Substation secondary voltage plate anti-misoperation auxiliary system

Abstract

The application relates to the technical field of substation secondary equipment operation and maintenance, and discloses a substation secondary pressure plate anti-misoperation auxiliary system which comprises a data acquisition module, a digital twin visualization module, an NLP semantic recognition module, a pressure plate state recognition module, an automatic closed-loop comparison module and an alarm reminding module. The output end of the data acquisition module is connected with the input ends of the digital twin visualization module, the NLP semantic recognition module and the pressure plate state recognition module respectively; the digital twin visualization module is bidirectionally connected with the NLP semantic recognition module and the automatic closed-loop comparison module in communication; and the output ends of the NLP semantic recognition module and the pressure plate state recognition module are connected with the input end of the automatic closed-loop comparison module. Through the cooperation of the modules, the system realizes closed-loop prevention and control of risks in all links of pressure plate operation, completes intelligent analysis and verification of operation tickets before operation, provides visual control guidance during operation, and completes state verification and closed-loop comparison after operation.

Description

Technical Field

[0001] This invention relates to the field of substation secondary equipment operation and maintenance technology, specifically to a substation secondary pressure plate anti-misoperation auxiliary system. Background Technology

[0002] As a core component of the substation relay protection system, the accuracy of the secondary switchboard's commissioning and decommissioning operations directly affects the reliable operation of the protection devices and the safe and stable operation of the power grid. With the continuous advancement of power grid construction, the voltage level of substations is constantly increasing, and the scale of secondary equipment is continuously expanding. The commissioning and decommissioning operations, status control, and ledger verification of secondary switchboards have become core control contents in the daily operation and maintenance, overhaul, and renovation and expansion projects of substations. The industry has formed a supporting operation ticket management system and manual verification operation process, and has gradually applied related technologies such as switchboard status image recognition and electronic ledger management to standardize switchboard operation and maintenance operations and prevent the risk of misoperation.

[0003] In existing secondary pressure plate operation and maintenance management technologies and systems, the operation ticket review, on-site operation control, and post-operation status verification processes often employ fragmented, independent management models. The technical means and data information at each stage cannot form an effective linkage, making it difficult to achieve closed-loop risk control throughout the entire operation process. Existing operation ticket verification technologies are mostly limited to basic text verification, failing to achieve in-depth analysis of operational logic and compliance verification. The review stage is disconnected from the on-site operation stage, making it difficult to identify and intercept logical defects and oversights before operation. Existing pressure plate status recognition technologies are mostly single image recognition functions, unable to achieve unified and real-time linkage with the target status of the operation ticket and standardized ledger data. This makes it difficult to complete a full, automated, closed-loop comparison of operation results, failing to meet the operational requirements of substation secondary equipment maintenance. Therefore, a substation secondary pressure plate anti-misoperation auxiliary system is proposed. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a substation secondary pressure plate anti-misoperation auxiliary system to solve the technical problems of disconnected control and insufficient risk prevention and control throughout the pressure plate operation and maintenance process.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a substation secondary pressure plate anti-misoperation auxiliary system, comprising a data acquisition module, a digital twin visualization module, an NLP semantic recognition module, a pressure plate status recognition module, an automated closed-loop comparison module, and an alarm reminder module: The output of the data acquisition module is connected to the input of the digital twin visualization module, the NLP semantic recognition module, and the pressure plate status recognition module, respectively, and is used to collect and verify multi-source heterogeneous data of the substation secondary pressure plate, and provide data input for each related module. The digital twin visualization module is bidirectionally connected to the NLP semantic recognition module and the automated closed-loop comparison module to construct a secondary pressure plate digital twin model, realize the mapping between physical entities and digital models and the visualization of operation data. The outputs of the NLP semantic recognition module and the pressure plate status recognition module are both connected to the input of the automated closed-loop comparison module. The NLP semantic recognition module is used to parse the operation ticket text and complete the logical verification, and the pressure plate status recognition module is used to identify the actual status of the pressure plate on site and generate an on-site status ledger. The output of the automated closed-loop comparison module is connected to the input of the alarm reminder module. It is used to complete the closed-loop comparison of the operation ticket, pressure plate ledger and on-site status, and to trigger the alarm reminder module to perform a warning action when the comparison is abnormal.

