationships are defined, and the efficiency and accuracy of diagnosis are further improved. In a dynamic environment containing a variety of communication devices and complex network topologies, the combination of time constraints and graph matching algorithms shows higher flexibility and adaptability, especially being able to quickly adapt to the topological changes and real-time diagnosis requirements of the power communication network. The experimental results verify the high feasibility and practicality of this method, which not only shows important value in promoting the intelligent process of power communication networks, but also reveals the wide application potential of time-constrained modeling and graph matching technology in future power communication network management, significantly reducing the cost and complexity of maintenance and operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them: Figure 1 It is a schematic flow chart of a method for diagnosing faults in a power communication network based on time-constrained graph matching provided by an embodiment of the present invention; Figure 2 It is another schematic flow chart of a method for diagnosing faults in a power communication network based on time-constrained graph matching provided by an embodiment of the present invention; Figure 3 It is another schematic flow chart of a method for diagnosing faults in a power communication network based on time-constrained graph matching provided by an embodiment of the present invention; Figure 4 It is a schematic diagram of an application of a method for diagnosing faults in a power communication network based on time-constrained graph matching provided by an embodiment of the present invention; Figure 5 This is a typical network topology diagram of a power communication network provided by an embodiment of the present invention; Figure 6 It is a query graph topology schematic diagram provided by an embodiment of the present invention; Figure 7 It is a query matching speed comparison diagram provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0022] The following is a definition of the graph matching problem in an embodiment of the present invention.

[0023] Definition 1: Graph: A directed, connected, labeled graph is denoted by .in, is a set of vertices, is the edge set, is a collection of tags, is a labeling function that labels each vertex or edge Map to A label in or .for The two edges in , if they share a common endpoint, then they are called and are adjacent. For each edge , its neighbor set is recorded as ,Right now The degree of the edge Defined as the number of its neighbors.

[0024] In the present invention, directed, connected, labeled graphs are collectively referred to as graphs.

[0025] Definition 2 Subgraph Isomorphism: Given a query graph and a data graph , if there is a arrive The injective mapping m , so that: 1. For any ,have ; 2. For any ,have and , then it is called yes The subgraphs of are isomorphic.

[0026] Will be from The vertices in The injective mapping of vertices in is called exist Isomorphic embedding of subgraphs in . Query graph And data graph The two edges in and , if satisfied ,and and (or and ), then it is called and is a matching edge, denoted by .

[0027] Definition 3 Flow Graph: Flow Graph is a growing graph consisting of a series of newly added edges, denoted by ,in Represents from the vertex To the top A directed edge with label and arrival time For two edges and ,if , then it is called Before , denoted as To simplify the representation, this paper only focuses on the edge insertion operation in the stream graph. It should be noted that our method can be easily extended to support edge deletion operations based on the time sliding window model.

[0028] Definition 4 Time Order: Given a query graph , its time sequence is defined in A strict partial order relation on . For two edges ,if , then it is represented in the data graph and The matching edges must also satisfy the partial order relation on arrival time.

[0029] if Not in O O In the definition, it is called is unordered, denoted by To simplify the presentation, this article will be presented in chronological order. O The query graph of .

[0030] Definition 5: Time Constraint Matching: Given a query graph And data graph A subgraph in ,if If the following two conditions are met, then yes A time constraint matching: Structural constraints (isomorphism): yes exist A structure embedded in .

[0031] Time order constraints: For Any two edges in ,if , then in The corresponding edge The time sequence must also satisfy .

[0032] Definition 6: Partial matching refers to the The query graph found in Partially similar substructures. A partial match result is a To the data graph A partial mapping that satisfies the following two conditions: Structural constraints (partial isomorphism): The edges and vertices in the partial matching results are structurally identical to the query graph. That is, the edges and vertices in the partial matching results are isomorphic to the data graph. A query graph is formed in A subgraph with the same structure as part of .

[0033] Temporal order constraint: The edges in the partial matching results not only match in structure but also satisfy the query graph The time order constraint defined in The edge Prior to the edge in time , then in the partial matching results, Matching Edges It must also precede in time Matching Edges .

[0034] Combination Figure 1 and Figure 4 As shown, Embodiment 1 of the present invention provides a method for diagnosing faults in a power communication network based on time-constrained graph matching, which specifically includes the following contents: S1: Based on the topological structure of the power communication network to be diagnosed, a data graph of the power communication network to be diagnosed is generated.

