Power distribution network non-power-cut maintenance bypass equipment indirect operation demand analysis and tool performance characteristic analysis method, equipment and medium

Abstract

The invention discloses a power distribution network non-power-cut maintenance bypass equipment indirect operation demand analysis and tool performance characteristic analysis method, equipment and a medium, and belongs to the technical field of power system automation and intelligent operation and maintenance, and the method comprises the steps: collecting and structuring on-site environment and equipment data; based on a domain knowledge graph and a semantic decomposition algorithm, disassembling the job task into a quantitative behavior unit and a job demand parameter; constructing a tool multi-dimensional performance index system, and extracting high-dimensional feature expression by using a neural network algorithm; a weighted matching and dynamic weight mechanism is adopted to carry out adaptive intelligent matching; and outputting a recommendation list and an applicable suggestion according to the matching score, and feeding back a continuous optimization result through man-machine cooperation. According to the method, the dynamic feature model is constructed through multi-source data fusion and semantic analysis, accurate matching of operation requirements and tool performance is realized in combination with an adaptive matching mechanism, an optimized recommendation scheme is finally output, and the intelligent level of maintenance operation is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of power system automation and intelligent operation and maintenance technology, specifically to a method, system, equipment, and medium for analyzing the demand for indirect operations and the performance characteristics of tools for uninterrupted power supply maintenance of bypass equipment in distribution networks. Background Technology

[0002] With the continuous improvement of the intelligence and automation level of power systems, the maintenance and operation of distribution networks face numerous challenges, including diverse equipment types, complex operating scenarios, varying personnel qualifications, and volatile environmental risks. Traditional methods of selecting tools and formulating work plans often rely on experience-based operations and manual judgment, lacking a systematic analysis of work requirements, tool performance, and the on-site environment. Especially under new and complex working conditions or emergencies, manual tool selection suffers from strong subjectivity, low recommendation accuracy, low efficiency, and significant safety hazards, making it difficult to meet the requirements of refined and intelligent operation and maintenance. At the same time, existing tool management information systems mostly focus on asset ledgers and basic information records, lacking cross-scenario and multi-dimensional data fusion and intelligent matching capabilities, and are unable to achieve dynamic adaptation of work requirements and tool performance under large-scale data-driven conditions. Summary of the Invention

[0003] In view of the above-mentioned problems, the present invention provides a method, system, equipment and medium for analyzing the indirect operation requirements and tool performance characteristics of bypass equipment for uninterrupted maintenance of power distribution networks.

[0004] Therefore, the technical problem solved by this invention is that existing methods are difficult to cope with diverse operational needs and complex field environments, fail to deeply model the multi-dimensional performance of tools and equipment such as safety insulation, structural strength and scene adaptability, and cannot accurately quantify the complex nonlinear relationship between operational needs and tool and equipment performance, thus leading to mismatch in selection, low operational efficiency and potential safety risks.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for analyzing the requirements of indirect operations and the performance characteristics of tools for uninterrupted maintenance of bypass equipment in power distribution networks, comprising, By employing multi-sensor fusion and natural language processing technologies, the system automatically collects and structures on-site environmental and equipment data. Based on domain knowledge graphs and semantic decomposition algorithms, the system breaks down work tasks into quantifiable behavioral units and work requirement parameters. It constructs a multi-dimensional performance index system for tools and equipment and uses neural network algorithms to extract high-dimensional feature expressions. It adopts a weighted matching and dynamic weight mechanism to perform adaptive intelligent matching between work requirement parameters and high-dimensional feature expressions of tools and equipment. Based on the matching score, it outputs a recommendation list and applicable suggestions, and continuously optimizes the results through human-machine collaborative feedback.

[0006] As a preferred embodiment of the method for analyzing the indirect operation requirements and tool performance characteristics of bypass equipment during uninterrupted power supply maintenance in a power distribution network as described in this invention, the step of automatically collecting and structuring on-site environment and equipment data through multi-sensor fusion and natural language processing technology includes... By deploying sensors and combining mobile terminal input and speech recognition technology, multi-dimensional data is collected in real time, while natural language processing methods are used to perform semantic understanding and structured annotation on historical documents.

[0007] Multi-dimensional data is synchronously collected according to timestamps and geographical locations, and a dynamic feature label generation algorithm for maintenance scenarios is introduced to automatically identify key scenario features and generate corresponding labels.

[0008] As a preferred embodiment of the method for analyzing the indirect operation requirements and tool performance characteristics of bypass equipment during uninterrupted power supply maintenance in a power distribution network as described in this invention, the step of decomposing the work task into quantifiable behavioral units and work requirement parameters includes: A multi-level knowledge graph is constructed based on knowledge in the field of power distribution network maintenance, forming a semantic network.

