Intelligent Work Ticket Generation System Based on Distribution Construction Specifications

Abstract

The intelligent work ticket generation system based on distribution construction specifications belongs to the technical field of power work tickets and aims to solve the problem of poor applicability of work tickets generated by existing work ticket generation systems. The present invention includes a task information collection module, a data storage and management module, a task feature analysis module, a work ticket template matching module, and a work ticket generation and optimization module. The task feature analysis module deeply excavates key task features to determine their positions and path lengths in the classification hierarchy. The work ticket template matching module first calculates the classification hierarchy similarity ratio and combines it with the feature weights to obtain a weighted similarity score, then uses a graph convolutional network to learn the knowledge graph to obtain a low-dimensional feature space similarity score, and finally combines the two and determines the final similarity score through a balance coefficient, so as to accurately screen out the template with the highest matching degree, significantly improving the accuracy of work ticket generation and effectively solving the problem of poor applicability of existing work ticket generation systems.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power work tickets, and particularly to an intelligent work ticket generation system based on distribution construction specifications. Background Art

[0002] In the field of electric power distribution construction, work tickets, as key documents to ensure the safety and standardization of construction, the accuracy and efficiency of their generation have always been the focus of the industry. The traditional way of generating work tickets mainly relies on manual experience and has many limitations. In terms of specific task types, whether it is equipment installation, fault diagnosis or maintenance, each has a set of specific operation specifications and safety procedures. For example, when installing equipment, it is necessary to focus on the positioning of the equipment, the fixing of the foundation and the correctness of wiring; for fault diagnosis, professional detection tools and technical means need to be used, and the fault causes are gradually investigated according to a certain diagnosis process; maintenance focuses on regular inspections, cleaning, lubrication of equipment and the replacement of vulnerable parts, etc. The traditional manual way of generating work tickets is difficult to comprehensively and accurately consider these complex factors.

[0003] With the rapid development of information technology, some intelligent work ticket generation systems have emerged. Most of the existing systems have the following problems: some systems simply retrieve work ticket templates based on keyword matching or single-dimensional feature comparison, and cannot deeply understand the complex relationships between task features, resulting in low accuracy of template matching; although some systems consider multiple factors, the allocation of the importance of each factor lacks a scientific basis, making it impossible to effectively distinguish the influence degree of different factors on the generation of work tickets in practical applications. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent work ticket generation system based on distribution construction specifications, which solves the problem of poor applicability of work tickets generated by existing work ticket generation systems in the background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions: an intelligent work ticket generation system based on distribution construction specifications, including: a task information collection module, a data storage and management module, a task feature analysis module, a work ticket template matching module, and a work ticket generation and optimization module;

[0006] The task information collection module: is responsible for comprehensively collecting various detailed information of new tasks, including regional information, environmental information, types of electric power projects, equipment models and specific tasks of the tasks; regional information includes geographical location and regional type; the environment includes indoor / outdoor, temperature and humidity, mountainous areas, information on being close to rivers or the coast; types of electric power projects cover project classifications of different voltage levels and equipment; detailed equipment models and specific task types, including equipment installation, repair or maintenance;

[0007] The data storage and management module: used to build and maintain the system data architecture; classify and store and manage various types of data by building multiple databases, including task information tables, area information tables, environmental information tables, power project type tables, equipment model tables, and work ticket template tables; also used for real-time updating and maintenance of data. When new task information is entered, task completion status is updated, area information changes, equipment models are updated, or new work ticket templates are generated, data update operations are performed on the corresponding database tables in a timely manner;

[0008] The task feature analysis module: used to deeply mine and analyze the collected task information, extract key task features and determine their positions in the classification hierarchy, and extract the key features of area information from the original task data; The key features include: categories and key parameters in environmental information; the exact hierarchy of power project types in the power project classification system; key technical parameters and category information of detailed equipment models; the positioning of specific task types in the task classification system, forming a comprehensive and representative task feature vector, and further determining the path length of each task feature in its respective classification hierarchy;

