Electricity larceny prevention system based on artificial intelligence
Through the anti-power stolen system based on artificial intelligence, using technical means such as data governance, model training and large language models, the comprehensive coverage of all users and accurate identification of power stolen behavior is achieved, and the problem of difficulty in efficient supervision of traditional systems is solved, which improves the efficiency of anti-power stolen work and the fairness of the power market.
Patent Information
- Application Number
- CN202411626320.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-14
- Publication Date
- 2025-03-28
AI Technical Summary
Traditional anti-powered power theft system is difficult to fully cover all users, which makes it difficult to supervise. Some areas lack systems and effective management mechanisms, which affects the efficient progress of anti-powered power theft work.
Adopt an anti-power stolen system based on artificial intelligence, through data governance service modules, model training service modules, model algorithm service modules (including large language models) and Agent service groups, we collect and analyze power consumption data in real time, and intelligently identify abnormal power consumption behaviors.
It has achieved comprehensive coverage of all users, accurately identified power theft behavior, reduced the cost and difficulty of manual inspection, improved the efficiency of anti-power theft work, reduced power loss and facility damage, and maintained a fair competition order in the power market.
Smart Images
Figure CN119722377A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of power system management, and in particular to an anti-electricity theft system based on artificial intelligence. Background Art
[0002] The anti-electricity theft system is a power system management tool specifically used to monitor and prevent illegal electricity use. Its core goal is to monitor and prevent illegal electricity use, especially electricity theft. Specifically, the anti-electricity theft system collects current, voltage and other data of electrical equipment through power sensors, transmits the data to the data collector, and then analyzes and processes the data through the cloud server, thereby realizing remote monitoring of electrical equipment and anti-electricity theft functions. The anti-electricity theft system is widely used in small and medium-sized enterprises, shopping malls, office buildings and other places, and can be applied to electrical equipment such as distribution boxes and power cables. By real-time monitoring of current, voltage and other data of electrical equipment, the system can promptly detect abnormal electricity use, issue alarms and handle them, and effectively prevent electricity theft.
[0003] The anti-electricity theft system is of great significance for protecting the safety of electricity use in enterprises and promoting sustainable development. It can not only improve the safety and management efficiency of electricity use in enterprises, reduce energy consumption and costs, but also maintain the fair order of the electricity market and prevent economic losses and social harm caused by illegal electricity use. However, in the actual anti-electricity theft work, due to the large number of electricity user groups and complex and diverse electricity use behaviors, traditional supervision methods are difficult to fully cover all users, resulting in great difficulty in supervision. In some areas, due to the lack of systematic and effective management mechanisms and measures, anti-electricity theft work is difficult to carry out efficiently.
[0004] Therefore, it is urgent to improve this shortcoming. The present invention studies and improves the existing technology and its shortcomings, and provides an anti-electricity theft system based on artificial intelligence. Summary of the invention
[0005] The purpose of the present invention is to provide an anti-electricity theft system based on artificial intelligence to solve the problems raised in the above background technology.
[0006] To achieve the above object, the present invention provides the following technical solution: an anti-electricity theft system based on artificial intelligence, comprising: Data governance service module, which is responsible for data collection, processing and management, ensuring that the system can obtain high-quality and reliable electricity consumption data, and providing a solid foundation for subsequent model training and algorithm prediction. It specifically includes data collection submodule, data processing submodule and data management submodule; Model training service module, which is responsible for building and training anti-electricity theft prediction models and ensuring that these models can run efficiently and accurately in different application scenarios, and specifically includes model training tools and model training support; Model algorithm service module (LLM, i.e. Large Language Model), which is responsible for predicting and analyzing electricity consumption data using the trained anti-electricity theft prediction model to identify potential electricity theft behaviors, including base large model prediction, fine-tuning large model prediction, and pre-training large model prediction; Agent service group, the Agent service group is a service system composed of several Agents (intelligent agents), and the Agents can complete specific tasks autonomously or collaboratively.
