Power transmission hidden danger elimination method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a power transmission hidden danger elimination method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining current hidden danger information of a power transmission line; determining a target thinking map reasoning framework corresponding to the current hidden danger information based on the current hidden danger information and a pre-constructed reference thinking map reasoning framework; determining a target hidden danger processing path corresponding to the current hidden danger information from the target thinking map reasoning framework, and generating an operation instruction based on the target hidden danger processing path; and analyzing the operation and maintenance resource information from the operation instruction, generating an electronic chemical order based on the operation and maintenance resource information and the operation instruction, and sending the electronic chemical order to a terminal device corresponding to the target operation and maintenance team, so that the target operation and maintenance team performs hidden danger elimination on the power transmission line based on the electronic chemical order. According to the scheme, the technical defects of low efficiency, insufficient accuracy and lack of autonomous deep reasoning ability due to dependence on manual research and judgment in the prior art are solved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, electronic device and storage medium for eliminating power transmission hazards. Background Technology

[0002] In the field of power transmission line operation and maintenance, timely and accurate identification and elimination of potential hazards are crucial to ensuring the safe and stable operation of the power grid. Currently, various technologies have been gradually introduced for hazard monitoring, including deploying sensor networks, using drones for automated inspections, and utilizing image recognition algorithms to analyze visible light and infrared images. When the monitoring system identifies an anomaly, the existing handling process is mainly based on a manual approach. Operation and maintenance experts or experienced technicians need to integrate and analyze information locally based on the reported anomaly, combined with their personal experience, by consulting safety regulations, equipment manuals, and limited historical cases. They then manually formulate specific handling plans, prepare work instructions, and finally assign them to the appropriate operation and maintenance teams for execution through the work order system.

[0003] However, the above-mentioned power transmission hazard elimination schemes have the following problems: 1. The decision-making process is mainly based on manual judgment, resulting in a long cycle and slow response speed from hazard identification to the generation of a treatment plan, which cannot meet the requirements for rapid handling of emergency hazards and is inefficient; 2. Manual analysis is difficult to systematically integrate and reason about multi-source heterogeneous information, which may lead to the formulation of treatment plans that are not well considered, and there may be omissions of optimal solutions or neglect of potential risks, making it difficult to guarantee accuracy; 3. It is impossible to automatically deduce, evaluate and optimize complex hazards through multi-path and multi-factor analysis, and it lacks autonomous and structured deep reasoning capabilities, and lacks the intelligence level and reliability of the overall power transmission hazard elimination process. Summary of the Invention

[0004] This application provides a method, device, electronic device, and storage medium for eliminating power transmission hazards, which solves the technical defects of the prior art, such as low efficiency, insufficient accuracy, and lack of autonomous deep reasoning ability, due to reliance on manual judgment.

[0005] According to one aspect of this application, a method for eliminating potential power transmission hazards is provided, the method comprising:

[0006] In response to the triggering of a power transmission hazard elimination event, the system acquires current hazard information for the power transmission line; wherein, the current hazard information includes hazard monitoring data and hazard description text;

[0007] Based on the current hidden danger information and the pre-constructed reference mind map reasoning framework, the target mind map reasoning framework corresponding to the current hidden danger information is determined; wherein, the reference mind map reasoning framework is a mind map containing different hidden danger handling paths constructed based on the historical hidden danger information of the transmission line.

[0008] The target hazard handling path corresponding to the current hazard information is determined from the target mind map reasoning framework, and a work instruction is generated based on the target hazard handling path;

[0009] The operation and maintenance resource information is parsed from the work instruction, and an electronic work order is generated based on the operation and maintenance resource information and the work instruction. The electronic work order is sent to the terminal device corresponding to the target operation and maintenance team, so that the target operation and maintenance team can eliminate hidden dangers on the transmission line based on the electronic work order.

[0010] According to one aspect of this application, a power transmission hazard elimination device is provided, the device comprising:

[0011] The current hazard information acquisition module is used to acquire the current hazard information of the transmission line in response to the triggering of a power transmission hazard elimination event; wherein, the current hazard information includes hazard monitoring data and hazard description text;

[0012] The target mind map reasoning framework determination module is used to determine the target mind map reasoning framework corresponding to the current hidden danger information based on the current hidden danger information and a pre-constructed reference mind map reasoning framework; wherein, the reference mind map reasoning framework is a mind map containing different hidden danger handling paths constructed based on the historical hidden danger information of the transmission line.

[0013] The work instruction generation module is used to determine the target hazard handling path corresponding to the current hazard information from the target mind map reasoning framework, and generate a work instruction based on the target hazard handling path;

[0014] The electronic work order generation module is used to parse maintenance resource information from the work instruction, generate an electronic work order based on the maintenance resource information and the work instruction, and send the electronic work order to the terminal device corresponding to the target maintenance team, so that the target maintenance team can eliminate hidden dangers on the transmission line based on the electronic work order.

[0015] According to another aspect of this application, an electronic device is provided, the electronic device comprising:

[0016] At least one processor; and

[0017] A memory that is communicatively connected to at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by at least one processor, which enables the at least one processor to perform the power transmission hazard elimination method of any embodiment of this application.

[0019] According to another aspect of this application, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement the power transmission hazard elimination method of any embodiment of this application.

[0020] According to another aspect of this application, a computer program product is provided, which includes a computer program that, when executed by a processor, implements the power transmission hazard elimination method of any embodiment of this application.