[0006] Preferably, the data acquisition module incorporates a multi-format file adaptation unit, a data verification unit, and a batch management unit. The multi-format file adaptation unit is used to support the batch uploading and receiving of different types of operation ticket files, pressure plate data files, and on-site acquired image files. The data verification unit is used to verify the compliance of uploaded files and perform preliminary structured preprocessing of uploaded data while eliminating invalid data. The batch management unit is used by users to create corresponding operation batches according to operation and maintenance needs, and to collect and manage related data in the same scenario. It is compatible with the batch uploading and receiving of various file formats in substation operation and maintenance scenarios, ensuring the compliance and standardization of input data, effectively eliminating interference from invalid data, and realizing unified collection and management of operation and maintenance data in the same scenario. This avoids data confusion and provides a standardized and reliable data source support for subsequent system analysis and comparison.

[0007] Preferably, the digital twin visualization module includes a physical and digital mapping submodule and a visualization display submodule. The physical and digital mapping submodule is used to perform structured parsing of the pressure plate data uploaded by the data acquisition module, extracting various basic information such as the name, type, cabinet to which it belongs, and standard status of the pressure plate, and automatically constructing a digital twin model corresponding one-to-one with the physical entity on site based on the basic information. The visualization display submodule is used to intuitively present various information and status of the pressure plate in a graphical way, and classifies and displays the pressure plate-related equipment according to a preset hierarchical relationship. It can accurately parse the basic data of the pressure plate and construct a digital twin model corresponding one-to-one with the physical entity on site, realizing accurate mapping between physical equipment and digital model. At the same time, through graphical and hierarchical display, the relevant information and status of the pressure plate are presented intuitively, which is convenient for maintenance personnel to quickly view and verify, reducing the risk of operational errors.

[0008] Preferably, the NLP semantic recognition module incorporates a text preprocessing unit, a semantic parsing unit, and a logic verification unit. The text preprocessing unit performs word segmentation and syntactic analysis on the operation ticket text transmitted by the data acquisition module to complete the standardized conversion of the text content. The semantic parsing unit extracts information related to the platen operation, such as the operation steps, platen name, and target status, from the preprocessed operation ticket text. The logic verification unit combines a preset expert rule base with historical operation data to review the compliance and logical rationality of the parsed platen operation content. This allows for standardized processing and deep semantic parsing of the operation ticket text, accurately extracting the core key information of the platen operation. Simultaneously, by combining the expert rule base with historical data, it completes the logical and compliance review of the operation content, identifying omissions and logical defects in the operation ticket content in advance, and intercepting security risks before the operation is executed.

[0009] Preferably, the pressure plate status recognition module integrates an image preprocessing unit, a pressure plate positioning unit, and a status recognition unit. The image preprocessing unit performs distortion correction, image enhancement, and noise reduction on the on-site pressure plate images transmitted by the data acquisition module, eliminating the adverse effects of the shooting environment and shooting method on image quality. The pressure plate positioning unit selects each pressure plate entity from the preprocessed image and extracts the corresponding feature information. The status recognition unit analyzes the feature information of the pressure plate, determines the actual working status and corresponding identity information of the pressure plate, and generates a pressure plate status ledger corresponding to the on-site situation. This effectively eliminates the adverse effects of the shooting environment and operating method on the on-site images, accurately completes the positioning and core feature extraction of the pressure plate entity, accurately determines the actual working status and identity information of the pressure plate, and generates a status ledger that fits the actual on-site situation, providing real and accurate on-site data support for subsequent closed-loop comparison work.

[0010] Preferably, the automated closed-loop comparison module has a built-in before-and-after operation ticket comparison unit. This unit is used by the user to divide and mark the uploaded operation tickets according to the actual operation process, marking the operation tickets as two different stages: before and after the operation. It automatically compares the pressure plate status information in the operation tickets of the two stages, locates the content where the pressure plate status differs between the two stages, and highlights the inconsistent parts to distinguish them. This can adapt to the full-process control requirements of on-site operations, supports maintenance personnel to divide the execution stages of the operation tickets according to the actual work process, automatically completes the before-and-after comparison of the pressure plate status, accurately locates the content of status differences and clearly marks them, so that maintenance personnel can quickly and comprehensively grasp the changes in the pressure plate status before and after the operation.

