Device anomaly trajectory tracking method and device, electronic device, and storage medium

Abstract

Embodiments of the present application provide a device abnormal trajectory tracking method and device, electronic equipment and storage medium, the method comprises: obtaining the device identification information of the device, recording the device information of the device according to the device identification information;Device information includes location information;When the device is repaired, obtain the device data in the preset time period;The device data is stored in the graph data structure with the device identification information as the vertex and the location information as the edge;The path of the graph data structure is tracked, and the abnormal trajectory of the device is obtained. By coding the device identification of the device, in addition to improving the real-time and accuracy of the device usage data record, the usage trajectory of the device can also be searched based on the device data stored in the graph data structure, so as to distinguish the abnormal trajectory, which can effectively improve the monitoring strength of the device and directly and effectively investigate the problem point and the person responsible for the device.

Description

Technical Field

[0001] This invention relates to the fields of industrial internet and big data technology, and in particular to a method for tracing abnormal equipment trajectories, a device for tracing abnormal equipment trajectories, a corresponding electronic device, and a corresponding computer storage medium. Background Technology

[0002] Industrial Internet Identifier Resolution Service is a crucial foundation for achieving full product lifecycle management. The Industrial Internet Identifier Resolution System provides public identifier resolution services, helping enterprises achieve information connection and interoperability across various stages and between enterprises. It transforms "information silos" into a free flow of information throughout the entire process based on unified identifiers, realizing comprehensive digitization and interaction of design, production, marketing, and after-sales information. This enhances enterprise knowledge value and sharing, optimizes product development and business processes, reduces the cost of full product lifecycle management, and ultimately improves the enterprise's market competitiveness.

[0003] Currently, the main application technologies of the Industrial Internet involve generating identification chains, which facilitate the parsing of identification chains when users query identified objects, thereby obtaining traceability and verification analysis results for the identified objects. However, the current application of Industrial Internet identification resolution and equipment management mainly focuses on the binding of Industrial Internet identifications, while the means of achieving equipment traceability management and visualization through big data analysis are still under development, lacking specific methods and means for monitoring large-scale equipment. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention are proposed to provide a method for tracing abnormal equipment trajectories, a device for tracing abnormal equipment trajectories, a corresponding electronic device, and a corresponding computer storage medium to overcome or at least partially solve the above problems.

[0005] This invention discloses a method for tracing abnormal equipment trajectories, the method comprising:

[0006] Obtain the device identification information of the device, and record the device information of the device based on the device identification information; the device information includes location information;

[0007] When the equipment malfunctions during maintenance, the equipment data within a preset time period is acquired; the equipment data is stored in a graph data structure with the equipment identification information as vertices and the location information as edges.

[0008] By performing path tracing on the graph data structure, the abnormal trajectory of the device can be obtained.

[0009] Optionally, recording the location information of the device based on the device identification information includes:

[0010] Record the daily management data, borrowing and usage data of the equipment, and the location information corresponding to the equipment's travel trajectory.

[0011] Optionally, the device information further includes the time-based usage length, and the location information includes the location information of the device within a preset time period; the step of recording the device information according to the device identification information further includes:

[0012] The device information is transformed by converting the device identification information into vertices, the location information of the device within a preset time period into edges, and the time usage length into edge weights, to construct a graph data structure to record the device information and obtain device data.

[0013] Optionally, the method further includes:

[0014] Device data in a graph data structure is stored based on a preset routing table;

[0015] The device data in the graph data structure includes vertex data and edge data; the storage of the device data in the graph data structure based on a preset routing table includes:

[0016] The vertex data is distributed and stored in the form of the multiple first partitions by performing hash partitioning based on the device identification information of the vertices.

[0017] Furthermore, the edge data is partitioned according to a preset partitioning strategy to obtain multiple second partitions, and the edge data is distributed and stored in the form of the multiple second partitions;

[0018] The relationship between each vertex in the first partition and each edge in the second partition is recorded based on a preset routing table.

[0019] Optionally, the graph data structure includes directed graph paths with multiple edges, and the step of tracing the path through the graph data structure to obtain the abnormal trajectory of the device includes:

[0020] Obtain the conditional parameter information of the multi-edge; the conditional parameter information is used to define the number of repetitions where an edge starts from a certain vertex;

[0021] The abnormal trajectory of the device is obtained by tracing the directed graph path with multiple edges in the graph data structure using the condition parameter information.

[0022] Optionally, the device data stored in the form of a graph data structure includes vertex data and edge data. The step of tracing directed graph paths with multiple edges in the graph data structure using the condition parameter information to obtain the abnormal trajectory of the device includes:

[0023] Obtain each vertex in the vertex data, and use a recursive method to traverse the edge data with each vertex as the starting vertex, and output the path information that meets the condition parameter information.

[0024] The path information that meets the condition parameter information is taken as the abnormal trajectory of the device.

[0025] Optionally, the step of recursively traversing the edge data using each vertex as a starting vertex to output path information that meets the condition parameter information includes:

[0026] Starting from any vertex, the edges in the edge data are searched recursively.

[0027] In response to the edges in the edge data that start from the initial point, perform an edge aggregation operation on the edges to obtain an edge set, and sort the aggregated edges in the edge set according to their weights.

[0028] Obtain the edges from the set of edges with the next vertex as the temporary starting point. If there is an edge with a weight greater than a preset weight among the edges with the next vertex as the starting point, then use the edge with the weight greater than the preset weight as the associated path until the next vertex that has been traversed is the same as the initial starting point. Output the path composed of the associated paths.

[0029] From the combined paths, obtain path information that is relevant to the initial starting point and meets the condition parameter information.

[0030] Optionally, after obtaining the abnormal trajectory of the device, the method further includes:

[0031] The device's operation records are obtained based on the abnormal trajectory.

[0032] The operation records are verified to determine whether there are any risks or violations.

[0033] This invention also discloses a device for tracing abnormal equipment trajectories, the device comprising:

[0034] The device recording module is used to acquire device identification information of the device and record device information based on the device identification information; the device information includes location information.

