An intelligent method for generating power line work tickets based on graphic topology analysis technology

Abstract

The present invention discloses a method for intelligently generating work tickets for power lines based on a graphic topology analysis technology, and relates to the technical field of data processing; by acquiring real-time monitoring data of a target power line and mapping it to a structural topology diagram of the target power line, a target topology diagram is obtained; fault prediction is performed according to the real-time monitoring data to obtain a fault node and a fault type; according to the fault type of the fault node, work tasks and safety measures are determined; node importance analysis is performed according to the target topology diagram to determine the task priority of the fault node; according to the task priority, the working time of each fault node is determined, a work ticket is generated, and the work ticket is executed after approval. Work tickets are automatically generated through real-time monitoring data and topology analysis, which reduces manual intervention, ensures the accuracy of data and information, reduces subsequent work and corrections caused by errors, improves the accuracy and generation efficiency of work tickets, and thus improves overall management efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent ticketing method for power line work tickets based on graphic topology analysis technology. Background Art

[0002] In the power system, whether it is daily inspection and maintenance or emergency fault repair, safety regulations and operating procedures must be strictly followed. As the core management tool in this process, the work ticket not only clarifies the specific content of the work task, the execution location, the required time, and the personnel composition of the work team, but also lists in detail the various safety measures that must be taken to ensure the safety of the operation. These measures include but are not limited to the scope of power outages, grounding protection, insulation isolation, the use of personal protective equipment, etc., aiming to minimize the safety risks during the operation.

[0003] Traditionally, the preparation, approval and execution of work tickets often rely on manual operations. The professional level and personal writing habits of the people filling out the tickets will result in the final content being non-standard or even unqualified, which brings a lot of repetitive work to the filling out and review of power work tickets, resulting in inefficient operation and maintenance management. Summary of the invention

[0004] The purpose of the present invention is to solve the problem of low efficiency of operation and maintenance management mentioned in the above background technology, and to propose an intelligent method for generating power line work tickets based on graphic topology analysis technology.

[0005] In a first aspect of the present invention, there is provided a method for intelligently generating a power line work ticket based on a graph topology analysis technique, the method comprising:

[0006] Acquire real-time monitoring data of the target power line and map it to a structural topology diagram of the target power line to obtain a target topology diagram; the nodes of the structural topology diagram represent power equipment, the edges represent the connection relationship between the equipment, and the type information and location information of each node equipment are marked;

[0007] Perform fault prediction based on the real-time monitoring data to obtain a set of fault nodes and fault types;

[0008] Determine the work tasks and safety measures of the target fault node according to the fault type of the target fault node; the target fault node is any fault node in the fault node set;

[0009] Perform node importance analysis according to the target topology map to determine the task priority of the target fault node;

[0010] Determine the work team, working time and working location of each fault node based on the work tasks and task priorities of all fault nodes;

[0011] According to the work tasks, working hours, working locations, working teams and safety measures of each fault node, a work ticket application document is generated and sent to the approval system to obtain a work ticket with a permission mark.

[0012] Optionally, performing fault prediction according to the real-time monitoring data to obtain a set of fault nodes and a fault type includes:

[0013] Acquire operation data of a target power device node in a preset time period; the target power device node is any node on a target topology map; the operation data includes at least one of voltage data, current data, power data, temperature data, vibration data and switch state data;

[0014] Preprocessing the operation data to obtain operation characteristic data; the preprocessing includes missing value filling and normalization processing;

[0015] The operating characteristic data is used as an input of a pre-trained deep learning model to obtain the operating state of the target power equipment; the operating state includes a fault-free state and a fault state divided into multiple fault types;

[0016] The power equipment nodes whose operating status is a faulty state are regarded as faulty nodes and put into the faulty node set.

[0017] Optionally, determining the work task and safety measures of the target faulty node according to the fault type of the target faulty node includes:

[0018] According to the equipment identification and fault type of the target fault node, a preset maintenance database is queried to obtain a work task; the work task includes multiple action contents;

[0019] Each action content is used as the input of the preset knowledge graph system to obtain multiple corresponding security measures.