[0011] Preferably, the automated closed-loop comparison module has a built-in three-end comparison unit. The three-end comparison unit is used to simultaneously retrieve the operation ticket data before the operation, the on-site pressure plate status data after the operation, and the standardized pressure plate ledger data, and perform a synchronous, item-by-item closed-loop comparison of the pressure plate status information in the three sets of data, thereby identifying pressure plate content with inconsistent status information in the three sets of data, and generating corresponding comparison statistical reports. It can simultaneously retrieve three types of data: the target status of the operation ticket, the actual status on site, and the standardized ledger, complete the item-by-item closed-loop comparison of the three sets of data, accurately identify abnormal content with inconsistent status, and generate standardized comparison statistical reports.

[0012] Preferably, the alarm reminder module establishes a bidirectional linkage triggering relationship with the NLP semantic recognition module and the automated closed-loop comparison module. When the NLP semantic recognition module identifies logical errors and compliance issues in the operation ticket content after completing the operation ticket review, it simultaneously sends a corresponding trigger signal to the alarm reminder module. When the automated closed-loop comparison module identifies an anomaly in the pressure plate status information after completing the status comparison, it also simultaneously sends a corresponding trigger signal to the alarm reminder module. Upon receiving the corresponding trigger signal, the alarm reminder module immediately initiates the corresponding early warning action and simultaneously marks the detailed information of the anomaly. This enables full-scenario linkage early warning for both logical anomalies in the operation ticket and anomalies in the pressure plate status comparison. When various anomalies are identified, early warning actions can be triggered immediately, while clearly marking the anomaly details and locking the subsequent operation process, thereby fundamentally avoiding relay protection device anomalies and power grid operation safety hazards caused by pressure plate misoperation.

[0013] Preferably, the physical and digital mapping submodule of the digital twin visualization module synchronously transmits the completed digital twin model to the visualization display submodule. Simultaneously, it receives operation ticket data parsed by the NLP semantic recognition module and comparison result data generated by the automated closed-loop comparison module, synchronously updating the corresponding data content to the digital twin model and presenting it through the visualization display submodule. This enables real-time linkage updates between the digital twin model and the operation ticket parsing data and comparison result data, allowing the changes in the pressure plate status and comparison results throughout the entire operation process to be intuitively presented through a visual interface. This achieves unified source of operation and maintenance data, ensuring that operation and maintenance personnel accurately grasp the operation dynamics and abnormal situations throughout the entire process.

[0014] Preferably, the batch management unit of the data acquisition module is used to classify, store, and quickly retrieve different batches created by the user. At the same time, by allowing the user to edit and adjust the batch information, all relevant files and data under the same operation and maintenance scenario are completely collected into the same batch for unified management. This enables the classified storage and quick retrieval of batch data in different operation and maintenance scenarios, supports operation and maintenance personnel to edit and adjust batch information as needed, and completely collects and manages all operation and maintenance data of the same scenario, ensuring the standardization and orderliness of data management, and facilitating subsequent historical operation tracing and operation and maintenance work review.

[0015] Compared with the prior art, the present invention provides an auxiliary system for preventing misoperation of secondary pressure plates in substations, which has the following beneficial effects: This substation secondary pressure plate misoperation prevention auxiliary system achieves closed-loop risk control throughout the entire operation process through the coordinated operation of various functional modules. Before operation, the NLP semantic recognition module completes intelligent parsing and logical verification of the operation ticket, identifying operational logic defects and content omissions in advance, and intercepting safety risks before operation execution. During operation, the digital twin visualization module realizes intuitive and visual control of the pressure plate status, providing clear guidance for operation and maintenance, and avoiding deviations and errors during operation. After operation, the pressure plate status recognition and automated closed-loop comparison module completes full verification of the operation results, and combined with the alarm reminder module, realizes immediate warning and operation interception of abnormal situations, effectively avoiding protection device failure or maloperation caused by secondary pressure plate misoperation. At the same time, the digital twin technology realizes the same source and real-time linkage of three types of data, realizing data traceability and closed-loop management of the entire pressure plate operation and maintenance process, effectively solving the problems of pressure plate status control gaps and handover traceability difficulties in long-cycle renovation and expansion projects and cross-time period operation and maintenance. Attached Figure Description

[0016] Figure 1 This is a system architecture block diagram of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 This invention provides a technical solution: a substation secondary pressure plate anti-misoperation auxiliary system, comprising a data acquisition module, a digital twin visualization module, an NLP semantic recognition module, a pressure plate status recognition module, an automated closed-loop comparison module, and an alarm reminder module. The output of the data acquisition module is connected to the input of the digital twin visualization module, the NLP semantic recognition module, and the pressure plate status recognition module, respectively. It is used to collect and verify multi-source heterogeneous data of the substation secondary pressure plate and provide data input for each related module.