[0035] The data graph of the power communication network to be diagnosed can be expressed as ,in, represents the vertex set of the power communication network data graph to be diagnosed, represents the edge set of the power communication network data graph to be diagnosed, Indicates mapping vertices or edges to The label function, Represents a set of labels, including vertex type, edge type, and edge time information.

[0036] The time information of an edge can be a time window condition, such as an alarm triggering time range, an alarm arrival time, etc.

[0037] Specifically, the original data such as the optical routing table, defect list table, and defect list alarm table in the power communication network are obtained, and standardized fields (such as device type and port number) are defined using a data dictionary to ensure data consistency. The data is abstracted into nodes and relationships using a non-relational graph database to construct a general data graph of the communication network. Sites, devices, and ports can be used as nodes, and optical path connections can be used as edges. By mapping optical paths with site, device, and port information, the topological structure of the power communication network to be diagnosed can be constructed, and defect alarm events can be mapped to edges in the graph structure, with timestamps and labels.

[0038] Combination Figure 5 As shown, V ( G )={Substation A, Substation B, Substation C, Equipment A, Equipment B, Equipment C, Equipment D, A(1), A(2), B(1), B(2), C(1), C(2), C(3), C(4), C(5)}, E ( G )={(Substation A, Equipment A),(Equipment A, A(1)),(Equipment A, A(2)),(Substation A, Substation B),(Substation B, Equipment B),(Equipment B, B(1)),(Equipment B, B(2)),(Substation B, Substation C),(Substation C, Equipment C),(Equipment C, C(1)),(Equipment C, C(2)),(Equipment C, C(3)),(Substation C, Equipment D),(Equipment D, C(4)),(Equipment D, C(5)),(A(1), B(1)),(B(2), C(1)),(A(2), C(5))}. The equipment in the system and its connection methods are represented by a collection of nodes and edges. S2: Determine typical types of power communication network faults, and generate a power communication network fault query graph containing time constraints under different faults based on the topological structure of the power communication network to be diagnosed.

[0039] The fault query graph of the power communication network can be expressed as ,in, represents the vertex set of the fault query graph of the power communication network, Represents the edge set of the power communication network fault query graph.

[0040] Specifically, by analyzing the defect fault information, the communication network fault is obtained, and the communication network defect fault includes transmission optical path interruption, optical cable interruption, equipment power supply interruption, equipment board fault, and equipment power supply fault.

[0041] It is understandable that these faults correspond to different power communication network fault query diagrams, and the fault categories are queried through different features.

[0042] The time constraint relationship refers to the restriction imposed on the time attributes of the edge or node in the data graph during the matching process, which can be used to describe the time law of fault propagation. These time attributes can include the creation time, update time or failure time of the edge or node. For example, the upper and lower limits of the propagation delay can be defined, indicating the time range required for the fault to propagate from one node to another.

[0043] Combination Figure 6 As shown, Figure 6 The corresponding query graph is shown in Table 1.

[0044] Table 1

[0045] In Table 1, the first module represents the basic information of the edge in the query graph. The basic information is (edge ​​ID, starting vertex ID, target vertex ID, edge attribute label, starting vertex attribute label, target vertex attribute label). Take the first row as an example (e 0 0 3 10 1). e represents the edge here, 0 represents the unique identifier of the edge, which is used to distinguish different edges. The second 0 is the ID of the starting vertex of the edge, and 3 represents the ID of the target vertex of the edge. The first 1 is used to describe the attribute or type of the edge to indicate the type of edge. Here, it indicates that the type is device connection. In the initial modeling, the types of edges are simplified into three categories: optical cable connection, device connection, and port connection, corresponding to labels 0, 1, and 2 respectively. The third 0 represents the label of the starting vertex. There are three types of vertices, namely site, device, and port, corresponding to labels 0, 1, and 2 respectively. The last 0 and 1 represent the label of the starting vertex and the label of the target vertex respectively. The second module adds time constraint information. Here, b indicates that this is a time constraint. Taking (b 3 4 2 1 0) as an example, the edge with ID 3 must arrive before 4, 4 arrives before 2, 2 arrives before 1, and 1 arrives before 0. This series of edge IDs indicates that these edges must occur in the specified order.

