Power distribution network overhead line ground wire anti-error management and control method and system based on model self-healing

Abstract

The invention provides a power distribution network overhead line ground wire anti-error management and control method and system based on model self-healing, and the method comprises the steps: recognizing missing tower equipment in graph model data through data comparison; determining a target pole tower related to the maintenance from the missing pole tower equipment; executing model self-healing operation on the target tower; obtaining the direction of the connecting line and the information of the connecting line associated equipment terminal, carrying out topology calculation and state analysis, and determining the live-line state of the connecting line and each node in the electrical connection network; and generating an operation ticket. According to the scheme, multi-source collaborative comparison is carried out by obtaining the machine account, the graph model data and the external work order data of the power distribution network equipment, missing tower equipment is automatically identified, the target tower is intelligently screened based on the operation order task, and then the model self-healing operation including logic-physical double-view modeling, model increment updating and graph consistency completion is executed. And automatic reconstruction and dynamic synchronization of the topology model are realized.

Description

Technical Field

[0001] This application relates to the field of distribution network overhead line technology, and more specifically, to a model-based self-healing method and system for preventing misoperation of the ground wire of distribution network overhead lines. Background Technology

[0002] In the operation and maintenance of power distribution networks, installing temporary grounding wires is a core technical measure to ensure the safety of maintenance personnel. Currently, the prevention and control of errors in distribution network management relies on static graphical data (including graphic data and model data). Graphical data is used to visualize the spatial layout of towers, equipment, and connecting lines, while model data stores structured information such as equipment parameters and topological connections for calculation and analysis. However, overhead distribution lines have significant characteristics such as open field operation environments, dispersed maintenance points, and short maintenance time windows. The location of the grounding wire needs to be flexibly determined based on the actual situation, such as the on-site maintenance scope and safety distance, making it difficult for existing static graphical data to maintain real-time consistency with the physical power grid on-site.

[0003] Existing anti-misoperation systems primarily rely on manually maintained static topology models, which are severely out of sync with the actual power grid on-site. On one hand, when lines are modified, towers are added, or relocated, the model data cannot automatically identify and adapt to these changes, resulting in discrepancies between the model and reality. On the other hand, the system struggles to interpret the electrical semantics of connecting lines in the diagrams and cannot finely manage complex structures such as multiple circuits on the same tower. This limits anti-misoperation verification to a coarse tower level, failing to achieve precise multi-level positioning of circuits, phases, and connection sides. This deficiency directly leads to an extremely high risk of malicious misoperations during distribution network maintenance, such as accidentally connecting grounding wires to energized circuits or omitting grounding wires during energization. Especially in scenarios with multiple circuits on the same tower, the electromagnetic induction voltage generated by adjacent energized circuits poses a serious safety threat to the outage maintenance circuits, and existing technologies cannot provide reliable anti-misoperation protection. Summary of the Invention

[0004] The purpose of this application is to provide a model-based self-healing method and system for preventing misoperation of ground wires in overhead power distribution networks, in order to solve the above-mentioned problems.

[0005] In a first aspect, embodiments of this application provide a model-based self-healing method for preventing misoperation of ground wires in overhead power distribution networks, comprising: acquiring equipment ledger data, graphic model data, and external work order data of the power distribution network, and identifying missing tower equipment in the graphic model data through data comparison; determining the target tower related to the current maintenance from the missing tower equipment according to the operation ticket task indicated by the external work order data; and performing a model self-healing operation on the target tower; wherein, the model self-healing operation includes: establishing a logical-physical dual-view tower model, updating the logical-physical dual-view tower model to the model data to complete the model, and adding tower elements at the corresponding positions in the graphic data to complete the graphics; the logical-physical dual view includes a physical model and a logical-physical dual-view model. The system comprises a physical model indicating the physical attributes of the target tower and a logical model indicating the circuit attributes of each circuit carried by the target tower. The physical model and the logical model are associated through an identifier. Based on the updated graphical model data, a topology search is performed on the connecting lines connected to the target tower at the graphical front end to obtain the routing of the connecting lines and the information of the terminals of the connected lines associated with the connecting lines. Topology calculation and state analysis are then performed to determine the energized state of the connecting lines and each node in the electrical connection network. An operation ticket is generated based on external work order data. The grounding wire connection position is bound to the logical connection point in the logical tower model corresponding to the circuit specified in the operation ticket. The binding relationship is then checked for error prevention based on the energized state.

[0006] In the implementation of the above solution, multi-source collaborative comparison is performed by acquiring distribution network equipment ledgers, diagram data, and external work order data. This automatically identifies missing tower equipment and intelligently filters target towers based on operation ticket tasks. Then, a model self-healing operation is executed, including logical-physical dual-view modeling, incremental model updates, and graphical consistency completion. This systematically solves the problem of model-site disconnect caused by lagging diagram maintenance and missing topological semantics in overhead distribution network lines. It achieves automated reconstruction and dynamic synchronization of the topology model, reduces manual maintenance costs and error probability, improves distribution network operation and maintenance efficiency, and significantly enhances the level of digitalization. On the other hand, based on the updated graphical model data, the topology search and real-time status calculation of the target tower and associated lines are performed on the graphical front end. The energized status of each node and connecting line in the electrical connection network is accurately determined. The grounding wire connection position is precisely bound to the loop logic connection point specified in the operation ticket and the error prevention logic is verified. This improves the error prevention control granularity from the traditional tower level to the loop-phase level, effectively eliminating the error prevention blind spots such as misjudgment of energized loops and insufficient safety distance in the scenario of multiple circuits on the same tower. It also significantly reduces the risk of malicious misoperation such as misconnecting grounding wires and missing grounding wires, ensuring the personal safety of operators and the stable operation of the power grid.

[0007] In one implementation of the first aspect, establishing the logical-physical dual-view tower model includes: Create a physical tower model of the target tower; wherein the physical tower model includes the spatial coordinates, tower material, and tower number attribute of the target tower; according to the number of circuits carried by the target tower, instantiate a logical tower model for each circuit; wherein the logical tower model includes the line name, phase information, circuit number, and equipment terminal information associated with the logical connection point of the corresponding circuit; through the globally unique identifier of the physical tower model and the physical tower reference attribute of the logical tower model, establish a one-to-many association relationship between the physical tower model and all the logical tower models to obtain a logical-physical dual-view tower model.

[0008] In the implementation of the above scheme, by constructing independent physical tower models and logical tower models, the decoupling management of geospatial attributes and electrical topology attributes is achieved. The physical model uses a globally unique identifier to solidify the spatial coordinates, material, and number of the tower, providing a precise spatial positioning benchmark for grounding wire connection. At the same time, multiple logical tower models instantiated based on the number of circuits establish a one-to-many relationship with the physical model through reference attributes, accurately representing the complex structural characteristics of multiple circuits on the same tower, solving the technical defects of traditional models where physical entities and logical circuits are mixed and cannot be finely distinguished. On the other hand, the dual-view structure can refine the management granularity of grounding wire connection position from the single tower level to the circuit-phase level. The circuit name, phase information, circuit number, and equipment terminal information contained in the logical model provide clear electrical connection semantics for subsequent topology search, enabling the anti-misoperation system to accurately verify the power outage status of the target circuit, identify adjacent energized circuits, and calculate the phase-to-phase safety distance. This effectively eliminates the risk of mistakenly connecting energized circuits due to circuit identification errors in the maintenance scenario of multiple circuits on the same tower, improving the accuracy and safety of grounding wire anti-misoperation control.

[0009] In one implementation of the first aspect, the step of performing a topology search on the line connecting the target tower at the graphics front end based on the updated graph data to obtain the direction of the connecting lines and the information of the associated equipment terminals, and performing topology calculation and state analysis to determine the energized state of the connecting lines and each node in the electrical connection network includes: performing a topology search on the line connecting the target tower, traversing the direction of the connecting lines to determine the connection relationship of the loop in each direction of the logical connection point, and extracting the connection relationship data between equipment terminals; performing topology calculation based on the connection relationship data and the real-time equipment status of the power grid to determine the energized state of each node and each connecting line in the electrical connection network; and driving the graph data to perform real-time topology coloring and visualization based on the energized state.

[0010] In the implementation of the above solution, by performing a topology search on the target tower's associated lines in the graphics front-end, automatically traversing the direction of the connecting lines and extracting the connection relationship data between equipment terminals, a deep analysis and structured expression of the electrical semantics of the visual connecting lines in the graphics is achieved. This solves the technical problems of missing topology attributes and ambiguous electrical connection relationships in traditional systems. Furthermore, by combining the real-time equipment status of the power grid for topology calculation, the energized status of each node and connecting line in the electrical connection network is dynamically determined, upgrading the static graphic model data into a dynamic topology model with real-time status perception capabilities, thus improving the response speed and accuracy to the power grid's operating status. On the other hand, the calculation results based on the energized status drive the graphic model data for real-time topology coloring and visualization, transforming abstract topology analysis into intuitive graphical information. This enables maintenance personnel to clearly identify the energized status of each logical connection point of the target tower and its adjacent circuits. Especially in the scenario of multi-circuit line maintenance on the same tower, it can quickly identify the spatial distribution and safe distance of energized circuits, effectively avoiding the risk of misoperation due to insufficient status perception, while also improving the safety verification efficiency of operation ticket generation and on-site execution.