[0020] Optionally, performing node importance analysis according to the target topology map to determine the task priority of the target fault node includes:

[0021] According to the target topology diagram, construct a target power line model in a power simulation environment;

[0022] According to the fault type of the target fault node, a first-level fault injection is performed to obtain system response data; the system response data includes load transfer data, voltage fluctuation data, frequency fluctuation data and cascading failure data;

[0023] Calculating the priority score of the target fault node according to the system response data;

[0024] According to the priority scores of multiple faulty nodes, the tasks are prioritized. The higher the priority score of the faulty node, the higher the task priority.

[0025] Optionally, calculating the priority score of the target faulty node according to the system response data includes:

[0026] The stability impact score is calculated based on the voltage fluctuation data and the frequency fluctuation data;

[0027] Based on the load transfer data, the load transfer impact score is calculated;

[0028] Based on the cascading failure data, the repair cost score is calculated;

[0029] The priority score is calculated based on the stability impact score, load transfer impact score and repair cost score:

[0030]

[0031] in, is the desired priority score; is the stability impact score; is the load transfer impact score; is the repair cost fraction; w 1 、w 2 、w 3 is the weight coefficient; when the target fault node has a fault of fault type ft, m is the number of affected nodes, and n is the number of secondary fault nodes that fail under the chain effect; is the voltage fluctuation amplitude of the i-th affected node; is the maximum allowable voltage fluctuation of the ith affected node; is the frequency fluctuation amplitude of the i-th affected node; is the maximum allowable frequency fluctuation of the ith affected node; is the load transfer amount; is the maximum permissible load transfer; is the repair cost of the target faulty node; is the repair cost required for the jth secondary faulty node; It is the maximum value of the cost of repairing a fault in historical data.

[0032] Optionally, working hours include working start time, working duration and working end time;

[0033] Determining the work team, work time and work location of each fault node according to the work tasks and task priorities of all fault nodes includes:

[0034] Determine the working duration of each faulty node according to the working tasks of each faulty node;

[0035] Determine the working location of each fault node according to the location information of each fault node;

[0036] Obtain the available team library for the current duty cycle and arrange tasks; task arrangement includes the following steps:

[0037] Step 1: Establish a work task queue according to the task priority of each faulty node; the higher the task priority of the faulty node, the earlier the corresponding work task will be queued;

[0038] Step 2: If the work task queue is not an empty queue, remove the work task at the head of the queue and proceed to step 3; otherwise, end the task scheduling process;

[0039] Step 3: According to the skill requirements of the work task, select candidate participating teams that meet the skill requirements;

[0040] Step 4: According to the most recent task arrangement of each candidate participating team, the starting time when each candidate participating team can carry out the work task is obtained;

[0041] Step 5: The candidate participating team with the earliest start time is selected as the working team for the work task, and its task schedule is updated, and then return to step 2.

[0042] Optionally, obtaining the start time when each candidate participating team can perform the work task according to the most recent task arrangement of each candidate participating team includes:

[0043] Determine the geographical location and departure time of the target candidate participating team according to the most recent work tasks of the target candidate participating team; the target candidate participating team is any one of the multiple candidate participating teams;

[0044] Determine the travel time based on the geographic location and the work location of the work task;

[0045] According to the departure time and the traffic time, the start time at which the target candidate participating team can perform the work task is obtained.

[0046] Optionally, the work ticket application document is generated according to the work tasks, working hours, working locations, working teams and safety measures of each fault node, and sent to the approval system to obtain the work ticket with the permission mark, including:

[0047] Fill the work tasks, working hours, working locations, work teams and safety measures of each fault node into the preset work ticket template according to the preset format to generate a work ticket application document;

[0048] Send the work ticket application document to the approval system, and after approval, obtain a work ticket with a permission mark;

[0049] For work tickets with permits, add unique numbers based on the execution time.

[0050] Beneficial effects of the present invention:

[0051] The present invention proposes an intelligent method for generating work tickets for power lines based on graph topology analysis technology, the method comprising: acquiring real-time monitoring data of a target power line, and mapping the data to a structural topology diagram of the target power line to obtain a target topology diagram; the nodes of the structural topology diagram represent power equipment, the edges represent the connection relationship between the equipment, and are marked with type information and location information of each node equipment; performing fault prediction based on the real-time monitoring data to obtain a set of fault nodes and fault types; determining the work tasks and safety measures of the target fault nodes based on the fault type of the target fault nodes; the target fault node is any fault node in the set of fault nodes; performing node importance analysis based on the target topology diagram to determine the task priority of the target fault node; determining the work team, work time and work location of each fault node based on the work tasks and task priorities of all fault nodes; generating a work ticket application document based on the work tasks, work time, work location, work team and safety measures of each fault node, and sending it to an approval system to obtain a work ticket with a permission mark.