[0046] Combination Figure 2 As shown, in S2, the typical type of power communication network fault is determined, and based on the topological structure of the power communication network to be diagnosed, a power communication network fault query diagram under different faults is generated, including: Obtain historical defect warning information; Analyze historical defect alarm information and filter out nodes and links affected by faults, determine the coverage of defect alarm information and typical types of power communication network faults; In the topological structure of the power communication network to be diagnosed, nodes within the coverage of the defect alarm information are connected to generate a power communication network fault query graph, and time information is added to the edges in the query graph.

[0047] Historical defect alarm information includes defect order alarm information and defect order information. Defect order alarm information records the alarm details when the fault is triggered, and defect order information stores the fault reporting content, such as time information, location information, and fault alarm type information.

[0048] In this embodiment, firstly, by analyzing the timing characteristics of the fault, the search scope of the fault can be quickly narrowed, thereby improving the diagnostic efficiency. Secondly, time-constrained modeling can combine network topology and time information to accurately identify the root cause and propagation path of the fault. In addition, this method can adapt to the dynamic changes of the power communication network and is suitable for real-time fault diagnosis scenarios. Finally, the diagnostic results based on time constraints have strong interpretability, which can help operation and maintenance personnel better understand the occurrence mechanism and propagation law of the fault.

[0049] S3: According to the edge labels and time constraint relationships of the power communication network fault query graph, all candidate edges are found from the power communication network data graph to be diagnosed, and the partial matching relationships between the candidate edges are recorded, and the candidate edges and their partial matching relationships are stored in a preset index structure. Based on the preset index structure, an incremental matching algorithm is used to query the power communication network data subgraph that matches the time constraint of the power communication network fault query graph in the power communication network data graph to be diagnosed in accordance with the generated matching order.

[0050] Optionally, the preset index structure includes node groups and links, each node group includes edges in the power communication network fault query graph and all candidate edges in the power communication network data graph to be diagnosed that match the edges in the power communication network fault query graph, and the links represent the connection relationship between the candidate edges in the power communication network data graph to be diagnosed, and the connection relationship satisfies the structural constraints and time order constraints in the power communication network fault query graph.

[0051] Specifically, the preset index structure groups the nodes, each group corresponds to an edge in the query graph, including all edges in the data graph that match the edge, and creates links between nodes to encapsulate partial matches (i.e., partial embedding) and time order information of the edges in the data graph. If two nodes in different groups have a common endpoint and follow the time constraint information, a corresponding link is added between the two nodes.

[0052] It can be understood that when nodes of different groups are adjacent, and their corresponding mapping edges are also adjacent, and the arrival time of the nodes complies with the time order defined by the mapping edge, a corresponding link is added between the two nodes.

[0053] Specifically, you can initialize a node set with a preset index structure and edge sets Then, process the data graph in ascending order of arrival time. The edge of and Specifically, given the data graph An edge in Matching edges in the query graph ,examine Is it an unordered edge, or for Each predecessor edge Does it exist? The corresponding node in is not empty, the partial order must hold, and the edges can be processed in order. Zhongwei Create a node, then Create corresponding links between nodes. Specifically, consider exist Each adjacent edge in , and retrieve its matching edges .if and The edge relationship and time order between them meet the conditions (nodes in different groups have common endpoints and follow the time constraint information), then find The corresponding edge Nodes and in and After building the preset index structure, set The status of the node.

[0054] The partial matching results are stored by constructing a preset index structure, where the partial matching results include structural constraints and time constraint information of the query graph.

[0055] Specifically, building a preset index structure can be completed in an offline stage and can be completed through a one-time operation, which reduces the backtracking search space and memory consumption, thereby improving the efficiency of locating candidate matches in the online stage.

[0056] Optional, combined Figure 3 As shown, multiple candidate edges with the same neighbor structure are merged into one node in the preset index structure.

[0057] Specifically, during the traversal process, if the adjacency relationships of multiple candidate edges are completely consistent in the current path, they are merged and processed. In this way, the number of candidates can be reduced, redundant candidate paths can be eliminated, redundant calculations can be reduced, and the calculation complexity can be reduced, thereby improving the efficiency of incremental matching.

[0058] Optionally, the nodes in the preset index structure include a node status attribute, and the node status attribute is used to indicate whether all of its adjacent edges in the data graph have been matched.