[0007] Furthermore, the data collection submodule is responsible for collecting power consumption data from various power monitoring equipment, power consumption information collection systems and other data sources, including key information such as current, voltage, power consumption, and power consumption time, and data collection specifically includes the following methods: RPA (Robotic Process Automation): It automatically performs a series of predefined tasks, including data extraction, data cleaning, data conversion, etc., by simulating user operations. RPA specifically includes: automated data extraction (automatically extracting electricity consumption data from the power company's internal system or database, such as electricity consumption, electricity consumption time, electricity charges, etc.), cross-system data integration (integrating data across multiple systems or platforms, integrating electricity consumption data from different sources into a unified database for subsequent analysis and processing); Information crawling: By writing crawler programs, visit relevant websites and extract the required data. Information crawling specifically includes: public data sources (some power companies or government agencies will publish electricity consumption data on the Internet in a public form, such as electricity consumption, electricity bills, etc. Through information crawling, the anti-electricity theft system can obtain these public data), social media and forums (on social media and forums, users will share their electricity consumption experience, electricity bills and other information. Through information crawling, the system can capture this information as a clue to identify abnormal electricity consumption behavior); File upload: Users or external systems submit electricity consumption data to the system in the form of files. These files contain original electricity consumption data, processed data reports, etc., and file upload specifically includes: manual upload by users (users organize their electricity consumption data into files, such as Excel, CSV, etc., and submit the files to the system through the upload function provided by the system), external system data import (some external systems such as power monitoring equipment, smart meters, etc., will generate electricity consumption data files. Through the file upload function, these systems can import data files into the anti-electricity theft system for further analysis and early warning).
[0008] Furthermore, the data processing submodule is responsible for preprocessing the collected raw power consumption data for subsequent analysis and early warning, which is specifically composed of three parts: data import, data cleaning, and data loading; The data import is responsible for importing raw data from external data sources (such as files, databases, etc.) into the system; The data cleaning is the core link in the data processing submodule, which aims to eliminate errors, redundancies and inconsistencies in the data and improve the accuracy and availability of the data; The data loading is responsible for loading the cleaned data into subsequent modules; Among them, data cleaning specifically includes the following operation processes: Text extraction: Extract useful text information from the raw data, such as user information, electricity usage time, electricity consumption, etc., to ensure that subsequent processing can proceed smoothly; Data conversion: converting data from one format to another to meet the requirements of subsequent analysis or system, for example, unifying the date and time format into a standard year-month-day-hour-minute-second format, and converting numeric data into a specific data type (such as floating point or integer); Data deduplication: Identify and delete duplicate records in a data set to ensure the uniqueness and accuracy of the data, thereby avoiding duplicate analysis, reducing the amount of calculation, and improving analysis efficiency; Data segmentation: Data sets are segmented according to specific rules or conditions for more detailed analysis. For example, data can be segmented into different subsets based on time intervals (such as daily, weekly, and monthly), or based on user types (such as residential users and commercial users).
[0009] Furthermore, the data management submodule is responsible for managing and configuring various types of data in the system to ensure the security and accessibility of the data, specifically including two major items: data management and data processing configuration; The data management is responsible for the storage, maintenance and update of various data in the system, and in the anti-electricity theft system, the data types include the following: Source data: original electricity consumption data directly obtained from sources such as power monitoring equipment, smart meters, and user information systems; Text data: including electricity application, electricity usage records, electricity bills and other data in text form filled out by users; Question and answer data: It comes from user consultation, system feedback and other channels. It usually exists in the form of questions and answers. It is used to understand user needs, system performance and other information, which helps to optimize the system and improve user experience. The data processing configuration is responsible for setting and managing various parameters and processes in the data processing process. In the anti-electricity theft system, the data processing configuration mainly includes platform management, which is used to manage and maintain the data processing platform, including platform configuration, upgrade, troubleshooting, etc.
[0010] Furthermore, the model training tool includes the following two aspects: 1) Model training Pre-training: The model is preliminarily trained on a large-scale data set to enable it to have basic electricity theft recognition capabilities. This process helps the model learn the common characteristics of electricity theft and lays the foundation for subsequent fine-tuning. Fine-tuning: Based on pre-training, the model is further optimized and adjusted for specific anti-electricity theft application scenarios. Fine-tuning is performed on a smaller data set to make the model better suited to electricity theft detection in specific scenarios. 2) Model processing Distillation compression: By training a small model (student model) to imitate the output of a large model (teacher model), the complexity and computational cost of the model can be reduced while ensuring its performance.