[0052] Work tickets are automatically generated through real-time monitoring data and topological analysis, which reduces manual intervention, ensures the accuracy of data and information, reduces subsequent work and corrections caused by errors, improves the accuracy and generation efficiency of work tickets, and thus improves overall management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The present invention will be further described below in conjunction with the accompanying drawings.

[0054] Figure 1 A flowchart of a method for intelligently generating power line work tickets based on graphic topology analysis technology is provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] The embodiment of the present invention provides a method for intelligently generating a power line work ticket based on a graph topology analysis technology. Figure 1, Figure 1 A flowchart of a method for intelligently generating a power line work ticket based on a graph topology analysis technique provided by an embodiment of the present invention. The method comprises the following steps:

[0057] S101, acquiring real-time monitoring data of a target power line, and mapping the real-time monitoring data to a structural topology diagram of the target power line to obtain a target topology diagram.

[0058] S102, performing fault prediction based on real-time monitoring data to obtain a set of fault nodes and fault types.

[0059] S103, determining the work task and safety measures of the target fault node according to the fault type of the target fault node.

[0060] S104, performing node importance analysis according to the target topology map, and determining the task priority of the target fault node.

[0061] S105, determining the work team, working time and work location of each fault node according to the work tasks and task priorities of all fault nodes.

[0062] S106, generating a work ticket application document according to the work tasks, working hours, working locations, work teams and safety measures of each fault node, and sending it to the approval system to obtain a work ticket with a permission mark.

[0063] The nodes of the structural topology diagram represent power equipment, the edges represent the connection relationship between the equipment, and are marked with the type information and location information of each node equipment; the target fault node is any fault node in the fault node set.

[0064] An intelligent method for generating work tickets for power lines based on a graphical topology analysis technique provided in an embodiment of the present invention automatically generates work tickets through real-time monitoring data and topology analysis, thereby reducing manual intervention, ensuring the accuracy of data and information, reducing subsequent work and corrections caused by errors, and improving the accuracy and generation efficiency of work tickets, thereby improving overall management efficiency.

[0065] In one implementation, through node importance analysis, the task priority of each faulty node can be reasonably determined according to the degree of fault impact, thereby optimizing resource scheduling, ensuring that the most critical faults can be handled in a timely manner, and avoiding delays caused by improper resource allocation.

[0066] In one implementation, substations, switches, transmission lines and other equipment in the power line are abstracted as nodes and edges to form a topological structure; attributes such as voltage level, switch type, line load, etc. can be assigned to each node according to the type, function and specification information of each device, and location information is added to each node to form a complete structural topological map. Mapping real-time monitoring information to the structural topological map can visualize the operating status of the power line in real time, helping managers to understand the power grid situation more intuitively.

[0067] In one embodiment, step S102 includes:

[0068] Step 1: Obtain the operating data of the target power equipment node in a preset time period.

[0069] Step 2: preprocess the operation data to obtain operation characteristic data.

[0070] Step three: Use the operating characteristic data as the input of the pre-trained deep learning model to obtain the operating status of the target power equipment.

[0071] Step 4: The power equipment nodes whose operating status is a faulty state are regarded as faulty nodes and put into the faulty node set.

[0072] Among them, the target power equipment node is any node on the target topology map; the operating data includes at least one of voltage data, current data, power data, temperature data, vibration data and switch status data; the preprocessing includes missing value filling and normalization processing; the operating status includes a fault-free state and a fault state divided into multiple fault types.

[0073] In one implementation, the method considers a variety of operating data as input, including voltage, current, power, temperature, vibration, and switch status. These diverse data provide a rich source of information for the model, which helps to more comprehensively evaluate the operating status of power equipment.

[0074] In one implementation, deep learning models have significant advantages in processing complex, nonlinear relationships, can capture hidden fault features in operating data, can accurately analyze and predict the operating status of power equipment, and identify potential faults, thereby alerting operation and maintenance personnel to perform preventive maintenance operations, thereby reducing equipment damage and downtime, and avoiding larger-scale power failures. The pre-trained deep learning model can be a long short-term memory network model or a convolutional neural network model.