[0019] The digital twin visualization module is bidirectionally connected to the NLP semantic recognition module and the automated closed-loop comparison module to construct a secondary pressure plate digital twin model, thereby realizing the mapping between the physical entity and the digital model and the visualization of the operation data.

[0020] The outputs of both the NLP semantic recognition module and the pressure plate status recognition module are connected to the input of the automated closed-loop comparison module. The NLP semantic recognition module is used to parse the operation ticket text and complete the logical verification, while the pressure plate status recognition module is used to identify the actual status of the pressure plate on site and generate an on-site status ledger.

[0021] The output of the automated closed-loop comparison module is connected to the input of the alarm reminder module to complete the closed-loop comparison of the operation ticket, pressure plate ledger and the on-site status, and to trigger the alarm reminder module to perform early warning actions when the comparison is abnormal.

[0022] The data acquisition module incorporates a multi-format file adaptation unit, a data verification unit, and a batch management unit. The multi-format file adaptation unit is used to batch upload and receive different types of operation ticket files, pressure plate data files, and on-site acquired image files. The data verification unit is used to verify the compliance of various uploaded files and perform preliminary structured preprocessing of uploaded data while removing invalid data. The batch management unit is used by users to create corresponding operation batches according to maintenance needs, and to collect and manage related data in the same scenario. The batch management unit of the data acquisition module is used to classify, store, and quickly retrieve different batches created by users. At the same time, by allowing users to edit and adjust the information of the created batches, all related files and data in the same maintenance operation scenario are completely collected into the same batch for unified management.

[0023] The digital twin visualization module includes a physical and digital mapping submodule and a visualization display submodule. The physical and digital mapping submodule is used to perform structured parsing of the pressure plate data uploaded by the data acquisition module, extracting various basic information such as the name, type, cabinet to which it belongs, and standard status of the pressure plate, and automatically constructing a digital twin model that corresponds one-to-one with the physical entity on site based on the basic information. The visualization display submodule is used to intuitively present various information and status of the pressure plate in a graphical way, and classifies and displays the pressure plate-related equipment according to a preset hierarchical relationship. The physical and digital mapping submodule of the digital twin visualization module synchronously transmits the completed digital twin model to the visualization display submodule. At the same time, it can receive the operation ticket data parsed by the NLP semantic recognition module and the comparison result data generated by the automated closed-loop comparison module, and synchronously update the corresponding data content into the digital twin model, and present it through the visualization display submodule.

[0024] The NLP semantic recognition module has a built-in text preprocessing unit, semantic parsing unit, and logic verification unit. The text preprocessing unit is used to perform word segmentation and syntactic analysis on the operation ticket text transmitted by the data acquisition module to complete the standardized conversion of the text content. The semantic parsing unit is used to extract information related to the platen operation, such as operation steps, platen name, and target status, from the preprocessed operation ticket text. The logic verification unit is used to combine the preset expert rule base and historical operation data to review the compliance and logical rationality of the parsed platen operation content.

[0025] The pressure plate status recognition module has a built-in image preprocessing unit, a pressure plate positioning unit, and a status recognition unit. The image preprocessing unit is used to perform distortion correction, image enhancement, and noise reduction on the on-site pressure plate images transmitted by the data acquisition module, eliminating the adverse effects of the shooting environment and shooting method on image quality. The pressure plate positioning unit is used to select each pressure plate entity from the preprocessed image and extract the corresponding feature information. The status recognition unit is used to analyze the feature information of the pressure plate, determine the actual working status and corresponding identity information of the pressure plate, and generate a pressure plate status log corresponding to the on-site situation.

[0026] The automated closed-loop comparison module has a built-in pre- and post-operation ticket comparison unit. This unit allows users to divide and mark the uploaded operation tickets according to the actual operation process, labeling each operation ticket as a pre-operation and post-operation stage. It automatically compares the pressure plate status information in the operation tickets of the two stages item by item, locating the content where the pressure plate status differs between the two stages, and highlighting the inconsistent parts. The automated closed-loop comparison module also has a built-in three-end comparison unit. This unit simultaneously retrieves the operation ticket data before the operation, the on-site pressure plate status data after the operation, and the standardized pressure plate ledger data, and performs a synchronous item-by-item closed-loop comparison of the pressure plate status information in the three sets of data. This identifies the pressure plate content with inconsistent status information in the three sets of data and generates corresponding comparison statistical reports.

