Intelligent work ticket generation system based on power distribution construction specifications

Abstract

The invention discloses an intelligent work ticket generation system based on power distribution construction specifications, belongs to the technical field of electric power work tickets, and aims to solve the problem that a work ticket generated by an existing work ticket generation system is poor in applicability. The system comprises a task information acquisition 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, key task features are deeply mined through the task feature analysis module, and the positions and path lengths of the key task features in a classification hierarchy system are determined; the work ticket template matching module firstly calculates a classification level similarity proportion and obtains a weighted similarity score in combination with a feature weight, then learns a knowledge graph by using a graph convolutional network to obtain a low-dimensional feature space similarity score, and finally combines the two scores and determines a final similarity score through a balance coefficient. Therefore, the template with the highest matching degree is accurately screened out, the accuracy of work ticket generation is remarkably improved, and the problem that an existing work ticket generation system is poor in applicability is effectively solved.

Description

Technical Field

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

[0002] In the field of power distribution construction, work tickets are key documents to ensure safe and standardized 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 specific set of operating specifications and safety procedures. For example, when installing equipment, it is necessary to focus on the positioning of the equipment, the foundation fixation and the correctness of the wiring; fault diagnosis requires the use of professional detection tools and technical means to gradually identify the cause of the fault according to a certain diagnostic process; maintenance focuses on regular inspection, cleaning, lubrication and replacement of wearing parts of equipment. The traditional manual work ticket generation method 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 begun to emerge. 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 are unable to deeply understand the complex relationship between task features, resulting in low template matching accuracy; although some systems take multiple factors into consideration, there is a lack of scientific basis for the allocation of the importance of each factor, making it impossible to effectively distinguish the degree of influence of different factors on work ticket generation 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 power distribution construction specifications, which solves the problem of poor applicability of work tickets generated by existing work ticket generation systems in the background technology.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solutions: A work ticket intelligent generation system based on power distribution construction specifications, comprising: 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, 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;

[0007] 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;

[0008] 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 position 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 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, 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;

[0010] 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.

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

[0012] Furthermore, 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 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; 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;

[0013] 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.

[0014] Furthermore, 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, and for regional information, converts it into quantifiable or classifiable features, including regional type and regional power load level; for environmental information, extracts environmental categories and environmental parameters, and environmental categories include indoor, outdoor, and special environments, and environmental parameters include temperature, humidity, and altitude; for power project types, determines its specific level in the power project classification system, "equipment maintenance-switch cabinet maintenance-10kV switch cabinet maintenance"; for detailed equipment models, extracts key parameters and model category information of the equipment; for specific task types, also clarifies its position in the task classification system, "maintenance task-fault maintenance", and forms a task feature vector;

[0015] The classification hierarchy determination unit determines the position and path length of each task feature in the respective classification hierarchy based on a pre-constructed classification hierarchy system; for the power project type, in the power project classification hierarchy, 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 regional information, from provincial-level regions, municipal-level regions, county-level regions, township-level regions, and finally to the specific location type, the path length is 5; for environmental information, starting from the top-level main environmental category, through indoor and outdoor distinction, spatial characteristics, environmental condition details, and finally to the special environmental identification, the path length is 5; for equipment models, starting from the top-level "power equipment category", through 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 power operation and maintenance inspection, equipment level tasks, equipment component tasks, and finally to the specific operation task, the path length is 5.

[0016] Furthermore, 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 combining classification hierarchical quantization with weighted similarity and integrating into a graph convolutional network. The specific steps are as follows:

[0017] 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;

[0018] 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 ;

[0019] 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;

[0020] 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 ;

[0021] 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.

[0022] Furthermore, 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 it 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.

[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 in the specific information of the new task into the selected or adjusted work ticket template, including the basic information of the task name, work location, work supervisor and estimated working time. According to the detailed equipment model and specific task type of the task, the 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 performs a logical check on the generated work ticket to check the information integrity, 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 the check finds problems, optimize according to the preset rules; if the safety measures are missing, refer to the safety measures or power distribution construction specifications of similar tasks for supplementation; if the construction steps are unreasonable, adjust the sequence of the construction steps or refine the content of the construction steps.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] The intelligent work ticket generation system based on the power distribution construction specification 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 mines the key task features, and determines its position and path length in the classification hierarchy system, and the work ticket template matching module first calculates the classification hierarchy similarity ratio and combines the feature weight to obtain the weighted similarity score, and then uses the graph convolution 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, overcome the problem of inaccurate template matching in the existing system, significantly improve the accuracy of work ticket generation, the adaptability of the system and the work efficiency, effectively ensure the safe and efficient distribution construction, effectively solve the problem of poor applicability of the existing work ticket generation system, and promote the power construction management to move towards intelligence and precision. 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

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] In order to solve the technical problem that the work tickets generated by the existing work ticket generation system are not suitable for use, such as Figure 1-Figure 2 As shown, the following preferred technical solutions are provided:

[0030] The intelligent work ticket generation system based on power 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 regional information, environmental information, power project type, equipment model and specific tasks of the task; regional confidence 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; by accurately collecting this information, a complete data foundation is provided for the intelligent generation of subsequent work tickets, ensuring that the system can accurately process the task according to the actual situation;

[0032] 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;

[0033] 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 position 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 hierarchy, providing a key basis for similarity calculation in the subsequent work ticket template matching process, so that the system can accurately measure the matching degree between tasks and templates;

[0034] 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; its 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, screen out the template with the highest matching degree with the new task, and provide an accurate template basis for the subsequent generation of work tickets, ensuring that the content of the work ticket is highly consistent with the actual requirements of the task;

[0035] 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.

[0036] 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.

[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 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.

[0038] 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 to ensure the timeliness and accuracy of the data.

[0039] 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, and converts the regional information into quantifiable or classifiable features, including the regional type and the regional power load level; for environmental information, the environmental category and environmental parameters are extracted, and the environmental categories include indoor, outdoor, and special environments, and the environmental parameters include temperature, humidity, and altitude; for the power project type, its specific level in the power project classification system is determined, "equipment maintenance-switch cabinet maintenance-10kV switch cabinet maintenance"; for the detailed equipment model, the key parameters and model category information of the equipment are extracted; for the specific task type, its position in the task classification system is also clarified, "maintenance task-fault maintenance", to form a task feature vector.

[0040] 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" and "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;

[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, 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:

[0042] 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;

[0043] 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 ;

[0044] 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;

[0045] 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 ;

[0046] 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.

[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 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.

[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 the new task into the selected or adjusted work ticket template, including the basic information of the task name, work location, work supervisor and estimated working time. According to the detailed equipment model and specific task type of the task, the 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, the standard safety measures for maintenance of this model of switchgear, such as power outage, electrical testing, grounding operation steps, measures to prevent misoperation, etc., and common construction steps such as trolley pull-out 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 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, optimization is performed according to preset rules; if safety measures are missing, they are supplemented by referring to safety measures for similar tasks or distribution construction specifications; if the construction steps are unreasonable, the sequence of construction steps is adjusted or the content of the construction steps is refined; so that the work ticket reaches the optimal 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, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0051] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which 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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