[0011] Furthermore, the model training support provides the necessary environment and conditions for the model training tool to ensure that the model training process can proceed smoothly, specifically including the following two aspects: 1) Version management: used to record and save each version of the model during pre-training and fine-tuning, so that you can go back to a specific version when needed, and track the improvement of the model to provide a reference for subsequent model optimization; 2) Training configuration: key parameter settings in the model training process, including learning rate, batch size, training rounds, etc., which are used to fine-tune and optimize the training configuration according to the specific application scenarios and data set characteristics during the pre-training and fine-tuning stages.
[0012] Furthermore, the base large model prediction: uses the pre-trained base large model to make a preliminary prediction of the electricity consumption data to obtain basic electricity consumption characteristics and trends, providing a basis for subsequent fine-tuning large model predictions and pre-trained large model predictions. The base large model refers to a model that has a wide range of electricity theft behavior recognition capabilities after large-scale data training. This model has been optimized on a large-scale electricity consumption data set so that it can capture the common characteristics of electricity theft behavior. When new electricity consumption data is input, the base large model uses these characteristics to make predictions to determine whether there is electricity theft behavior, providing a solid foundation for subsequent electricity theft behavior analysis.
[0013] Furthermore, the fine-tuning of the large model prediction: on the basis of the base large model, fine-tuning is performed for electricity theft behaviors in specific areas, specific time periods or specific electricity usage types. Through fine-tuning, the model is adapted to the characteristics of electricity theft behaviors in these specific scenarios to improve the accuracy and reliability of the prediction, accurately capture the details of electricity theft behaviors, and provide power companies with accurate anti-electricity theft strategies. For example, in the anti-electricity theft system, the LLM is fine-tuned according to the characteristics of electricity theft behaviors, and the model weights are adjusted so that it can more accurately identify electricity theft behaviors.
[0014] Furthermore, the pre-trained large model prediction: the power consumption data is predicted and analyzed using a model pre-trained on large-scale data to identify potential power theft behaviors. Since the pre-trained model has learned a large number of power consumption characteristics and patterns, it can adapt to new power consumption scenarios and power theft behaviors more quickly. The application of the pre-trained large model prediction in the anti-power theft system includes: Power usage behavior analysis: Detailed analysis of power usage data, including power usage trends, power usage time distribution, etc., to identify potential power theft by comparing normal power usage behavior with abnormal power usage behavior; Electricity theft warning: Based on the analysis results of electricity consumption data, electricity theft warning information is generated. When the model detects anomalies in the electricity consumption data, it issues a warning in time to remind the power company to conduct further investigation and processing; Formulate anti-electricity theft strategies: By analyzing the characteristics and trends of electricity theft, we can provide reference for power companies to formulate anti-electricity theft strategies, help power companies determine key monitoring areas and time periods, and take corresponding technical means and management measures to prevent the occurrence of electricity theft.
[0015] Furthermore, the Agent service group specifically includes the following submodules: Task scheduling submodule: receives anti-electricity theft task requests from users and performs reasonable task decomposition and scheduling according to task requirements; Intention recognition submodule: accurately identifies the user's intention or potential electricity theft behavior characteristics by analyzing user input or electricity usage data; Subtask processing submodule: decomposes the complex anti-electricity theft task into multiple subtasks (including data analysis, model prediction, result verification, etc.) and assigns them to the model algorithm service module for processing; Post-processing submodule: further analyzes and processes the processing results of the subtasks to obtain the final judgment or suggestion on the electricity theft behavior; Summary and induction submodule: summarize and summarize the processing results to form an easy-to-understand report or suggestion for reference by users or power companies.