[0035] The equipment data acquisition module is used to acquire equipment data within a preset time period when the equipment malfunctions during maintenance; the equipment data is stored in a graph data structure with the equipment identification information as vertices and the location information as edges;

[0036] The path tracing module is used to perform path tracing on the graph data structure to obtain the abnormal trajectory of the device.

[0037] Optionally, the device recording module includes:

[0038] The equipment information recording submodule is used to record the daily management data, borrowing and usage data of the equipment, and the location information corresponding to the equipment's travel trajectory.

[0039] Optionally, the device information further includes the time period for requisition, and the location information includes the location information of the device within a preset time period; the device information recording module includes:

[0040] The device data generation submodule is used to perform data transformation on the device information, using the device identification information as vertices, the location information of the device within a preset time period as edges, and the time usage length as edge weights, to construct a graph data structure to record the device information and obtain device data.

[0041] Optionally, the device recording module further includes:

[0042] The device data storage submodule is used to store device data in a graph data structure based on a preset routing table;

[0043] Optionally, the device data in the graph data structure includes vertex data and edge data; the device data storage submodule includes:

[0044] The first distributed storage unit is used to perform hash partitioning according to the device identification information of the vertex to obtain multiple first partitions, and to distribute and store the vertex data in the form of the multiple first partitions.

[0045] The second distributed storage unit is used to partition the data according to a preset partitioning strategy to obtain multiple second partitions, and to distribute and store the edge data in the form of the multiple second partitions.

[0046] The device data storage unit is used to record the relationship between each vertex in the first partition and each edge in the second partition based on a preset routing table.

[0047] Optionally, the graph data structure includes directed graph paths with multiple edges, and the path tracing module includes:

[0048] The condition parameter information acquisition submodule is used to acquire the condition parameter information of multiple edges; the condition parameter information is used to define the number of repetitions where an edge starts from a certain vertex.

[0049] The path tracing submodule is used to trace the directed graph path with multiple edges in the graph data structure using the condition parameter information to obtain the abnormal trajectory of the device.

[0050] Optionally, the device data stored in the form of a graph data structure includes vertex data and edge data, and the path tracing submodule includes:

[0051] The edge data traversal unit is used to obtain each vertex in the vertex data, and traverse the edge data in a recursive manner using each vertex as the starting vertex, and output the path information that meets the condition parameter information.

[0052] An abnormal trajectory output unit is used to take the path information that meets the condition parameter information as the abnormal trajectory of the device.

[0053] Optionally, the edge data traversal unit includes:

[0054] The edge search subunit is used to search for each edge in the edge data in a recursive manner, starting from any vertex.

[0055] An edge clustering subunit is used to respond to the edges in the edge data that have the initial starting point, perform edge aggregation operation on the edges to obtain an edge set, and sort the aggregated edges in the edge set according to the edge weights.

[0056] The associated path acquisition subunit is used to acquire the edges with the next vertex as the temporary starting point from the edge set. If there is an edge with a weight greater than a preset weight among the edges with the next vertex as the starting point, the edge with the weight greater than the preset weight is taken as the associated path until the next vertex traversed is the same as the initial starting point, and the path composed of the associated paths is output.

[0057] The path information acquisition subunit is used to acquire path information from the composed path that is relevant to the initial starting point and meets the condition parameter information.

[0058] Optionally, after obtaining the abnormal trajectory of the device, the device further includes:

[0059] The operation record acquisition module is used to acquire the operation record of the device based on the abnormal trajectory.

[0060] The operation record verification module is used to verify the operation record and determine whether there are any usage risks or violations.

[0061] This invention also discloses an electronic device, including: a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements any of the methods for tracing abnormal trajectories of the device.

[0062] This invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the methods for tracing abnormal device trajectories.

[0063] The embodiments of the present invention have the following advantages:

[0064] In this embodiment of the invention, device identification information can be obtained, and device information can be recorded based on the obtained device identification information. This allows for the acquisition of device data within a preset time period when a device malfunctions during maintenance. The acquired device data can be stored in a graph data structure with device identification information as vertices and position information as edges. Path tracing can then be performed on the graph data structure to obtain the abnormal trajectory of the device. By assigning device identification codes, the real-time performance and accuracy of device usage data recording are improved. Furthermore, based on the device data stored in the graph data structure, the device's usage trajectory can be searched to identify abnormal trajectories. This effectively enhances device monitoring, directly and effectively identifies device problems and responsible parties, and visualizes the device trajectory based on path tracing of the graph data structure. Attached Figure Description

[0065] Figure 1 This is a flowchart illustrating the steps of an embodiment of a method for tracing abnormal equipment trajectories according to the present invention.

[0066] Figure 2 This is a flowchart illustrating the steps of another embodiment of the device anomaly trajectory tracing method of the present invention;

[0067] Figure 3 This is a schematic diagram of the storage of the graph data structure provided in the embodiments of the present invention;

[0068] Figure 4 This is a schematic diagram of the process of constructing an RDD provided in an embodiment of the present invention;

[0069] Figure 5 This is a schematic diagram of a directed graph with multiple edges provided in an embodiment of the present invention;

[0070] Figure 6 This is a schematic diagram of the path tracing process provided in an embodiment of the present invention;

[0071] Figure 7This is a schematic diagram illustrating an application scenario of the abnormal trajectory of the tracing device provided in an embodiment of the present invention;

[0072] Figure 8 This is a structural block diagram of an embodiment of a device for tracing abnormal equipment trajectories according to the present invention. Detailed Implementation

[0073] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0074] Currently, the main point of integration between industrial internet identifier resolution and equipment management lies in the binding of industrial internet identifiers. For example, in related industrial internet applications, this can be achieved by generating identifier chains to clarify the traceability path of identifiers (goods, transactions, etc.), verifying the authenticity of the identifier process. This allows users to parse the identifier chain when querying an identifier object, thereby obtaining traceability, verification, and analysis results, and enabling predictive analysis of the target. However, this approach suffers from insufficient real-time performance and accuracy of equipment data, significant challenges in reliable data collection from terminal devices, considerable difficulty in tracing existing equipment, and a lack of specific monitoring methods and techniques for large-scale equipment.