[0059] Specifically, in the online stage, based on the preset index structure, an incremental matching algorithm is used to query the power communication network data subgraph that matches the time constraint of the power communication network fault query graph in the power communication network data graph to be diagnosed in accordance with the generated matching order.

[0060] Specifically, the preset index structure is dynamically updated in the online stage. First, the query edge corresponding to the newly inserted edge is determined, and its timestamp is verified to be in the time order constraint, for example, whether it is later than the matching edge of the predecessor edge. If so, the newly inserted edge is added to the corresponding group and a link with the neighbor edge is established. If the new link makes the node meet all neighbor conditions, its node status attribute is updated to 1.

[0061] In this way, during the matching stage, only the nodes whose matching node status attribute is 1 in the preset index structure are traversed, where 1 indicates a match. This can be dynamically updated to ensure that the preset index structure reflects the data graph status in real time, and only the layout is adjusted to avoid reconstruction overhead, thereby reducing redundant partial matches.

[0062] Optionally, when the timestamp of an edge of the data graph of the power communication network to be diagnosed exceeds a preset time sliding window range, the edge of the data graph is deleted from the data graph of the power communication network to be diagnosed.

[0063] Specifically, initialize the time sliding window size , preset time sliding window Represents a time range, indicating the time range of the data currently considered. If the time sliding window size Set to 10 minutes. Indicates the data within 10 minutes before the current time. Record each edge in the data graph , record its timestamp, which indicates the occurrence time, update time or arrival time of the edge, and calculate the starting time of the preset time sliding window as the current time minus , traverse each edge of the data graph. If its timestamp is less than the start time of the preset time sliding window, delete the corresponding edge and update the data graph.

[0064] Optionally, in S3, when time-constrained matching is performed using an incremental matching algorithm, when an edge in the power communication network data graph to be diagnosed is updated, a corresponding update operation is performed in a preset index structure by searching for a partial match related to the edge update.

[0065] Specifically, the update operation includes edge insertion and edge deletion. When processing the edge insertion operation, the newly inserted edge is first added to the data graph, and the preset index structure is updated to include the relevant information of the new edge. Subsequently, all incremental matching results containing the new edge are searched based on the preset index structure to ensure that these matches meet the structural constraints and time order constraints of the query graph. When processing the edge deletion operation, all incremental matching results containing the edge to be deleted are first searched and recorded. Subsequently, the edge to be deleted is removed from the data graph, and the preset index structure is updated to remove the candidate edges and partial matching relationships related to the edge.

[0066] In this way, for each update of the data graph, only the partial matches related to the updated edges are processed through incremental calculation, and the relevant candidate edges and partial matches are quickly located through the preset index structure, avoiding the recalculation of the entire graph and improving the efficiency of time-constrained matching. In addition, all incremental matching results of the query graph can be output in real time. Optionally, the generated matching order includes: Calculate the number of connected nodes for each node in the preset index structure; Sort the partially matched relationships according to the number of connected nodes for each node; Generate a matching order based on the sorting results.

[0067] Specifically, the connection node refers to other nodes directly connected to the node in the preset index structure, which are represented by links. Sorting the partial matching relationships according to the number of connection nodes of each node means sorting the partial matching relationships according to the number of connection nodes corresponding to them, and placing the edges with fewer connection nodes in front. According to the sorting result, the edges of the query graph corresponding to the node are added to the matching order in sequence, so that the partial matching relationships with fewer connection nodes are processed first, and the feasible matching relationships and infeasible matching relationships are determined more quickly.

[0068] Specifically, we can start from the node corresponding to the newly inserted edge as the trigger point for incremental update, and give priority to the query edge with the least number of candidate edges in the current partial match for expansion to reduce branches.

[0069] You can also use the edge adjacency and time order constraints stored in the preset index structure to extend the match along the index links. If the candidate edges of a path are pruned, the algorithm will automatically skip the branch and generate a new order according to the remaining links.

[0070] Optionally, the generated matching order includes: The matching order is determined by combining the frequency of occurrence of each node in the data graph and the number of connected nodes of each node in the preset index structure.

[0071] Optionally, the matching order is determined by combining the frequency of occurrence of each node in the data graph and the number of connected nodes of each node in the preset index structure, including: Calculate the priority index based on the frequency of each node in the data graph and the number of connected nodes of each node in the preset index structure; The matching order is determined according to the priority index.