[0021] The technical solution of this application embodiment, in response to the triggering of a power transmission hazard elimination event, acquires current hazard information of the power transmission line; wherein, the current hazard information includes hazard monitoring data and hazard description text; based on the current hazard information and a pre-constructed reference mind map reasoning framework, determines the target mind map reasoning framework corresponding to the current hazard information; wherein, the reference mind map reasoning framework is a mind map containing different hazard handling paths constructed based on historical hazard information of the power transmission line; determines the target hazard handling path corresponding to the current hazard information from the target mind map reasoning framework, and generates a work instruction based on the target hazard handling path; parses maintenance resource information from the work instruction, and generates an electronic work order based on the maintenance resource information and the work instruction, and sends the electronic work order to the terminal device corresponding to the target maintenance team, so that the target maintenance team can eliminate the hazard of the power transmission line based on the electronic work order. The technical solution provided in this application, through deep semantic understanding and fusion of multi-source heterogeneous hidden danger information, uses a mind map structure to simulate the systematic reasoning process of human experts, determines the target hidden danger handling path, and ultimately realizes full-process automation from hidden danger identification to resource scheduling. This not only solves the technical defects of existing technologies, such as low efficiency, insufficient accuracy, and lack of autonomous deep reasoning ability due to reliance on manual judgment, but also improves the comprehensiveness and accuracy of hidden danger handling solutions, thereby further improving the intelligence level and reliability of power grid operation and maintenance.

[0022] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A flowchart illustrating a method for eliminating power transmission hazards provided in this application embodiment;

[0025] Figure 2 This is a schematic diagram of the structure of a power transmission hazard elimination device provided in an embodiment of this application;

[0026] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0028] It should be noted that the terms "first," "second," "third," "fourth," "actual," "preset," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Figure 1 This is a flowchart illustrating a method for eliminating power transmission hazards provided in an embodiment of this application. This embodiment is applicable to situations involving the elimination of power transmission hazards. The method can be executed by a power transmission hazard elimination device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0030] S110. In response to the triggering of a power transmission hazard elimination event, obtain current hazard information of the power transmission line; wherein, the current hazard information includes hazard monitoring data and hazard description text.

[0031] In this embodiment, when a power transmission hazard elimination command is received, it is determined that a power transmission hazard elimination event has been triggered. In response to the triggering of the power transmission hazard elimination event, current hazard information of the power transmission line is acquired; wherein, current hazard information can be understood as any indication or state at the current moment that the power transmission line or its auxiliary equipment deviates from normal operating conditions, potentially causing a fault or accident. Current hazard information may include hazard monitoring data and hazard description text. Hazard monitoring data refers to structured numerical or status-based data collected by various automated monitoring devices that indicates the existence of safety hazards in the power transmission line, specifically including but not limited to image and video data, electrical measurement data, and meteorological and environmental data.

[0032] A. Image and video data can be visual information captured by drones, fixed cameras, or inspection robots, including visible light images, infrared thermal images, and laser point cloud data. Among them, visible light images are used to identify potential hazards such as insulator spontaneous explosion, damage, broken conductor strands, hanging foreign objects, bending and deformation of tower materials, and illegal buildings and excessively tall trees in passageways. Infrared thermal images are used to detect overheating of contact points and wire clamps. Laser point cloud data is used to accurately measure changes in conductor sag and distances to crossing objects.

[0033] B. Electrical measurement data refers to the electrical parameters of the online monitoring system, which may include leakage current data, partial discharge data, and abnormal fluctuations in voltage and current. Among them, leakage current data indicates that the pollution level of the insulator may exceed the standard; partial discharge data indicates that there are insulation defects inside the equipment; and abnormal fluctuations in voltage and current may indicate that there is an intermittent grounding or short circuit risk in the line.

[0034] C. Meteorological and environmental data are real-time data from micro-weather stations, which may include wind speed and direction data, precipitation and humidity data, and ambient temperature data. Among them, wind speed and direction data are used to assess the risk of wind-induced discharge; precipitation and humidity data are used to assess the risk of insulator flashover; and ambient temperature data are used to assist in the analysis of conductor current-carrying capacity.

[0035] Hazard description text can be understood as unstructured information in natural language that describes, records, or reports a hazard situation. Hazard description text specifically includes, but is not limited to, manual inspection reports, historical hazard case reports, summaries of power safety regulations and equipment operation manuals, and expert experience records.

[0036] A. Manual inspection reports can be descriptive text recorded by the inspectors. For example: The third insulator on the large side of phase B of tower #36 of the 110kV Yunshan line has an obvious crack, about 5 centimeters long; During the inspection, it was found that a crane was working in the #12-#13 passage of the 110kV Donghu line, and the safety distance was insufficient.

[0037] B. Historical hazard case reports can be complete reports of similar hazards that have been dealt with in the past, including experiential knowledge such as malfunction phenomena, cause analysis, handling methods, and final results.

[0038] C. Summaries of electrical safety regulations and equipment operation manuals can be relevant normative text excerpts. For example, when the potential hazard involves live-line work, the relevant clauses in the safety regulations concerning safe distances, the use of insulated tools, and work permit systems can serve as important binding descriptive information.

[0039] D. Expert experience records can be written records of the opinions or decision-making logic of domain experts. For example: For towers located in windy areas, the inspection cycle of fastening bolts should be shortened to half of the usual frequency.

[0040] In this embodiment, objective monitoring data from "objects" (sensors, cameras) and subjective experience descriptions from "people" (inspectors, experts) are effectively integrated to form hidden danger information that far exceeds the traditional methods in terms of information dimension and completeness, laying a solid data foundation for subsequent steps of deep semantic understanding and intelligent reasoning.

[0041] S120. Based on the current hidden danger information and the pre-constructed reference mind map reasoning framework, determine the target mind map reasoning framework corresponding to the current hidden danger information; wherein, the reference mind map reasoning framework is a mind map containing different hidden danger handling paths constructed based on the historical hidden danger information of the transmission line.