[0075] One of the core ideas of this invention is to improve the real-time performance and accuracy of recording equipment usage data by assigning equipment identification codes to the equipment, thereby achieving full data recording and storage of the terminal usage process, ensuring data accumulation, and realizing reliable data collection and management. Furthermore, based on the equipment data stored according to the graph data structure, the usage trajectory of the equipment can be searched to identify abnormal trajectories, directly and effectively diagnosing equipment problems and identifying responsible parties. Additionally, it enables the supervision of the usage process of large enterprise equipment, effectively improving the monitoring of equipment and visualizing the equipment trajectory based on path tracing of the graph data structure.

[0076] Reference Figure 1 The diagram illustrates a step flowchart of an embodiment of a method for tracing abnormal equipment trajectories according to the present invention, which may specifically include the following steps:

[0077] Step 101: Obtain the device identification information and record the device information based on the device identification information;

[0078] In this embodiment of the invention, by assigning a device identifier to the device, the real-time performance and accuracy of recording device usage data can be improved, enabling full recording and storage of terminal usage data, ensuring data accumulation, and achieving reliable data collection and management.

[0079] Specifically, the acquired device identification information can be represented as a device identification code, so that the device can be assigned a code based on the acquired device identification code, thereby realizing the recording of device information during use based on the device identification code.

[0080] In practical applications, the device's unique code can be used as the suffix of the Industrial Internet identifier code. Then, the identifier can be registered with the secondary node via an interface to obtain the device identifier code. For example, taking equipment in a repair center as an example, assuming it is connected to a provincial Industrial Internet identifier resolution secondary node, and the enterprise identifier prefix is ​​86.107.XXX, the device's unique code can be used as the suffix of the Industrial Internet identifier code. The identifier registration can then be performed by calling the secondary node interface, and the obtained identifier code will be "86.107.XXX / device unique code".

[0081] After the equipment obtains an identification code, equipment information can be recorded based on that code. Specifically, daily management data, borrowing and usage data, and equipment travel trajectories can all be recorded through the identification traceability application. Equipment information during use can include relevant data from daily management, borrowing and usage, and equipment travel. This relevant data can include equipment identification information, equipment location information, and the duration of equipment use. In other words, the corresponding equipment identification information, location information, and duration of equipment use for daily management data, borrowing and usage data, and equipment travel trajectories can be recorded as raw data for equipment trajectory traceability.

[0082] The raw data types involved in equipment trajectory tracing can be mainly divided into equipment identification data types and barcode data types.

[0083] Equipment identification data mainly refers to the unit directory, workshop directory, and equipment directory established when maintenance equipment enters the factory. This includes the recorded standard classification information of the equipment, such as type, brand, specifications, model, location, purpose, function, serial number, manufacturer, production time, service life, and price. When equipment already in the system is borrowed, its detailed information needs to be recorded by scanning the code, including equipment name, equipment type, reason for borrowing, estimated return time, and warehouse location. When the return time is approaching, the system automatically generates a reminder to alert equipment managers to retrieve the borrowed equipment. When the borrowed equipment is returned, the borrowing record must be checked for proper processing. This ensures complete recording of equipment entry and exit information via barcode scanning, facilitating personnel management.

[0084] The main fields used in its data samples are shown in Table 1 below:

[0085] Serial Number Equipment identification code Equipment Name Equipment location Location ID 1 86.107.108 / 158400242610012 Excavator 1 XXX Coal Mining Plant 1 158400242610012 2 86.107.108 / 27264512012 Mainframe 2 XXX Coal Mining Plant 2 27264512012 3 86.107.108 / 39169401012 Detection device 1 XXX Coal Mining Plant 3 39169401012 4 86.107.108 / 40110052274120 Detection device 2 XXX Coal Mining Plant 4 40110052274120 5 86.107.108 / 540110052274120 Detection device 3 XXX Coal Mining Plant 5 540110052274120 6 86.107.108 / 67023165427 Detection device 4 XXX Coal Mining Plant 6 67023165427

[0086] Table 1

[0087] The equipment scanning data mainly refers to the scanning of the equipment by the borrower when they arrive at each usage point after borrowing the equipment to record the equipment usage. At the same time, the system will record the location of the usage point and the scanning time.

[0088] The main fields used in its data samples are shown in Table 2 below:

[0089] Previous position ID Current Location ID Issuance time Duration of use (h) 158400242610012 40110052274120 2021-09-2210:00:00 10 158400242610012 67023165427 2021-09-2200:00:00 20 27264512012 158400242610012 2021-09-2500:00:00 10 40110052274120 39169401012 2021-09-2309:00:00 18 39169401012 27264512012 2021-08-2113:00:00 10 39169401012 27264512012 2021-09-2411:00:00 12 158400242610012 540110052274120 2021-09-2211:30:00 12 40110052274120 540110052274120 2021-09-1500:00:00 10 27264512012 67023165427 2021-09-2309:00:00 18

[0090] Table 2

[0091] In the specific implementation, to facilitate data association, the location information in the scanned data can be replaced with location IDs, and the replacement results are shown in Table 3 below:

[0092] Previous position ID Current Location ID Issuance time Duration of use (h) 1 4 2021-09-2210:00:00 10 1 6 2021-09-2200:00:00 20 2 1 2021-09-2500:00:00 10 4 3 2021-09-2309:00:00 18 3 2 2021-08-2113:00:00 10 3 2 2021-09-2411:00:00 12 1 5 2021-09-2211:30:00 12 4 5 2021-09-1500:00:00 10 2 6 2021-09-2309:00:00 18

[0093] Table 3

[0094] In this embodiment of the invention, key elements can be data such as device usage time, scanning location information, and usage duration to achieve device path tracing. This embodiment of the invention does not impose any limitations on this aspect.