[0009] The work ticket template matching module: based on the task feature vector provided by the task feature analysis module, performs efficient retrieval and matching in the work ticket template database, and uses an advanced algorithm that combines classification hierarchy quantization and weighted similarity and incorporates a graph convolutional network, comprehensively considering the relationship between task features and work ticket template features; Its detailed matching steps are: first calculate the classification hierarchy similarity ratio of each feature, and then combine the feature weights to obtain the weighted similarity score based on the classification hierarchy; at the same time, use the graph convolutional network to perform feature learning on the power domain knowledge graph to obtain the similarity score between the work ticket template and the new task in the low-dimensional feature space, and finally combine the two into the final similarity score through a balance coefficient; sort the work ticket templates according to this score, and screen out the template with the highest matching degree with the new task;

[0010] The work ticket generation and optimization module: generates a personalized work ticket based on the template selected by the work ticket template matching module, and accurately fills the specific information of the new task into the corresponding columns of the work ticket template; after generating the work ticket, perform a comprehensive logical check to check the integrity of the information and ensure that all required fields have been accurately filled.

[0011] Furthermore, the task information collection module provides a user interface, and the staff inputs the detailed information of the new task, including area information, environmental information, power project type, equipment model, and specific task.

[0012] Further, the data storage and management module includes a database construction unit and a data update and maintenance unit. The database construction unit constructs multiple related database tables, including a task information table for storing the basic information and detailed feature information of tasks; a regional information table for storing the geographical features, climate conditions, and power infrastructure information of different regions; an environmental information table containing various environmental types and related parameters, specifically including the temperature, humidity ranges, and special environmental factors of indoor and outdoor environments; a power project type table listing common power project types and their detailed descriptions; an equipment model table recording the models, specifications, and technical parameters of various power equipment; a work ticket template table storing the content of work ticket templates corresponding to different types of tasks and associated with other tables through specific fields to ensure data integrity and consistency;

[0013] The data update and maintenance unit is responsible for real-time updating of the information in the database. When new task information is entered or a task is completed, it timely updates the relevant data in the task information table, including the actual start time, actual end time, and task execution status. At the same time, if there are changes in regional information, updates to equipment models, or generation of new work ticket templates, it can also correspondingly update the corresponding database tables.

[0014] Further, the task feature analysis module includes a feature extraction unit and a classification hierarchy determination unit. The feature extraction unit extracts key features from the original task data obtained from the task information collection module. For regional information, it converts it into quantifiable or classifiable features, including regional type and regional power load level; for environmental information, it extracts the environmental category and environmental parameters, where the environmental category includes indoor, outdoor, and special environments, and the environmental parameters include temperature, humidity, and altitude; for the power project type, it determines its specific level in the power project classification system, "equipment maintenance - switchgear maintenance - 10kV switchgear maintenance"; for the detailed equipment model, it extracts the key parameters and model category information of the equipment; for the specific task type, it also clarifies its position in the task classification system, "maintenance task - fault repair", to form a task feature vector;

[0015] The classification hierarchy determination unit determines the position and path length of each task feature in its respective classification hierarchy according to the pre-constructed classification hierarchy system; for the power project type, in the power project classification hierarchy, starting from the top-level "Power Project", passing through "Equipment Maintenance", "Switchgear Maintenance" to "10kV Switchgear Maintenance", its path length is determined to be 3; for the regional information from provincial regions, municipal regions, county regions, township regions to the specific location type, its path length is 5; for the environmental information, starting from the top-level environmental main category, passing through indoor / outdoor distinction, spatial characteristics, environmental condition details to the special environmental identifier, its path length is 5; for the equipment model, starting from the top-level "Power Equipment Category", passing through power distribution equipment, switchgear category, switchgear series to the specific switchgear model, its path length is 5; for the specific task type "Switchgear", starting from the top-level task domain, passing through power operation and maintenance, equipment-level tasks, equipment component tasks to the specific operation task, its path length is 5.