[0027] The alarm reminder module establishes a two-way linkage trigger relationship with the NLP semantic recognition module and the automated closed-loop comparison module. When the NLP semantic recognition module identifies logical errors and compliance issues in the operation ticket after completing the operation ticket review, it simultaneously sends a corresponding trigger signal to the alarm reminder module. When the automated closed-loop comparison module identifies an anomaly in the pressure plate status information after completing the status comparison, it also simultaneously sends a corresponding trigger signal to the alarm reminder module. Upon receiving the corresponding trigger signal, the alarm reminder module immediately initiates the corresponding early warning action and simultaneously marks the detailed information of the anomaly.

[0028] Through the coordinated operation of various functional modules, a closed-loop risk control system is achieved across the entire operation process. Before operation, the NLP semantic recognition module intelligently parses and logically verifies the operation ticket. Combined with an expert rule base (built by senior operations and maintenance experts) and artificial intelligence technology, the system logically reviews the platen engagement / disengagement sequence and status settings in the operation ticket, identifying operational logic defects and omissions in advance and intercepting security risks before execution. During operation, the digital twin visualization module provides intuitive and visual control of the platen status, offering clear guidance for operations and maintenance and avoiding deviations and errors. Simultaneously, the system, in conjunction with the expert rule base and artificial intelligence technology, verifies the correctness of the platen engagement / disengagement sequence. After operation, the system uses platen status recognition and automation... The closed-loop comparison module completes full verification of operation results, and combined with the alarm reminder module, it realizes real-time early warning and operation interception of abnormal situations, effectively avoiding the problem of protection device failure or malfunction caused by secondary pressure plate misoperation. At the same time, through digital twin technology, it realizes the same source and real-time linkage of three types of data, realizing data traceability and closed-loop management of the entire pressure plate operation and maintenance process. It effectively solves the problems of pressure plate status control gap and handover traceability difficulties in long-cycle renovation and expansion projects and cross-time period operation and maintenance. It also improves the efficiency of review, reduces fatigue error of manual review, and reduces the workload of operation and maintenance personnel. The company can save about 13,000 man-hours per year.