[0095] Step 102: When a fault occurs during equipment maintenance, acquire equipment data within a preset time period;

[0096] In the process of tracing the path of equipment, when equipment malfunctions during maintenance, such as when equipment malfunctions or missing parts are found during maintenance, the path tracing algorithm can be executed on the equipment data within a preset time period to quickly retrieve and output the abnormal trajectory route of the equipment based on the aforementioned key elements.

[0097] In practical applications, the path tracing algorithm executed can be implemented using the DFS (Depth First Search) path tracing algorithm implemented in Spark GraphX. GraphX ​​is a component in Spark used for graphs and graph computations. GraphX ​​can introduce new graph abstraction data structures by extending Spark RDDs (Resilient Distributed Datasets, an abstract concept of distributed memory), placing effective information into directed multigraphs of vertices and edges.

[0098] The preset time period can refer to the time interval between the current equipment maintenance point and the previous equipment maintenance point, the time the previous location was used is less than the current location's usage time, or the difference between two borrowing times is less than n days, etc. The acquired equipment data can be stored in a graph data structure with equipment identification information as vertices and location information as edges. Specifically, during the recording of equipment information, the recorded equipment information can be transformed, and storage is achieved based on the transformed structure. That is, the equipment information recorded after being transformed into a graph data structure can be called equipment data.

[0099] In the specific implementation, the recorded device information can include device identification information, time-based usage duration, and location information. Location information typically refers to the device's location within a preset time period. The recorded device information can then be organized and abstracted into a graph data structure in Spark GraphX. This is represented by abstracting device identification information (i.e., device ID) as vertices, transforming the device's location within a fixed time period (i.e., its location within the preset time period) into edges, and using the usage duration as the edge weight, thus realizing the flow of the graph data structure for device information. It should be noted that there may be multiple flows between different locations; that is, the generated graph data structure can contain multiple edges.

[0100] For example, data such as device usage time, scanning location information, and usage duration can be used as key elements. Data conditions can be set, such as the usage time of the previous location being less than the usage time of the current location, the difference between two usage times being less than n days, and the usage time being after the last routine maintenance. This enables the tracking of the path from device usage to return, preventing path interference between different batches of usage. The device identifier ID is abstracted as a vertex, the two location transfers are edges, and the transfer time and usage duration are the weights of the edges. There may be multiple transfers between each location, that is, the graph data structure generated by the transfer has multiple edges.

[0101] Step 103: Perform path tracing on the graph data structure to obtain the abnormal trajectory of the device.

[0102] The basic idea of ​​the path tracing algorithm is to read data files from the Hadoop Distributed File System (HDFS) to create RDDs, and then use map operations to create RDDs for points and edges respectively, thus forming the RDD of the graph. In practical applications, we can first traverse the set of points and retrieve all edges originating from a vertex from the set of edges, sort them by time weight, record the information of each edge, find the edges in the graph that start from the target vertex of that edge, record their information, and determine whether the weight meets the conditions. Based on the idea of ​​depth-first search, we iterate until we find the edge with the target vertex as the initial vertex. Otherwise, we backtrack to ensure that every valid edge is traversed and all paths are eventually found.

[0103] In some embodiments of the present invention, after obtaining the abnormal trajectory of the device based on the path tracing algorithm, the device's operation records can also be obtained based on the abnormal trajectory. These operation records can then be verified to determine if there are any usage risks or violations. Specifically, the device's usage trajectory can be searched based on the device data stored in the graph data structure, thereby identifying abnormal trajectories, directly and effectively identifying equipment problems and responsible parties, and supervising the use of large enterprise equipment. This effectively improves the monitoring of the equipment and enables visualization of the device trajectory based on path tracing using the graph data structure.

[0104] In this embodiment of the invention, device identification information can be obtained, and device information can be recorded based on the obtained device identification information. This allows for the acquisition of device data within a preset time period when a device malfunctions during maintenance. The acquired device data can be stored in a graph data structure with device identification information as vertices and position information as edges. Path tracing can then be performed on the graph data structure to obtain the abnormal trajectory of the device. By assigning device identification codes, the real-time performance and accuracy of device usage data recording are improved. Furthermore, based on the device data stored in the graph data structure, the device's usage trajectory can be searched to identify abnormal trajectories. This effectively enhances device monitoring, directly and effectively identifies device problems and responsible parties, and visualizes the device trajectory based on path tracing of the graph data structure.

[0105] Reference Figure 2 The diagram illustrates a flowchart of another embodiment of the device anomaly trajectory tracing method of the present invention, which may specifically include the following steps:

[0106] Step 201: Perform data transformation on the device information, using device identification information as vertices, device location information within a preset time period as edges, and time usage length as edge weights, to construct a graph data structure to record device information and obtain device data.

[0107] In this embodiment of the invention, by assigning a device identifier to the device, the real-time performance and accuracy of recording device usage data can be improved, enabling full recording and storage of terminal usage data, ensuring data accumulation, and achieving reliable data collection and management.

[0108] Specifically, the acquired device identification information can be represented as a device identification code, so that the device can be assigned a code based on the acquired device identification code, thereby realizing the recording of device information during use based on the device identification code.

[0109] In practical implementation, the recorded equipment information can include relevant data on the daily management, borrowing and use of the equipment, and the equipment's operation. This relevant data can include equipment identification information, equipment location information, and the length of time the equipment is used. In other words, the equipment identification information, location information, and the length of time the equipment is used for daily management data, borrowing and use data, and the corresponding equipment identification information, location information, and length of time the equipment is used for the original data involved in equipment trajectory tracing.

[0110] In one embodiment of the present invention, when a fault occurs during equipment maintenance, such as when a fault is found in the equipment or a part is missing, a path tracing algorithm is executed on the equipment data for a preset time period to quickly retrieve and output the abnormal trajectory route of the equipment by judging key elements.