[0016] Further, the work ticket template matching module includes a template retrieval unit and a template adjustment unit. The template retrieval unit performs retrieval and matching in the work ticket template database according to the task feature vector obtained by the task feature analysis module, using an algorithm that combines classification hierarchy quantization and weighted similarity and incorporates a graph convolutional network. The specific steps are as follows:

[0017] S1. First, calculate the classification hierarchy similarity ratio of each feature , where is the path length from the common ancestor node of the classification hierarchy of the new task feature j to the new task node, is the path length from the common ancestor node to the feature j node of the work ticket template i;

[0018] S2. Let the feature vector of the new task be , and the corresponding weight vector be . The weight vector of the new task is evaluated by senior engineers, project managers, and safety experts according to their many years of practical experience in power projects for the importance of each feature in the work ticket template matching process; the feature vector of the work ticket template is , and calculate the weighted similarity score based on the classification hierarchy ;

[0019] S3. Use the graph convolutional network technology to perform feature learning and representation on the constructed power domain knowledge graph, map both the work ticket template and the new task to the same low-dimensional feature space, and the knowledge graph is represented as , where V is the node set containing work ticket template nodes, new task nodes, and various power domain related entity nodes, and E is the edge set representing the relationships between nodes; for each node , whose initial feature vector is ; After L layers of graph convolution operations, the feature vector update formula for node is , where represents the feature vector of node v at the l-th layer, is the set of neighboring nodes of node v, is the normalization constant, and are the learnable weight matrix and bias vector at the l-th layer respectively, is the ReLU activation function;

[0020] S3. After L layers of graph convolution, the final feature vector of the work ticket template node i and the final feature vector of the new task node are obtained. Then the cosine similarity calculation formula between them is . Let the similarity score based on the graph convolutional network be ;

[0021] S4. Comprehensively calculate the similarity score between the work ticket template i and the new task, where is the balance coefficient, which is adjusted according to the actual situation to balance the weights of the hierarchical weighted similarity and the graph convolutional network similarity in the final score. Sort the work ticket templates according to the similarity score , and select the template with a higher matching degree as the basic template.

[0022] Further, the template adjustment unit is used to adjust the template according to the task characteristics when there are partial differences between the matched work ticket template and the new task. If the environmental information of the new task has special requirements (such as high altitude areas) that are not considered in the matched template, the special safety measures and construction precautions for power distribution construction in high altitude areas can be extracted from the environmental information database and supplemented to the work ticket template to ensure that the work ticket template can fully meet the requirements of the new task.

[0023] Furthermore, the work ticket generation and optimization module includes an information filling unit and a work ticket verification and optimization unit. The information filling unit is used to fill the specific information of the new task into the selected or adjusted work ticket template, including basic information such as task name, work location, work responsible person, and estimated working time. According to the detailed equipment model and specific task type of the task, relevant safety measures, construction steps, and tool and material list information are extracted from the equipment knowledge base and the task operation specification library and filled into the corresponding columns of the work ticket template. The work ticket verification and optimization unit conducts logical verification on the generated work ticket, checks the information integrity, including that all required items have been filled; the rationality of safety measures, including whether the safety measures match the geographical environment and equipment conditions of the task; the coherence of construction steps, including whether the construction steps conform to the normal construction sequence; if problems are found in the verification, optimization is carried out according to the preset rules; if safety measures are missing, refer to the safety measures of similar tasks or the distribution construction specifications for supplementation; if the construction steps are unreasonable, adjust the construction step sequence or refine the content of the construction steps.