[0011] In one implementation of the first aspect, the step of performing error-prevention logic verification on the binding relationship based on the energized state includes: before executing the operation ticket, simulating the binding relationship based on the logical-physical dual-view pole model and the energized state of the connecting line; during the simulation, performing verification; wherein the verification includes: verifying whether the energized state of the logical connection point corresponding to the grounding wire connection position is in a de-energized state, and verifying whether the direction of the small side, large side, or branch side of the logical connection point meets the requirements of the operation ticket task; and calling the energized state of all logical pole models associated with the physical pole model to verify the safe distance between adjacent energized circuits in multi-circuit lines on the same pole and the grounding wire connection position; when it is verified that the energized state of the logical connection point is abnormal, the direction is inconsistent, or the safe distance between the energized circuit and the grounding wire connection position does not meet the preset threshold, generating a safety warning information and terminating the operation ticket generation process.

[0012] In the implementation of the above scheme, a simulation pre-run mechanism is introduced before executing the operation ticket. By calling the logical-physical dual-view tower model and the live state calculated in real-time topology, the binding relationship is checked in a multi-dimensional collaborative manner. This includes confirming the power outage status of the logical connection point, verifying the compliance of the connection direction, and quantitatively assessing the safety distance between adjacent live circuits in the scenario of multiple circuits on the same pole. This achieves a leap from single-state verification to three-dimensional error prevention logic. It can proactively identify and block potential risks such as live grounding wires, incorrect connection positions, and insufficient safety distances during the operation ticket generation stage, moving the safety control point forward and avoiding safety hazards caused by post-event correction. On the one hand, by linking and calling the energized state of all logical tower models associated with physical towers during the simulation and pre-run, the real-time energized distribution of each circuit in a multi-circuit line on the same tower can be accurately identified. Based on the preset safety distance threshold, the spatial relationship between each energized circuit and the target grounding wire connection position is quantitatively verified. This effectively solves the problem of safety distance assessment failure caused by the inability to accurately identify the electrical coupling relationship between circuits in the scenario of multiple circuits sharing a tower. It reduces the risk of serious accidents such as induced voltage injury from adjacent energized circuits and phase-to-phase short circuits, and improves the reliability and intelligence level of grounding wire anti-misoperation control in complex distribution network environments.

[0013] In one implementation of the first aspect, determining the target pole related to the current maintenance from the missing pole equipment based on the operation ticket task indicated by the external work order data includes: performing a correlation analysis between the missing pole equipment and the operation ticket task indicated by the external work order data; if the missing pole equipment is referenced by the operation ticket task, it is marked as a target pole with high completion priority, and completion prompt information is generated; if the missing pole equipment is not referenced by the operation ticket task, it is marked as a low-priority pole to be completed, and recorded in the missing equipment report; performing a model self-healing operation on the target pole includes: in response to a completion confirmation instruction for the high-priority target pole, performing the model self-healing operation on the target pole.

[0014] In the implementation of the above solution, a task-driven intelligent priority ranking mechanism was constructed by performing correlation analysis between missing tower equipment and operation ticket tasks. This mechanism can accurately identify key missing equipment affecting the current maintenance operation and mark it as a high-priority completion target, while irrelevant equipment is downgraded and only reports are generated for future reference. Thus, under the reality of a large-scale distribution network with numerous missing data, the mechanism optimizes the allocation of computing resources and improves the efficiency of model completion, avoiding the system performance loss and redundant data processing burden caused by the traditional full completion mode. On the other hand, a manual confirmation instruction step is introduced before the model self-healing operation, forming a human-machine collaborative closed loop. This not only ensures the timeliness and accuracy of model completion of equipment associated with the operation ticket, but also avoids the risk of misjudgment that may be generated by the automated algorithm through manual intervention. At the same time, non-urgent missing equipment is included in the report management instead of directly modifying the model, effectively isolating the potential interference of irrelevant changes to the current operation ticket execution process, and improving the robustness and controllability in complex distribution network environments.

[0015] In one implementation of the first aspect, the method further includes: performing real-time anti-misoperation verification on the grounding wire connection position during the execution of the operation ticket; wherein the real-time anti-misoperation verification includes re-verification of the energized state of the logical connection point and the energized state of adjacent circuits; if the real-time anti-misoperation verification passes, the grounding wire connection operation is executed, and the graphic data is updated based on the status information of the successful grounding wire connection; if the real-time anti-misoperation verification fails, the grounding wire connection operation is aborted, and the operation abort position and failure reason are marked in the graphic data.

[0016] In the implementation of the above scheme, by introducing a real-time error prevention and verification mechanism during the execution of the operation ticket, the energized status of the logical connection point and the energized status of the adjacent circuit are re-verified. This effectively solves the problem of the failure of the pre-verification result caused by the possible change of the power grid operation status within the time window from operation ticket generation to on-site execution. It realizes a closed-loop safety management and control of the entire process from pre-rehearsal to in-process monitoring, and improves the system's safety response capability and prevention and control timeliness under dynamic operating conditions. On the other hand, by sending the status feedback after the verification is passed to the incremental update map model data, the real-time synchronization between the on-site operation result and the topology model status is ensured, avoiding information silos caused by the disconnect between operation execution and system records. The operation suspension and cause marking mechanism when the verification fails solidifies the abnormal location and fault cause into the map model data, providing accurate data anchors for subsequent operation and maintenance investigation, responsibility tracing and model correction, and enhancing the safety traceability of distribution network operations and the integrity and consistency of system data.

[0017] In one implementation of the first aspect, updating the logical-physical dual-view pole model to the model data includes: incrementally updating the logical-physical dual-view pole model to the model data.

[0018] In the implementation of the above scheme, an incremental update mechanism is used to write the logical-physical dual-view tower model into the model data. Compared with the full replacement method, only the local model fragments of the target tower and its associated circuits are differentially modified and persisted. This reduces the computational overhead and data I / O load in the maintenance of large-scale distribution network models, avoids the system performance bottlenecks and long-term service interruptions caused by full updates, and improves the response speed of the model self-healing process and the overall system throughput. On the other hand, the incremental update strategy ensures the stability and consistency of the model data of unaffected equipment by minimizing the scope of changes, effectively avoiding the risks of unexpected data overwriting, loss of correlation, or topology fragment conflicts that may be introduced by full coverage, and enhancing the controllability and traceability of model updates. Furthermore, the incremental update strategy is easy to integrate with the version management system, enabling accurate tracking of model changes, difference comparison, and rollback operations, providing more refined and reliable technical support for the long-term evolution management of distribution network models.

[0019] Secondly, embodiments of this application provide a model-based self-healing distribution network ground wire misoperation prevention and control system, comprising a data acquisition layer, a tower analysis layer, a model self-healing layer, a topology analysis layer, and a graphical invoicing layer connected in sequence, wherein: The data acquisition layer is used to acquire equipment ledger data, diagram data and external work order data of the power distribution network, and identify the missing tower equipment in the diagram data through data comparison; The tower analysis layer is used to determine the target tower related to this maintenance from the missing tower equipment based on the operation ticket task indicated by the external work order data. The model self-healing layer is used to perform model self-healing operations on the target tower. The model self-healing operations include: establishing a logical-physical dual-view tower model; updating the logical-physical dual-view tower model to the model data to complete the model; and adding tower primitives to the corresponding positions in the graphic data to complete the graphics. The logical-physical dual view includes a physical model and a logical model. The physical model indicates the entity attributes of the target tower, and the logical model indicates the loop attributes of each loop carried by the target tower. The physical model and the logical model are associated through an identifier. The topology analysis layer is used to perform a topology search on the line connecting the target tower at the front end of the graphics based on the updated graphic model data, obtain the direction of the connecting line and the information of the terminal of the equipment associated with the connecting line, and perform topology calculation and state analysis to determine the energized state of the connecting line and each node in the electrical connection network. The graphical invoicing layer is used to generate operation tickets based on external work order data, bind the grounding wire connection position to the logical connection point in the logical pole model corresponding to the circuit specified in the operation ticket, and perform anti-misoperation logic verification on the binding relationship based on the energized state.

[0020] Thirdly, embodiments of this application provide an electronic device, including: a processor, a memory, and a communication bus, wherein the processor and the memory communicate with each other through the communication bus; the memory stores computer program instructions that can be executed by the processor, and the computer program instructions are read and executed by the processor to perform the method provided in the first aspect or any possible implementation of the first aspect.

[0021] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions, which, when read and executed by a processor, perform the method provided in the first aspect or any possible implementation thereof.

[0022] Fifthly, embodiments of this application provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements the method provided by the first aspect or any possible implementation of the first aspect.