[0111] The preset time period can refer to the time period between the current equipment maintenance point and the previous equipment maintenance point, the time of borrowing at the previous location being less than the time of use at the current location, the time difference between two borrowing periods being less than n days, etc.

[0112] When equipment malfunctions during maintenance, equipment data within a preset time period can be retrieved. This retrieved data can be stored using a graph data structure with equipment identification information as vertices and location information as edges. Specifically, during the recording of equipment information, the recorded information can be transformed and stored based on the transformed structure. In other words, the equipment information recorded after being transformed into a graph data structure can be referred to as equipment data.

[0113] In practice, the recorded device information can be organized and abstracted into a graph data structure in Spark GraphX. This is represented by abstracting device identification information (i.e., device ID) as vertices, and transforming the device's location within a fixed time period (i.e., its location information within a preset time period) into edges, with the transformation time and usage duration serving as the edge weight. This achieves the transformation of the graph data structure of device information. It should be noted that there may be multiple transformations between different locations, meaning that the resulting graph data structure can contain multiple edges.

[0114] When storing device data in a graph data structure based on a preset routing table, the device data in the graph data structure includes vertex data and edge data. Multiple first partitions (i.e., vertex RDDs) can be obtained by hashing the device identifier information used as vertices, and the vertex data can be distributed and stored in the form of multiple first partitions. In addition, multiple second partitions (i.e., edge RDD partitions) can be obtained by partitioning according to a preset partitioning strategy, and the edge data can be distributed and stored in the form of multiple second partitions. Then, the relationship between each vertex in the first partition and each edge data in the second partition is recorded based on the preset routing table.

[0115] Specifically, refer to Figure 3 This diagram illustrates the storage of the graph data structure provided in an embodiment of the present invention. The abstracted graph data structure is stored using GraphX's vertex partitioning method, implemented through three RDDs: VertexTable, RoutingTable, and Edge Table. Directed graphs are stored using GraphX's graph data structure, as shown below. Figure 3 As shown, GraphX ​​can distribute the directed graph data as RDDs across two nodes in the cluster. The vertices with IDs 1 and 3 are split. The Vertex RDD stores the vertex IDs and attribute information, while the Edge RDD stores information about all edges. The Vertex RDD distributes the vertex data across the cluster in a multi-partition format by hashing the vertex IDs. The Edge RDD distributes the edge data across the cluster in a multi-partition format by partitioning according to a specified partition strategy. The routing table records the relationship between vertices within a partition and all edge RDD partitions. When an edge RDD needs vertex data, the vertex RDD sends the vertex data to the edge RDD partition according to the routing table.

[0116] The process of building an RDD can be as follows: Figure 4As shown, when constructing the RDD of a directed graph, the original data text file can be uploaded to HDFS, then the file can be read from HDFS and partitioned to create RDDs for points and edges respectively, generating the RDD of the graph. Furthermore, `graph.edges.persist()` can be used to cache edges to avoid repeated disk reads during iteration, thereby improving algorithm performance. This embodiment of the invention does not impose any limitations on this approach.

[0117] Step 202: Use conditional parameter information to trace the directed graph path with multiple edges in the graph data structure to obtain the abnormal trajectory of the device;

[0118] When equipment is under maintenance, such as when equipment malfunctions or parts are missing, a path tracing algorithm can be executed to trace the path of the graph data structure within a preset time period. By judging the aforementioned key elements, the abnormal trajectory route of the equipment can be quickly retrieved and output.

[0119] Graph data structures can include directed graph paths with multiple edges. For example, data such as device usage time, scan location information, and usage duration can be used as key elements. Data conditions can be set, such as the usage time of the previous location being less than the usage time of the current location, the difference between two usage times being less than n days, and the usage time being after the last routine maintenance. This enables path tracing from device usage to return, preventing path interference between different batches of usage. The device identifier ID is abstracted as a vertex, the two location transitions are edges, and the transition time and usage duration are the edge weights. There may be multiple transitions between locations, meaning the generated graph data structure contains multiple edges, as shown in Tables 1 and 3. Figure 5 The path in the directed graph shown is a multi-edge path.

[0120] In this embodiment of the invention, conditional parameter information of multiple edges can be obtained, and the conditional parameter information can be used to trace the directed graph path with multiple edges in the graph data structure to obtain the abnormal trajectory of the device.

[0121] The condition parameter information can be used to define the number of repetitions of an edge starting from a certain vertex. In this case, the essence of searching for a device path that meets the condition is to realize path tracing for a directed graph with multiple edges. Assuming the condition parameter n = 2, the device with position ID 1 in Table 3 will flow to position IDs 4, 3, and 2 in sequence, and finally return to its original position, which is a device path that meets the condition.

[0122] In the process of tracing directed graph paths with multiple edges in a graph data structure using conditional parameter information, the device data stored in the form of a graph data structure includes vertex data and edge data. Each vertex in the vertex data can be obtained, and the edge data can be traversed in a recursive manner using each vertex as the starting vertex. The path information that meets the conditional parameter information is then output as the abnormal trajectory of the device.

[0123] The basic idea of ​​the path tracing algorithm is to read data files from the Hadoop Distributed File System (HDFS), a software framework that can perform distributed processing of large amounts of data, to create RDDs, and then use map operations to create RDDs for nodes and edges respectively, thus forming the RDD of the graph.

[0124] Specifically, in the process of recursively traversing the edge data using each vertex as the starting vertex and outputting path information that meets the condition parameter information, any vertex is used as the initial starting point. The edge data is searched one by one using a recursive method. Then, the edges with the initial starting point are aggregated to obtain an edge set. The aggregated edges in the edge set are sorted according to their weights. The edges with the next vertex as the temporary starting point are then obtained from the edge set. If there is an edge with a weight greater than a preset weight among the edges with the next vertex as the starting point, the edge with the weight greater than the preset weight is used as the associated path. This process continues until the next vertex is the same as the initial starting point. The path composed of the associated paths is then output so that the path information that meets the condition parameter information for the initial starting point can be obtained from the composed path.