[0042] The mind graph reasoning framework is a dynamic and visual computational model used to simulate the advanced thinking process of human experts when dealing with complex power transmission hazards, involving multi-path deduction, trade-offs, and intuitive associations. In this embodiment, a pre-constructed reference mind graph reasoning framework is obtained. This framework is a mind graph containing different hazard handling paths, constructed based on historical hazard information of power transmission lines. The mind graph reasoning framework includes state nodes, operation nodes, evaluation nodes, sequential edges, and associated edges. State nodes reflect the state information of the power transmission line during hazard handling; operation nodes reflect the operational methods used; evaluation nodes reflect the risk assessment results of the corresponding handling path; sequential edges are connecting lines between any two key nodes with a chronological order; and associated edges are connecting lines between any two logically related key nodes belonging to different handling paths, or between two non-adjacent logically related key nodes belonging to the same handling path. Key nodes include state nodes and operation nodes, and the handling path is a path composed of state nodes, operation nodes, and sequential edges.

[0043] For example, a status node represents a fact, situation, or stage result in the process of handling a potential hazard, describing the status information of the transmission line at a certain moment. A status node can include an initial status node, intermediate status nodes, and a final status node. For instance, the initial status node could be "Hazard identified: Insulator string of tower #1101 severely damaged"; an intermediate status node could be "Safety measures confirmed to be implemented on-site, required spare parts have been issued from the central warehouse, and drone inspection confirmed no other hidden defects"; and the final status node could be "Hazard eliminated, power restored to the line." An operation node represents a specific, executable action or decision, that is, the operational means or steps used in handling transmission line hazards. It is the processing step used to shift the transmission line from one problem state to another. For example, an operation node could be "Execute a power outage operation and apply for permission from the dispatch center," or "Dispatch a working group, carrying safety belts, damaged insulators, and tensioners to the site," or "Activate the backup power supply scheme to ensure important downstream loads," or "Decid to use equipotential bonding for replacement according to regulation DL / T 741." An assessment node represents the risk assessment result formed by evaluating, verifying, or judging the risks of a hazard handling path. For example, an assessment node could be "The cost of hazard handling path A is ¥5,000, which is within the budget," or "The first operation step in hazard handling path A is consistent with the requirements of Article 5.2.1 of the Safety Regulations," or "The handling plan corresponding to hazard handling path B requires operation in severe weather, and the risk level is high," or "The estimated power outage time of hazard handling path C is 2 hours shorter than that of hazard handling path D."

[0044] For example, a sequential edge represents the temporal order and causal logic of thought or operation. It is a connecting line between any two state nodes or operation nodes with a temporal sequence. Sequential edges can connect the key nodes that constitute a complete processing path. For example, the hazard handling path is: insulator damage (state node) → perform power outage operation (operation node) → power is off on site (state node) → replace insulator (operation node). In this hazard handling path, adjacent key nodes are connected by sequential edges. An associative edge represents the logical relationship between any two key nodes in different processing paths, or the logical relationship between two non-adjacent key nodes in the same processing path. It simulates the moment when an expert suddenly realizes that the tool for one solution can be used for another solution, or that the two solutions cannot be executed simultaneously. The logical relationship between the two key nodes connected by an associative edge can include complementary, conflicting, and dependent relationships. For example, complementary relationship: the operation node "call a crane for operation" in hazard handling path A and the operation node "transport large equipment" in hazard handling path B are complementary, meaning that hazard handling path A and hazard handling path B can share resources and be merged into a more efficient hazard handling path. Conflict Relationship: The operation node "Perform live water flushing" in hazard treatment path C conflicts with the status node "Ambient humidity greater than 80%". According to safety regulations, live water flushing is prohibited under high humidity conditions, which could trigger the rejection or modification of hazard treatment path C. Dependency Relationship: The evaluation node "Solution feasibility assessment passed" for hazard treatment path D is based on the status node "Geological subsidence monitoring report result" of hazard treatment path E. This means that the advancement of hazard treatment path D depends on the result of hazard treatment path E.

[0045] In this embodiment of the application, the construction process of the reference mind map reasoning framework may include the following steps:

[0046] S1. Create an initial state node based on historical potential hazards information, and use the initial state node as the root node of the reference mind map reasoning framework;

[0047] S2. Based on the initial state node, the large language model generates multiple possible subsequent operation nodes and determines the state information of the transmission line corresponding to each operation node. The state information is used as the state node, and the operation nodes and state nodes with time sequence and correlation are connected by sequential edges to form multiple concurrent inference paths.

[0048] S3. While expanding the path, drive the large language model to scan and analyze the key nodes of different paths, and actively establish the aforementioned related edges.

[0049] S4. By evaluating nodes, each path is scored, and the relationships revealed by the associated edges are used to trim, merge, and optimize multiple paths, ultimately converging to the optimal processing path, and generating a reference mind map reasoning framework.