[0009] We use a deep semantic understanding model to parse task text and perform entity alignment and relationship matching with a knowledge graph to identify key semantic nodes.

[0010] A multi-level job semantic decomposition algorithm is adopted to recursively decompose maintenance tasks into job behavior units.

[0011] As a preferred embodiment of the method for analyzing the indirect operation requirements and tool performance characteristics of bypass equipment for uninterrupted power supply maintenance in a power distribution network as described in this invention, the step of constructing a multi-dimensional performance index system for tools and extracting high-dimensional feature representations using a neural network algorithm includes, Establish a multi-dimensional performance index system for various tools and equipment used in power distribution network maintenance.

[0012] Feature learning is used for feature extraction and normalization to uncover the deep nonlinear correlations between various tool performance parameters.

[0013] As a preferred embodiment of the method for analyzing the indirect operation requirements and tool performance characteristics of bypass equipment during uninterrupted power supply maintenance in a power distribution network as described in this invention, the method for establishing a multi-dimensional performance index system for various tools used in power distribution network maintenance includes: Each tool is expressed as a feature vector containing multiple specific index values ​​through a multi-dimensional performance index system.

[0014] The deep nonlinear correlation between the performance parameters of the various excavation tools includes: An improved sparse autoencoder neural network is used to extract nonlinear features from the multidimensional performance parameters of tools.

[0015] The encoder maps multidimensional performance parameters to hidden feature representations.

[0016] The beneficial effects of this preferred technical solution are as follows: by establishing a multi-dimensional performance index system, the complex characteristics of tools are transformed into standardized feature vectors, realizing a unified quantitative representation of performance data; furthermore, an improved sparse autoencoder neural network is used to extract nonlinear features from multi-dimensional parameters, effectively mining the deep correlation between performance indicators, and mapping the original parameters into hidden feature expressions with higher information density and stronger discriminative power.

[0017] As a preferred embodiment of the method for analyzing the indirect operation requirements and tool performance characteristics of bypass equipment for uninterrupted power supply maintenance in a distribution network as described in this invention, the adaptive intelligent matching of operation requirement parameters and high-dimensional feature representation of tools includes: The task requirement feature vector is structurally aligned with the high-dimensional performance feature vector of the tools and equipment, and a deep correlation scoring algorithm based on weighted matching is introduced to calculate the weighted correlation score of the task requirement feature vector and the high-dimensional performance feature vector of the tools and equipment in each dimension.

[0018] Through a dynamic weight adjustment mechanism, the matching weights of each item are dynamically optimized in real time based on the operational risk level, environmental parameters, and task priority.

[0019] The beneficial effects of this preferred technical solution are that by structuring the alignment of the task requirement feature vector with the high-dimensional performance feature vector of the tools and equipment, and introducing a deep correlation scoring algorithm based on weighted matching, accurate quantitative matching of requirements and performance in the multi-dimensional feature space is achieved. Furthermore, by combining a dynamic weight adjustment mechanism, the matching weight can be optimized in real time according to the risk level, environmental parameters and task priority in the actual operation, so that the recommendation results can not only meet the dynamic changing needs of the operation scenario, but also effectively improve the overall operation efficiency.

[0020] As a preferred embodiment of the method for analyzing the requirements and tool performance characteristics of indirect operations for uninterrupted power supply maintenance bypass equipment in a power distribution network as described in this invention, the step of outputting a recommended list and applicable suggestions based on matching scores, and continuously optimizing the results through human-machine collaborative feedback, includes: All tools and equipment are sorted based on weighted relevance scores, generating a recommended list and personalized application suggestions including usage order and safety precautions.

[0021] By collecting feedback from operators, a closed-loop learning mechanism is constructed to dynamically optimize the weight parameters.

[0022] The beneficial effects of this preferred technical solution are as follows: by generating a recommendation list containing personalized suggestions through intelligent sorting based on weighted relevance scores, the accuracy of tool selection and the guidance of on-site operations are improved; at the same time, by constructing a closed-loop learning mechanism that integrates human feedback and operational data, the matching weights and model parameters are continuously and dynamically optimized, enabling the invention to have self-evolution capabilities, thereby continuously improving recommendation quality and operational efficiency.

[0023] This invention provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the method for analyzing the indirect operation requirements and tool performance characteristics of bypass equipment for uninterrupted maintenance of power distribution networks.

[0024] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the steps of the method for analyzing the requirements of indirect operation of bypass equipment for uninterrupted maintenance of power distribution networks and analyzing the performance characteristics of tools.