[0016] The present invention provides an anti-electricity theft system based on artificial intelligence, which has the following beneficial effects: The present invention collects electricity consumption data in real time through RPA, information crawling, and file uploading, and decomposes the anti-electricity theft task into multiple subtasks through the Agent service group, and then uses the algorithm model to deeply mine and analyze the electricity consumption data, so as to intelligently identify abnormal electricity consumption behaviors, such as electricity theft, leakage, etc., and provide timely feedback to management personnel, which greatly reduces the cost and difficulty of manual inspections and is conducive to the efficient implementation of anti-electricity theft work. Compared with traditional supervision methods, this system can fully cover all users and accurately identify electricity theft behaviors, thereby helping to reduce power loss, reduce damage to power facilities and safety hazards, effectively curb the occurrence of electricity theft, and is conducive to maintaining fair competition order in the power market and promoting the healthy development of the power market. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] Figure 1 This is a logic block diagram of an anti-electricity theft system based on artificial intelligence of the present invention; Figure 2 This is a schematic diagram of a data management service module of an anti-electricity theft system based on artificial intelligence of the present invention; Figure 3 The present invention is a schematic diagram of a model training service module of an anti-electricity theft system based on artificial intelligence. DETAILED DESCRIPTION
[0018] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.
[0019] like Figure 1-Figure 3 As shown, an anti-electricity theft system based on artificial intelligence includes: Data governance service module: responsible for data collection, processing and management, ensuring that the system can obtain high-quality and reliable electricity consumption data, providing a solid foundation for subsequent model training and algorithm prediction, including data collection submodule, data processing submodule, and data management submodule; In this embodiment, the data collection submodule is responsible for collecting power consumption data from various power monitoring equipment, power consumption information collection systems and other data sources, including key information such as current, voltage, power consumption, and power consumption time, and the data collection specifically includes the following ways: RPA (Robotic Process Automation): It automatically performs a series of predefined tasks, including data extraction, data cleaning, data conversion, etc., by simulating user operations. RPA specifically includes: automated data extraction (automatically extracting electricity consumption data from the power company's internal system or database, such as electricity consumption, electricity consumption time, electricity charges, etc.), cross-system data integration (integrating data across multiple systems or platforms, integrating electricity consumption data from different sources into a unified database for subsequent analysis and processing); Information crawling: By writing crawler programs, visit relevant websites and extract the required data. Information crawling specifically includes: public data sources (some power companies or government agencies will publish electricity consumption data on the Internet in a public form, such as electricity consumption, electricity bills, etc. Through information crawling, the anti-electricity theft system can obtain these public data), social media and forums (on social media and forums, users will share their electricity consumption experience, electricity bills and other information. Through information crawling, the system can capture this information as a clue to identify abnormal electricity consumption behavior); File upload: Users or external systems submit electricity consumption data to the system in the form of files. These files contain original electricity consumption data, processed data reports, etc., and file upload specifically includes: manual upload by users (users organize their own electricity consumption data into files, such as Excel, CSV, etc., and submit the files to the system through the upload function provided by the system), external system data import (some external systems such as power monitoring equipment, smart meters, etc., will generate electricity consumption data files. Through the file upload function, these systems can import data files into the anti-electricity theft system for further analysis and early warning); In this embodiment, the data processing submodule is responsible for preprocessing the collected raw power consumption data for subsequent analysis and early warning, and specifically consists of three parts: data import, data cleaning, and data loading. Data import is responsible for importing raw data from external data sources (such as files, databases, etc.) into the system; data cleaning is the core link in the data processing submodule, which aims to eliminate errors, redundancies and inconsistencies in the data and improve the accuracy and availability of the data; data loading is responsible for loading the cleaned data into subsequent modules; Among them, data cleaning specifically includes the following operation processes: Text extraction: Extract useful text information from the raw data, such as user information, electricity usage time, electricity consumption, etc., to ensure that subsequent processing can proceed smoothly; Data conversion: converting data from one format to another to meet the requirements of subsequent analysis or system, for example, unifying the date and time format into a standard year-month-day-hour-minute-second format, and