[0050] In this embodiment, a target mind map reasoning framework corresponding to the current hidden danger information is determined based on the current hidden danger information and a pre-constructed reference mind map reasoning framework. For example, the current hidden danger information and the pre-constructed reference mind map reasoning framework are input into a large language model, so that the large language model analyzes the current hidden danger information with reference to the reference mind map reasoning framework, and obtains the target mind map reasoning framework corresponding to the current hidden danger information output by the large language model. Optionally, determining the target mind map reasoning framework corresponding to the current hidden danger information based on the current hidden danger information and the pre-constructed reference mind map reasoning framework includes: based on the current hidden danger information and the pre-constructed reference mind map reasoning framework, determining, through the large language model, the various operational means used to handle the hidden danger of the transmission line and the state information of the transmission line after handling the transmission line based on each operational means;

[0051] Using the current hidden danger information as the root node, each of the aforementioned operational methods as the corresponding operational node, and each of the aforementioned state information as the state node, a corresponding hidden danger handling path branch is generated based on the root node, the operational node, and the state node connected by sequential edges; wherein, the sequential edge is a connection line between any two nodes with a chronological order; based on the large language model, target key node pairs with logical relationships are determined, and association lines are generated between the target key node pairs; wherein, the target key node pairs include any two of the aforementioned state nodes and / or operational nodes, and the two key nodes in the target key node pairs belong to different hidden danger handling path branches; a risk assessment is performed on each of the hidden danger handling path branches, the risk assessment result is determined, and the risk assessment result is added to the hidden danger handling path branch as an assessment node; the topology graph formed by each of the hidden danger handling path branches and the association lines is used as the target mind map reasoning framework.

[0052] In this embodiment, based on current hazard information and a pre-built reference mind map reasoning framework, a large language model is invoked to generate various operational methods for handling potential hazards on transmission lines, as well as the state information of the transmission line after handling each operational method. Each operational method is treated as an operational node, and each state information is treated as a state node. The advantage of this setup is that it allows for parallel exploration of multiple possible handling directions for transmission line hazards, avoiding a single-path approach and ensuring comprehensive decision-making. Optionally, based on the current hidden danger information and a pre-constructed reference mind map reasoning framework, a large language model is used to determine the various operational methods adopted for handling the hidden dangers of the transmission line and the state information of the transmission line after handling the transmission line based on each operational method. This includes: inputting the current hidden danger information and the state data contained in the pre-constructed reference mind map reasoning framework into the large language model to obtain the various operational methods adopted for handling the hidden dangers of the transmission line output by the large language model; for each operational method, inputting the operational method into the large language model to obtain the state information of the transmission line after handling the transmission line based on the operational method output by the large language model.

[0053] For example, referencing the state data (all nodes, edge relationships) and current hazard information contained in the mind map reasoning framework, the context of the prompt words is used as the context. Using a specific prompt word template, the context is input into a large language model, yielding multiple independent operation nodes output by the model. These operation nodes represent the various operational methods used to handle hazards on the transmission line; that is, specific actions that can be immediately executed based on the current hazard state of the transmission line. For example, for the hazard of insulator damage, parallel operation methods might include requesting a power outage, assessing the feasibility of live-line work, and dispatching drones for detailed inspection. For each operation method, the method is input into the large language model, yielding the state information of the transmission line after processing based on the operation method. This state information is then used as the corresponding state node. It can be understood that a state node represents the new state the transmission line might enter after executing the operation node, based on domain knowledge prediction. For example, for the operation node "Request a power outage," the determined state node is "The line is de-energized, safety measures are being implemented."

[0054] Using the current hazard information as the root node (i.e. the initial state node), the corresponding hazard handling path branch is generated based on the root node, operation node and state node connected by sequential edges. It can be understood that sequential edges are used to connect each operation node with a time sequence to its derived state node to form an operation-state sequence. Starting from the root node (initial hazard), a hazard handling path branch is formed through multiple operation-state sequences.

[0055] In this embodiment, for any two key nodes in different hazard handling path branches or any two non-adjacent key nodes in the same hazard handling path branch, it is determined whether there is a deep logical relationship between the two key nodes that transcends the order relationship. If so, the two key nodes are taken as a target key node pair, and an association line is generated between the target key node pairs. Optionally, determining the target key node pairs with logical relationships based on the large language model includes: determining candidate key node pairs consisting of two non-adjacent candidate key nodes belonging to different hazard handling path branches or belonging to the same hazard handling path branch based on a pre-set association strategy; for each candidate key node pair, inputting the candidate key node pair and the context information of each candidate key node in the candidate key node pair in its respective hazard handling path branch into the large language model, and determining whether there is a logical relationship between the two candidate key nodes in the candidate key node pair based on the output result of the large language model. If so, the candidate key node pair is taken as the target key node pair.

[0056] In this embodiment, candidate key node pairs that may have a relationship are determined from different hazard handling path branches based on a pre-defined association strategy, or candidate key node pairs that may have a relationship but are not adjacent are determined from the same hazard handling path branch based on a pre-defined association strategy. For example, nodes of the same type (such as those involving crane calls), nodes that may overlap in time and space, or nodes that are semantically related (such as a detection operation and a repair operation) in different hazard handling path branches are selected as candidate key node pairs. For each candidate key node pair, the candidate key node pair and the context information of each candidate key node in the candidate key node pair in its respective hazard handling path branch are input into a large language model. Through well-designed prompt words, the large language model determines whether there is a deep logical relationship beyond the order relationship between the two candidate key nodes in the candidate key node pair, obtains the output result of the large language model, and determines whether there is a logical relationship between the two candidate key nodes in the candidate key node pair. If so, the candidate key node pair is taken as the target key node pair. The advantage of this setup is that it can uncover potential systemic connections and avoid resource conflicts or loss of collaboration opportunities that may result from the isolated evolution of various hidden danger handling paths.

[0057] A risk assessment is conducted for each branch of the hazard handling path, and the risk assessment result is determined. This process can be understood as simulating an expert review and scoring of the solution from multiple dimensions, transforming qualitative judgments into quantitative evidence. For example, a large language model is invoked, based on domain knowledge, to score each key node in each hazard handling path branch on dimensions such as safety (risk level), cost (materials, labor), time consumption (operation duration), and reliability (success probability) (using a percentage system or graded scores). The weighted sum of the scores for each operational node is used as the risk assessment result for the hazard handling path branch. The risk assessment result is encapsulated as a new assessment node and connected to the last node in the assessed hazard handling path branch via sequential edges. The topology graph formed by the various hazard handling path branches and their connecting lines serves as the target mind map reasoning framework.