[0025] The beneficial effects of this invention are: Through the collaboration of multiple types of intelligent sensors and mobile terminals, multi-dimensional data collection of on-site environmental parameters, equipment operating status, and historical operation data is achieved. Natural language processing technology is used to perform semantic structured parsing of maintenance process documents and operation logs.

[0026] Based on a dynamic feature label generation algorithm for maintenance scenarios, this system constructs a structured feature vector that flexibly reflects operational needs and environmental changes through adaptive feature weighted aggregation and a maximum mutual information weight optimization mechanism. During the matching analysis, multiple factors, including risk level and environmental adaptability, are comprehensively considered, and the weights of each feature are dynamically adjusted to achieve optimal adaptive matching between demand features and tool performance features. Through weighted correlation scoring and ranking, the system intelligently recommends the optimal tool list and operation configuration suggestions, improving the adaptability of maintenance operations to complex and uncertain environments, as well as the matching of tool selection and the overall intelligence level of the entire operation process. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart illustrating the overall process of a method for analyzing the requirements and performance characteristics of tools for uninterrupted power supply maintenance of bypass equipment in a power distribution network, as provided in one embodiment of the present invention. Detailed Implementation

[0029] To make the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0030] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a method for analyzing the indirect operation requirements and tool performance characteristics of bypass equipment for uninterrupted maintenance in a power distribution network, including: S1. Automatically collect and structure on-site environmental and equipment data through multi-sensor fusion and natural language processing technology.

[0031] S2. Based on domain knowledge graphs and semantic decomposition algorithms, the job tasks are decomposed into quantifiable behavioral units and job requirement parameters.

[0032] S3. Construct a multi-dimensional performance index system for tools and equipment, and use neural network algorithms to extract high-dimensional feature representations.

[0033] S4. Adaptive intelligent matching of job requirement parameters and high-dimensional feature representation of tools is performed by adopting weighted matching and dynamic weighting mechanism.

[0034] S5. Output a recommendation list and applicable suggestions based on the matching score, and continuously optimize the results through human-machine collaborative feedback.

[0035] This invention achieves automated collection and structured processing of maintenance site data through multi-sensor fusion and natural language processing technologies, effectively solving the inefficiency problem caused by traditional reliance on manual experience. It improves the accuracy of requirements analysis by accurately breaking down complex tasks into quantifiable behavioral units using domain knowledge graphs and semantic decomposition algorithms. A multi-dimensional performance characteristic model of tools built based on neural networks deeply explores the nonlinear correlations between performance parameters. Combined with an intelligent matching mechanism that dynamically adjusts weights, it achieves adaptive and precise matching between work requirements and tool performance. Finally, through human-machine collaborative feedback, it continuously optimizes the recommendation results, forming a closed-loop evolutionary system and improving the intelligence level of power distribution network maintenance operations.

[0036] Example 2, an embodiment of the present invention, provides a method for analyzing the indirect operation requirements and tool performance characteristics of bypass equipment for uninterrupted power supply maintenance in a power distribution network, based on the previous embodiment. The method includes: In the embodiments of this application, the multi-sensor fusion and natural language processing technology in S1 is the natural language processing (NLP) method, which specifically includes an end-to-end processing flow based on deep learning: First, an unstructured text such as maintenance procedures, historical work orders, and equipment files is intelligently segmented and word vector mapped through a pre-trained language model; then, a hybrid model combining conditional random fields and bidirectional long short-term memory networks is used to accurately identify named entities such as equipment models, technical parameters, and operation actions in the text; finally, a graph neural network based on an attention mechanism extracts the semantic relationships between entities to form a triplet structured knowledge of "operation object-action requirements-parameter constraints".

[0037] At the data fusion level, the system acquires environmental and equipment status parameters in real time through various intelligent sensing devices deployed at the work site (including high-precision temperature and humidity sensors, 3D laser scanners, ultraviolet corona detectors, and embedded current and voltage acquisition modules). All sensor data, along with inspection records entered via mobile terminals and voice-converted operation logs, are spatiotemporally aligned using a timestamp-based sliding window mechanism and fused using a Kalman filter algorithm to effectively eliminate measurement errors and acquisition delays. Simultaneously, the system establishes a unified spatial coordinate reference system, precisely associating various data types with their corresponding physical locations to ensure accurate mapping of data in three-dimensional space.

[0038] To ensure data quality, the system also incorporates a multi-layered data verification mechanism: threshold detection and outlier filtering are implemented at the acquisition end; data integrity check codes are used at the transmission layer; and data logic verification based on business rules is implemented at the platform layer. The processed, standardized data will be stored according to a three-dimensional "time-space-device" index, forming a structured dataset with complete spatiotemporal labels.