[0029] The operation flow of the above system is as follows: System initialization and basic data construction process: Maintenance personnel create a dedicated operation batch based on the current maintenance scenario using the system's batch management unit. They fill in the batch's basic information, select the batch type, and after batch creation, upload the corresponding pressure plate data, historical operation tickets, and relevant maintenance documents to the data acquisition module in batches via the multi-format file adaptation unit. The data verification unit of the data acquisition module performs compliance checks on all uploaded files, verifying file format and integrity, removing invalid data and corrupted files. Simultaneously, it performs preliminary structured preprocessing on the verified files, completing the standardized conversion of the data format. The data acquisition module then synchronously transmits the preprocessed pressure plate basic data to the digital twin visualization module. The digital mapping submodule performs deep structured analysis on the uploaded pressure plate data, extracting all basic information such as the name, type, cabinet to which it belongs, row number, and standard status of each pressure plate. It automatically constructs a digital twin model that corresponds one-to-one with the physical pressure plate entities on site, generating a standardized pressure plate ledger. The physical and digital mapping submodules synchronously transmit the completed digital twin model to the visualization display submodule. The visualization display submodule displays the pressure plates, cabinets to which they belong, and other equipment in a hierarchical manner through a graphical approach, distinguishing pressure plate types and marking standard status according to preset rules, completing system initialization and basic digital base construction. At the same time, the batch management unit classifies and stores all basic data of this batch, supporting subsequent quick retrieval and editing adjustments. Intelligent review and risk assessment process for operation tickets before operation: Maintenance personnel upload the operation ticket file corresponding to this operation to the created operation batch. After the data acquisition module completes file format verification and structured preprocessing, the operation ticket text data is synchronously transmitted to the NLP semantic recognition module and the digital twin visualization module. The semantic recognition module's text preprocessing unit performs word segmentation and syntactic analysis on the received operation ticket text, standardizing the text content and eliminating parsing obstacles caused by differences in text format and non-standard expressions. The semantic parsing unit performs deep semantic decomposition on the preprocessed operation ticket text, extracting all key information related to the platen operation, including platen operation steps, corresponding platen names, operation target status, and drop / return order requirements. The logic verification unit retrieves a preset expert rule base and historical compliant operation data to conduct a full compliance and logical rationality review of the parsed platen operation content, verifying whether the drop / return order in the operation ticket is compliant, whether the platen name and status match, and whether there are logical defects in the operation steps. Simultaneously, the review results are transmitted to the automated closed-loop comparison module and the digital twin visualization module. After receiving the parsed operation ticket data, the digital twin visualization module updates the required platen target status in the digital twin model and presents it graphically through the visualization display submodule, intuitively displaying the platen position, target status, and operation order corresponding to this operation, providing visual guidance for subsequent on-site operations. (If NLP...) If the semantic recognition module identifies logical errors, omissions, or compliance issues in the operation ticket during the review process, it immediately sends a trigger signal to the alarm reminder module to initiate the early warning process. Only after the operation ticket is corrected and approved by the maintenance personnel can the on-site operation be carried out. Visualized control process for on-site operation: During on-site operation, maintenance personnel can access the corresponding operation batch through the system terminal. Through the visualization display sub-module of the digital twin visualization module, they can view the list of pressure plates, target status and operation sequence corresponding to this operation according to the hierarchy of cabinet and number. The system clearly distinguishes the pressure plates to be operated, the pressure plates that have been operated and the pressure plates that are prohibited from operation through graphical icons, providing full-process visual guidance for on-site operation and avoiding deviations in operation objects and operation status. During the operation, maintenance personnel can upload photos of pressure plates and stage operation records in stages. After the data acquisition module completes data verification, it is synchronized to the corresponding module to realize dynamic tracking and recording of the operation process and ensure that the entire operation process is traceable. Post-operation pressure plate status identification and closed-loop comparison process: After the on-site pressure plate operation is completed, maintenance personnel take photos of the on-site cabinet pressure plates and upload them in batches to the corresponding operation batch. After the data acquisition module completes the compliance verification of the image files, it synchronously transmits the on-site pressure plate images to the pressure plate status identification module. The image preprocessing unit of the pressure plate status identification module performs distortion correction, image enhancement, and noise reduction on the received on-site images to eliminate the influence of factors such as shooting angle, uneven lighting, and image shaking on image quality, and outputs standardized and clear images. The pressure plate positioning unit then uses the preprocessed image... The system precisely selects each pressure plate entity, extracting core feature information such as its outline, label, and connection angle to achieve accurate positioning and identity matching for each pressure plate. The status recognition unit performs in-depth analysis of the extracted pressure plate feature information, determining its actual working status through the connection angle and confirming its identity by recognizing the pressure plate label information. This process generates a real-world status log that perfectly matches the actual situation on-site, and simultaneously transmits this log to the automated closed-loop comparison module and the digital twin visualization module. The automated closed-loop comparison module initiates the corresponding comparison based on the operation instructions from the maintenance personnel. For the selected operation ticket comparison mode, the maintenance personnel divide and mark the uploaded operation tickets into two stages: before and after the operation. The before-and-after operation ticket comparison unit automatically compares the pressure plate status information in the operation tickets of the two stages, accurately locates the differences in status, and distinguishes the inconsistent parts by highlighting them, generating the before-and-after operation comparison results. If the three-end comparison mode is selected, the three-end comparison unit simultaneously retrieves the target status data parsed from the operation ticket, the actual on-site status data generated by the pressure plate status identification module, and the standardized ledger data corresponding to the digital twin model. It performs a closed-loop comparison of the pressure plate status information of the three sets of data, accurately identifies the pressure plate content with inconsistent status in the three sets of data, generates a full comparison statistical report, and clarifies the equipment information of consistent, inconsistent, and abnormal status. The automated closed-loop comparison module synchronously sends the final comparison results back to the digital twin visualization module. The physical and digital mapping sub-module updates the comparison results to the digital twin model. The visualization display sub-module intuitively displays the comparison results, highlights the location and information of abnormal pressure plates, and simultaneously stores the comparison results in the corresponding operation batch. Anomaly Warning and Closed-Loop Handling Process: When the automated closed-loop comparison module completes the comparison and identifies anomalies such as inconsistent pressure plate status information or mismatch between accounts, cards, and physical items, it immediately sends a trigger signal to the alarm reminder module. Upon receiving the trigger signal from the NLP semantic recognition module or the automated closed-loop comparison module, the alarm reminder module immediately activates an audible and visual warning action. At the same time, an anomaly prompt window pops up on the system interface, clearly indicating the name of the abnormal pressure plate, the cabinet to which it belongs, the anomaly type, and the anomaly details. This locks the subsequent operation process of the system, preventing maintenance personnel from completing the closed loop of this operation. According to the warning prompt, the maintenance personnel go to the site to verify the abnormal pressure plate situation, complete the error correction and status adjustment, and re-upload the on-site pressure plate photos. The system then re-executes the status recognition and closed-loop comparison until all comparison results are compliant and all anomalies are handled. At this point, the alarm reminder module deactivates the warning and unlocks the system operation process. Full-process data archiving and traceability: After the entire process of this pressure plate operation is completed and all abnormalities are handled in a closed loop, the system batch management unit will collect and classify all data of this operation, including basic pressure plate ledger, operation ticket documents, audit records, on-site image data, status recognition results, closed-loop comparison reports, abnormal handling records, etc., and store them in a full range. The archived batch data supports subsequent rapid retrieval, query and traceability, which can meet the needs of pressure plate status handover, historical operation backtracking and operation and maintenance responsibility traceability for long-cycle renovation and expansion projects and cross-time period operation and maintenance operations, and realize the digital, traceable closed-loop management of the entire pressure plate operation and maintenance process.