[0058] Optionally, after determining whether there is a logical relationship between the two candidate key nodes in the candidate key node pair based on the output of the large language model, and if so, taking the candidate key node pair as the target key node pair, the method further includes: obtaining the logical relationship type between the two target key nodes in the target key node pair output by the large language model; wherein, the logical relationship type includes complementary relationship, conflict relationship, and dependency relationship; generating an association line between the target key node pairs includes: generating an association line between the target key node pairs that matches the logical relationship type.

[0059] In this embodiment, if a logical relationship is determined between two candidate key nodes in a candidate key node pair, the logical relationship type between two target key nodes in the target key node pair output by the large language model is further determined. The logical relationship types include complementary, conflicting, and dependent relationships. Specifically, when two candidate key nodes can share resources, be executed concurrently, or synergize, their logical relationship can be determined to be complementary. The criteria for this determination include whether they are different processes for the same equipment, whether the required tools overlap, or whether the execution time and space are consistent (allowing for merging plans). For example, the operation node "calling a crane to replace tower materials" in hazard handling path branch A and the operation node "using a crane to install bird guards" in hazard handling path branch B are determined to be complementary because they share crane resources. When two candidate critical nodes are determined to be mutually exclusive in terms of safety procedures, resource usage, or logic, their logical relationship can be identified as a conflict. The criteria for this include whether they violate provisions of the "Electric Power Safety Regulations" (e.g., conflicting safety distance requirements between two operations), whether they compete for the same scarce resource (e.g., the only special-operation vehicle), or whether they contradict each other in terms of timing logic (e.g., an operation that requires a power outage conflicts with a live operation). For example, the operation node "perform a live water flushing operation" and the operation node "the ambient humidity at the meteorological monitoring node is greater than 80%" are determined to be in conflict because they violate safety regulations. When the execution of one candidate operation node depends on the output of another candidate operation node, their logical relationship can be identified as a dependency. The criteria for this include whether the former candidate operation node provides data for the latter (e.g., test results provide a basis for a repair plan), changes its state (e.g., maintenance can only be carried out after a power outage), or prepares resources. For example, the status node "foundation settlement test report" is a prerequisite for the operation node "determine the final reinforcement plan," and is therefore determined to be a dependency.

[0060] A connection line matching the logical relationship type is generated between pairs of target key nodes. That is, if the logical relationship types of the two target key nodes in a pair are different, the corresponding connection line representation will be different. Optionally, the logical relationship type can also be used as the attribute information of the connection edge. Before performing a risk assessment on each of the aforementioned hazard handling path branches and determining the risk assessment result, the method further includes: for each connection line in the target mind graph reasoning framework, processing the hazard handling path branches to which the two target key nodes connected by the connection line belong based on the logical relationship type corresponding to the connection line, in order to update the target mind graph reasoning framework.

[0061] For example, traversing all the connecting edges in the target mind graph reasoning framework, when the logical relationship type corresponding to the connecting edge is complementary, the path segments belonging to the two target key nodes connected by the connecting line (i.e., two key nodes with a complementary relationship) are merged to generate a new hazard handling path that combines the strengths of both. For example, merging two paths that both require a crane allows for scheduling the crane only once, generating a new, more efficient path. When the logical relationship type corresponding to the connecting edge is conflicting, the hazard handling path branches belonging to the two target key nodes connected by the connecting line (i.e., two key nodes with a conflicting relationship) are selected based on the evaluation node scores. For example, the hazard handling path branch with the lower score is discarded to ensure the overall quality of the hazard handling solutions corresponding to each hazard handling path branch in the target mind graph reasoning framework.

[0062] S130. Determine the target hazard handling path corresponding to the current hazard information from the target mind map reasoning framework, and generate a work instruction based on the target hazard handling path.

[0063] In this embodiment, the target hazard handling path corresponding to the current hazard information is determined from the target mind graph reasoning framework. For example, the hazard handling path branch with the highest comprehensive score corresponding to the evaluation node in the target mind graph reasoning framework is taken as the target hazard handling path. This target hazard handling path represents the Pareto optimal solution under multiple objective constraints such as safety, cost, and efficiency in the current knowledge state. A work instruction is generated based on the target hazard handling path. For example, the target hazard handling path, consisting of state nodes, operation nodes, and evaluation nodes, is mapped to the inherent framework of pre-operation preparation, operation steps, safety measures, and precautions in the work instruction to generate the work instruction. The target hazard handling path contains a complete and logically rigorous hazard handling logic. The operation node sequence provides the core step skeleton of the work instruction. The state nodes clearly define the preconditions required before each step and the acceptance standards to be achieved after execution. The evaluation nodes include key information such as safety risks and quality control points, which will be transformed into specific safety measures and quality standards. The safety regulations stored in the attributes of associated edges, especially conflict edges, serve as the authoritative basis for compiling safety measures.