[0072] Specifically, the priority index is calculated according to the frequency of each node in the data graph and the number of connected nodes of each node in the preset index structure as follows: , in, It is a priority index. The larger the priority index value is, the higher the priority is, and the earlier it is matched. The normalized value of the frequency of nodes in the preset index structure appearing in the data graph. The normalized value of the number of connected nodes of the node in the preset index structure is used to match nodes with high frequency of appearance in the data graph and small number of connected nodes. , are the corresponding weights, is a small constant.

[0073] It is understandable that the matching order determines the order in which the edges are expanded during the matching process, which directly affects the size of the search space and the matching efficiency. When matching, start with the partial match with fewer connected nodes, and gradually expand the partial match until a complete match result is found or it is determined that it cannot be expanded. In this way, by giving priority to the partial match with fewer connected nodes, instead of considering all the edges in the data graph, the search space is reduced and the matching efficiency is improved. In addition, the nodes and links in the preset index structure have encapsulated the edges and adjacency relationships in the data graph, so that incremental matching can be performed more efficiently. Moreover, by dynamically selecting the starting point and book order, it is ensured that the search starts from the path that is most likely to form a complete match, thereby improving efficiency.

[0074] Optionally, in S3, searching the power communication network data subgraph that matches the time constraint of the power communication network fault query graph in the power communication network data graph to be diagnosed by using an incremental matching algorithm according to the generated matching order based on the preset index structure includes: Recursively searching for candidate edges that partially match each edge in the power communication network fault query graph in a preset index structure according to a matching order through an incremental matching algorithm, and adding the found candidate edges to a dynamic matching set; If the dynamic matching set corresponding to the first candidate edge found does not contain all the edges in the power communication network fault query graph, then the first candidate edge found is deleted from the dynamic matching set, and the second candidate edge partially matched with the corresponding edge in the power communication network fault query graph is traced back to continue the time constraint matching; If the dynamic matching set contains all the edges in the electric power communication network fault query graph, the recursion is stopped to obtain the electric power communication network data subgraph that matches the time constraint of the electric power communication network fault query graph.

[0075] Specifically, each edge in the query graph is processed step by step in the matching order. For each edge in the query graph, all possible candidate edges are found from the preset index structure. These candidate edges are the matches between the data graph and the edges in the query graph. These candidate edges are added to the partial matching results, and then the next edge is expanded according to the matching order until the partial matching result includes all edges in the query graph or cannot be expanded.

[0076] It can be understood that the dynamic matching set is a dynamically constructed temporary data structure used to record the current matching status.

[0077] S4: Determine the defect and fault results of the power communication network to be diagnosed according to the power communication network data subgraph.

[0078] S4 includes: respectively calculating the matching degree of a plurality of power communication network data subgraphs and the query graph; Selecting the electric power communication network data subgraph corresponding to the maximum matching degree; Defect and fault results of the power communication network to be diagnosed are determined from the selected power communication network data sub-graph.

[0079] Specifically, the matching degree of multiple power communication network data subgraphs is analyzed, and the power communication network data subgraphs that cover the most alarm events and meet the time constraints are preferentially selected to determine the defect and fault results of the power communication network to be diagnosed. The defect and fault results of the power communication network to be diagnosed include faulty equipment or links.

[0080] Optionally, respectively calculating the matching degree between a plurality of power communication network data subgraphs and the query graph includes: Calculate the structural matching degree and time matching degree of multiple power communication network data subgraphs and the query graph respectively; The structural matching degree and the temporal matching degree are weightedly summed to obtain the matching degree between multiple power communication network data subgraphs and the query graph.

[0081] Optionally, the matching degree between multiple power communication network data subgraphs and the query graph is calculated as follows: , in, is the i-th power communication network data subgraph Fault query diagram of power communication network The matching degree, is the weight of the structural matching degree, ranging from 0 to 1. is the structural matching degree between the ith power communication network data subgraph and the query graph, for The alarm events in The matching degree of the edges or vertices in time is selected to analyze and determine the defect and fault results of the power communication network to be diagnosed.

[0082] Optionally, the structural matching degree between the i-th power communication network data subgraph and the query graph is calculated as follows: , in, Query diagram for power communication network fault The edge set of The data sub-map for the power communication network The edge set of Query diagram for power communication network fault and power communication network data sub-map The number of common edges.