[0024] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0025] The intelligent work ticket generation system based on distribution construction specifications provided by the present invention comprehensively collects various detailed information of new tasks through the task information collection module. The data storage and management module constructs multiple database tables and updates and maintains them in real time. The task feature analysis module deeply excavates key task features, determines its position and path length in the classification hierarchy. The work ticket template matching module first calculates the classification hierarchy similarity ratio and combines the feature weights to obtain the weighted similarity score, then uses the graph convolutional network to learn the knowledge graph to obtain the low-dimensional feature space similarity score, and finally combines the two and determines the final similarity score through the balance coefficient, so as to accurately screen out the template with the highest matching degree, overcoming the problem of inaccurate template matching in the existing system, significantly improving the accuracy of work ticket generation, the adaptability of the system, and work efficiency, effectively ensuring the safe and efficient progress of distribution construction, effectively solving the problem of poor applicability of the existing work ticket generation system, and promoting the intelligent and precise development of electric power construction management. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the module structure of the present invention;

[0027] Figure 2 It is a schematic diagram of the overall process of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] To solve the technical problem of the poor applicability of the work tickets generated by the existing work ticket generation system, as Figure 1 - Figure 2 shown, the following preferred technical solutions are provided:

[0030] A work ticket intelligent generation system based on distribution construction specifications includes a task information collection module, a data storage and management module, a task feature analysis module, a work ticket template matching module, and a work ticket generation and optimization module.

[0031] The task information collection module: is responsible for comprehensively collecting various detailed information of new tasks, including task area information, environmental information, power project types, equipment models, and specific tasks; the area information includes geographical location and area type; the environment includes indoor / outdoor, temperature / humidity, mountainous areas, near rivers or coastal information; power project types cover project classifications of different voltage levels and equipment; detailed equipment models and specific task types, including equipment installation, repair, or maintenance; by accurately collecting this information, it provides a complete data basis for the intelligent generation of subsequent work tickets, ensuring that the system can perform accurate processing according to the actual situation of the task;

[0032] The data storage and management module: is used to construct and maintain the system data architecture; by constructing multiple databases, including task information tables, area information tables, environmental information tables, power project type tables, equipment model tables, and work ticket template tables, it classifies and stores and manages various types of data; it is also used for the real-time update and maintenance of data. When new task information is entered, the task completion situation is updated, the area information changes, the equipment model is updated, or a new work ticket template is generated, it timely performs data update operations on the corresponding database tables;

[0033] The task feature analysis module: It is used to deeply mine and analyze the collected task information, extract key task features and determine their positions in the classification hierarchy, and refine the key features of regional information from the original task data; the key features include: the categories and key parameters in the environmental information; the exact hierarchy of the power project type in the power project classification system; the key technical parameters and category information of the detailed equipment model; the positioning of the specific task type in the task classification system, forming a comprehensive and representative task feature vector, and further determining the path length of each task feature in its respective classification hierarchy, providing a key basis for the similarity calculation in the subsequent work ticket template matching process, enabling the system to accurately measure the matching degree between the task and the template;

[0034] The work ticket template matching module: Based on the task feature vector provided by the task feature analysis module, it performs efficient retrieval and matching in the work ticket template database, using an advanced algorithm that combines classification hierarchy quantization and weighted similarity and incorporates a graph convolutional network, comprehensively considering the relationship between task features and work ticket template features; its detailed matching steps are: first calculate the classification hierarchy similarity ratio of each feature, and then combine the feature weights to obtain the weighted similarity score based on the classification hierarchy; at the same time, use the graph convolutional network to perform feature learning on the power domain knowledge graph to obtain the similarity score between the work ticket template and the new task in the low-dimensional feature space, and finally synthesize the two into the final similarity score through a balance coefficient; sort the work ticket templates according to this score, screen out the template with the highest matching degree with the new task, provide an accurate template basis for the subsequent generation of work tickets, and ensure that the content of the work ticket highly conforms to the actual requirements of the task;

[0035] The work ticket generation and optimization module: Generate personalized work tickets according to the template selected by the work ticket template matching module, and accurately fill the specific information of the new task into the corresponding columns of the work ticket template; after generating the work ticket, conduct a comprehensive logical check to check the integrity of the information and ensure that all required items have been accurately filled.

[0036] The task information collection module provides a user interface, and the staff inputs the detailed information of the new task, including regional information, environmental information, power project type, equipment model, and specific task.