[0064] Optionally, generating a work instruction based on the target hazard handling path includes: extracting target work elements from the target hazard handling path; wherein the target work elements include a tool and equipment list, personnel qualifications and division of labor, a set of safety measures, and a process sequence; filling the target work elements into the corresponding fields in the work instruction template to generate the work instruction. For example, the target hazard handling path is traversed and parsed to extract the target work elements, which include a tool and equipment list, personnel qualifications and division of labor, a set of safety measures, and a process sequence. The tool and equipment list contains the names, specifications, and quantities of tools and equipment necessary for execution at all operation nodes. Personnel qualifications and division of labor are inferred based on the complexity and risk level of the operation nodes, determining the required supervisor, operators, and their qualification requirements. The set of safety measures is a collection of mandatory safety measures generated by comprehensively considering safety risk warnings in all assessment nodes and the relevant clauses of the Safety Regulations referenced in conflict relationship edges. The process sequence transforms the sequence of operation nodes in the target hazard handling path into logically coherent and sequentially fixed work steps, specifying the work standards, methods, and acceptance requirements for each step. The target task elements are filled into the corresponding fields in the task instruction template to generate the task instruction. The task instruction template can be a structured electronic form containing predefined fields. The table below shows a task instruction template provided in an embodiment of this application:

[0065]

[0066] In this embodiment, the generated work instructions meet the requirements of the enterprise's safety management system, avoiding problems such as format confusion and missing content caused by differences in personnel skill levels; each step and each measure is derived from the optimal path rigorously deduced through a large model and mind map framework, ensuring the rationality and safety of the technical solution to the greatest extent; the key requirements in the instructions can be associated with the source nodes in the mind map, facilitating post-event review and auditing.

[0067] S140. Parse the operation and maintenance resource information from the work instruction, and generate an electronic work order based on the operation and maintenance resource information and the work instruction. Send the electronic work order to the terminal device corresponding to the target operation and maintenance team, so that the target operation and maintenance team can eliminate hidden dangers on the transmission line based on the electronic work order.

[0068] In this embodiment, maintenance resource information is parsed from the work instruction manual. This maintenance resource information may include a list of tools and personnel skills required for all operation nodes in the target hazard handling path. For example, the required tools are automatically extracted from the descriptions of the operation nodes in the work instruction manual. For instance, from the operation node "replacing tension clamps," tools such as hydraulic pliers, corresponding model tension clamps, wire brushes, and conductive paste can be extracted. Similarly, based on the attributes of the operation nodes, the required personnel qualifications and skills are inferred. For example, for the operation node "performing equipotential live-line work," the operator must possess a live-line work qualification certificate and a high-altitude work permit; while for the operation node "using drones for detailed inspection," the personnel must possess a drone pilot's license.

[0069] The system matches maintenance resource information with all available maintenance teams. From all candidate maintenance teams that match the matching information, it selects the target maintenance team that is located within the same power grid as the location of the potential hazard and whose current work order load is not exceeded. For example, the matching of maintenance resource information with all available maintenance teams can include primary matching and refined matching. Primary matching can be understood as capability matching, that is, selecting maintenance teams from all available maintenance teams whose skills cover the qualifications required for this operation. Refined matching can be understood as efficiency and balance matching, including: 1. Among all teams that pass the primary matching, prioritizing teams located within the same power grid as the hazard-affected tower; 2. The system checks the current work order load of these teams and selects maintenance teams whose current work order load is not exceeded as the target maintenance team. Here, "load not exceeded" means that the total number of work orders currently dispatched but not completed by the maintenance team does not exceed its preset processing capacity threshold.

[0070] Optionally, if multiple target maintenance teams are selected, the estimated time required for each team to move from its current location to the hazard site can be calculated by integrating geographic information systems and real-time traffic data. The team with the shortest arrival time is then selected as the final hazard elimination assignment. The work instruction, parsed maintenance resource information, hazard location, and contact information are packaged into a structured electronic work order. This order is then directly pushed to the target maintenance team's mobile work terminal or work system via a message queue or interface, enabling the target maintenance team to eliminate the hazard on the transmission line based on the electronic work order.

[0071] In this embodiment, a mind-map reasoning framework is constructed to transform the traditional linear decision-making process, which relies on individual expert experience, into a computationally achievable, parallel, multi-path deduction and optimization-based intelligent decision-making process. On one hand, this automates and streamlines the generation of hazard mitigation solutions, significantly shortening the response time from hazard discovery to solution formulation. On the other hand, through deep semantic understanding and cross-path correlation analysis using a large language model, it uncovers collaborative opportunities and potential conflicts that are easily overlooked manually, thereby improving the comprehensiveness and accuracy of the solutions. Furthermore, based on quantitative evaluation and multi-objective fusion, it ensures the comprehensive optimality of the output solution in terms of safety, cost, and efficiency, effectively enhancing the intelligence level and reliability of power grid operation and maintenance.

[0072] The technical solution of this application embodiment, in response to the triggering of a power transmission hazard elimination event, acquires current hazard information of the power transmission line; wherein, the current hazard information includes hazard monitoring data and hazard description text; based on the current hazard information and a pre-constructed reference mind map reasoning framework, determines the target mind map reasoning framework corresponding to the current hazard information; wherein, the reference mind map reasoning framework is a mind map containing different hazard handling paths constructed based on historical hazard information of the power transmission line; determines the target hazard handling path corresponding to the current hazard information from the target mind map reasoning framework, and generates a work instruction based on the target hazard handling path; parses maintenance resource information from the work instruction, and generates an electronic work order based on the maintenance resource information and the work instruction, and sends the electronic work order to the terminal device corresponding to the target maintenance team, so that the target maintenance team can eliminate the hazard of the power transmission line based on the electronic work order. The technical solution provided in this application, through deep semantic understanding and fusion of multi-source heterogeneous hidden danger information, uses a mind map structure to simulate the systematic reasoning process of human experts, determines the target hidden danger handling path, and ultimately realizes full-process automation from hidden danger identification to resource scheduling. This not only solves the technical defects of existing technologies, such as low efficiency, insufficient accuracy, and lack of autonomous deep reasoning ability due to reliance on manual judgment, but also improves the comprehensiveness and accuracy of hidden danger handling solutions, thereby further improving the intelligence level and reliability of power grid operation and maintenance.