[0030] This solution achieves closed-loop risk control throughout the entire operation process through the coordinated operation of various functional modules. Before operation, the NLP semantic recognition module performs intelligent parsing and logical verification of the operation ticket, identifying operational logic defects and content omissions in advance, and intercepting safety risks before operation execution. During operation, the digital twin visualization module enables intuitive and visual management of the pressure plate status, providing clear guidance for operation and maintenance and avoiding deviations and errors during operation. After operation, the pressure plate status recognition and automated closed-loop comparison module completes full verification of the operation results, and combined with the alarm reminder module, it realizes immediate early warning and operation interception of abnormal situations, effectively avoiding the problem of protection device failure or malfunction caused by secondary pressure plate misoperation. At the same time, the digital twin technology realizes the same source and real-time linkage of three types of data, realizing data traceability and closed-loop management of the entire pressure plate operation and maintenance process, effectively solving the problems of pressure plate status control gaps and handover traceability difficulties in long-cycle renovation and expansion projects and cross-time period operation and maintenance.

[0031] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0032] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A substation secondary pressure plate anti-misoperation auxiliary system, characterized in that, It includes a data acquisition module, a digital twin visualization module, an NLP semantic recognition module, a pressure plate status recognition module, an automated closed-loop comparison module, and an alarm notification module. The output of the data acquisition module is connected to the input of the digital twin visualization module, the NLP semantic recognition module, and the pressure plate status recognition module, respectively, and is used to collect and verify multi-source heterogeneous data of the substation secondary pressure plate, and provide data input for each related module. The digital twin visualization module is bidirectionally connected to the NLP semantic recognition module and the automated closed-loop comparison module to construct a secondary pressure plate digital twin model, realize the mapping between physical entities and digital models and the visualization of operation data. The outputs of the NLP semantic recognition module and the pressure plate status recognition module are both connected to the input of the automated closed-loop comparison module. The NLP semantic recognition module is used to parse the operation ticket text and complete the logical verification, and the pressure plate status recognition module is used to identify the actual status of the pressure plate on site and generate an on-site status ledger. The output of the automated closed-loop comparison module is connected to the input of the alarm reminder module. It is used to complete the closed-loop comparison of the operation ticket, pressure plate ledger and on-site status, and to trigger the alarm reminder module to perform a warning action when the comparison is abnormal.

2. The substation secondary pressure plate anti-misoperation auxiliary system according to claim 1, characterized in that: The data acquisition module incorporates a multi-format file adaptation unit, a data verification unit, and a batch management unit. The multi-format file adaptation unit is used to batch upload and receive different types of operation ticket files, pressure plate data files, and on-site acquired image files. The data verification unit is used to verify the compliance of various uploaded files and to perform preliminary structured preprocessing of the uploaded data while removing invalid data. The batch management unit is used by users to create corresponding operation batches according to operation and maintenance needs, and to collect and manage related data in the same scenario in a unified manner.