[0037] The data storage and management module includes a database construction unit and a data update and maintenance unit. The database construction unit constructs multiple related database tables, including a task information table for storing the basic information and detailed feature information of tasks; a regional information table for storing the geographical features, climate conditions, and power infrastructure information of different regions; an environmental information table containing various environmental types and related parameters, specifically including the temperature and humidity ranges and special environmental factors of indoor and outdoor environments; a power project type table listing common power project types and their detailed descriptions; an equipment model table recording the models, specifications, and technical parameters of various power equipment; and a work ticket template table storing the content of work ticket templates corresponding to different types of tasks and associated with other tables through specific fields to ensure data integrity and consistency.

[0038] The data update and maintenance unit is responsible for real-time updating of the information in the database. When new task information is entered or a task is completed, it promptly updates the relevant data in the task information table, including the actual start time, actual end time, and task execution status. At the same time, if there are changes in regional information, updates to equipment models, or generation of new work ticket templates, it can also correspondingly update the corresponding database tables to ensure the timeliness and accuracy of the data.

[0039] The task feature analysis module includes a feature extraction unit and a classification hierarchy determination unit. The feature extraction unit extracts key features from the original task data obtained from the task information collection module. For regional information, it converts it into quantifiable or classifiable features, including regional type and regional power load level; for environmental information, it extracts the environmental category and environmental parameters. The environmental category includes indoor, outdoor, and special environments, and the environmental parameters include temperature, humidity, and altitude; for the power project type, it determines its specific level in the power project classification system, "equipment maintenance - switchgear maintenance - 10kV switchgear maintenance"; for the detailed equipment model, it extracts the key parameters and model category information of the equipment; for the specific task type, it also clarifies its position in the task classification system, "maintenance task - fault repair", to form a task feature vector.

[0040] The classification hierarchy determination unit determines the position and path length of each task feature in its respective classification hierarchy according to the pre-constructed classification hierarchy system; for the power project type, in the power project classification hierarchy, starting from the top-level "Power Project", passing through "Equipment Maintenance", "Switchgear Maintenance" to "10kV Switchgear Maintenance", the determined path length is 3; for the regional information from provincial regions, municipal regions, county regions, township regions to the specific location type, its path length is 5; for the environmental information, starting from the top-level environmental main category, passing through indoor / outdoor distinction, spatial characteristics, environmental condition details to the special environmental identifier, its path length is 5; for the equipment model, starting from the top-level "Power Equipment Category", passing through distribution equipment, switchgear category, switchgear series to the specific switchgear model, its path length is 5; for the specific task type "Switchgear", starting from the top-level task domain, passing through power operation and maintenance, equipment-level tasks, equipment component tasks to the specific operation task, its path length is 5;

[0041] The work ticket template matching module includes a template retrieval unit and a template adjustment unit. The template retrieval unit performs retrieval and matching in the work ticket template database according to the task feature vector obtained by the task feature analysis module, using an algorithm that combines classification hierarchy quantization and weighted similarity and incorporates graph convolutional networks. The specific steps are as follows:

[0042] S1. First, calculate the classification hierarchy similarity ratio of each feature , where is the path length from the common ancestor node of the new task feature j to the new task node, is the path length from the common ancestor node to the feature j node of the work ticket template i;

[0043] S2. Let the feature vector of the new task be , and the corresponding weight vector be . The weight vector of the new task is evaluated by senior engineers, project managers, and safety experts according to their years of practical experience in power projects for the importance of each feature in the work ticket template matching process; the feature vector of the work ticket template is , and calculate the weighted similarity score based on the classification hierarchy ;

[0044] S3. Use graph convolutional network technology to perform feature learning and representation on the constructed power domain knowledge graph, map both the work ticket template and the new task to the same low-dimensional feature space, and the knowledge graph is represented as , where V is the set of nodes including work ticket template nodes, new task nodes, and various power domain related entity nodes, and E is the set of edges representing the relationships between nodes; for each node , whose initial feature vector is ; After L layers of graph convolution operations, the feature vector update formula for node is , where represents the feature vector of node v at the l-th layer, is the set of neighbor nodes of node v, is the normalization constant, and are the learnable weight matrix and bias vector at the l-th layer respectively, is the ReLU activation function;

[0045] S3. After L layers of graph convolution, the final feature vector of the work ticket template node i and the final feature vector of the new task node are obtained. Then the cosine similarity calculation formula between them is . Let the similarity score based on the graph convolutional network be ;

[0046] S4. Comprehensively calculate the similarity score between the work ticket template i and the new task, where is the balance coefficient, which is adjusted according to the actual situation to balance the weights of the hierarchical weighted similarity and the graph convolutional network similarity in the final score. Sort the work ticket templates according to the similarity score and select the template with a higher matching degree as the basic template.