[0073] Figure 2 This is a schematic diagram of a power transmission hazard elimination device provided in an embodiment of this application. This device can execute the power transmission hazard elimination method provided in any embodiment of this application, and possesses the corresponding functional modules and beneficial effects for executing the method. Figure 2 As shown, the device includes:

[0074] The current hazard information acquisition module 210 is used to acquire the current hazard information of the transmission line in response to the triggering of a power transmission hazard elimination event; wherein, the current hazard information includes hazard monitoring data and hazard description text;

[0075] The target mind map reasoning framework determination module 220 is used to determine the target mind map reasoning framework corresponding to the current hidden danger information based on the current hidden danger information and a pre-constructed reference mind map reasoning framework; wherein, the reference mind map reasoning framework is a mind map containing different hidden danger handling paths constructed based on the historical hidden danger information of the transmission line.

[0076] The work instruction generation module 230 is used to determine the target hazard handling path corresponding to the current hazard information from the target mind map reasoning framework, and generate a work instruction based on the target hazard handling path;

[0077] The electronic work order generation module 240 is used to parse the operation and maintenance resource information from the work instruction, generate an electronic work order based on the operation and maintenance resource information and the work instruction, and send the electronic work order to the terminal device corresponding to the target operation and maintenance team, so that the target operation and maintenance team can eliminate hidden dangers on the transmission line based on the electronic work order.

[0078] Optional, the target mind map reasoning framework determination module includes:

[0079] The operation method determination unit is used to determine, based on the current hidden danger information and a pre-constructed reference mind map reasoning framework, each operation method used to handle the hidden danger of the transmission line and the state information of the transmission line after handling the transmission line based on each operation method through a large language model.

[0080] The hazard handling path branch generation unit is used to take the current hazard information as the root node, each of the operation methods as the corresponding operation node, and each of the status information as the status node, and generate a corresponding hazard handling path branch based on the root node, the operation node and the status node connected by sequential edges; wherein, the sequential edge is a connection line between any two nodes with a chronological order.

[0081] The association line generation unit is used to determine the target key node pairs with logical relationships based on the large language model, and generate association lines between the target key node pairs; wherein, the target key node pairs include any two node pairs consisting of the state nodes and / or operation nodes, and the two key nodes in the target key node pairs belong to different hidden danger handling path branches or are non-adjacent nodes in the same hidden danger handling path branch;

[0082] An assessment node construction unit is used to perform risk assessment on each of the hazard handling path branches, determine the risk assessment result, and add the risk assessment result as an assessment node to the hazard handling path branch;

[0083] The target mind graph reasoning framework generation unit is used to take the topology graph formed by each of the hidden danger handling path branches and the associated lines as the target mind graph reasoning framework.

[0084] Optionally, the operation means determination unit is used for:

[0085] The current hidden danger information and the state data contained in the pre-constructed reference mind map reasoning framework are input into the large language model to obtain the various operation methods used to handle the hidden dangers of the transmission line output by the large language model.

[0086] For each operation method, the operation method is input into the large language model to obtain the state information of the transmission line after processing the transmission line based on the operation method, which is output by the large language model.

[0087] Optional, the association line generation unit is used for:

[0088] Based on a pre-defined association strategy, candidate key node pairs are determined, consisting of two non-adjacent candidate key nodes belonging to different branches of the hazard handling path or belonging to the same branch of the hazard handling path.

[0089] For each candidate key node pair, the candidate key node pair and the context information of each candidate key node in the candidate key node pair in the corresponding hidden danger handling path branch are input into the large language model. Based on the output of the large language model, it is determined whether there is a logical relationship between the two candidate key nodes in the candidate key node pair. If so, the candidate key node pair is taken as the target key node pair.

[0090] Optional, also includes:

[0091] The logical relationship type acquisition unit is used to determine whether there is a logical relationship between two candidate key nodes in the candidate key node pair based on the output result of the large language model. If so, after taking the candidate key node pair as the target key node pair, the logical relationship type between the two target key nodes in the target key node pair output by the large language model is obtained. The logical relationship type includes complementary relationship, conflict relationship and dependency relationship.

[0092] The correlation line generation unit is used for:

[0093] Generate association lines between the target key node pairs that match the logical relationship type.

[0094] Optional, also includes:

[0095] The target mind graph reasoning framework update module is used to process the hidden danger handling path branches to which the two target key nodes connected by the connection line belong, based on the logical relationship type corresponding to the connection line, for each connection line in the target mind graph reasoning framework before performing risk assessment on each of the hidden danger handling path branches and determining the risk assessment result, so as to update the target mind graph reasoning framework.

[0096] Optional, a work instruction generation module is used for:

[0097] Extract target operational elements from the target hazard handling path; wherein, the target operational elements include a list of tools and equipment, personnel qualifications and division of labor, a set of safety measures, and a sequence of processes and procedures;

[0098] Fill the target task elements into the corresponding fields in the task instruction template to generate the task instruction.

[0099] The power transmission hazard elimination device provided in this application embodiment can execute a power transmission hazard elimination method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of the method.

[0100] Figure 3 A schematic diagram of an electronic device 10, which can be used to implement embodiments of this application, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0101] like Figure 3As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0102] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of monitors, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0103] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for eliminating power transmission hazards.

[0104] In some embodiments, the power transmission hazard elimination method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the power transmission hazard elimination method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the power transmission hazard elimination method by any other suitable means (e.g., by means of firmware).

[0105] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0106] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable power transmission hazard elimination device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0107] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0108] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0109] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0110] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0111] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the power transmission hazard elimination method provided in any embodiment of this application.

[0112] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0113] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired information of the technical solution of this application can be achieved, and this is not limited herein.