[0023] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing embodiments of this application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims and drawings. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart illustrating the model-based self-healing method for preventing misoperation of ground wires in overhead power distribution networks, provided in this application embodiment; Figure 2 A flowchart illustrating a model-based self-healing method for preventing misoperation of ground wires in overhead power distribution lines in a specific application scenario provided in this application embodiment; Figure 3 A schematic diagram of the structure of the model-based self-healing distribution network overhead line ground wire anti-misoperation control system provided in the embodiments of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0026] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0027] In actual operation and maintenance scenarios of overhead power distribution lines, the deployment of grounding wire management technology exhibits differentiated designs. Closed operating environments such as substations generally employ fixed grounding wire management schemes, where physical interlocking devices are deployed at pre-defined grounding connection points and rigidly bound to the five-prevention switching operation ticket system. Maintenance personnel execute operations according to a predefined logical sequence. While this scheme offers high safety and reliability in substation scenarios, its implementation relies on fixed physical infrastructure and uniform grounding locations, resulting in high investment costs and making it unsuitable for the open, field-based operating environments of power distribution networks and the flexible, ever-changing grounding locations required. In contrast, anti-misoperation systems for overhead power distribution lines are primarily built along two technical routes: one is logical verification based on static topology diagrams, and the other is business rule verification using a pure rule base. Static topology diagram solutions verify operational logic through predefined power grid models, but model updates rely entirely on manual maintenance. They cannot automatically detect and synchronize with on-site changes after line modifications, pole additions, or relocations, leading to a continuous accumulation of discrepancies between the diagram and reality. Furthermore, the system lacks the ability to parse the electrical semantics of connecting lines in the diagram, making it difficult to construct a complete network of electrical connections. Error prevention verification granularity is limited to the pole level, failing to achieve multi-level precise positioning. While pure rule-based solutions can focus on the correctness of the operation sequence, their error prevention effectiveness depends entirely on the completeness and accuracy of the underlying topology model. They lack the ability to perceive the real-time operating status of the power grid, performing rule matching only at the logical level, making it difficult to cope with the complex and ever-changing operating environment and temporary adjustment needs of distribution networks. The above technical routes have revealed systemic deficiencies in the actual operation and maintenance of power distribution networks, such as extensive management, static prevention of errors, fragmented processes and blind spots in monitoring. Specifically: (1) Extensive management is reflected in the inability to achieve refined management from the pole level to the spatial location level, and the record of grounding wire connection position is vague; (2) Static prevention of errors is reflected in the system relying on preset static models and rules, lacking the ability to adapt to dynamic changes in the power grid; (3) Fragmented processes lead to poor connection between grounding wire management and other production business processes, forming information silos; and blind spots in monitoring result in the lack of effective technical supervision and process closure throughout the entire life cycle of the grounding wire.

[0028] However, given the characteristics of operation and maintenance of overhead power distribution lines—namely, the large number of locations, wide coverage, and short time windows—existing technologies face even more severe implementation challenges. The open working environment means there are no fixed grounding connection points or power sources on-site, making it impossible to deploy fixed control equipment. The flexible and variable nature of operations requires that the grounding wire connection location be determined temporarily based on the maintenance scope, safety distance, and other actual conditions, exhibiting a high degree of dynamism. In scenarios with multiple circuits on the same pole, limited physical space leads to tight safety distances between circuits, and electromagnetic induction risks mean that circuits under maintenance during power outages may generate dangerous induced voltages due to adjacent energized circuits. Difficult circuit identification necessitates that on-site personnel accurately identify specific circuits to prevent accidental connection to energized lines, while existing systems lack the ability to accurately identify and verify multiple circuits, resulting in a severe deficiency in verification mechanisms. When working on long lines with multiple tasks and multiple shifts, the use, connection, removal, and return of temporary grounding wires rely entirely on manual inspection, counting, and memorization, leading to extremely high management complexity and a lack of effective technical supervision methods.

[0029] In summary, the core technical bottlenecks in current distribution network grounding wire misoperation prevention and control ultimately lie in two main dimensions: inconsistent diagrams and unclear topology. First, inconsistent diagrams manifest as the static topology model upon which existing misoperation prevention systems rely, which is difficult to update dynamically with the actual power grid. When lines are modified, towers are added, or relocated, the system cannot automatically identify and adapt to these changes, leading to a severe disconnect between the model and the field, rendering the misoperation prevention function ineffective. Second, unclear topology manifests as the system's inability to interpret the electrical meaning of connecting lines in the diagram, its inability to construct complete electrical connection relationships, and its lack of refined modeling capabilities for complex structures such as multiple circuits on the same tower. This results in misoperation prevention verification remaining at a coarse tower level, failing to achieve precise positioning and safety management at the circuit level.

[0030] In view of this, this application provides a model self-healing-based method for preventing errors in the ground wire management of overhead distribution lines. This method acquires distribution network equipment ledgers, diagram data, and external work order data for multi-source collaborative comparison, automatically identifies missing tower equipment, and intelligently filters target towers based on operation ticket tasks. It then performs a model self-healing operation including logical-physical dual-view modeling, incremental model updates, and graphical consistency completion. This systematically solves the problem of model-site disconnect caused by lagging diagram maintenance and missing topological semantics in overhead distribution lines. It achieves automated reconstruction and dynamic synchronization of the topology model, reduces manual maintenance costs and error probability, and improves distribution network operation and maintenance. Efficiency and digitalization levels have been significantly improved. On the other hand, based on the updated graphical model data, topology search and real-time status calculation are performed on the target tower and associated lines at the graphical front end. The energized status of each node and connecting line in the electrical connection network is accurately determined. The grounding wire connection position is precisely bound to the loop logic connection point specified in the operation ticket and the error prevention logic is verified. This improves the granularity of error prevention control from the traditional tower level to the loop-phase level, effectively eliminating the error prevention blind spots such as misjudgment of energized loops and insufficient safety distance in the scenario of multiple circuits on the same tower. It greatly reduces the risk of serious misoperations such as misconnecting grounding wires and missing grounding wires, and ensures the personal safety of operators and the stable operation of the power grid.

[0031] Please see Figure 1 The illustrated flowchart illustrates a model-based self-healing method for preventing misoperation of overhead line ground wires in distribution networks, as provided in this embodiment. This model-based self-healing method can be applied to electronic devices, which may include physical devices such as servers, PCs, tablets, or smartphones, or virtual devices such as virtual machines or containers. The electronic device can be a single device, a combination of multiple devices, or a cluster of numerous devices. The aforementioned model-based self-healing method for preventing misoperation of overhead line ground wires in distribution networks may include: Step S110: Obtain equipment ledger data, diagram data, and external work order data of the power distribution network, and identify the missing pole equipment in the diagram data through data comparison.

[0032] The aforementioned equipment ledger data is a structured collection of information describing the basic attributes of overhead line equipment in the distribution network. It mainly includes the inherent technical parameters and management attributes of power grid equipment such as poles, conductors, switches, and transformers. Specifically, the ledger data for pole equipment may include static attribute information such as a globally unique identifier (mRID), equipment name, pole material, geospatial coordinates (e.g., latitude and longitude), voltage level, dispatch number, and commissioning date. Equipment ledger data is typically stored in a relational database or in data tables conforming to relevant standards. It serves as a fundamental information source for the full lifecycle management of power grid assets, providing an authoritative data benchmark for equipment comparison, missing data identification, and attribute completion during the model self-healing process.

[0033] The aforementioned graphical model data is a digital representation of the distribution network topology and its geospatial relationships, comprising two parts: graphical data and model data. The graphical data, in Scalable Vector Graphics (SVG) format, primarily describes the visual primitives, coordinate positions, layer structures, and visual connections between power grid equipment, supporting topology display and graphical ticketing functions in the human-machine interface. The model data, defined in XML (Extensible Markup Language) format based on relevant standards, precisely depicts the electrical topology connections of equipment through classes and attributes, including electrical semantic information such as terminals, connectivity nodes, and equipment containers. Together, the graphical model data constitutes the static topology framework of the power grid, serving as the core data foundation for topology search, state calculation, and error prevention logic verification.

[0034] The aforementioned external work order data refers to task instruction data originating from upstream business systems such as the Distribution Network Production Management System (PMS), dispatch management system, or emergency repair platform. External work order data can be transmitted in the form of structured messages or interface calls. Its main content may include maintenance task identifier, operation ticket type, information on the line circuit to be operated, planned operation time, work team information, and a list of tower equipment involved in the operation task. External work order data serves as dynamic input to trigger the self-healing and error prevention control processes of the trigger model, providing task context and target equipment range for intelligently generating operation tickets, filtering high-priority towers to be supplemented, and binding grounding wire connection positions.

[0035] Step S110 above uses equipment ledger data as a baseline data source. A multi-dimensional matching algorithm is used to verify consistency with the set of pole and tower equipment in the model data, identifying missing equipment entities on the model side, and thus identifying missing pole and tower equipment in the model data. An example implementation is as follows: Standardized preprocessing is performed on the pole and tower records in the equipment ledger and model data to unify data format and coding standards. Then, precise matching is performed based on globally unique identifiers or scheduling numbers. If a record exists in the ledger but not in the model, it is directly determined to be missing equipment. For cases of incompatible identification systems or non-standard coding, a fuzzy matching strategy based on key attributes can be adopted. This involves calculating the text similarity of equipment names (natural language processing techniques can be used to parse naming semantics, such as segmenting and extracting features from compound names based on N-gram models or edit distance algorithms), judging spatial coordinate distance thresholds, and verifying voltage level consistency to establish a mapping relationship between ledger equipment and model equipment. Ledger equipment for which a mapping cannot be established is marked as a missing object in the model. Building upon this foundation, a topology connectivity check is performed, and a graph search analysis is conducted on the topology connection points around the missing equipment. If isolated nodes or broken electrical paths are found, the missing equipment determination results are further strengthened. Finally, all identified missing tower equipment are prioritized according to their relevance to external work order data, providing an accurate target list for subsequent model self-healing operations.

[0036] Step S120: Based on the operation ticket task indicated by the external work order data, identify the target tower related to this maintenance from the missing tower equipment.