[0125] In practical applications, we can first traverse the set of points and cache all edges starting from a vertex from the set of edges, sort them by time weight, take an edge, record its information, find the edges in the graph that start from the target vertex of that edge, record their information, and determine whether the weight meets the conditions. Based on the idea of ​​depth-first search algorithm, we iterate until we find the edge that starts from the initial vertex. Otherwise, we backtrack to ensure that every valid edge is traversed and all paths are eventually found.

[0126] For example, such as Figure 6 The diagram illustrates the path tracing process provided in this embodiment of the invention. The path tracing part is mainly implemented using loop and recursion. The cached edge set is traversed, and then the recursive method is used to search each edge until a path that meets the conditions is found and output. Then, the path continues from a new vertex until all vertices have been visited as the starting vertex.

[0127] The basic steps of path tracing are as follows: ① Traverse the vertices of the RDD graph, arbitrarily select a vertex V as the starting vertex StartId of the path, and continue until all vertices have been visited; ② Filter to see if there is an edge starting with StartId. If there is, aggregate and cache the edges, and sort the edge set in ascending order of time attribute; otherwise, return to Step 1; ③ Take an edge starting with StartId and record the edge information SrcId, DstId, Tf, and Af; ④ Determine if there is an edge in the graph that starts with DstId. If there is, aggregate and cache the edges, and sort the edge set in ascending order of time attribute; otherwise, return to Step 3 to continue execution. Until all edges in the edge set have been visited, if still not, clear the edge set cache starting with StartId and return to Step 1; ⑤ Read the edges in the edge set starting with DstId whose time weight is greater than Tf, and determine whether their weight meets the condition. If not, execute Step 5 until all edges in the edge set have been visited. If still not, clear the edge set cache starting with StartId and return to Step 1. If it meets the condition, execute the next step; ⑥ Determine whether the DstId of the current edge is equal to the StartId. If it is equal, output the path and the associated point information and return to Step 1. If not, return to Step 4.

[0128] The algorithm uses recursive calls to implement path tracing, and the pseudocode can be as follows:

[0129] varj = 0 / / Set flag

[0130] while (iterate through the edge set) {

[0131] Determine if the current transaction time conforms to the time series {

[0132] Set constraints

[0133] Determine if the constraint condition is met {return None if not}

[0134] else {check if the target position is the initial position {satisfies the output path requirements}}

[0135] else{findTargetTradeInfo(FirstStartId,

[0136] TargetId,gragh,allLinkedInfo,edgeAll(j).attr._1,edgeAll(j).attr._2)}}

[0137] j = j + 1

[0138] }

[0139] The embodiments of the present invention do not impose any limitations on this.

[0140] Step 203: Obtain the device's operation record based on the abnormal trajectory, verify the operation record, and determine whether there are any usage risks or violations.

[0141] After obtaining the abnormal trajectory of the equipment based on the path tracing algorithm, the operation records of the equipment can be retrieved based on the abnormal trajectory, and the operation records can be verified to determine whether there are any usage risks or violations. That is, based on the equipment data stored according to the graph data structure, the usage trajectory of the equipment can be searched to identify abnormal trajectories, directly and effectively investigate equipment problems and identify responsible parties, and supervise the use of large equipment in enterprises. This effectively improves the monitoring of equipment and enables visualization of equipment trajectories based on the path tracing of the graph data structure.

[0142] In this embodiment of the invention, device identification information can be obtained, and device information can be recorded based on the obtained device identification information. This allows for the acquisition of device data within a preset time period when a device malfunctions during maintenance. The acquired device data can be stored in a graph data structure with device identification information as vertices and position information as edges. Path tracing can then be performed on the graph data structure to obtain the abnormal trajectory of the device. By assigning device identification codes, the real-time performance and accuracy of device usage data recording are improved. Furthermore, based on the device data stored in the graph data structure, the device's usage trajectory can be searched to identify abnormal trajectories. This effectively enhances device monitoring, directly and effectively identifies device problems and responsible parties, and visualizes the device trajectory based on path tracing of the graph data structure.

[0143] Reference Figure 7 The illustration shows an application scenario diagram of the device abnormal trajectory tracing system provided by the embodiment of the present invention. It is applied to the device abnormal trajectory tracing system 710. The device abnormal trajectory tracing system can trace the abnormal trajectory of a device when a fault occurs during maintenance, which can be used to troubleshoot the device problem and the responsible person.

[0144] The tracing process implemented by the abnormal trajectory tracing system 710 can be specifically described as follows:

[0145] ① Obtain Equipment Code: The code is directly read from the equipment's built-in barcode or existing enterprise-customized code; ② Register Equipment: The equipment changes obtained in Step 1 are registered in the computer system; ③ Supplement Data: In this step, daily usage data and location information of the equipment are read and uploaded to the system; ④ Regular Maintenance: The system sets maintenance intervals and maintenance status data segments for the equipment, reminding the corresponding work teams to perform equipment maintenance and registering the maintenance data; ⑤ Determine Equipment Status: Based on the registered maintenance data segments and the equipment's code information, the location trajectory and usage status of the corresponding equipment are confirmed. ⑥ If the equipment is faulty, report the fault to the relevant personnel based on the equipment status in Step 5, and simultaneously report the equipment's phased location trajectory and usage status to determine the responsibility for the equipment fault; ⑦ If the equipment is not faulty in Step 5, proceed directly to this step to end the current traceability process; if the equipment is faulty, proceed to Step 6 and then to this step to end the traceability; ⑧ After the traceability ends, if the equipment status is normal, seal the equipment trajectory and equipment code data into a data packet with the corresponding date number, and use the date after the equipment returns to normal as the start date of the next traceability process.

[0146] Specifically, the reported phased location trajectory and usage status of the equipment when a malfunction occurs can be achieved by executing a path tracing algorithm on equipment data within a preset time period when a malfunction is discovered during equipment maintenance, such as when a malfunction or missing parts is found during maintenance. It should be noted that the specific implementation process of the path tracing algorithm can be found in the preceding text, and will not be repeated here in this embodiment of the invention.