[0075] In one embodiment, step S103 includes:

[0076] Step 1: According to the equipment identification and fault type of the target fault node, query the preset maintenance database to obtain the work task.

[0077] Step 2: Use each action content as input to the preset knowledge graph system to obtain multiple corresponding security measures.

[0078] Among them, the work tasks include multiple action contents.

[0079] In one implementation, maintenance knowledge and safety specifications are systematically stored in databases and knowledge graphs, and knowledge is effectively utilized and transferred through automatic query and application. This helps standardize the operation and maintenance process, and can perform high-quality maintenance and repair work even when personnel are replaced or experience is insufficient, and ensure that safety measures are not missed.

[0080] In one embodiment, step S104 includes:

[0081] Step 1: Build a target power line model in a power simulation environment according to the target topology diagram.

[0082] Step 2: Perform a first-level fault injection according to the fault type of the target fault node to obtain system response data.

[0083] Step 3: Calculate the priority score of the target fault node based on the system response data.

[0084] Step 4: sort the task priorities according to the priority scores of multiple faulty nodes. The higher the priority score of the faulty node, the higher the task priority.

[0085] Among them, the system response data includes load transfer data, voltage fluctuation data, frequency fluctuation data and cascading failure data.

[0086] In one implementation, the system response data includes cascading failure data, which not only considers the impact of a single failure, but also can evaluate the cascading failures that may be caused. The faulty node predicted by the monitoring system is called a primary faulty node, and its failure is called a primary fault; when a primary faulty node fails, it causes other nodes to fail, and these nodes that fail due to the cascading effect are called secondary faulty nodes.

[0087] By giving priority to the first-level fault nodes that may cause cascading failures, the propagation of faults in a wider range of the power system can be effectively prevented, thereby improving the overall stability of the system.

[0088] In one embodiment, calculating the priority score of the target fault node according to the system response data includes:

[0089] Step 1: Calculate the stability impact score based on the voltage fluctuation data and frequency fluctuation data: .

[0090] Step 2: Calculate the load transfer impact score based on the load transfer data: .

[0091] Step 3: Calculate the repair cost score based on the cascading failure data: .

[0092] Step 4: Calculate the priority score based on the stability impact score, load transfer impact score and repair cost score: .

[0093] in, is the desired priority score; is the stability impact score; is the load transfer impact score; is the repair cost fraction; w 1 、w 2 、w 3 is the weight coefficient; when the target fault node has a fault of fault type ft, m is the number of affected nodes, and n is the number of secondary fault nodes that fail under the chain effect; is the voltage fluctuation amplitude of the i-th affected node; is the maximum allowable voltage fluctuation of the ith affected node; is the frequency fluctuation amplitude of the i-th affected node; is the maximum allowable frequency fluctuation of the ith affected node; is the load transfer amount; is the maximum permissible load transfer; is the repair cost of the target faulty node; is the repair cost required for the jth secondary faulty node; It is the maximum value of the cost of repairing a fault in historical data.

[0094] In one implementation, multiple key factors such as voltage fluctuation, frequency fluctuation, load transfer and repair cost are incorporated into the calculation of the priority score. By comprehensively considering these factors, the impact of faulty nodes on system stability and economy can be fully evaluated, ensuring the comprehensiveness and accuracy of the priority evaluation.

[0095] In one implementation, the weight coefficient w 1 、w 2 、w 3 It can be determined by technicians based on historical experience data, for example, w 1 、w 2 、w 3 Set to 0.3, 0.3, 0.4.

[0096] In one implementation, cost assessment can be performed based on historical operation and maintenance data to obtain the time and cost of various operation and maintenance operations (work tasks).

[0097] In one embodiment, the working time includes working start time, working duration and working end time; step S105 includes:

[0098] According to the working tasks of each faulty node, the working duration of each faulty node is determined.

[0099] According to the location information of each faulty node, the working location of each faulty node is determined.

[0100] Obtain the available team library for the current duty cycle and arrange tasks. Task arrangement includes the following steps:

[0101] Step 1: Establish a work task queue according to the task priority of each faulty node; the higher the task priority of the faulty node, the earlier the corresponding work task enters the queue.

[0102] Step 2: If the work task queue is not an empty queue, the work task at the head of the queue is removed from the queue and the process proceeds to step 3; otherwise, the task scheduling process ends.