[0037] Optionally, step S120 may include: performing a correlation analysis between the missing pole equipment and the operation ticket task indicated by the external work order data; if the missing pole equipment is referenced by the operation ticket task, marking it as a target pole with high completion priority and generating completion prompt information; if the missing pole equipment is not referenced by the operation ticket task, marking it as a low-priority pole to be completed and recording it in the missing equipment report; performing a model self-healing operation on the target pole, including: in response to the completion confirmation instruction for the high-priority target pole, performing a model self-healing operation on the target pole.

[0038] The aforementioned correlation analysis can be achieved through multi-dimensional data matching algorithms. For example, the implementation method for correlation analysis between missing tower equipment and the operation ticket tasks indicated by external work order data is as follows: Extract key information such as the target line name, tower number range of the maintenance section, and topological path of the work point involved in the operation ticket task from the external work order data to construct a task-related equipment reference feature vector; then, for the missing tower equipment identified in step S110, analyze its scheduling number, naming name, affiliated line, and geographical coordinate attributes in the equipment ledger, and use a strategy combining precise matching and fuzzy matching for comparison and judgment. At the precise matching level: directly verify whether the scheduling number or globally unique identifier of the missing tower is explicitly referenced by the operation ticket task. At the fuzzy matching level: use natural language processing technology to segment and calculate semantic similarity of the equipment naming text, and evaluate the textual correlation between the missing equipment name and the line name and section description within the task scope using cosine similarity or edit distance algorithms. Simultaneously, combine spatial topology analysis to determine whether the geographical coordinates of the missing tower fall within the maintenance operation buffer zone. If a missing device is successfully matched in any dimension, it is determined to be related to this maintenance and marked as high priority for completion; otherwise, it is classified into the low priority set and a missing device report is generated, thereby achieving task-driven precise target selection and optimization of computing resources.

[0039] The generation of the aforementioned completion confirmation command supports two execution modes: human-machine collaboration and automation. In human-machine collaboration mode, before executing the model self-healing operation, completion prompts for high-priority target towers can be pushed to maintenance personnel through the human-machine interface. The prompts can include the scheduling number, line, physical coordinates, and related operation ticket task details of the missing equipment. Based on on-site verification or existing ledger data, maintenance personnel can issue explicit manual approval commands by clicking the confirmation button or entering an authorization password. The completion confirmation command serves as the trigger condition for the model self-healing operation, ensuring that critical topology changes are under controllable human supervision. In automation mode, completion commands can be generated autonomously based on a preset confidence assessment strategy. When the missing judgment result of a high-priority target tower simultaneously has precise attribute matching (such as completely consistent mRID or scheduling number), consistent cross-validation of multi-source ledger data, and a spatial topology anomaly level higher than a preset threshold, the confidence assessment strategy automatically outputs a high-confidence flag, triggering model completion and graphical update operations without manual intervention. This improves response efficiency in emergency maintenance scenarios while ensuring data reliability.

[0040] It is understandable that the number of overhead power line devices in the distribution network is large and widely distributed. The number of missing devices identified through data comparison may be numerous. Performing model self-healing operations on all missing devices could lead to huge computational overhead, storage resource consumption, and system response delays due to full topology reconstruction, thus limiting the efficiency of operation ticket generation. More importantly, operation ticket tasks driven by external work order data have clear spatiotemporal boundaries, involving only equipment within specific maintenance sections. Missing devices not directly related to the task, even if not promptly supplemented, will not affect the accuracy of current error prevention verification. Therefore, the above solution can classify missing devices into high and low priorities through correlation analysis, allowing computational resources to focus on key equipment referenced in the operation ticket. High-priority target towers are prioritized for supplementation to ensure the topology model meets the integrity requirements of current operation error prevention. Low-priority devices are only recorded in the missing device report, avoiding potential data disturbance risks introduced by irrelevant changes and providing a data foundation for subsequent periodic ledger management. This achieves a precise match between computational investment and business value while ensuring the security of core business operations. Furthermore, the cost constraints of manual confirmation instructions reinforce the necessity of this strategy. High-priority equipment directly affects maintenance safety, and manual verification can ensure the accuracy of the information. Low-priority equipment is numerous and does not affect the current task. If all of them are submitted for manual confirmation, it will cause a surge in maintenance burden and may delay the execution of critical operation tickets.

[0041] The above-mentioned solution constructs a task-driven intelligent priority ranking mechanism by performing correlation analysis between missing tower equipment and operation ticket tasks. This mechanism can accurately identify key missing equipment affecting the current maintenance operation and mark it as a high-priority completion target, while downgrading irrelevant equipment and generating only reports for future reference. In the context of a large-scale distribution network with numerous missing data, this solution optimizes the allocation of computing resources and improves model completion efficiency, avoiding the system performance loss and redundant data processing burden caused by the traditional full completion mode. On the other hand, the introduction of a manual confirmation instruction step before executing the model self-healing operation forms a human-machine collaborative closed loop. This ensures the timeliness and accuracy of model completion for equipment associated with the operation ticket, while avoiding the risk of misjudgment that may be generated by the automated algorithm through manual intervention. At the same time, by including non-urgent missing equipment in report management instead of directly modifying the model, it effectively isolates the potential interference of irrelevant changes to the current operation ticket execution process, improving robustness and controllability in complex distribution network environments.

[0042] Step S130: Perform a model self-healing operation on the target tower; wherein, the model self-healing operation includes: establishing a logical-physical dual-view tower model, updating the logical-physical dual-view tower model to the model data to complete the model, and adding tower elements at the corresponding positions in the graphic data to complete the graphics; the logical-physical dual view includes a physical model and a logical model. The physical model is used to indicate the entity attributes of the target tower, and the logical model is used to indicate the loop attributes of each loop carried by the target tower. The physical model and the logical model are associated through an identifier.

[0043] Optionally, the above-mentioned establishment of a logical-physical dual-view pole model includes: creating a physical pole model of the target pole; wherein the physical pole model includes the spatial coordinates, pole material, and pole number attributes of the target pole; instantiating a logical pole model for each circuit according to the number of circuits carried by the target pole; wherein the logical pole model includes the line name, phase information, circuit number, and equipment terminal information associated with the logical connection point of the corresponding circuit; and establishing a one-to-many association between the physical pole model and all logical pole models through the globally unique identifier of the physical pole model and the physical pole reference attribute of the logical pole model to obtain the logical-physical dual-view pole model.

[0044] The aforementioned logical-physical dual-view pole model is a hierarchical modeling architecture built based on relevant standards. It is used to accurately represent the complex mapping relationships of a single physical pole carrying multiple circuits in an overhead power distribution network. The logical-physical dual-view pole model includes a physical pole model and a logical pole model. The physical pole model is instantiated using the `cim:Pole` class, defining the entity attributes of the target pole, including static geographic and asset information such as spatial coordinates, pole material, and pole number, serving as a unique digital twin of the physical entity in the real world. The logical pole model uses the `cim:PoleSite` class as a template, and is instantiated multiple times according to the number of circuits carried by the target pole. Each instance corresponds to an independent circuit and includes electrical attributes such as line name, phase information, circuit number, and equipment terminal information associated with the logical connection point, used to describe the topological connection relationship and connection position of each circuit. A one-to-many relationship can be established between physical tower models and logical tower models through a globally unique identifier. That is, one physical tower model is associated with multiple logical tower models, forming a precise mapping structure of one base and multiple circuits. This decouples the electrical characteristics of multiple circuits on the same tower into independent logical entities, while retaining their spatial binding relationship with the physical entities.

[0045] It is understood that the purpose of establishing the above-mentioned logical-physical dual-view tower model in this application embodiment is to solve the technical defect of the ambiguous mapping relationship between physical entities and electrical circuits in the scenario of multiple circuits on the same tower in a distribution network, and to achieve a leap in the fine-grained control of grounding wire connection positions from the tower level to the circuit-phase level. Traditional models treat the tower as a single node and cannot distinguish the differences in connection points of different circuits, resulting in only coarse-grained judgment for false alarm verification, making it difficult to identify the safety risks of adjacent energized circuits. However, by constructing a dual-view model, this application embodiment can accurately locate the logical connection point of each circuit in topology analysis, and combine the electrical semantics of the connection lines with the equipment terminal information to provide a structured data foundation for the calculation of the energized state of multiple circuits on the same tower, safety distance verification, and accurate binding of grounding wires. Furthermore, the logical-physical dual-view tower model can decouple physical spatial attributes from electrical topology attributes, enabling independent maintenance of tower geographic coordinate updates and circuit topology changes. This not only meets the practical needs of flexible changes in connection positions during distribution network operations but also ensures the consistency between the topology model and the physical power grid on site, fundamentally eliminating the potential for malicious operations such as incorrect grounding wire connection or missed grounding wire removal caused by insufficient model granularity.

[0046] The following section uses a logical-physical dual-view pole model established in a certain application scenario as an example to introduce the working principle of the above scheme for constructing a logical-physical dual-view pole model. The physical pole model and logical pole model established in this application scenario are shown in Table 1 and Table 2, respectively.