[0039] In one alternative implementation, multi-sensor fusion and natural language processing technology can be achieved by deploying IoT sensor terminals and mobile data acquisition devices. The sensor terminals collect physical parameters such as ambient temperature and humidity, device current and voltage waveforms, and geographic location coordinates in real time, while the mobile devices record on-site operation commands and device status descriptions simultaneously through voice recognition technology. Finally, the multi-source heterogeneous data is uniformly transmitted to the central processing platform for integrated analysis through a data fusion gateway.

[0040] In another alternative implementation, multi-sensor fusion and natural language processing technology can also be based on a distributed acquisition network built on edge computing architecture and 5G communication technology. By deploying intelligent sensor nodes with local computing capabilities at the work site, real-time edge preprocessing of vibration signals and infrared thermal imaging data of key equipment status can be achieved. At the same time, combined with AR glasses worn by on-site workers, visual environment capture and voice interaction can be performed. The processed feature data and NLP parsing results are transmitted to the cloud knowledge base with low latency through the 5G network for deep fusion and modeling analysis.

[0041] Furthermore, in S1, the automatic collection and structuring of on-site environmental and equipment data through multi-sensor fusion and natural language processing technology includes steps A1-A2: A1. By deploying sensors and combining mobile terminal input and speech recognition technology, multi-dimensional data is collected in real time. At the same time, natural language processing methods are used to perform semantic understanding and structured annotation on historical documents.

[0042] A2. Synchronously collect multi-dimensional data according to timestamps and geographical locations, and introduce a dynamic feature label generation algorithm for maintenance scenarios to automatically identify key scenario features and generate corresponding labels.

[0043] Furthermore, in the data extraction stage, a dynamic feature label generation algorithm tailored to maintenance scenarios is employed. The core of this algorithm lies in adaptive feature aggregation, which automatically identifies the most representative scenario features for different work scenarios and generates corresponding feature labels. In specific implementation, it is assumed that the collected data samples are a set... Each sample This is a vector containing multiple types of features. An adaptive feature weight adjustment mechanism is used to assign weights to each feature dimension. Construct a weighted aggregated feature vector The mathematical expression for this process is: in, Represents the total dimension of the features. The weights are the standardized result of the j-th feature. The adaptive adjustment can be achieved through optimization based on the maximum mutual information criterion: in, Representation of features The mutual information between the feature and the target output (such as job classification, scene label, etc.) is used to measure the importance of the feature, where y represents the target variable. This weighting strategy can be updated in real time during model training to adapt to dynamic job scenarios. The final result of this feature aggregation is to automatically transform large-scale, heterogeneous raw data into structured scene feature vectors with semantic labels, laying a solid data foundation for subsequent steps such as requirements analysis and tool adaptation.

[0044] In this embodiment, the domain knowledge graph and semantic decomposition algorithm in S2, namely the multi-level job semantic decomposition algorithm, specifically includes automatically breaking down complex maintenance tasks into a series of quantifiable job behavior units. This algorithm uses job sequences in the knowledge graph as support and, combined with the contextual semantics of the task text, recursively extracts job steps. Assuming that K job behavior units are ultimately decomposed, each behavior unit uses... express.

[0045] in, The action type of this behavior, For the equipment objects involved, These are the corresponding requirement parameters (such as required tools, safety thresholds, etc.). The decomposition process employs dynamic programming for optimal matching, aiming to maximize the overall consistency between the decomposed behavioral units and the original task semantics. The formula is expressed as: in, This represents the embedding of the text segment corresponding to the k-th task in the original task. This represents a description of a behavioral unit generated from a knowledge graph. This is the similarity function.

[0046] In one optional implementation, the multi-level job semantic decomposition algorithm employs a hybrid decomposition strategy based on rule templates and syntactic parsing. Specifically, standardized job procedure templates are pre-set for different major job categories (such as cable installation, switch replacement, etc.), which define the necessary sequence of steps for the job. The algorithm first identifies the core verbs and noun phrases in the task text through dependency parsing and performs preliminary matching with the steps in the template. For specific tasks or complex instructions not covered by the template, a syntactic parser is activated to directly extract behavioral elements from the sentence structure according to the general grammatical rules of "action-goal-condition," thereby jointly completing the hierarchical decomposition from macro-level tasks to micro-level operational instructions.

[0047] In another optional implementation, the multi-level task semantic decomposition algorithm employs a sequence generation method based on semantic role annotation. This method does not rely on predefined rigid templates. The algorithm first performs deep semantic role annotation on the task text, identifying the core semantic roles of each predicate verb, such as agent, patient, time, and location. Subsequently, the annotated semantic structure sequence is mapped to atomic operation nodes stored in the knowledge graph. By calculating the overlap and association strength of semantic roles, a series of logically ordered task behavior units are dynamically assembled and generated, achieving more flexible and context-aware task decomposition.