[0103] Step three: Based on the skill requirements of the work task, select candidate participants who meet the skill requirements.

[0104] Step 4: According to the most recent task arrangement of each candidate participating team, the start time when each candidate participating team can carry out the work task is obtained.

[0105] Step 5: The candidate participating team with the earliest start time is selected as the working team for the work task, and its task schedule is updated, and then return to step 2.

[0106] In one implementation, a work task queue based on task priority is established to ensure that high-priority fault nodes are processed first. This mechanism ensures that the potential faults with the greatest impact on the power system can be repaired first, maintaining the stability and reliability of the system.

[0107] In one implementation, in task scheduling, qualified candidate teams are selected based on the skill requirements of the work tasks, ensuring the matching of tasks and personnel skills. This matching mechanism not only improves the quality and efficiency of task completion, but also reduces rework or safety hazards caused by skill mismatch.

[0108] In one implementation, by querying the most recent task schedule of each candidate team, the earliest start time for the new task is determined, and then the team with the earliest start time is selected to perform the task, and the earliest start time is used as the work start time of the work task, and then the work end time is obtained according to the duration of the work task. This method effectively reduces the task waiting time and improves the overall work efficiency.

[0109] In one embodiment, the working time includes the working start time, working duration and working end time; according to the most recent task arrangement of each candidate participating team, the starting time at which each candidate participating team can perform the work task includes:

[0110] Step 1: Determine the geographical location and departure time of the target candidate team based on the most recent work tasks of the target candidate team.

[0111] Step 2: Determine the travel time based on the geographical location and the work location of the work task.

[0112] Step three, based on the departure time and travel time, obtain the start time when the target candidate participating team can carry out the work task.

[0113] Among them, the target candidate participating team is any one of multiple candidate participating teams.

[0114] In one implementation, by analyzing the candidate participating team's recent work task location and departure time, and calculating the travel time to the new task location, the earliest start time that the team can perform the new task can be accurately predicted. This accurate time estimation helps reduce delays in starting tasks and improves the efficiency of task scheduling. It also helps to improve the transparency of task execution and makes the scheduling of subsequent tasks more orderly.

[0115] In one embodiment, step S106 includes:

[0116] Step 1: Fill in the work tasks, working hours, working locations, work teams and safety measures of each fault node into the preset work ticket template according to the preset format to generate a work ticket application document.

[0117] Step 2: Send the work ticket application document to the approval system. After approval, you will get a work ticket with a permission mark.

[0118] Step three, add a unique number to the work ticket with permission according to the execution time.

[0119] In one implementation, work tickets are generated and managed through a systematic and standardized process, ensuring the standardization, safety and traceability of power maintenance tasks.

[0120] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for intelligently generating power line work tickets based on graphic topology analysis technology, characterized in that: The method comprises: Acquire real-time monitoring data of the target power line and map it to a structural topology diagram of the target power line to obtain a target topology diagram; the nodes of the structural topology diagram represent power equipment, the edges represent the connection relationship between the equipment, and the type information and location information of each node equipment are marked; Perform fault prediction based on the real-time monitoring data to obtain a set of fault nodes and fault types; Determine the work tasks and safety measures of the target fault node according to the fault type of the target fault node; the target fault node is any fault node in the fault node set; Perform node importance analysis according to the target topology map to determine the task priority of the target fault node; Determine the work team, working time and working location of each fault node based on the work tasks and task priorities of all fault nodes; Generate a work ticket application document based on the work tasks, work time, work location, work team and safety measures of each fault node, and send it to the approval system to obtain a work ticket with a permission mark; The performing node importance analysis according to the target topology map to determine the task priority of the target fault node includes: According to the target topology diagram, construct a target power line model in a power simulation environment; According to the fault type of the target fault node, a first-level fault injection is performed to obtain system response data; the system response data includes load transfer data, voltage fluctuation data, frequency fluctuation data and cascading failure data; Calculating the priority score of the target fault node according to the system response data; According to the priority scores of multiple faulty nodes, the tasks are prioritized. The higher the priority score of the faulty node, the higher the task priority.