[0047] Table 1 Physical Tower Model

[0048] Table 2 Logic Tower Model

[0049] As shown in Tables 1 and 2 above, the establishment principle of the logical-physical dual-view pole model is based on the inheritance and association mechanism of the Pole and PoleSite classes in relevant standards. It achieves decoupling modeling and semantic association between physical entities and logical connection points through the Resource Description Framework (RDF). The physical pole model is based on the instantiation of the cim:Pole class, whose rdf:ID attribute is assigned a globally unique identifier "POLE_24000862739400". The identity code of this physical entity is solidified through cim:Naming.mRID, while also carrying static attributes such as spatial coordinates, pole material, and geographic coordinates, forming a digital twin of the real pole. The logical pole model is instantiated with the cim:PoleSite class, whose key attribute cim:PoleSite.Pole establishes an explicit reference to the physical pole model through rdf:resource="#POLE_24000862739400", forming a directional association from logic to physical. The essence of this association mechanism is that a physical pole model can correspond to multiple logical pole model instances. Each logical pole model is bound to a specific circuit (such as #CIRCUIT_24000496600100) through the cim:Equipment.MemberOf_EquipmentContainer property, and associated with a specific line segment (such as #CIRSEC_24013060107100) through the cim:Equipment.MemberOf_CircuitSection property. This decouples the electrical attributes such as line name, phase information, and dispatch number of different circuits on the same physical pole into independent logical entities. This one-to-many referencing architecture solves the technical defects of traditional models where multiple lines share a single tower, resulting in mixed topological connections and the inability to distinguish the independent connection points of each circuit. During topology analysis, it can accurately locate the specific logical connection point of the circuit represented by POLESITE_24000862742100, rather than the general tower entity. This provides a calculable and verifiable semantic basis for the precise binding of the subsequent grounding wire connection position with the three-level coordinates of circuit-phase-connection side.

[0050] The above solution achieves decoupled management of geospatial attributes and electrical topology attributes by constructing independent physical tower models and logical tower models. The physical model uses a globally unique identifier to solidify the spatial coordinates, material, and number of the tower, providing a precise spatial positioning benchmark for grounding wire connection. Simultaneously, multiple logical tower models instantiated based on the number of circuits establish a one-to-many relationship with the physical model through reference attributes, accurately representing the complex structural characteristics of multiple circuits on the same tower. This solves the technical defects of traditional models where physical entities and logical circuits are mixed and cannot be finely distinguished. On the other hand, the dual-view structure can refine the management granularity of grounding wire connection positions from the single tower level to the circuit-phase level. The circuit name, phase information, circuit number, and equipment terminal information contained in the logical model provide clear electrical connection semantics for subsequent topology searches, enabling the anti-misoperation system to accurately verify the de-energized status of the target circuit, identify adjacent energized circuits, and calculate the phase-to-phase safety distance. This effectively eliminates the risk of mistakenly connecting energized circuits due to circuit identification errors in multi-circuit line maintenance scenarios on the same tower, improving the accuracy and safety of grounding wire anti-misoperation management.

[0051] Optionally, updating the logical-physical dual-view pole model to the model data includes: incrementally updating the logical-physical dual-view pole model to the model data.

[0052] The incremental update mechanism for the aforementioned logical-physical dual-view pole model can focus on minimizing the scope of changes and ensuring data consistency, achieving efficient data completion through local differential updates. Based on the high-priority target pole set determined in step S120, the incremental update mechanism locates the physical pole model and associated logical pole model nodes to be updated from the existing model data, and then generates a CIM / XML fragment containing only the target equipment change information, rather than a full model reconstruction. In practice, the path of the corresponding pole node in the original model can be parsed first, and precise replacement or insertion operations can be performed on the spatial coordinates and material properties of the physical pole model, as well as the loop associations and equipment terminal connection relationships of the logical pole model. A transaction lock mechanism is used to lock the node to be updated and its upstream and downstream topology connection points to prevent data inconsistency caused by concurrent access. After the update is completed, the incremental change record is synchronously written to the model version log, recording the changed equipment mRID, timestamp, and operation type for subsequent auditing and rollback. Compared to full updates, incremental updates avoid the overhead of overall serialization and deserialization of large-scale power distribution network models, significantly reducing computational resource consumption and I / O waiting time, and compressing the model self-healing operation response time from minutes to seconds. At the same time, the local update strategy isolates the stability of non-associated devices, avoids the risk of unexpected disturbances from unrelated topology segments, and improves the controllability and traceability of model evolution.

[0053] The above-mentioned solution uses an incremental update mechanism to write the logical-physical dual-view tower model into the model data. Compared with the full replacement method, it only performs differential modification and persistence on local model fragments of the target tower and its associated circuits, reducing the computational overhead and data I / O load in the maintenance process of large-scale distribution network models. It avoids system performance bottlenecks and long-term service interruptions caused by full updates, and improves the response speed of the model self-healing process and the overall system throughput. On the other hand, the incremental update strategy ensures the stability and consistency of the model data of unaffected equipment by minimizing the scope of changes, effectively avoiding the risks of unexpected data overwriting, loss of correlation, or topology fragment conflicts that may be introduced by full coverage, and enhancing the controllability and traceability of model updates. Furthermore, the incremental update strategy is easy to integrate with version management systems, enabling accurate tracking of model changes, difference comparison, and rollback operations, providing more refined and reliable technical support for the long-term evolution management of distribution network models.

[0054] After establishing the logical-physical dual-view pole model in step S130 above, an incremental CIM / XML fragment injection mechanism can be used to accurately integrate the logical-physical dual-view pole model into the existing power distribution network topology model. An example implementation of this scheme is as follows: based on relevant standards (e.g., IEC 61970 CIM), the physical pole model and the logical pole model are serialized into standard XML format fragments, which are then inserted into the original model data through an incremental update mechanism. Specifically, the physical pole model is instantiated as a globally unique node using the `cim:Pole` class, supplementing it with static attributes such as coordinates, material type, and pole number; the logical pole model is instantiated as an independent node using the `cim:PoleSite` class, filled with electrical attributes such as circuit name, phase information, and equipment terminal connection relationships, and a one-to-many association is established with the physical pole model through the `PoleSite.Pole` reference attribute. During the update process, based on the electrical connection path determined by topology analysis, the ConnectivityNode nodes of the upstream and downstream equipment of the tower can be located in the model data. The terminals of the logical tower model are then bound to the corresponding nodes to ensure that the newly added model fragments are seamlessly connected with the existing topology network, ultimately achieving the completion of model data and consistency of electrical connectivity.

[0055] To complete the graphic data by adding pole and tower primitives at corresponding locations, the following implementation method can be used: Based on the updated model data, the spatial coordinate attributes of the physical pole and tower model are parsed, and pole and tower primitive objects are created at the corresponding geographic coordinates of the SVG graphic. These primitives contain the geometry, layer attributes, and display style of the pole and tower symbols. For multi-loop scenarios on the same pole, multiple loop identifiers can be overlaid on a single pole and tower primitive to distinguish the spatial distribution of different logical connection points. Simultaneously, through a unique identifier matching mechanism, the ID attribute of the pole and tower primitive can be bound to the rdf:ID of the physical pole and tower model, and the loop information of the logical pole and tower model can be attached to the primitive attributes in the form of tags, achieving consistency between the graphic and model ends.

[0056] Step S140: Based on the updated graphic model data, perform a topology search on the line connection lines connected to the target tower at the graphic front end to obtain the direction of the connection lines and the information of the terminals of the equipment associated with the connection lines, and perform topology calculation and status analysis to determine the energized status of the connection lines and each node in the electrical connection network.

[0057] Optionally, step S140 may include: performing a topology search on the line connecting to the target tower, traversing the direction of the connecting lines to determine the connection relationship of the loop in each direction of the logical connection point, and extracting the connection relationship data between the equipment terminals; performing topology calculation based on the connection relationship data and the real-time equipment status of the power grid to determine the energized status of each node and each connecting line in the electrical connection network; and performing real-time topology coloring and visualization based on the energized status driving the graph data.

[0058] Step S140 above can employ a depth-first traversal algorithm to perform electrical semantic parsing on the line connections associated with the target tower. Specifically, it involves obtaining the logical connection points (corresponding to the small side, large side, and branch side) and their associated equipment terminal information of the target tower from the logical-physical dual-view model completed in step S130. Using these terminals as the starting nodes for the search, the primitive paths of the connection lines are recursively traversed in the SVG graphic data. During the traversal, the vector path data of the connection lines is parsed to trace their spatial direction, identify the starting and ending equipment nodes of their connections, and extract the topological relationship between the corresponding terminal nodes and connection nodes in the model data through unique identifier matching, thereby constructing a complete electrical path from each direction of the logical connection point to adjacent towers, switches, or transformers. In addition, the conductive equipment (such as overhead line segments or cables) to which the equipment terminal belongs can be located by querying the cim:Terminal.ConductingEquipment attribute, and key information such as terminal serial number and connection phase can be extracted. This transforms the visual connection relationship at the graphic level into structured connection data at the model level, providing accurate electrical connection relationship input for backend topology calculation.

[0059] It is understandable that the anti-misoperation logic for grounding wire connection operations requires a safety assessment based on the real-time electrical status of the target connection point and its adjacent circuits and connecting lines. If it is not possible to accurately determine whether each node is energized, de-energized, or grounded, the system cannot verify whether the grounding wire is mistakenly connected to a energized circuit or whether the safety distance requirements are met, resulting in the loss of the anti-misoperation function. Simultaneously, the calculation results of the energized status can also provide a topology coloring basis for graphical invoicing, enabling maintenance personnel to intuitively identify the boundary between the maintenance section and the energized section, avoiding human error. The above implementation method for determining the energized status based on connection relationship data and real-time power grid equipment status is as follows: the backend receives the equipment terminal connection relationship data extracted by the frontend through traversal, constructs an undirected graph network containing connection nodes and terminals; then, it obtains the real-time power grid equipment status, including switch open / closed status, disconnector position, grounding disconnector on / off status, and power injection status, and performs connectivity analysis and state propagation calculations according to the topology rules in relevant standards. The calculation process can start from a known energized power source node (such as a substation outgoing busbar) and recursively propagate the energized state along closed switching equipment to the entire network, marking the nodes and connecting lines along the path as energized. For switches in the open state or grounding switches that have been put into operation, the state propagation is blocked and downstream nodes are marked as de-energized or grounded. Finally, the energized state enumeration values ​​(de-energized, energized, grounded) of each connected node and the energized state of each connecting line are output. This drives the front-end SVG graphics to perform topology coloring rendering based on the state enumeration values, presenting the real-time electrical state of the power grid in a visual manner.