[0048] Furthermore, in S2, the breakdown of the task into quantifiable behavioral units and task requirement parameters includes steps B1-B3: B1. Construct a multi-level knowledge graph based on knowledge in the field of power distribution network maintenance to form a semantic network.

[0049] B2. Use a deep semantic understanding model to parse the task text and perform entity alignment and relationship matching with the knowledge graph to identify key semantic nodes.

[0050] B3. A multi-level job semantic decomposition algorithm is adopted to recursively decompose maintenance tasks into job behavior units.

[0051] In this embodiment, the deep semantic understanding model in B2 is the BERT pre-trained model. Specifically, based on knowledge from the power distribution network maintenance domain, a multi-level knowledge graph is constructed to form a work-related semantic network. This knowledge graph uses core elements such as work content, expected goals, major risks, and safety measures as nodes, and semantic connections are established between nodes through various relationships (such as "containment," "association," and "constraint"). Knowledge Graph middle.

[0052] in, It is a set of nodes, representing work actions, equipment components, environmental conditions, etc. This is an edge set, representing the semantic relationships between nodes. Each node... Attached attribute vector This includes information such as risk level and action type. For maintenance task text, a deep semantic understanding model, such as a pre-trained model like BERT, is first used to obtain the embedded representation of the task text. Subsequently, the text content is aligned with the knowledge graph for entity alignment and relation matching. Based on the principle of maximum semantic similarity, the set of knowledge nodes most relevant to the semantics of the task text is identified. The mathematical representation of semantic alignment is: in, Functions for calculating semantic similarity (such as cosine similarity). This is the threshold parameter.

[0053] In one alternative implementation, the deep semantic understanding model can be an ERNIE pre-trained model based on an encoder architecture. This model learns the relationships between professional concepts and terms in the power industry knowledge graph during the pre-training phase by introducing a knowledge masking mechanism, enabling it to accurately understand the semantic connotations of professional terms. In practice, after inputting the maintenance task description into the model, a text vector representation containing professional semantics can be obtained. This vector can effectively distinguish the semantic differences of the "disconnect switch" operation in different scenarios of live-line work and power outage maintenance.

[0054] In another alternative implementation, the deep semantic understanding model can also be a T5 pre-trained model based on an encoder-decoder architecture. This model converts the original task description into a standardized sequence of work instructions through text-to-text generation. In specific implementation, the model receives an original description such as "check for abnormal sounds from switchgear 302 on 10kV line," and after semantic understanding, generates a standardized expression: "perform acoustic detection of switchgear - analyze abnormal sound sources - record detection results." This standardized output significantly improves the matching efficiency with preset work nodes in the knowledge graph.

[0055] Through the above process, it is possible to automatically extract, quantify, and structure operational requirements from complex text, and map them to specific, operable operational behavior units and detailed requirement parameters, providing an efficient and accurate input foundation for subsequent intelligent tool adaptation and risk analysis.

[0056] Furthermore, in S3, the construction of a multi-dimensional performance index system for tools and the extraction of high-dimensional feature representations using neural network algorithms include steps C1-C2: C1. Establish a multi-dimensional performance index system for various tools and equipment used in power distribution network maintenance.

[0057] Specifically, a comprehensive, multi-dimensional performance index system will be established for various tools and equipment used in power distribution network maintenance. This system covers key attributes such as safety insulation level (e.g., withstand voltage, leakage current), structural strength (e.g., tensile and drop resistance), and adaptability parameters (e.g., applicable temperature, humidity, and compatibility with different equipment types), and can be extended to additional indicators such as weight, size, ease of operation, and special functions. Each type of tool and equipment will be represented by a feature vector. The form of expression.

[0058] in, Let m be the performance parameters of the i-th type of tool, and m be the total number of performance indicators. This represents the specific value of the tool under the j-th indicator.

[0059] C2. Feature learning is used for feature extraction and normalization to uncover the deep nonlinear correlations between various tool performance parameters.

[0060] In this embodiment, the feature learning in C2, namely the improved Sparse Autoencoder (SAE) neural network algorithm, is used for feature extraction and normalization. Specifically, the performance feature matrices of all tools are first set as... Where n is the number of tools and equipment. The input layer receives the normalized parameter vector, and for each sample... The encoder maps the image to a low-dimensional hidden representation as follows: , in, For encoding the weight matrix, For bias terms, The activation function is (e.g., ReLU or sigmoid). This is achieved through sparse constraint terms. To ensure the sparsity of feature representation, and to incorporate reconstruction error. The overall loss function is: in, For decoding output, and For decoding layer parameters, For decoding activation function, For sparse regularization coefficients, For Kullback-Leibler divergence constraints, For the desired sparsity, is the average activation value of hidden unit j.