[0114] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for eliminating potential power transmission hazards, characterized in that, The method includes: In response to the triggering of a power transmission hazard elimination event, the system acquires current hazard information for the power transmission line; wherein, the current hazard information includes hazard monitoring data and hazard description text; Based on the current hidden danger information and the pre-constructed reference mind map reasoning framework, the target mind map reasoning framework corresponding to the current hidden danger information is determined; wherein, the reference mind map reasoning framework is a mind map containing different hidden danger handling paths constructed based on the historical hidden danger information of the transmission line. The target hazard handling path corresponding to the current hazard information is determined from the target mind map reasoning framework, and a work instruction is generated based on the target hazard handling path; The operation and maintenance resource information is parsed from the work instruction, and an electronic work order is generated based on the operation and maintenance resource information and the work instruction. The electronic work order is sent to the terminal device corresponding to the target operation and maintenance team, so that the target operation and maintenance team can eliminate hidden dangers on the transmission line based on the electronic work order.

2. The method according to claim 1, characterized in that, Based on the current hazard information and the pre-constructed reference mind map reasoning framework, the target mind map reasoning framework corresponding to the current hazard information is determined, including: Based on the current hidden danger information and the pre-constructed reference mind map reasoning framework, the various operational methods used to handle the hidden dangers of the transmission line and the state information of the transmission line after handling the transmission line based on each of the operational methods are determined through the large language model. The current hidden danger information is taken as the root node, each of the operation methods is taken as the corresponding operation node, and each of the status information is taken as the status node. A corresponding hidden danger processing path branch is generated based on the root node, the operation node and the status node connected by sequential edges; wherein, the sequential edge is a connection line between any two nodes with a chronological order. Based on the large language model, target key node pairs with logical relationships are determined, and association lines are generated between the target key node pairs; wherein, the target key node pair includes any two state nodes and / or operation nodes, and the two key nodes in the target key node pair belong to different hidden danger handling path branches or are non-adjacent nodes in the same hidden danger handling path branch; A risk assessment is performed on each of the hazard handling path branches, the risk assessment result is determined, and the risk assessment result is added as an assessment node to the hazard handling path branch; The topology diagram formed by the various hazard handling path branches and the associated lines is used as the target mind map reasoning framework.

3. The method according to claim 2, characterized in that, Based on the current hidden danger information and the pre-constructed reference mind map reasoning framework, the various operational methods used to handle the hidden dangers of the transmission line are determined through a large language model, along with the state information of the transmission line after handling each operational method, including: The current hidden danger information and the state data contained in the pre-constructed reference mind map reasoning framework are input into the large language model to obtain the various operation methods used to handle the hidden dangers of the transmission line output by the large language model. For each operation method, the operation method is input into the large language model to obtain the state information of the transmission line after processing the transmission line based on the operation method, which is output by the large language model.

4. The method according to claim 2, characterized in that, Based on the aforementioned large language model, target key node pairs with logical relationships are identified, including: Based on a pre-defined association strategy, candidate key node pairs are determined, consisting of two non-adjacent candidate key nodes belonging to different branches of the hazard handling path or belonging to the same branch of the hazard handling path. For each candidate key node pair, the candidate key node pair and the context information of each candidate key node in the candidate key node pair in the corresponding hidden danger handling path branch are input into the large language model. Based on the output of the large language model, it is determined whether there is a logical relationship between the two candidate key nodes in the candidate key node pair. If so, the candidate key node pair is taken as the target key node pair.

5. The method according to claim 4, characterized in that, After determining whether there is a logical relationship between the two candidate key nodes in the candidate key node pair based on the output of the large language model, and if so, taking the candidate key node pair as the target key node pair, the method further includes: Obtain the logical relationship type between two target key nodes in the target key node pair output by the large language model; wherein, the logical relationship type includes complementary relationship, conflict relationship and dependency relationship; Generating association lines between the target key node pairs includes: Generate association lines between the target key node pairs that match the logical relationship type.

6. The method according to claim 4, characterized in that, Before conducting a risk assessment for each of the aforementioned hazard handling path branches and determining the risk assessment results, the process also includes: For each connection line in the target mind map reasoning framework, the hidden danger handling path branches of the two target key nodes connected by the connection line are processed based on the logical relationship type corresponding to the connection line, so as to update the target mind map reasoning framework.

7. The method according to claim 1, characterized in that, Based on the target hazard handling path, a work instruction manual is generated, including: Extract target operational elements from the target hazard handling path; wherein, the target operational elements include a list of tools and equipment, personnel qualifications and division of labor, a set of safety measures, and a sequence of processes and procedures; Fill the target task elements into the corresponding fields in the task instruction template to generate the task instruction.

8. A device for eliminating potential power transmission hazards, characterized in that, include: The current hazard information acquisition module is used to acquire the current hazard information of the transmission line in response to the triggering of a power transmission hazard elimination event; wherein, the current hazard information includes hazard monitoring data and hazard description text; The target mind map reasoning framework determination module is used to determine the target mind map reasoning framework corresponding to the current hidden danger information based on the current hidden danger information and a pre-constructed reference mind map reasoning framework; wherein, the reference mind map reasoning framework is a mind map containing different hidden danger handling paths constructed based on the historical hidden danger information of the transmission line. The work instruction generation module is used to determine the target hazard handling path corresponding to the current hazard information from the target mind map reasoning framework, and generate a work instruction based on the target hazard handling path; The electronic work order generation module is used to parse maintenance resource information from the work instruction, generate an electronic work order based on the maintenance resource information and the work instruction, and send the electronic work order to the terminal device corresponding to the target maintenance team, so that the target maintenance team can eliminate hidden dangers on the transmission line based on the electronic work order.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the power transmission hazard elimination method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the method for eliminating power transmission hazards as described in any one of claims 1-7.

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