2. According to claim 1, a method for intelligently generating power line work tickets based on graphic topology analysis technology is characterized in that: The fault prediction is performed according to the real-time monitoring data to obtain the fault node set and the fault type, including: Acquire operation data of a target power device node in a preset time period; the target power device node is any node on a target topology map; the operation data includes at least one of voltage data, current data, power data, temperature data, vibration data and switch state data; Preprocessing the operation data to obtain operation characteristic data; the preprocessing includes missing value filling and normalization processing; The operating characteristic data is used as an input of a pre-trained deep learning model to obtain the operating state of the target power equipment; the operating state includes a fault-free state and a fault state divided into multiple fault types; The power equipment nodes whose operating status is a faulty state are regarded as faulty nodes and put into the faulty node set.

3. According to claim 1, a method for intelligently generating power line work tickets based on graphic topology analysis technology is characterized in that: Determining the work tasks and safety measures of the target fault node according to the fault type of the target fault node includes: According to the equipment identification and fault type of the target fault node, a preset maintenance database is queried to obtain a work task; the work task includes multiple action contents; Each action content is used as the input of the preset knowledge graph system to obtain multiple corresponding security measures.

4. According to claim 1, a method for intelligently generating power line work tickets based on graphic topology analysis technology is characterized in that: Calculating the priority score of the target fault node according to the system response data includes: The stability impact score is calculated based on the voltage fluctuation data and the frequency fluctuation data; Based on the load transfer data, the load transfer impact score is calculated; Based on the cascading failure data, the repair cost score is calculated; The priority score is calculated based on the stability impact score, load transfer impact score and repair cost score: Among them, S priority is the priority score required; S stability is the stability impact score; S load is the load transfer impact fraction; S recovery is the repair cost fraction; w1, w2, w3 are weight coefficients; when the target fault node has a fault of fault type ft, m is the number of affected nodes, and n is the number of secondary fault nodes that fail under the chain effect; ΔV i,ft is the voltage fluctuation amplitude of the ith affected node; ΔV i,max is the maximum allowable voltage fluctuation of the ith affected node; ΔF i,ft is the frequency fluctuation amplitude of the i-th affected node; ΔF i,max is the maximum allowable frequency fluctuation of the ith affected node; L ft is the load transfer; L max is the maximum allowable load transfer; T ft is the repair cost of the target faulty node; T j,ft is the repair cost required for the jth secondary fault node; T max It is the maximum value of the cost of repairing a fault in historical data.

5. According to claim 1, a method for intelligently generating power line work tickets based on graphic topology analysis technology is characterized in that: Working hours include work start time, work duration and work end time; Determining the work team, work time and work location of each fault node according to the work tasks and task priorities of all fault nodes includes: Determine the working duration of each faulty node according to the working tasks of each faulty node; Determine the working location of each fault node according to the location information of each fault node; Obtain the available team library for the current duty cycle and arrange tasks; task arrangement includes the following steps: Step 1: Establish a work task queue according to the task priority of each faulty node; the higher the task priority of the faulty node, the earlier the corresponding work task will be queued; Step 2: If the work task queue is not an empty queue, remove the work task at the head of the queue and proceed to step 3; otherwise, end the task scheduling process; Step 3: According to the skill requirements of the work task, select candidate participating teams that meet the skill requirements; Step 4: According to the most recent task arrangement of each candidate participating team, the starting time when each candidate participating team can carry out the work task is obtained; Step 5: The candidate participating team with the earliest start time is selected as the working team for the work task, and its task schedule is updated, and then return to step 2.

6. According to claim 5, a method for intelligently generating power line work tickets based on graphic topology analysis technology is characterized in that: The starting time when each candidate participating team can perform the work task according to the most recent task arrangement of each candidate participating team includes: Determine the geographical location and departure time of the target candidate participating team according to the most recent work tasks of the target candidate participating team; the target candidate participating team is any one of the multiple candidate participating teams; Determine the travel time based on the geographic location and the work location of the work task; According to the departure time and the traffic time, the start time at which the target candidate participating team can perform the work task is obtained.

7. According to claim 1, a method for intelligently generating power line work tickets based on graphic topology analysis technology is characterized in that: The work ticket application document is generated according to the work tasks, working hours, working locations, working teams and safety measures of each fault node, and sent to the approval system to obtain the work ticket with the permission mark, including: Fill the work tasks, working hours, working locations, work teams and safety measures of each fault node into the preset work ticket template according to the preset format to generate a work ticket application document; Send the work ticket application document to the approval system, and after approval, obtain a work ticket with a permission mark; For work tickets with permits, add unique numbers based on the execution time.

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