[0061] The optimized encoder can effectively map the input multidimensional performance parameters into low-dimensional, information-dense, and highly discriminative feature representation vectors. Finally, the feature representation matrix of all tools and implements. It depicts the distribution of essential attributes of various tools and implements across multiple performance dimensions.

[0062] In one alternative implementation, feature learning can be achieved through probability distribution learning via a variational autoencoder. This method maps tool performance parameters to a probability distribution in the latent space. By introducing random sampling and KL divergence constraints during the encoding process, the learned feature representations exhibit good continuity and smoothness, better reflecting the gradual relationships and potential correlations between different tool performances.

[0063] In another alternative implementation, feature learning can also be representation learning achieved through contrastive learning. This method constructs positive and negative sample pairs of tool performance parameters, trains a neural network encoder to learn feature representations that distinguish between similar and dissimilar tools, and clusters tools with similar performance characteristics in the feature space, thereby enhancing the ability of feature representations to distinguish subtle performance differences between tools.

[0064] Furthermore, the exploration of deep nonlinear relationships between various tool performance parameters in C2 includes steps C21-C22: C21. An improved sparse autoencoder neural network is used to extract nonlinear features from the multidimensional performance parameters of tools.

[0065] C22. The encoder maps multidimensional performance parameters into hidden feature representations.

[0066] Furthermore, the adaptive intelligent matching of job requirement parameters and high-dimensional feature representations of tools in S4 includes steps D1-D2: D1. The task requirement feature vector is structurally aligned with the high-dimensional performance feature vector of the tools and equipment, and a deep correlation scoring algorithm based on weighted matching is introduced to calculate the weighted correlation score of the task requirement feature vector and the high-dimensional performance feature vector of the tools and equipment in each dimension.

[0067] Specifically, the task requirement feature vector obtained through semantic parsing and behavior decomposition will be... High-dimensional performance feature vectors of tools and equipment Structured alignment is performed. To this end, a weighted matching-based deep relevance scoring algorithm is introduced to establish a one-to-one correlation mapping between various attributes of the job requirements and the performance dimensions of the tools. Specifically, the performance feature matrix of all tools to be matched is represented as follows: , where n is the number of tools and equipment, and d is the feature dimension. For each pair of job demand vectors Vector of tool performance Construct a weighted correlation scoring function: in, To determine the similarity between the j-th dimension demand feature and the tool performance feature, indices such as normalized Euclidean distance and cosine similarity can be selected. Let be the matching weight of the j-th feature, quantifying its influence on the overall matching quality. The score set of all tools and equipment. Based on this, the optimal matching or recommended sequence can be obtained.

[0068] D2. Through a dynamic weight adjustment mechanism, the matching weights of each item are dynamically optimized in real time based on the operational risk level, environmental parameters, and task priority.

[0069] Specifically, matching weights are applied based on the risk level of the task, the working environment (such as high temperature, high humidity, high pressure, etc.), and the task priority. Real-time dynamic optimization. Let the risk level be l, and the environmental parameter vector be... Then the weight adjustment function can be defined as in, The base weights are learned from historical data; This is a correction coefficient under the risk level, which increases the weight of relevant dimensions under high-risk conditions; This serves as a dynamic adjustment factor for environmental adaptability, automatically corrected based on current environmental parameters. This multi-factor adaptive weighting strategy ensures that the matching algorithm not only reflects the ontological correlation between demand and performance but also flexibly adapts to the real-time complexity of actual operations. The final optimal matching combination not only guarantees operational safety and scenario adaptability but also improves tool utilization efficiency and operational intelligence.

[0070] Furthermore, S5 outputs a recommendation list and applicable suggestions based on the matching score, and continuously optimizes the results through human-machine collaborative feedback, including steps E1-E2: E1. Sort all tools and equipment according to weighted relevance scores, generate a recommended list and personalized application suggestions including usage order and safety precautions.

[0071] E2. By collecting feedback information from operators, a closed-loop learning mechanism is constructed to dynamically optimize the weight parameters.

[0072] Based on the weighted correlation score between the calculated job requirements and the performance of the tools, all available tools are ranked. The tools with the highest matching score are automatically included in the preferred recommendation list. The system further combines the characteristics of the job requirements to output specific applicable suggestions, including: the order of use of the preferred tools, operating precautions, and personalized configuration instructions based on the current working environment.

[0073] Work plan documents are automatically generated based on the complexity of the requirements and the level of risk, organically integrating the most suitable tools, operation sequences and safety protection measures, making it easy for on-site workers to refer to and implement.