[0083] Optional, calculated as follows The alarm events in The degree of temporal matching of the edges or vertices in : , in, For warning events timestamp, for Zhongyu The timestamp of the associated edge or vertex, is the indicator function, if If yes, it is 1, otherwise it is 0. is a set of alarm events, each of which is associated with is associated with an edge or vertex in .

[0084] S4 specifically includes determining the fault type and the corresponding quantity according to the power communication network data subgraph obtained by graph matching, and providing the range of defect information.

[0085] In the power communication system, fault phenomena can generally be divided into three categories. The first category is a fault limited to a single network unit. This type of fault will not affect other network units and is usually captured by the faulty unit and the network management system that manages it. In this case, at least two nodes will be affected. The second category is that the fault of a single network unit will propagate to other network units, forming a larger range of fault impact. The third category is a failure of the communication medium between network units, such as an optical cable or optical path interruption, which usually affects multiple nodes or sites. For the second and third types of faults, the farthest node affected by the fault is usually the boundary of the fault range, that is, the farthest network unit reporting the fault. Time constraints also play a key role in the diagnosis of these two types of faults. By analyzing the time sequence of alarms, the propagation path of the fault can be determined, and the starting point and range of the fault can be further inferred, thereby achieving accurate positioning and diagnosis of complex faults.

[0086] It should be noted that, when the queried power communication network data subgraph is a subgraph with only one vertex and / or a subgraph with more than 2 vertices and a node degree of 1, there is no defect fault in the power communication network. This can be used as a filter condition for pruning during matching, reducing invalid matches and improving the efficiency of fault diagnosis.

[0087] In this embodiment, the judgment method of equipment board failure and equipment power supply failure in communication network defect failure is relatively special. The characteristics of equipment board failure are that it is issued by the same board or different boards under the same device, and the equipment power supply failure is issued by the board. Generally, a substation board issues POWER ABNORMAL or a substation protection network issues POWER FAIL. Their failures are all board-level failures, and the failure point is relatively single, so these two types of easily identifiable failures can be directly queried or the method provided by the embodiment of the present invention can be used for fault query. The main difference between transmission optical path interruption and optical cable interruption is at the substation site level. If there is no connection between substations, it is an optical cable interruption, and if there is no connection between substations, it is an optical path interruption.

[0088] Therefore, based on the above situation in the application context of power communication network defect diagnosis, the embodiment of the present invention excludes some special cases. For data graph subgraphs that meet any of the following conditions, the embodiment of the present invention does not regard these as cases where defect faults exist: a subgraph with only one vertex, no connecting edges connected to it, a subgraph with a number of vertices greater than 2 and a node degree of 1, and no alarm subgraph exists.

[0089] Embodiment 2 of the present invention provides a power communication network fault diagnosis system, which runs the power communication network fault diagnosis method based on time-constrained graph matching as described in Embodiment 1, and the system includes: A first generating module, configured to generate a data graph of the power communication network to be diagnosed based on the topological structure of the power communication network to be diagnosed; The second generation module is used to determine the typical type of power communication network faults, and based on the topological structure of the power communication network to be diagnosed, generate a power communication network fault query graph containing time constraint relationships under different faults; A time constraint graph matching module is used to find all candidate edges from the power communication network data graph to be diagnosed according to the edge labels and time constraint relationships of the power communication network fault query graph, and record the partial matching relationships between the candidate edges, and store the candidate edges and their partial matching relationships in a preset index structure, and query the power communication network data subgraph that matches the time constraint of the power communication network fault query graph in the power communication network data graph to be diagnosed according to the generated matching order through an incremental matching algorithm based on the preset index structure; The determination module is used to determine the defect and fault results of the power communication network to be diagnosed according to the power communication network data sub-graph.

[0090] Embodiment 3 of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is loaded into the processor, the method for diagnosing faults in a power communication network based on time-constrained graph matching as described in Embodiment 1 is implemented.

[0091] Embodiment 4 of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for diagnosing faults in a power communication network based on time-constrained graph matching according to Embodiment 1 is implemented.

[0092] Example 5 of the present invention provides a verification example of the method described in Example 1.

[0093] Combination Figure 7 As shown in the figure, a large-scale power communication network in China is selected for analysis. The network has 208 nodes and 297 computing edges. The operating environment of the algorithm below is CPU: Intel(R) Core i9-13900HX, main frequency 5.40GHz, memory 16GB, operating system Ubunte18.04.