[0060] The above solution performs a topology search on the target tower's associated lines at the graphical front end, automatically traversing the routes of connecting lines and extracting connection relationship data between equipment terminals. This achieves deep analysis and structured expression of the electrical semantics of visual connecting lines in the graphics, solving the technical problems of missing topology attributes and ambiguous electrical connection relationships in traditional systems. Furthermore, it combines the real-time equipment status of the power grid to perform topology calculations, dynamically determining the energized status of each node and connecting line in the electrical connection network. This upgrades static graphical data into a dynamic topology model with real-time status awareness, improving the response speed and accuracy to the power grid's operating status. On the other hand, the calculation results based on the energized status drive the graphical data for real-time topology coloring and visualization, transforming abstract topology analysis into intuitive graphical information. This allows maintenance personnel to clearly identify the energized status of each logical connection point of the target tower and its adjacent circuits. Especially in the scenario of multi-circuit line maintenance on the same tower, it can quickly identify the spatial distribution and safe distance of energized circuits, effectively avoiding the risk of misoperation due to insufficient status awareness, while also improving the safety verification efficiency of operation ticket generation and on-site execution.

[0061] Step S150: Generate an operation ticket based on external work order data, bind the grounding wire connection position to the logical connection point in the logic tower model corresponding to the circuit specified in the operation ticket, and perform anti-misoperation logic verification on the binding relationship based on the energized state.

[0062] Step S150 above can take structured external work order data as input and achieve this through the collaborative implementation of task parsing and model data retrieval. For example, the implementation method involves parsing key information carried in the external work order, such as the maintenance task type, target line identifier, work area range, and operation sequence requirements; identifying the operation nature (e.g., power outage maintenance, live-line work); matching it with a pre-set operation ticket template library; and calling the standard ticket template framework corresponding to the task type. Then, based on the pole scheduling number or line name referenced in the work order, the corresponding logical-physical dual-view pole model is retrieved from the completed diagram data. The logical pole model instance of the circuit specified in the operation ticket and its logical connection point are precisely located. The equipment terminal information, phase attributes, and spatial orientation (small side, large side, or branch side) of the connection point are extracted. The grounding wire connection position is explicitly bound to the grounding operation item in the operation ticket in the form of a four-tuple of circuit-phase-connection side-logical pole unique identifier, forming an operation instruction set with precise location semantics.

[0063] Optionally, the above-mentioned error-proof logic verification of the binding relationship based on the energized state includes: before executing the operation ticket, simulating the binding relationship based on the energized state of the logical-physical dual-view tower model and the connecting lines; during the simulation, performing verification; wherein, the verification includes: verifying whether the energized state of the logical connection point corresponding to the grounding wire connection position is in a de-energized state, and verifying whether the direction of the small side, large side, or branch side of the logical connection point meets the requirements of the operation ticket task; and calling the energized state of all logical tower models associated with the physical tower model to verify the safe distance between adjacent energized circuits and the grounding wire connection position in multi-circuit lines on the same tower; when the verification shows that the energized state of the logical connection point is abnormal, the direction is inconsistent, or the safe distance between the energized circuit and the grounding wire connection position does not meet the preset threshold, generating a safety warning information and stopping the operation ticket generation process.

[0064] The aforementioned simulation pre-run is a simulation process that performs a full-element logical deduction and safety boundary verification of the planned grounding wire connection operation sequence in a virtual environment before the actual execution of the operation ticket. The simulation pre-run process in the above scheme uses the generated operation ticket as the driving input, calls the logical-physical dual-view tower model established in the above steps and the calculated real-time topology energized state data, reconstructs the three-dimensional electrical topology and spatial relationship digital mirror of the target maintenance section, and sequentially simulates the connection action of each grounding operation item.

[0065] The aforementioned verification of whether the energized state of the logical connection point corresponding to the grounding wire connection location is in a de-energized state refers to querying the node energized state enumeration value output by the topology calculation during the simulation and pre-running process to verify whether the target logical connection point is in a de-energized or grounded state. This verification is the first line of defense against erroneous logic, aiming to prevent arcing short circuits, equipment damage, and electric shock accidents caused by the grounding wire being mistakenly connected to a energized circuit. If the verification finds that the connection point is energized, it is immediately identified as a risk of erroneous operation while energized, the operation ticket generation is stopped, and an emergency warning is issued to ensure that grounding operations are only performed on the side of the circuit that has been confirmed to be de-energized, fundamentally meeting the core requirement of "verify energization before grounding" in the power safety work procedures.

[0066] The aforementioned verification of whether the direction of the small-number side, large-number side, or branch line side of the logical connection point conforms to the requirements of the operation ticket task refers to comparing the explicit work orientation description in the operation ticket text (such as "10kV line 1#5 pole small-number side A phase") with the orientation attribute field of the connection point in the logical pole model to verify whether the two are strictly consistent. The necessity of this verification stems from the fact that different orientations of the same physical pole in a distribution network overhead line may correspond to different circuits or different phases of the same circuit. If the connection side direction is incorrect, it will result in the grounding wire being installed in a circuit outside the maintenance scope, causing the power outage area to expand or missing dangerous grounding points. Especially in multi-circuit shared-tower scenarios, incorrect direction can easily lead to accidental grounding of live circuits. This verification ensures that the spatial orientation of the grounding position accurately matches the maintenance task, achieving refined operation positioning from the pole level to the connection side level.

[0067] The aforementioned verification of the safe distance between adjacent energized circuits and grounding wire connection points in multi-circuit lines on the same pole refers to obtaining all associated logical pole model instances through the globally unique identifier of the physical pole model, querying the real-time energized status of each instance, identifying adjacent energized circuits, and calculating the minimum air gap distance between the conductor connection point of the energized circuit and the target grounding wire connection point based on the pole structure parameters. This verification verifies whether the distance meets the safe distance threshold specified in relevant standards. This verification is designed for the special complexity of multi-circuit lines on the same pole, aiming to prevent adjacent energized circuits from generating electrostatic or electromagnetic induction voltages on outage maintenance circuits, avoiding discharge injuries caused by insufficient safe distance when workers are connecting or disconnecting grounding wires. It fills the gap in traditional error prevention systems that cannot quantitatively assess spatial safety risks.

[0068] The aforementioned solution introduces a simulation and pre-run mechanism before executing the operation ticket. By calling the logical-physical dual-view tower model and the live state calculated in real-time topology, it performs multi-dimensional collaborative verification of the binding relationship. This includes confirming the power outage status of the logical connection point, verifying the compliance of the connection direction, and quantitatively assessing the safety distance between adjacent live circuits in multi-circuit scenarios on the same tower. This achieves a leap from single-state verification to a three-dimensional error prevention logic, which can proactively identify and block potential risks such as live grounding wires, incorrect connection positions, and insufficient safety distances during the operation ticket generation stage. This moves the safety control checkpoint forward and avoids safety hazards caused by post-event error correction. On the other hand, By linking and calling the energized state of all logical tower models associated with physical towers during the simulation and pre-run, the real-time energized distribution of each circuit in a multi-circuit line on the same tower can be accurately identified. Based on the preset safety distance threshold, the spatial relationship between each energized circuit and the target grounding wire connection position is quantitatively verified. This effectively solves the problem of safety distance assessment failure caused by the inability to accurately identify the electrical coupling relationship between circuits in the scenario of multiple circuits sharing a tower. It reduces the risk of serious accidents such as induced voltage injury from adjacent energized circuits and phase-to-phase short circuits, and improves the reliability and intelligence level of grounding wire misoperation control in complex distribution network environments.

[0069] Optionally, the above-mentioned model-based self-healing method for preventing grounding wire misoperation in overhead power distribution lines may further include: performing real-time anti-misoperation verification on the grounding wire connection position during the execution of the operation ticket; wherein, the real-time anti-misoperation verification includes re-verification of the energized state of the logical connection point and the energized state of adjacent circuits; if the real-time anti-misoperation verification passes, the grounding wire connection operation is executed, and the model data is updated based on the status information of the successful grounding wire connection; if the real-time anti-misoperation verification fails, the grounding wire connection operation is stopped, and the operation stoppage position and failure reason are marked in the model data.

[0070] It is understandable that there is a certain time window between the topology energized state data relied upon for ticket generation and the actual grid state at the time of execution. During this period, the grid operation mode may change due to events such as superior dispatch instructions, fault isolation, and load transfer, causing the verification conclusions of the pre-simulation stage to become invalid. If only the simulation results are relied upon and the actual state at the time of execution is ignored, it may lead to the grounding wire being mistakenly connected to a circuit that has become energized due to mode adjustment, or the safety distance verification of the newly energized circuit being omitted, causing the time dimension of the error prevention logic to fail. Therefore, re-verifying the energized state of the logic connection point and adjacent circuits during the actual execution of the operation ticket constitutes a real-time perception and blocking mechanism for safety risks under dynamic operating conditions.