[0074] We collect subjective evaluations, operation logs, and work completion data from operators through multiple channels when using recommended tools and implementing maintenance plans. Operators provide real-time feedback on tool compatibility, ease of operation, and safety, and the system automatically captures actual performance data, including objective data such as work order completion time and frequency of abnormal events.

[0075] Human-machine feedback is labeled and archived in real time, serving as the reward signal input for the reinforcement learning model. The feature matching weights, relevance scoring functions, and autoencoder model parameters are dynamically adjusted based on the feedback information, thereby achieving closed-loop self-evolutionary optimization of the entire system.

[0076] Example 3 is an embodiment of the present invention, which provides a method for analyzing the requirements of indirect operations of bypass equipment for uninterrupted maintenance of power distribution networks and the performance characteristics of tools. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0077] I. Simulation Experiment Objectives The verification results show that the "Intelligent Recommendation and Tool Matching System for Indirect Operations of Bypass Equipment in Distribution Network During Live-Line Maintenance" (hereinafter referred to as the "Intelligent Solution") has significant advantages over the traditional conventional solution (manual experience + manual data review) in terms of work efficiency, safety, tool selection accuracy, and worker satisfaction. II. Experimental Scenario Setup Job task category: Indirect maintenance of bypass equipment in uninterruptible power distribution networks (such as replacement of disconnecting switches, replacement of wiring terminals, etc.).

[0078] Operating environment types: divided into three types: "sunny day at normal temperature", "summer high temperature", and "rainy day with high humidity"; geographical distribution: urban / suburban.

[0079] Historical data sample: 100 typical maintenance operation scenarios.

[0080] Workers are divided into 5 teams of 4 people each, with a balanced skill level.

[0081] III. Key Comparison Indicators 1. Task preparation time (min): The time required from task assignment to tool and equipment readiness.

[0082] 2. Tool matching accuracy (%): The proportion of recommended tools that match the actual needs.

[0083] 3. Operational safety incident rate (%): The percentage of minor errors / near misses caused by tool or matching errors during operation.

[0084] 4. Worker satisfaction (out of 5): assessed through a questionnaire.

[0085] 5. System Response and Recommendation Time (s): The average time from input task to output recommendation.

[0086] IV. Experimental Procedure 1. Task Assignment: 100 maintenance tasks are randomly assigned to two groups (intelligent solution and conventional solution).

[0087] 2. Information Collection and Tool Preparation: Intelligent solution: Full-process automatic data collection, recommendation, feedback and optimization.

[0088] Conventional approach: Manually consult process manuals and rely on rules of thumb for judgment.

[0089] 3. Perform the task and record the data.

[0090] 4. Questionnaire / Record security incidents.

[0091] 5. Data statistics and comparative analysis.

[0092] V. Experimental Data (Examples of Some Typical Tasks)

[0093] Detailed scene breakdown

[0094] VI. Analysis and Explanation 1. Preparation time for assignments is reduced by approximately 60%. Intelligent solutions significantly shorten the time required for equipment and tool selection and process streamlining, thereby improving the efficiency of work teams.

[0095] 2. Extremely high matching accuracy, significantly reducing security risks. By using NLP, knowledge graphs, and deep learning algorithms, tool matching becomes more accurate, reducing safety incidents caused by selection errors.

[0096] 3. Safer work process The incidence of safety incidents has decreased, ensuring the safety of on-site personnel.

[0097] 4. Improved employee satisfaction The intelligent system reduces the psychological pressure on the work team, allowing them to focus more on the operation itself.

[0098] 5. The system has a fast response time, making it suitable for emergency response and large-scale deployment. Recommendations take only a few seconds and are suitable for a variety of complex scenarios.

[0099] This embodiment also provides an electronic device applicable to a method for analyzing the indirect operation requirements and tool performance characteristics of bypass equipment for uninterrupted power supply maintenance in a power distribution network. The device includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement the method for analyzing the indirect operation requirements and tool performance characteristics of bypass equipment for uninterrupted power supply maintenance in a power distribution network as proposed in the above embodiment.

[0100] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements a method for analyzing the requirements of indirect operations and the performance characteristics of tools for uninterrupted power supply maintenance of bypass equipment in a power distribution network, as proposed in the above embodiments.