[0147] In this application scenario, the abnormal trajectory of the equipment is retrieved through the traceability algorithm and the equipment identification data ledger is correlated and analyzed to find the discrepancy between the equipment trajectory and the equipment usage data, down to the specific point of use. By analyzing whether there are missing usage point information records, usage timeouts, etc., the time of equipment failure and the specific person responsible can be determined, thereby achieving the supervision of equipment use.

[0148] In this embodiment of the invention, by integrating industrial internet identifier resolution technology and big data analysis technology, the entire process of equipment use can be recorded through the identifier resolution system, and the compliance of equipment usage data before the failure point can be quickly determined using big data analysis methods. This ensures the traceability and standardized use of large / expensive equipment. When equipment failure or loss of key components occurs, the problematic link and responsible personnel can be quickly found, increasing the supervision of equipment use by enterprises and helping to achieve visualization of equipment trajectory.

[0149] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0150] Reference Figure 8 The diagram illustrates a structural block diagram of an embodiment of a device for tracing abnormal equipment trajectories according to the present invention, which may specifically include the following modules:

[0151] The device recording module 801 is used to acquire the device identification information of the device and record the device information based on the device identification information; the device information includes location information.

[0152] The equipment data acquisition module 802 is used to acquire equipment data within a preset time period when a fault occurs during equipment maintenance; the equipment data is stored in a graph data structure with equipment identification information as vertices and location information as edges.

[0153] The path tracing module 803 is used to perform path tracing on the graph data structure to obtain the abnormal trajectory of the device.

[0154] In one embodiment of the present invention, the device recording module 801 may include the following sub-modules:

[0155] The equipment information recording submodule is used to record daily management data, borrowing and usage data, and location information corresponding to the equipment's travel trajectory.

[0156] In one embodiment of the present invention, the device information further includes the time period for requisition, and the location information includes the location information of the device within a preset time period; the device information recording module may include the following sub-modules:

[0157] The device data generation submodule is used to transform device information. It uses device identification information as vertices, device location information within a preset time period as edges, and time usage length as edge weights to construct a graph data structure to record device information and obtain device data.

[0158] In one embodiment of the present invention, the device recording module 801 may further include the following sub-modules:

[0159] The device data storage submodule is used to store device data in a graph data structure based on a preset routing table;

[0160] In one embodiment of the present invention, the device data in the graph data structure includes vertex data and edge data; the device data storage submodule may include the following units:

[0161] The first distributed storage unit is used to perform hash partitioning according to the device identification information of the vertices to obtain multiple first partitions, and to distribute and store the vertex data in the form of multiple first partitions.

[0162] The second distributed storage unit is used to partition the data according to a preset partitioning strategy to obtain multiple second partitions, and to distribute and store the edge data in the form of multiple second partitions.

[0163] The device data storage unit is used to record the relationship between each vertex in the first partition and each edge in the second partition based on a preset routing table.

[0164] In one embodiment of the present invention, the graph data structure includes a directed graph path with multiple edges, and the path tracing module 803 may include the following sub-modules:

[0165] The condition parameter information acquisition submodule is used to obtain the condition parameter information of multiple edges; the condition parameter information is used to define the number of repetitions where an edge starts from a certain vertex.

[0166] The path tracing submodule is used to trace the path of a directed graph with multiple edges in a graph data structure using conditional parameter information to obtain the abnormal trajectory of the device.

[0167] In one embodiment of the present invention, the device data stored in the form of a graph data structure includes vertex data and edge data, and the path tracing submodule may include the following units:

[0168] The edge data traversal unit is used to obtain each vertex in the vertex data. It uses a recursive approach to traverse the edge data, starting with each vertex, and outputs the path information that meets the condition parameter information.

[0169] The abnormal trajectory output unit is used to take the path information that meets the condition parameter information as the abnormal trajectory of the device.

[0170] In one embodiment of the present invention, the edge data traversal unit may include the following sub-units:

[0171] The edge search sub-unit is used to search for each edge in the edge data recursively, starting from any vertex.

[0172] The edge clustering subunit is used to respond to the edges in the edge data that have an initial starting point, perform edge aggregation operations to obtain an edge set, and sort the aggregated edges in the edge set according to the edge weight.

[0173] The associated path acquisition sub-unit is used to obtain the edges with the next vertex as the temporary starting point from the edge set. If there is an edge with a weight greater than the preset weight among the edges with the next vertex as the starting point, the edge with the weight greater than the preset weight is used as the associated path. This continues until the next vertex that has been traversed is the same as the initial starting point. The path composed of the associated paths is then output.

[0174] The path information acquisition subunit is used to obtain path information from the composed path that meets the condition parameter information for the initial starting point.

[0175] In one embodiment of the present invention, after obtaining the abnormal trajectory of the device, the device abnormal trajectory tracing device provided in this embodiment of the present invention may further include the following modules:

[0176] The operation log acquisition module is used to obtain the device's operation log based on abnormal trajectories;

[0177] The operation log verification module is used to verify the operation log and determine whether there are any risk points or violations.

[0178] In this embodiment of the invention, the device for tracing abnormal equipment trajectories can acquire the equipment identification information of the equipment and record the equipment information based on the acquired identification information. This allows for the acquisition of equipment data within a preset time period when a fault occurs during equipment maintenance. The acquired equipment data can be stored in a graph data structure with the equipment identification information as vertices and position information as edges. Path tracing can then be performed on the graph data structure to obtain the abnormal trajectory of the equipment. By assigning codes to the equipment identification, the real-time performance and accuracy of equipment usage data recording are improved. Furthermore, based on the equipment data stored in the graph data structure, the usage trajectory of the equipment can be searched to identify abnormal trajectories. This effectively enhances equipment monitoring, directly and effectively identifies equipment problems and responsible parties, and visualizes the equipment trajectory based on path tracing of the graph data structure.