converting numeric data into a specific data type (such as floating point or integer); Data deduplication: Identify and delete duplicate records in a data set to ensure the uniqueness and accuracy of the data, thereby avoiding duplicate analysis, reducing the amount of calculation, and improving analysis efficiency; Data segmentation: segmenting data sets according to specific rules or conditions for more detailed analysis. For example, data can be segmented into different subsets based on time intervals (such as daily, weekly, and monthly), or segmented based on user types (such as residential users and commercial users). In this embodiment, the data management submodule is responsible for managing and configuring various types of data in the system to ensure the security and accessibility of the data, specifically including two major items: data management and data processing configuration; Data management is responsible for the storage, maintenance and update of various data in the system. In the anti-electricity theft system, the data types include the following: Source data: original electricity consumption data directly obtained from sources such as power monitoring equipment, smart meters, and user information systems; Text data: including electricity application, electricity usage records, electricity bills and other data in text form filled out by users; Question and answer data: It comes from user consultation, system feedback and other channels. It usually exists in the form of questions and answers. It is used to understand user needs, system performance and other information, which helps to optimize the system and improve user experience. Data processing configuration is responsible for setting and managing various parameters and processes in the data processing process. In the anti-electricity theft system, data processing configuration mainly includes platform management, which is used to manage and maintain the data processing platform, including platform configuration, upgrade, troubleshooting, etc. Model training service module: responsible for building and training anti-electricity theft prediction models and ensuring that these models can run efficiently and accurately in different application scenarios, including model training tools and model training support; In this embodiment, the model training tool includes the following two aspects: 1) Model training Pre-training: The model is preliminarily trained on a large-scale data set to enable it to have basic electricity theft recognition capabilities. This process helps the model learn the common characteristics of electricity theft and lays the foundation for subsequent fine-tuning. Fine-tuning: Based on pre-training, the model is further optimized and adjusted for specific anti-electricity theft application scenarios. Fine-tuning is performed on a smaller data set to make the model better suited to electricity theft detection in specific scenarios. 2) Model processing Distillation compression: By training a small model (student model) to imitate the output of a large model (teacher model), the complexity and computational cost of the model can be reduced while ensuring the performance of the model. In this embodiment, the model training support provides the necessary environment and conditions for the model training tool to ensure that the model training process can proceed smoothly, specifically including the following two aspects: 1) Version management: used to record and save each version of the model during pre-training and fine-tuning, so that you can go back to a specific version when needed, and track the improvement of the model to provide a reference for subsequent model optimization; 2) Training configuration: key parameter settings in the model training process, including learning rate, batch size, training rounds, etc., used to fine-tune and optimize the training configuration according to specific application scenarios and data set characteristics during pre-training and fine-tuning stages; Model algorithm service module (LLM, i.e. Large Language Model): responsible for predicting and analyzing electricity consumption data using the trained anti-electricity theft prediction model to identify potential electricity theft behaviors, including base large model prediction, fine-tuning large model prediction, and pre-training large model prediction; In this embodiment, the base large model prediction: the pre-trained base large model is used to make a preliminary prediction of the electricity consumption data to obtain basic electricity consumption characteristics and trends, providing a basis for the subsequent fine-tuning large model prediction and pre-training large model prediction. The base large model refers to a model that has the ability to identify a wide range of electricity theft behaviors after large-scale data training. This model has been optimized on a large-scale electricity consumption data set, so that it can capture the general characteristics of electricity theft behaviors. When new electricity consumption data is input, the base large model uses these characteristics to make predictions and determine whether there is electricity theft behavior, providing a solid foundation for the subsequent analysis of electricity theft behaviors; In this embodiment, the large model prediction is fine-tuned: based on the base large model, the electricity theft behavior in a specific area, a specific time period or a specific type of electricity consumption is fine-tuned. Through fine-tuning, the model is adapted to the characteristics of the electricity theft behavior in these specific scenarios to improve the accuracy and reliability of the prediction, and the details of the electricity theft behavior are captured precisely to provide the power company with an accurate anti-electricity theft strategy. For example, in the anti-electricity theft system, the LLM is fine-tuned according to the