[0071] Furthermore, real-time error prevention verification is a crucial link in constructing a complete safety closed loop encompassing pre-event simulation, in-event control, and post-event traceability, ensuring the continuous consistency between the model data and the actual physical power grid on site. When on-site personnel perform grounding wire connection operations, the actual operation results must be fed back to the error prevention system via status feedback to achieve synchronous updates of the topology model and closed-loop monitoring of the grounding wire's entire lifecycle status. Real-time verification, as the final safety barrier before operation execution, not only reconfirms the power outage conditions at the connection point to prevent misjudgments due to communication delays or untimely status updates, but also writes the successful grounding status into the model data after successful verification and connection execution, enabling real-time tracking of the location and quantity of deployed grounding wires. If verification fails, the operation abort location and failure reason are marked in the model data, providing precise data anchors for operation and maintenance analysis, responsibility definition, and subsequent model correction, thereby avoiding information silos and improving the level of precision in safety management.

[0072] The above solution introduces a real-time error prevention and verification mechanism during the execution of operation tickets to re-verify the energized status of logical connection points and adjacent circuits. This effectively solves the problem of pre-verification results becoming invalid due to changes in the power grid operating status within the time window from operation ticket generation to on-site execution. It achieves a closed-loop safety management system from pre-rehearsal to in-process monitoring, improving the system's safety response capability and prevention and control timeliness under dynamic operating conditions. On the other hand, by transmitting the status feedback after verification, incremental updates to the topology model data are ensured, and the real-time synchronization between on-site operation results and topology model status is ensured, avoiding information silos caused by the disconnect between operation execution and system records. The operation suspension and cause marking mechanism when verification fails solidifies the abnormal location and fault cause into the topology model data, providing accurate data anchors for subsequent operation and maintenance investigation, responsibility tracing, and model correction, enhancing the safety traceability of distribution network operations and the integrity and consistency of system data.

[0073] To facilitate understanding of the working principle of the model-based self-healing method for preventing misoperation of ground wires in overhead distribution networks, this application provides an application example of this method in a specific scenario. Please refer to... Figure 2 In this application scenario, the model-based self-healing method for preventing misoperation of the ground wire in overhead power distribution lines mainly includes: Step 1: Data Input and Missing Data Identification; The system receives multi-source data, including equipment ledgers, graphics and models, and external work orders. Through data parsing, it extracts information about lines and tower equipment relevant to the operation ticket tasks. The equipment ledger provides static technical parameters of the power grid equipment, the graphics and models offer a visual representation and structured description of the power grid topology, and the external work orders carry dynamic maintenance task instructions from upstream business modules such as the PMS (Power Management System). Together, these forms the data foundation for subsequent model self-healing and error prevention verification. By comparing multi-source data, missing tower equipment in the graphics and model data is identified, and its mRID, dispatch number, and spatial coordinates are marked.

[0074] Step 2: Correlation analysis and priority determination; A correlation analysis is performed on the missing tower equipment input and the operation ticket tasks indicated by external work orders to determine whether each tower is related to the current maintenance. If the missing equipment is directly referenced by the operation ticket task, it is immediately marked as high priority, and a "one-click completion" prompt message is pushed through the graphical interface to remind the user; after user confirmation, the model self-healing operation is triggered first. If the missing equipment is not referenced by the operation ticket, it is downgraded to low priority and only recorded in the missing equipment report, to be processed during subsequent periodic ledger management, thereby avoiding irrelevant changes from interfering with the current maintenance task. This priority allocation mechanism ensures that computing resources are focused on the key equipment associated with the operation ticket, improving the efficiency of model completion.

[0075] Step 3: Model self-healing; For high-priority target towers, a self-healing model operation is performed. At the model level, the completion location is determined based on topological relationships. A physical tower model (cim:Pole) is created to carry the tower's entity attributes (such as spatial coordinates, material, and number). Based on the multiple circuits carried by the tower, a logical tower model (cim:PoleSite) is instantiated for each circuit, filling in electrical attributes such as circuit name, phase information, and equipment terminals. A one-to-many relationship is established between the globally unique identifier (rdf:ID) of the physical tower model and the physical tower reference attribute (PoleSite.Pole) of the logical tower model, forming a logical-physical dual-view tower model that accurately describes the multi-circuit structure on the same tower. At the graphical level, tower primitives are simultaneously added to the corresponding geographic coordinates of the SVG graphics to ensure consistency between the model and the graphics. Finally, the completed dual-view model is written to the model data incrementally, avoiding the performance overhead of full reconstruction and achieving automatic completion and accurate generation of the topology model.

[0076] Step 4: Topology search and state calculation; After model completion, a topology search is performed on the web-based network for the relevant line connections of the target tower. By traversing the path of the connection lines, the connection relationships between device terminals in the graph are obtained, and the associated device terminal information is extracted. Based on these connection relationship data and the real-time equipment status of the power grid (switch position, grounding switch status, etc.), the backend performs topology calculation and status analysis to accurately analyze the energized status (no power, energized, grounded, etc.) of each node and connection line in the electrical connection network. This drives the frontend SVG graphics to perform real-time topology coloring and dynamic rendering, making the power grid status clear at a glance and providing accurate energized status basis for subsequent error prevention verification.

[0077] Step 5: Graphical invoicing and error prevention verification; On the graphical interface, operation tickets are intelligently created based on external work order data. In multi-circuit scenarios on the same pole, the operation ticket must accurately specify the line, pole, and connection position (e.g., small side, large side, branch side, etc.). Based on the logical-physical dual-view pole model, the grounding wire connection position is precisely bound to the logical connection point corresponding to the circuit specified in the operation ticket, and a simulation is performed. During the simulation, the topology status (whether there is a power outage), spatial relationships (e.g., safe distance under multiple circuits on the same pole) of the location are verified. The energized status of all logical pole models associated with the physical pole model is called, and the energized information of adjacent circuits is displayed to ensure the safety of the operation logic. After the simulation passes, the operation ticket enters the execution queue, waiting for on-site operation.

[0078] Step Six: Closed-loop management of on-site operation safety; During on-site operations, the grounding wire connection is performed based on the operation ticket steps. A final error-prevention logic check is conducted before connection, re-verifying the energized status of the connection point and adjacent circuits, and displaying safety information such as multiple circuits on the same pole being energized and safe working distances. If the check passes, the grounding wire connection is executed, and the successful connection status information is fed back to the method, triggering the topology model's status feedback and update (e.g., updating the grounding status), forming a closed loop for safety management. If the check fails, the operation is immediately stopped, an error message is displayed, manual intervention is required, and the operation stoppage location and failure reason are marked in the model data to provide a basis for subsequent analysis and correction.

[0079] Please see Figure 3 Based on the same inventive concept, this application also provides a model-based self-healing distribution network ground wire anti-misoperation control system 200, including a data acquisition layer 210, a tower analysis layer 220, a model self-healing layer 230, a topology analysis layer 240, and a graphic invoicing layer 250 connected in sequence, wherein: The data acquisition layer 210 is used to acquire equipment ledger data, diagram data and external work order data of the power distribution network, and to identify the missing tower equipment in the diagram data through data comparison; The tower analysis layer 220 is used to identify the target towers related to this maintenance from the missing tower equipment based on the operation ticket tasks indicated by the external work order data. Model self-healing layer 230 is used to perform model self-healing operations on the target tower. The model self-healing operations include: establishing a logical-physical dual-view tower model, updating the logical-physical dual-view tower model to the model data to complete the model, and adding tower primitives at the corresponding positions in the graphic data to complete the graphics. The logical-physical dual view includes a physical model and a logical model. The physical model is used to indicate the entity attributes of the target tower, and the logical model is used to indicate the loop attributes of each loop carried by the target tower. The physical model and the logical model are associated through an identifier. The topology analysis layer 240 is used to perform topology search on the line connection lines connected to the target tower at the front end of the graphics based on the updated graphic model data, obtain the direction of the connection lines and the information of the terminals of the equipment associated with the connection lines, and perform topology calculation and state analysis to determine the energized state of the connection lines and each node in the electrical connection network. The graphic ticketing layer 250 is used to generate operation tickets based on external work order data, bind the grounding wire connection position to the logical connection point in the logic tower model corresponding to the circuit specified in the operation ticket, and perform anti-misoperation logic verification on the binding relationship based on the energized state.

[0080] It is understood that the model-based self-healing distribution network ground wire misoperation prevention and control system 200 provided in this application embodiment can be used to execute the model-based self-healing distribution network ground wire misoperation prevention and control method provided in this application embodiment. Its implementation principle and the resulting technical effects have been introduced in the foregoing method embodiment. For the sake of brevity, any part not mentioned in the system embodiment can be referred to the corresponding content in any of the foregoing method embodiments.

[0081] Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of this application. (Refer to...) Figure 4 The electronic device 300 includes a processor 310, a memory 320, and a communication interface 330. These components are interconnected and communicate with each other via a communication bus 340 and / or other forms of connection mechanism (not shown).

[0082] The memory 320 includes one or more (only one is shown in the figure), which may be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The processor 310 and other possible components may access the memory 320 to read and / or write data therein.