[0179] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0180] This invention also provides an electronic device, comprising:

[0181] It includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When the computer program is executed by the processor, it implements the various processes of the above-described method embodiment for tracing abnormal device trajectories and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0182] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described method for tracing abnormal device trajectories and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0183] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0184] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0185] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0186] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0187] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0188] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0189] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0190] The present invention has provided a detailed description of a method for tracing abnormal equipment trajectories, a device for tracing abnormal equipment trajectories, a corresponding electronic device, and a corresponding computer storage medium. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for tracing abnormal equipment trajectories, characterized in that, The method includes: Obtain the device identification information of the device, and record the device information of the device based on the device identification information; the device information includes location information; When the equipment malfunctions during maintenance, the equipment data within a preset time period is acquired; the equipment data is stored in a graph data structure with the equipment identification information as vertices and the location information as edges. Path tracing is performed on the graph data structure to obtain the abnormal trajectory of the device; The graph data structure includes directed graph paths with multiple edges. The step of tracing the path through the graph data structure to obtain the abnormal trajectory of the device includes: Obtain the conditional parameter information of the multi-edge; the conditional parameter information is used to define the number of repetitions where an edge starts from a certain vertex; The abnormal trajectory of the device is obtained by tracing the directed graph path with multiple edges in the graph data structure using the condition parameter information. Device data stored in a graph data structure includes vertex data and edge data. The step of tracing directed graph paths with multiple edges in the graph data structure using the conditional parameter information to obtain the device's abnormal trajectory includes: Obtain each vertex in the vertex data, and use a recursive method to traverse the edge data with each vertex as the starting vertex, and output the path information that meets the condition parameter information. The path information that meets the condition parameter information is taken as the abnormal trajectory of the device; The method of recursively traversing the edge data using each vertex as a starting vertex to output path information that meets the condition parameter information includes: Starting from any vertex, the edges in the edge data are searched recursively. In response to the edges in the edge data that start from the initial point, perform an edge aggregation operation on the edges to obtain an edge set, and sort the aggregated edges in the edge set according to their weights. Obtain the edges from the set of edges with the next vertex as the temporary starting point. If there is an edge with a weight greater than a preset weight among the edges with the next vertex as the starting point, then use the edge with the weight greater than the preset weight as the associated path until the next vertex that has been traversed is the same as the initial starting point. Output the path composed of the associated paths. From the combined paths, obtain path information that is relevant to the initial starting point and meets the condition parameter information.

2. The method according to claim 1, characterized in that, The step of recording the location information of the device based on the device identification information includes: Record the daily management data, borrowing and usage data of the equipment, and the location information corresponding to the equipment's travel trajectory.

3. The method according to claim 1 or 2, characterized in that, The device information also includes the time period for requisition, and the location information includes the location information of the device within a preset time period; The step of recording the device information based on the device identification information further includes: The device information is transformed by converting the device identification information into vertices, the location information of the device within a preset time period into edges, and the time usage length into edge weights, to construct a graph data structure to record the device information and obtain device data.

4. The method according to claim 3, characterized in that, The method further includes: Device data in a graph data structure is stored based on a preset routing table; The device data in the graph data structure includes vertex data and edge data; the storage of the device data in the graph data structure based on a preset routing table includes: The vertex data is distributed and stored in the form of the multiple first partitions by performing hash partitioning based on the device identification information of the vertices. Furthermore, the edge data is partitioned according to a preset partitioning strategy to obtain multiple second partitions, and the edge data is distributed and stored in the form of the multiple second partitions; The relationship between each vertex in the first partition and each edge in the second partition is recorded based on a preset routing table.

5. The method according to claim 1, characterized in that, After obtaining the abnormal trajectory of the device, the process further includes: The device's operation records are obtained based on the abnormal trajectory. The operation records are verified to determine whether there are any risks or violations.

6. A device for tracing abnormal equipment trajectories, characterized in that, The device includes: The device recording module is used to acquire device identification information of the device and record device information based on the device identification information; the device information includes location information. The equipment data acquisition module is used to acquire equipment data within a preset time period when the equipment malfunctions during maintenance; the equipment data is stored in a graph data structure with the equipment identification information as vertices and the location information as edges; The path tracing module is used to perform path tracing on the graph data structure to obtain the abnormal trajectory of the device. The graph data structure includes directed graph paths with multiple edges. The path tracing module includes the following sub-modules: The condition parameter information acquisition submodule is used to obtain the condition parameter information of multiple edges; the condition parameter information is used to define the number of repetitions where an edge starts from a certain vertex. The path tracing submodule is used to trace the path of a directed graph with multiple edges in the graph data structure using conditional parameter information to obtain the abnormal trajectory of the device. Device data stored in the form of a graph data structure includes vertex data and edge data. The path tracing submodule includes the following units: The edge data traversal unit is used to obtain each vertex in the vertex data. It uses a recursive approach to traverse the edge data, starting with each vertex, and outputs the path information that meets the condition parameter information. An abnormal trajectory output unit is used to take path information that meets the condition parameter information as the abnormal trajectory of the device. The edge data traversal unit includes the following sub-units: The edge search sub-unit is used to search for each edge in the edge data recursively, starting from any vertex. The edge clustering subunit is used to respond to the edges in the edge data that have an initial starting point, perform edge aggregation operations to obtain an edge set, and sort the aggregated edges in the edge set according to the edge weight. The associated path acquisition sub-unit is used to obtain the edges with the next vertex as the temporary starting point from the edge set. If there is an edge with a weight greater than the preset weight among the edges with the next vertex as the starting point, the edge with the weight greater than the preset weight is used as the associated path. This continues until the next vertex that has been traversed is the same as the initial starting point. The path composed of the associated paths is then output. The path information acquisition subunit is used to obtain path information from the composed path that meets the condition parameter information for the initial starting point.

7. An electronic device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the method for tracing abnormal device trajectories as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the method for tracing abnormal device trajectories as described in any one of claims 1 to 5.

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