[0094] In order to study the application effect of graph matching technology in the target communication network, this paper compares and analyzes the performance differences between graph matching technology and the relational database currently used in the power grid when processing complex related data. In relational databases, one of the commonly used SQL operations is to combine the first rows of two or more tables through join connections. This method is very useful when processing complex related data. However, as the scale of data increases, especially when multiple table connections are involved, join operations may become time-consuming due to performance bottlenecks and difficult to meet the needs of efficient processing. In contrast, for large-scale communication networks, graph matching technology has better scalability and performance advantages when processing complex topological relationships and multi-dimensional data. Especially in communication networks, fault alarm data usually has strong correlations and a certain time sequence. Graph matching technology can capture these complex relationships more efficiently and further optimize the matching process through time constraints, thereby improving the accuracy and efficiency of fault diagnosis.

[0095] In actual defect ticket data, there is a problem of duplicate dispatching, which needs to be reduced and optimized through data processing. The generation of duplicate defect tickets mainly comes from the following two situations: First, in the actual operation environment, the same problem triggers an alarm again after a period of time because the defect is not detected and repaired in time. Due to the long time interval between two alarms, traditional systems often regard multiple alarms caused by the same root cause as different independent events, thereby generating multiple defect reports. After introducing time constraints, the timestamps of the alarms can be analyzed to determine whether these alarms belong to repeated events within the same time range. For example, when the time interval between two alarms is less than a certain set threshold, they can be regarded as the same event, thereby effectively reducing the generation of duplicate defect tickets. Second, when multiple geographically dispersed sites share the same line or the same equipment, if this line or equipment fails, the diagnosis system may mistakenly believe that it is caused by multiple independent defects based on manual judgment or preset rules, and thus generate multiple independent defect records. By introducing time constraints, we can analyze whether the alarms of these sites occur in a short period of time in combination with the time sequence of the alarms. This can determine whether these alarms are caused by the same fault cause and then merge related defect tickets.

[0096] The introduction of time constraints plays an important role in solving the problem of duplicate defect tickets. By comparing timestamps and analyzing time intervals, alarm events with the same root cause can be merged to avoid misjudgments caused by long time intervals or overly dispersed alarm distribution. At the same time, combined with the topological analysis capabilities of graph matching technology, the correlation between alarms can be identified more efficiently, especially in complex communication networks. The combination of time constraints and graph matching technology can more comprehensively capture the propagation path and impact range of alarms, thereby providing a more accurate basis for defect diagnosis and optimized dispatch.

[0097] From the experimental results, the response time of the traditional relational database is much slower than that of the graph matching algorithm when facing a variety of different fault types. Among the more novel graph matching algorithms, the graph matching algorithm in Example 1 is more efficient than the rapidflow algorithm in terms of time processing. Among the three fault type queries, the power supply equipment terminal is more efficient in querying the transmission optical path interruption and optical cable interruption time than the other two fault types, because its characteristics are more obvious than the graph matching modes of the other two categories. Through experimental comparison, it can be seen that graph matching has a better effect on fault diagnosis.

[0098] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0099] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0100] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the above. The computer-readable storage medium used herein is not to be interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0101] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0102] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect through the Internet). In some embodiments, by using the state information of the computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit may execute the computer-readable program instructions, thereby implementing various aspects of the present disclosure.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for fault diagnosis of power communication network based on time-constrained graph matching, characterized in that: include: S1: generating a data graph of the power communication network to be diagnosed based on the topological structure of the power communication network to be diagnosed; S2: Determine the typical types of power communication network faults, and generate a power communication network fault query graph containing time constraints under different faults based on the topological structure of the power communication network to be diagnosed; S3: According to the edge labels and time constraint relationships of the power communication network fault query graph, all candidate edges are found from the power communication network data graph to be diagnosed, and the partial matching relationships between the candidate edges are recorded, and the candidate edges and their partial matching relationships are stored in a preset index structure, and based on the preset index structure, an incremental matching algorithm is used to query the power communication network data subgraph that matches the time constraint of the power communication network fault query graph in the power communication network data graph to be diagnosed according to the generated matching order; S4: Determine the defect and fault results of the power communication network to be diagnosed according to the power communication network data subgraph.