[0047] The template adjustment unit is used to adjust the template according to the task characteristics when there are partial differences between the matched work ticket template and the new task. If the environmental information of the new task has special requirements (such as high altitude areas) that are not considered in the matched template, the special safety measures and construction precautions for power distribution construction in high altitude areas can be extracted from the environmental information database and supplemented into the work ticket template to ensure that the work ticket template can fully meet the requirements of the new task.

[0048] The work ticket generation and optimization module includes an information filling unit and a work ticket verification and optimization unit. The information filling unit is used to fill the specific information of a new task into a selected or adjusted work ticket template, including basic information such as task name, work location, work responsible person, and estimated working time. According to the detailed equipment model and specific task type of the task, relevant safety measures, construction steps, and tool and material list information are extracted from the equipment knowledge base and the task operation specification library and filled into the corresponding columns of the work ticket template. For example, for a 10kV switchgear maintenance task, standard safety measures for the maintenance of this type of switchgear, such as power outage, voltage testing, grounding operation steps, measures to prevent misoperation, etc., and common construction steps such as drawer unit pulling out for inspection, contact maintenance, secondary circuit testing, etc., are obtained from the knowledge base and filled into the work ticket.

[0049] The work ticket verification and optimization unit conducts logical verification on the generated work ticket, checks the information integrity, including that all required items have been filled; the rationality of safety measures, including whether the safety measures match the geographical environment and equipment conditions of the task; the coherence of construction steps, including whether the construction steps conform to the normal construction sequence; if problems are found during the verification, optimization is carried out according to the preset rules; if safety measures are missing, refer to the safety measures of similar tasks or the distribution construction specifications for supplementation; if the construction steps are unreasonable, adjust the construction step sequence or refine the content of the construction steps; so that the work ticket reaches the best state in terms of content accuracy, safety, and operability, meets the actual execution requirements of the distribution construction task, and ensures the smooth progress and safe implementation of the construction process.

[0050] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0051] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent replacements or changes, and should be covered by the protection scope of the present invention.

Claims

1. The intelligent work ticket generation system based on power distribution construction specifications is characterized by: include: Task information collection module, data storage and management module, task feature analysis module, work ticket template matching module and work ticket generation and optimization module; The task information collection module is responsible for comprehensively collecting various detailed information of new tasks, including regional information, environmental information, power project type, equipment model and specific tasks of the task; regional information includes geographical location and regional type; environment includes indoor and outdoor, temperature and humidity, mountainous area, near rivers or coastal information; power project type, covering project classification of different voltage levels and equipment; detailed equipment model and specific task type, including equipment installation, repair or maintenance; The data storage and management module is used to build and maintain the system data architecture; by building multiple databases, including task information tables, regional information tables, environmental information tables, power project type tables, equipment model tables, and work ticket template tables, various types of data are classified, stored, and managed; it is also used for real-time data update and maintenance. When new task information is entered, task completion status is updated, regional information changes, equipment model updates, or new work ticket templates are generated, the corresponding database tables are updated in a timely manner; The task feature analysis module is used to perform in-depth mining and analysis on the collected task information, extract key task features and determine their positions in the classification hierarchy system, and extract key features of regional information from the original task data; Key features include: categories and key parameters in environmental information; precise levels of power project types in the power project classification system; key technical parameters and category information of detailed equipment models; positioning of specific task types in the task classification system, forming a comprehensive and representative task feature vector, and further determining the path length of each task feature in its respective classification level; The work ticket template matching module: based on the task feature vector provided by the task feature analysis module, performs efficient retrieval and matching in the work ticket template database, uses an advanced algorithm based on classification level quantification and weighted similarity, and integrates graph convolutional networks to comprehensively consider the relationship between task features and work ticket template features; the detailed matching steps are: first calculate the classification level similarity ratio of each feature, and then combine the feature weights to obtain a weighted similarity score based on the classification level; at the same time, use the graph convolutional network to perform feature learning on the knowledge graph in the power field, obtain the similarity score between the work ticket template and the new task in the low-dimensional feature space, and finally combine the two into the final similarity score through the balance coefficient; sort the work ticket templates according to this score, and screen out the template with the highest matching degree with the new task; The work ticket generation and optimization module generates personalized work tickets based on the template selected by the work ticket template matching module, and accurately fills the specific information of the new task into the corresponding column of the work ticket template; after the work ticket is generated, it is subjected to a comprehensive logical check to check the integrity of the information and ensure that all required items have been accurately filled in.