[0083] Processor 310 includes one or more (only one is shown in the figure), which can be an integrated circuit chip with signal processing capabilities. The processor 310 described above can be a general-purpose processor, including a central processing unit (CPU), a microcontroller unit (MCU), a network processor (NP), or other conventional processors; it can also be a special-purpose processor, including a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0084] Communication interface 330 includes one or more (only one is shown in the figure) that can be used to communicate directly or indirectly with other devices to exchange data. For example, communication interface 330 can be an Ethernet interface; it can be a mobile communication network interface, such as an interface for 3G, 4G, or 5G networks; or it can be other types of interfaces with data transmission and reception capabilities.

[0085] One or more computer program instructions may be stored in the memory 320. The processor 310 may read and run these computer program instructions to implement the model-based self-healing method for preventing misoperation of the ground wire of overhead distribution lines in the present application and other desired functions.

[0086] Understandable. Figure 4 The structure shown is for illustrative purposes only; the electronic device 300 may also include components that are more advanced than those shown. Figure 4 The more or fewer components shown, or having the same Figure 4 The different configurations shown. Figure 4 The components shown can be implemented using hardware, software, or a combination thereof. For example, electronic device 300 can be a single server (or other device with computing power), a combination of multiple servers, a cluster of a large number of servers, etc., and can be either a physical device or a virtual device.

[0087] This application also provides a computer-readable storage medium storing computer program instructions. These instructions are read and executed by a processor to perform the model-based self-healing method for preventing ground wire misoperation in overhead power distribution lines provided in this application. For example, the computer-readable storage medium can be implemented as... Figure 4 The memory 320 in the electronic device 300, or a separate storage product (such as a USB flash drive, portable hard drive, etc.).

[0088] This application also provides a computer program product, which includes computer program instructions. These computer program instructions are read and executed by a processor to perform the model-based self-healing method for preventing misoperation of ground wires in overhead power distribution lines provided in this application. For example, these computer program instructions can be stored in... Figure 4 The memory 320 in the electronic device 300 is located inside the memory, or it is stored in a separate storage product (such as a USB flash drive, portable hard drive, etc.).

[0089] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0090] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0091] Furthermore, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0092] It should be noted that if the function is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0093] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0094] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A model-based self-healing method for preventing misoperation of ground wires in overhead distribution networks, characterized in that, The method includes: Acquire equipment ledger data, diagram data, and external work order data of the power distribution network, and identify the missing tower equipment in the diagram data through data comparison; Based on the operation ticket tasks indicated by the external work order data, identify the target towers related to this maintenance from the missing tower equipment; Perform a model self-healing operation on the target tower; wherein, the model self-healing operation includes: establishing a logical-physical dual-view tower model, updating the logical-physical dual-view tower model to the model data to complete the model, and adding tower primitives at corresponding positions in the graphic data to complete the graphics; the logical-physical dual view includes a physical model and a logical model, the physical model is used to indicate the entity attributes of the target tower, and the logical model is used to indicate the loop attributes of each loop carried by the target tower, the physical model and the logical model are formed by association through an identifier; Based on the updated graphic model data, a topology search is performed on the line connecting the target tower at the graphic front end to obtain the direction of the connecting line and the information of the terminal of the equipment associated with the connecting line. Topology calculation and status analysis are then performed to determine the energized status of the connecting line and each node in the electrical connection network. An operation ticket is generated based on external work order data. The grounding wire connection position is bound to the logical connection point in the logic tower model corresponding to the circuit specified in the operation ticket. The binding relationship is then checked for error prevention based on the energized state.

2. The method for preventing misoperation of ground wires in overhead distribution networks based on model self-healing as described in claim 1, characterized in that, The establishment of the logical-physical dual-view tower model includes: Create a physical tower model of the target tower; wherein, the physical tower model includes the spatial coordinates, tower material, and tower number attribute of the target tower; Based on the number of circuits carried by the target tower, a logical tower model is instantiated for each circuit; wherein, the logical tower model includes the line name, phase information, circuit number, and equipment terminal information associated with the logical connection point of the corresponding circuit; By using the globally unique identifier of the physical pole model and the physical pole reference attribute of the logical pole model, a one-to-many association relationship is established between the physical pole model and all the logical pole models to obtain a logical-physical dual-view pole model.

3. The method for preventing misoperation of ground wires in overhead distribution networks based on model self-healing as described in claim 1, characterized in that, Based on the updated graphical model data, a topology search is performed on the line connections to the target tower at the graphical front end to obtain the routing of the connection lines and information on the terminals of the associated equipment. Topology calculations and state analysis are then performed to determine the energized state of the connection lines and nodes in the electrical connection network, including: Perform a topology search on the line connecting to the target tower, traverse the direction of the line to determine the connection relationship of the loop in each direction of the logical connection point, and extract the connection relationship data between the device terminals; Based on the connection relationship data and the real-time equipment status of the power grid, topology calculation is performed to determine the energized status of each node and each connection line in the electrical connection network. Based on the charged state, the graph data is driven to perform real-time topology coloring and visualization.

4. The method for preventing misoperation of ground wires in overhead distribution networks based on model self-healing as described in claim 1, characterized in that, The step of performing error-proofing logic verification on the binding relationship based on the energized state includes: Before executing the operation ticket, the binding relationship is simulated and pre-performed based on the logical-physical dual-view tower model and the energized state of the connecting line; During the simulation, a verification is performed; the verification includes: verifying whether the energized state of the logical connection point corresponding to the grounding wire connection position is in a de-energized state, and verifying whether the direction of the small side, large side, or branch side of the logical connection point meets the requirements of the operation ticket task; and calling the energized state of all logical pole models associated with the physical pole model to verify the safe distance between adjacent energized circuits in multi-circuit lines on the same pole and the grounding wire connection position. When the verification shows that the energized state of the logic connection point is abnormal, the direction is incorrect, or the safe distance between the energized circuit and the grounding wire connection position does not meet the preset threshold, a safety warning message is generated and the process of generating the operation ticket is stopped.

5. The method for preventing misoperation of ground wires in overhead distribution networks based on model self-healing as described in claim 1, characterized in that, The step of determining the target towers related to this maintenance from the missing tower equipment based on the operation ticket tasks indicated by external work order data includes: Perform a correlation analysis between the missing tower equipment and the operation ticket tasks indicated by the external work order data; If the missing pole or tower equipment is referenced by the operation ticket task, it is marked as a target pole or tower with high completion priority, and a completion prompt message is generated. If the missing tower equipment is not referenced by the operation ticket task, it is marked as a low-priority tower to be completed and recorded in the missing equipment report; The step of performing a model self-healing operation on the target tower includes: performing the model self-healing operation on the target tower in response to a completion confirmation command for a high-priority target tower.

6. The method for preventing misoperation of ground wires in overhead distribution lines based on model self-healing according to any one of claims 1 to 5, characterized in that, The method further includes: During the execution of the operation ticket, the grounding wire connection position is checked in real time to prevent errors; wherein, the real-time error prevention check includes a second check of the energized state of the logic connection point and the energized state of the adjacent circuit. If the real-time anti-misoperation verification passes, the grounding wire connection operation is performed, and the pattern data is updated based on the status information of the successful grounding wire connection. If the real-time anti-misoperation verification fails, the grounding wire connection operation is stopped, and the operation stop location and failure reason are marked in the pattern data.

7. The method for preventing misoperation of ground wires in overhead distribution networks based on model self-healing according to any one of claims 1 to 5, characterized in that, The step of updating the logical-physical dual-view tower model to the model data includes: The logical-physical dual-view tower model is incrementally updated to the model data.

8. A model-based self-healing distribution network ground wire misoperation prevention and control system, characterized in that, This includes, in sequence, a data acquisition layer, a tower analysis layer, a model self-healing layer, a topology analysis layer, and a graphical invoicing layer, wherein: The data acquisition layer is used to acquire equipment ledger data, diagram data and external work order data of the power distribution network, and identify the missing tower equipment in the diagram data through data comparison; The tower analysis layer is used to determine the target tower related to this maintenance from the missing tower equipment based on the operation ticket task indicated by the external work order data. The model self-healing layer is used to perform model self-healing operations on the target tower. The model self-healing operations include: establishing a logical-physical dual-view tower model; updating the logical-physical dual-view tower model to the model data to complete the model; and adding tower primitives to the corresponding positions in the graphic data to complete the graphics. The logical-physical dual view includes a physical model and a logical model. The physical model indicates the entity attributes of the target tower, and the logical model indicates the loop attributes of each loop carried by the target tower. The physical model and the logical model are associated through an identifier. The topology analysis layer is used to perform a topology search on the line connecting the target tower at the front end of the graphics based on the updated graphic model data, obtain the direction of the connecting line and the information of the terminal of the equipment associated with the connecting line, and perform topology calculation and state analysis to determine the energized state of the connecting line and each node in the electrical connection network. The graphical invoicing layer is used to generate operation tickets based on external work order data, bind the grounding wire connection position to the logical connection point in the logical pole model corresponding to the circuit specified in the operation ticket, and perform anti-misoperation logic verification on the binding relationship based on the energized state.

9. An electronic device, characterized in that, include: A processor, a memory, and a communication bus, wherein the processor and the memory communicate with each other via the communication bus; The memory stores program instructions that can be executed by the processor, and the processor can execute the method as described in any one of claims 1 to 7 by calling the program instructions.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a computer, cause the computer to perform the method as described in any one of claims 1 to 7.

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