characteristics of the electricity theft behavior, and the model weight is adjusted so that it can more accurately identify the electricity theft behavior; In this embodiment, the pre-trained large model predicts: the power consumption data is predicted and analyzed using a model that has been pre-trained on large-scale data to identify potential power theft behaviors. Since the pre-trained model has learned a large number of power consumption characteristics and patterns, it can adapt to new power consumption scenarios and power theft behaviors more quickly. The application of the pre-trained large model prediction in the anti-power theft system includes: Power usage behavior analysis: Detailed analysis of power usage data, including power usage trends, power usage time distribution, etc., to identify potential power theft by comparing normal power usage behavior with abnormal power usage behavior; Electricity theft warning: Based on the analysis results of electricity consumption data, electricity theft warning information is generated. When the model detects anomalies in the electricity consumption data, it issues a warning in time to remind the power company to conduct further investigation and processing; Formulate anti-electricity theft strategies: By analyzing the characteristics and trends of electricity theft, provide reference for power companies to formulate anti-electricity theft strategies, help power companies determine key monitoring areas and time periods, and take corresponding technical means and management measures to prevent electricity theft; Agent service group: A service system composed of several agents (intelligent agents). Agents can complete specific tasks autonomously or collaboratively, and the Agent service group specifically includes the following sub-modules: Task scheduling submodule: receives anti-electricity theft task requests from users and performs reasonable task decomposition and scheduling according to task requirements; Intention recognition submodule: accurately identifies the user's intention or potential electricity theft behavior characteristics by analyzing user input or electricity usage data; Subtask processing submodule: decomposes the complex anti-electricity theft task into multiple subtasks (including data analysis, model prediction, result verification, etc.) and assigns them to the model algorithm service module for processing; Post-processing submodule: further analyzes and processes the processing results of the subtasks to obtain the final judgment or suggestion on the electricity theft behavior; Summary and induction submodule: summarize and summarize the processing results to form an easy-to-understand report or suggestion for reference by users or power companies.
[0020] The embodiments of the present invention are given for the purpose of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.
Claims
1. An anti-electricity theft system based on artificial intelligence, characterized in that: include: Data governance service module, which is responsible for data collection, processing and management, and specifically includes a data collection submodule, a data processing submodule and a data management submodule; Model training service module, which is responsible for building and training anti-electricity theft prediction models and ensuring that these models can run efficiently and accurately in different application scenarios, and specifically includes model training tools and model training support; The model algorithm service module is responsible for predicting and analyzing electricity consumption data using the trained anti-electricity theft prediction model to identify potential electricity theft behaviors, including prediction of base large model, prediction of fine-tuned large model, and prediction of pre-trained large model; The Agent service group is a service system composed of several Agents, and the Agents can complete specific tasks autonomously or collaboratively.
2. The anti-electricity theft system based on artificial intelligence according to claim 1 is characterized in that: The data collection submodule is responsible for collecting power consumption data from various power monitoring equipment and power consumption information collection systems, and data collection specifically includes the following methods: RPA: Automatically executes a series of predefined tasks by simulating user operations. RPA specifically includes: automated data extraction and cross-system data integration; Information crawling: By writing crawler programs, visit relevant websites and extract the required data. Information crawling specifically includes: public data sources, social media and forums; File upload: Users or external systems submit electricity consumption data to the system in the form of files, and file upload specifically includes: manual upload by users and data import from external systems.
3. The anti-electricity theft system based on artificial intelligence according to claim 1 is characterized in that: The data processing submodule is responsible for preprocessing the collected raw power consumption data, which is specifically composed of three parts: data import, data cleaning, and data loading; The data import is responsible for importing original data from external data sources into the system; The data cleaning is responsible for eliminating errors, redundancies and inconsistencies in the data; The data loading is responsible for loading the cleaned data into subsequent modules; Among them, data cleaning specifically includes the following operation processes: Text extraction: extract useful text information from raw data; Data conversion: converting data from one format to another to suit the requirements of subsequent analysis or system; Data deduplication: identifying and removing duplicate records in a data set; Data segmentation: segment the data set according to specific rules or conditions.