2. The intelligent work ticket generation system based on power distribution construction specifications as claimed in claim 1 is characterized by: The task information collection module provides a user interface through which staff can input detailed information of new tasks, including regional information, environmental information, power project type, equipment model and specific tasks.

3. The intelligent work ticket generation system based on power distribution construction specifications as claimed in claim 1 is characterized by: The data storage and management module includes a database construction unit and a data update and maintenance unit. The database construction unit constructs multiple related database tables, including a task information table for storing basic information and detailed feature information of the task; a regional information table for storing geographical features, climate conditions and power infrastructure information of different regions; an environmental information table containing various environmental types and related parameters, including temperature, humidity range and special environmental factors of indoor and outdoor environments; a power project type table listing common power project types and their detailed descriptions; an equipment model table recording the models, specifications and technical parameters of various power equipment; a work ticket template table storing the work ticket template content corresponding to different types of tasks, and being associated with other tables through specific fields to ensure data integrity and consistency.

4. The intelligent work ticket generation system based on power distribution construction specifications as claimed in claim 3 is characterized by: The data update and maintenance unit is responsible for updating the information in the database in real time. When new task information is entered or the task is completed, the relevant data in the task information table is updated in a timely manner, including the actual start time, actual end time and task execution status. At the same time, if there is a change in regional information, an update of the equipment model or a new work ticket template is generated, the corresponding database table can also be updated accordingly.

5. The intelligent work ticket generation system based on power distribution construction specifications as claimed in claim 1 is characterized by: The task feature analysis module includes a feature extraction unit and a classification level determination unit. The feature extraction unit extracts key features from the original task data obtained by the task information acquisition module. For regional information, it converts it into quantifiable or classifiable features, including regional type and regional power load level; for environmental information, it extracts environmental categories and environmental parameters, and environmental categories include indoor, outdoor, and special environments. Environmental parameters include temperature, humidity, and altitude; for power project types, it determines its specific level in the power project classification system, "equipment maintenance-switch cabinet maintenance-10kV switch cabinet maintenance"; for detailed equipment models, it extracts key parameters and model category information of the equipment; for specific task types, it also clarifies its position in the task classification system, "maintenance task-fault maintenance", to form a task feature vector.

6. The intelligent work ticket generation system based on power distribution construction specifications as claimed in claim 5 is characterized by: The classification level determination unit determines the position and path length of each task feature in the respective classification levels according to the pre-constructed classification level system; for the power project type, in the power project classification level, starting from the top-level "power project", through "equipment maintenance" "switch cabinet maintenance" to "10kV switch cabinet maintenance", the path length is determined to be 3; for the regional information, from the provincial level, municipal level, county level, township level, and finally to the specific location type, the path length is 5; for the environmental information, starting from the top-level main environmental category, through the indoor and outdoor distinction, spatial characteristics, environmental condition details, and finally to the special environmental identification, the path length is 5; for the equipment model, starting from the top-level "power equipment category", through the distribution equipment, switch cabinet category, switch cabinet series, and finally to the specific switch cabinet model, the path length is 5; for the specific task type "switch cabinet", starting from the top-level task field, through the power operation and maintenance inspection, equipment level tasks, equipment component tasks, and finally to the specific operation task, the path length is 5.