[0101] The storage medium proposed in this embodiment and the method for analyzing the requirements and performance characteristics of tools for uninterrupted power supply maintenance bypass equipment in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0102] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0103] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for analyzing the requirements and tool performance characteristics of indirect operations for uninterrupted power supply maintenance of bypass equipment in power distribution networks, characterized in that: The application relates to a method for recommending tools and instruments for power distribution network maintenance. The method comprises the following steps: Automatic collection and structuring of field environment and equipment data through multi-sensor fusion and natural language processing technology; Decomposition of the job task into quantified behavior units and job requirement parameters based on a domain knowledge graph and a semantic decomposition algorithm; Construction of a multi-dimensional performance index system of tools and instruments and extraction of high-dimensional feature expressions by using a neural network algorithm; Adaptive intelligent matching of job requirement parameters and high-dimensional feature expressions of tools and instruments by using a weighted matching and dynamic weight mechanism; 2. The method of claim 1, wherein the method is characterized in that: Output of a recommended list and application suggestions according to the matching score and continuous optimization of the results through man-machine collaborative feedback. The automatic collection and structuring of field environment and equipment data through multi-sensor fusion and natural language processing technology comprises the following steps: Real-time collection of multi-element data by deploying sensors and combining mobile terminal input and voice recognition technology, and semantic understanding and structured labeling of historical documents by using a natural language processing method; 3. The method of claim 2, wherein the method further comprises: determining the indirect work requirement of the power distribution network maintenance bypass device; and analyzing the performance characteristics of the tool based on the indirect work requirement of the power distribution network maintenance bypass device. Synchronous collection of multi-element data according to time stamps and geographical positions, and introduction of a dynamic feature label generation algorithm for maintenance scenes to automatically identify key scene features and generate corresponding labels. The decomposition of the job task into quantified behavior units and job requirement parameters comprises the following steps: Construction of a multi-level knowledge graph based on the knowledge of power distribution network maintenance to form a semantic network; Analysis of task texts by using a deep semantic understanding model and entity alignment and relationship matching with the knowledge graph to identify key semantic nodes; 4. The method of claim 3, wherein the method further comprises: determining the indirect work requirement of the power distribution network maintenance bypass device; and analyzing the performance characteristics of the tool based on the indirect work requirement of the power distribution network maintenance bypass device. Recursive decomposition of the maintenance task into job behavior units by using a multi-level job semantic decomposition algorithm. The construction of a multi-dimensional performance index system of tools and instruments and the extraction of high-dimensional feature expressions by using a neural network algorithm comprise the following steps: Establishment of a multi-dimensional performance index system for various tools and instruments used in power distribution network maintenance; 5. The method for analyzing the requirements and tool performance characteristics of indirect operations for uninterrupted power supply maintenance bypass equipment in a power distribution network as described in claim 4, characterized in that: Feature learning is used for feature extraction and normalization to mine deep nonlinear correlations between performance parameters of various tools and instruments. The establishment of a multi-dimensional performance index system for various tools and instruments used in power distribution network maintenance comprises the following steps: Each tool and instrument is expressed as a feature vector containing specific index values through the multi-dimensional performance index system; The mining of deep nonlinear correlations between performance parameters of various tools and instruments comprises the following steps: Nonlinear feature extraction of multi-dimensional performance parameters of tools and instruments by using an improved sparse auto-encoder neural network; 6. The method for analyzing the indirect operation requirement of the power distribution network non-stop maintenance bypass device and the performance characteristics of the tool according to claim 4, characterized in that: Mapping of the multi-dimensional performance parameters into hidden feature expressions by using an encoder. The adaptive intelligent matching of job requirement parameters and high-dimensional feature expressions of tools and instruments comprises the following steps: Structured alignment of a job requirement feature vector and high-dimensional performance feature vectors of tools and instruments, and introduction of a deep correlation scoring algorithm based on weighted matching to calculate the weighted correlation scores of the job requirement feature vector and the high-dimensional performance feature vectors of the tools and instruments in each dimension; 7. The method of claim 4, wherein the method further comprises: determining the indirect work requirement of the power distribution network maintenance bypass device; and analyzing the performance characteristics of the tool based on the indirect work requirement of the power distribution network maintenance bypass device. Real-time dynamic optimization of each matching weight according to the job risk level, environmental parameters and task priority through a dynamic weight adjustment mechanism. The output of a recommended list and application suggestions according to the matching score and the continuous optimization of the results through man-machine collaborative feedback comprises the following steps: Ranking of all tools and instruments according to the weighted correlation scores to generate a recommended list and personalized application suggestions containing use sequences and safety precautions. By collecting the feedback information of the operation personnel, a closed-loop learning mechanism is constructed to dynamically optimize the weight parameters.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor implements the steps of the power distribution network non-stop maintenance bypass device indirect operation demand analysis and tool performance characteristic analysis method of any one of claims 1-7 when executing the computer program.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the power distribution network non-stop maintenance bypass device indirect operation demand analysis and tool performance characteristic analysis method of any one of claims 1-7.

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