4. The anti-electricity theft system based on artificial intelligence according to claim 1 is characterized in that: The data management submodule is responsible for managing and configuring various types of data in the system to ensure the security and accessibility of the data, specifically including two major items: data management and data processing configuration; The data management is responsible for the storage, maintenance and update of various data in the system, and in the anti-electricity theft system, the data types include the following: Source data: raw electricity consumption data directly obtained from sources such as power monitoring equipment, smart meters, and user information systems; Text data: including data in text form such as electricity application filled out by users, electricity usage records, and electricity bills; Question and answer data: It comes from user consultation and system feedback channels. It exists in the form of questions and answers and is used to understand user needs and system performance. The data processing configuration is responsible for setting and managing various parameters and processes in the data processing process. In the anti-electricity theft system, the data processing configuration includes platform management, which is used to manage and maintain the data processing platform.
5. The anti-electricity theft system based on artificial intelligence according to claim 1 is characterized in that: The model training tool includes the following two aspects: 1) Model training Pre-training: The model is preliminarily trained on a large-scale data set to enable it to have basic electricity theft behavior recognition capabilities; Fine-tuning: Based on pre-training, the model is further optimized and adjusted for specific anti-electricity theft application scenarios; 2) Model processing Distillation compression: By training a small model to imitate the output of a large model, the complexity and computational cost of the model can be reduced while ensuring its performance.
6. The anti-electricity theft system based on artificial intelligence according to claim 1 is characterized in that: The model training support provides the necessary environment and conditions for the model training tool to ensure that the model training process can proceed smoothly, specifically including the following two aspects: 1) Version management: used to record and save each version of the model during pre-training and fine-tuning, so that you can go back to a specific version when needed, and track the improvement of the model to provide a reference for subsequent model optimization; 2) Training configuration: key parameter settings in the model training process, used to fine-tune and optimize the training configuration according to specific application scenarios and data set characteristics during the pre-training and fine-tuning stages.
7. The anti-electricity theft system based on artificial intelligence according to claim 1 is characterized in that: The base large model prediction: uses the pre-trained base large model to make preliminary predictions on electricity consumption data to obtain basic electricity consumption characteristics and trends. The base large model refers to a model that has the ability to identify a wide range of electricity theft behaviors after large-scale data training. When new electricity consumption data is input, the base large model uses these characteristics to make predictions to determine whether there is electricity theft behavior.
8. The anti-electricity theft system based on artificial intelligence according to claim 1 is characterized in that: The fine-tuning large model predicts: based on the base large model, fine-tuning is performed for electricity theft behaviors in specific areas, specific time periods or specific electricity usage types. Through fine-tuning, the model is adapted to the characteristics of electricity theft behaviors in these specific scenarios, and the details of electricity theft behaviors are accurately captured, providing power companies with accurate anti-electricity theft strategies.
9. The anti-electricity theft system based on artificial intelligence according to claim 1 is characterized in that: The pre-trained large model prediction: uses a model pre-trained on large-scale data to predict and analyze electricity consumption data to identify potential electricity theft behaviors, and the application of the pre-trained large model prediction in the anti-electricity theft system includes: Power usage behavior analysis: Detailed analysis of power usage data, including the trend of power usage and the distribution of power usage time. By comparing normal power usage behavior with abnormal power usage behavior, potential power theft can be identified. Electricity theft warning: Based on the analysis results of electricity consumption data, electricity theft warning information is generated. When the model detects anomalies in the electricity consumption data, a warning is issued in time; Formulate anti-electricity theft strategies: By analyzing the characteristics and trends of electricity theft, we can provide reference for power companies to formulate anti-electricity theft strategies, help power companies determine key monitoring areas and time periods, and take corresponding technical means and management measures to prevent the occurrence of electricity theft.
10. The anti-electricity theft system based on artificial intelligence according to claim 1, characterized in that: The Agent service group specifically includes the following submodules: Task scheduling submodule: receives anti-electricity theft task requests from users and performs reasonable task decomposition and scheduling according to task requirements; Intention recognition submodule: accurately identifies the user's intention or potential electricity theft behavior characteristics by analyzing user input or electricity usage data; Subtask processing submodule: decomposes the complex anti-electricity theft task into multiple subtasks and assigns them for processing; Post-processing submodule: further analyzes and processes the processing results of the subtasks to obtain the final judgment or suggestion on the electricity theft behavior; Summary and induction submodule: summarize and summarize the processing results to form an easy-to-understand report or suggestion for reference by users or power companies.