7. The intelligent work ticket generation system based on power distribution construction specifications as claimed in claim 6 is characterized by: The work ticket template matching module includes a template retrieval unit and a template adjustment unit. The template retrieval unit performs retrieval and matching in the work ticket template database according to the task feature vector obtained by the task feature analysis module, and adopts an algorithm based on the combination of classification level quantization and weighted similarity and integrated into the graph convolution network. The specific steps are as follows: S1. First calculate the classification level similarity ratio of each feature ,in is the path length from the common ancestor node of the classification hierarchy of the new task feature j to the new task node, is the path length from the common ancestor node to the feature j node of work ticket template i; S2. Let the feature vector of the new task be , the corresponding weight vector is ,The weight vector of the new task is evaluated by senior engineers, project managers and safety experts based on their many years of practical experience in power projects. The importance of each feature in the work ticket template matching process is evaluated; the feature vector of the work ticket template is , calculate the weighted similarity score based on the classification hierarchy ; S3. Use graph convolutional network technology to learn and represent the features of the constructed power field knowledge graph, map the work ticket template and the new task into the same low-dimensional feature space, and the knowledge graph is represented as , where V is a node set including work ticket template nodes, new task nodes, and various power-related entity nodes, and E is an edge set, which represents the relationship between nodes; for each node , whose initial eigenvector is ; After L layers of graph convolution operations, nodes The eigenvector update formula is ,in represents the feature vector of node v at layer l, is the set of neighboring nodes of node v, is the normalization constant, and are the learnable weight matrix and bias vector of the lth layer, is the ReLU activation function; S3. After L layers of graph convolution, the final feature vector of work ticket template node i is obtained and the final feature vector of the new task node , then the cosine similarity calculation formula between them is , let the similarity score based on graph convolutional network be ; S4. Comprehensively calculate the similarity score between work ticket template i and the new task ,in is the balance coefficient, which is adjusted according to the actual situation to balance the weighted similarity of the classification level and the graph convolution network similarity in the final score. Sort the work ticket templates and select the template with the highest matching degree as the basic template.

8. The intelligent work ticket generation system based on power distribution construction specifications as claimed in claim 7 is characterized by: The template adjustment unit is used to adjust the template according to the task characteristics when there are partial differences between the matched work ticket template and the new task. If the environmental information of the new task has special requirements for high-altitude areas, and the matching template does not take this into account, special safety measures and construction precautions for power distribution construction in high-altitude areas can be extracted from the environmental information library and added to the work ticket template to ensure that the work ticket template can fully adapt to the new task requirements.

9. The intelligent work ticket generation system based on power distribution construction specifications as claimed in claim 1 is characterized by: The work ticket generation and optimization module includes an information filling unit and a work ticket verification and optimization unit. The information filling unit is used to fill in the specific information of the new task into the selected or adjusted work ticket template, including the task name, work location, work supervisor and estimated working time basic information. According to the detailed equipment model and specific task type of the task, relevant safety measures, construction steps and tool and material list information are extracted from the equipment knowledge base and task operation specification library, and filled into the corresponding columns of the work ticket template.

10. The intelligent work ticket generation system based on power distribution construction specifications according to claim 9, characterized in that: The work ticket verification and optimization unit performs a logical verification on the generated work ticket to check the integrity of the information, including whether all required items have been filled in; the rationality of the safety measures, including whether the safety measures match the geographical environment and equipment conditions of the task; the consistency of the construction steps, including whether the construction steps conform to the normal construction sequence; if problems are found during the verification, they are optimized according to preset rules; if safety measures are missing, they are supplemented by referring to the safety measures of similar tasks or the power distribution construction specifications; if the construction steps are unreasonable, the sequence of the construction steps is adjusted or the content of the construction steps is refined.

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