2. The method for fault diagnosis of a power communication network based on time-constrained graph matching according to claim 1, characterized in that: In S3, the preset index structure includes node groups and links, each node group includes edges in the power communication network fault query graph and all candidate edges in the power communication network data graph to be diagnosed that match the edges in the power communication network fault query graph, and the links represent the connection relationship between the candidate edges in the power communication network data graph to be diagnosed, and the connection relationship satisfies the structural constraints and time order constraints in the power communication network fault query graph.

3. The method for fault diagnosis of electric power communication network based on time-constrained graph matching according to claim 2, characterized in that: Multiple candidate edges with the same neighbor structure are merged into one node in a preset index structure.

4. The method for fault diagnosis of a power communication network based on time-constrained graph matching according to claim 1, characterized in that: In S3, when time-constrained matching is performed by the incremental matching algorithm, when an edge in the power communication network data graph to be diagnosed is updated, a corresponding update operation is performed in the preset index structure by searching for partial matches related to the edge update.

5. The method for fault diagnosis of electric power communication network based on time-constrained graph matching according to claim 1 or 3, characterized in that: In S3, the matching sequence is generated as follows: Calculate the number of connected nodes for each node in the preset index structure; Sort the partially matched relationships according to the number of connected nodes for each node; Generate a matching order based on the sorting results.

6. The method for fault diagnosis of electric power communication network based on time-constrained graph matching according to claim 5, characterized in that: In S3, based on the preset index structure, an incremental matching algorithm is used to search the power communication network data graph to be diagnosed for a power communication network data subgraph that matches the time constraint of the power communication network fault query graph in accordance with the generated matching order, including: Recursively searching for candidate edges that partially match each edge in the power communication network fault query graph in a preset index structure according to a matching order through an incremental matching algorithm, and adding the found candidate edges to a dynamic matching set; If the dynamic matching set corresponding to the first candidate edge found does not contain all the edges in the power communication network fault query graph, then the first candidate edge found is deleted from the dynamic matching set, and the second candidate edge partially matched with the corresponding edge in the power communication network fault query graph is traced back to continue the time constraint matching; If the dynamic matching set contains all the edges in the electric power communication network fault query graph, the recursion is stopped to obtain the electric power communication network data subgraph that matches the time constraint of the electric power communication network fault query graph.

7. The method for fault diagnosis of electric power communication network based on time-constrained graph matching according to claim 1, characterized in that: In S4, the defect and fault result of the power communication network to be diagnosed is determined according to the power communication network data subgraph, including: respectively calculating the matching degree between a plurality of power communication network data subgraphs and the query graph; Selecting the electric power communication network data subgraph corresponding to the maximum matching degree; Defect and fault results of the power communication network to be diagnosed are determined from the selected power communication network data sub-graph.

8. The method for fault diagnosis of electric power communication network based on time-constrained graph matching according to claim 7, characterized in that: Calculate the matching degree of multiple power communication network data subgraphs and the query graph respectively, including: Calculate the structural matching degree and time matching degree of multiple power communication network data subgraphs and the query graph respectively; The structural matching degree and the temporal matching degree are weightedly summed to obtain the matching degree between multiple power communication network data subgraphs and the query graph.

9. A power communication network fault diagnosis system, characterized in that: The system comprises: A first generating module, configured to generate a data graph of the power communication network to be diagnosed based on the topological structure of the power communication network to be diagnosed; The second generation module is used to determine the typical type of power communication network faults, and based on the topological structure of the power communication network to be diagnosed, generate a power communication network fault query graph containing time constraint relationships under different faults; A time constraint graph matching module is used to find all candidate edges from the power communication network data graph to be diagnosed according to the edge labels and time constraint relationships of the power communication network fault query graph, and record the partial matching relationships between the candidate edges, and store the candidate edges and their partial matching relationships in a preset index structure, and query the power communication network data subgraph that matches the time constraint of the power communication network fault query graph in the power communication network data graph to be diagnosed according to the generated matching order through an incremental matching algorithm based on the preset index structure; The determination module is used to determine the defect and fault results of the power communication network to be diagnosed according to the power communication network data sub-graph.

10. An electronic device, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the power communication network fault diagnosis method based on time-constrained graph matching according to any one of claims 1-8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the power communication network fault diagnosis method based on time-constrained graph matching described in any one of claims 1 to 8 are implemented.

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