3. The substation secondary pressure plate anti-misoperation auxiliary system according to claim 1, characterized in that: The digital twin visualization module includes a physical and digital mapping submodule and a visualization display submodule. The physical and digital mapping submodule is used to perform structured parsing of the pressure plate data uploaded by the data acquisition module, extract various basic information such as the name, type, cabinet to which it belongs, and standard status of the pressure plate, and automatically construct a digital twin model that corresponds one-to-one with the physical entity on site based on the basic planetary system. The visualization display submodule is used to intuitively present various information and status of the pressure plate in a graphical way, and at the same time, classify and display the pressure plate-related equipment according to a preset hierarchical relationship.

4. The substation secondary pressure plate anti-misoperation auxiliary system according to claim 1, characterized in that: The NLP semantic recognition module incorporates a text preprocessing unit, a semantic parsing unit, and a logic verification unit. The text preprocessing unit performs word segmentation and syntactic analysis on the operation ticket text transmitted by the data acquisition module to complete the standardized conversion of the text content. The semantic parsing unit extracts information related to the pressure plate operation, such as the operation steps, pressure plate name, and target status, from the preprocessed operation ticket text. The logic verification unit combines a preset expert rule base with historical operation data to review the compliance and logical rationality of the parsed pressure plate operation content.

5. The substation secondary pressure plate anti-misoperation auxiliary system according to claim 1, characterized in that: The pressure plate status recognition module integrates an image preprocessing unit, a pressure plate positioning unit, and a status recognition unit. The image preprocessing unit is used to perform distortion correction, image enhancement, and noise reduction on the on-site pressure plate images transmitted by the data acquisition module, eliminating the adverse effects of the shooting environment and shooting method on image quality. The pressure plate positioning unit is used to select each pressure plate entity from the preprocessed image and extract the corresponding feature information. The status recognition unit is used to analyze the feature information of the pressure plate, determine the actual working status and corresponding identity information of the pressure plate, and generate a pressure plate status ledger corresponding to the on-site situation.

6. The substation secondary pressure plate anti-misoperation auxiliary system according to claim 1, characterized in that: The automated closed-loop comparison module has a built-in before-and-after operation ticket comparison unit. The before-and-after operation ticket comparison unit is used by the user to divide and mark the uploaded operation ticket according to the actual operation process. The operation ticket is marked as two different stages: before operation and after operation. The unit automatically compares the pressure plate status information in the operation tickets of the two stages one by one, locates the content where the pressure plate status differs between the two stages, and distinguishes and presents the inconsistent parts by highlighting.

7. The substation secondary pressure plate anti-misoperation auxiliary system according to claim 1, characterized in that: The automated closed-loop comparison module has a built-in three-end comparison unit. The three-end comparison unit is used to simultaneously retrieve the operation ticket data before the operation, the on-site pressure plate status data after the operation, and the standardized pressure plate ledger data, and perform a synchronous item-by-item closed-loop comparison of the pressure plate status information in the three sets of data, thereby identifying the pressure plate content with inconsistent status information in the three sets of data, and generating corresponding comparison statistical reports.

8. The substation secondary pressure plate anti-misoperation auxiliary system according to claim 1, characterized in that: The alarm reminder module establishes a two-way linkage trigger relationship with the NLP semantic recognition module and the automated closed-loop comparison module. When the NLP semantic recognition module identifies logical errors and compliance issues in the operation ticket content after completing the operation ticket review, it simultaneously sends a corresponding trigger signal to the alarm reminder module. When the automated closed-loop comparison module identifies an abnormal situation where the pressure plate status information is inconsistent after completing the status comparison, it also simultaneously sends a corresponding trigger signal to the alarm reminder module. Upon receiving the corresponding trigger signal, the alarm reminder module immediately initiates the corresponding early warning action and simultaneously marks the detailed information of the abnormal content.

9. The substation secondary pressure plate anti-misoperation auxiliary system according to claim 3, characterized in that: The physical and digital mapping submodule of the digital twin visualization module synchronously transmits the completed digital twin model to the visualization display submodule. At the same time, it can receive the operation ticket data parsed by the NLP semantic recognition module and the comparison result data generated by the automated closed-loop comparison module, and synchronously update the corresponding data content to the digital twin model, and present it through the visualization display submodule.

10. The substation secondary pressure plate anti-misoperation auxiliary system according to claim 2, characterized in that: The batch management unit of the data acquisition module is used to classify, store, and quickly retrieve different batches created by the user. At the same time, by allowing the user to edit and adjust the information of the created batches, all relevant files and data under the same operation and maintenance scenario are completely collected into the same batch for unified management.

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