Electric energy meter verification optimization scheduling system based on multi-agent large model algorithm

Abstract

The invention discloses an electric energy meter verification optimization scheduling system based on a multi-agent large model algorithm. The system comprises a data acquisition module, a multi-agent optimization scheduling module, a scheduling plan generation module and a user interaction module. The data acquisition module is used for data acquisition and monitoring, data preprocessing and cleaning and data storage and management; the multi-agent optimization scheduling module is used for an agent cooperation mechanism, agent adaptive learning and agent management; the scheduling plan generation module is used for prediction, analysis and integration, scheduling scheme generation and self-optimization and learning; and the user interaction module is used for interface design and implementation, user-defined functions and user feedback and improvement. According to the invention, the efficiency, the accuracy and the automation level of the electric energy meter verification process are improved through technical innovation and intelligent means.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy meter calibration, and in particular to an electric energy meter calibration optimization scheduling system based on a multi-agent large model algorithm. Background Art

[0002] In modern power systems, the performance of energy meters, as key equipment for power metering, is directly related to the operational efficiency and customer service quality of power companies. With the continuous development of smart grids, the operation of power systems has become more and more complex, which has put forward higher requirements for the calibration and dispatch of energy meters. Traditionally, power companies rely on fixed calibration strategies and dispatch models, which are usually formulated based on historical data and empirical rules. Although this method can meet basic needs in a stable and expected environment, it is inadequate in the face of the increasingly diversified and dynamic power market today. The intelligent and information development of power systems has led to an increasing amount and complexity of data in energy meters. At the same time, users' demand for power services has become more diversified and personalized, which has posed new challenges to the flexibility, accuracy and timeliness of calibration and dispatch. Traditional calibration and dispatch methods are difficult to respond to these changes quickly, resulting in obvious deficiencies in resource utilization efficiency and user satisfaction.

[0003] First, traditional optimization algorithms such as wolf pack algorithms have insufficient flexibility when dealing with the problem of meter calibration scheduling. These algorithms are usually optimized based on fixed constraints and a single goal, and it is difficult to adapt to the real-time changing calibration requirements and environmental conditions. For example, when an emergency occurs or user demand changes, traditional algorithms often cannot be adjusted in time, resulting in a rigid calibration plan and affecting overall efficiency. In addition, traditional algorithms have limited capabilities in multi-objective optimization. Meter calibration scheduling usually involves a balance of multiple objectives, such as calibration efficiency, cost control, and service quality. Existing optimization algorithms are difficult to take into account these objectives at the same time, and usually need to compromise in some aspects, resulting in poor overall solution results. In addition, traditional algorithms have weak search capabilities and the ability to solve complex constraint problems, and are prone to fall into local optimal solutions and cannot achieve global optimality. Finally, traditional algorithms are difficult to deeply integrate with modern intelligent technologies and fail to give full play to the advantages of intelligent systems. With the development of artificial intelligence and big data technologies, it is possible to use intelligent algorithms to comprehensively optimize meter calibration scheduling. However, traditional algorithms do not perform well in intelligent applications and lack intelligent decision-making capabilities and adaptive learning capabilities. They cannot fully utilize the information gain brought by big data, and it is difficult to achieve personalized and customized scheduling optimization.

[0004] In order to solve the shortcomings of traditional electricity meter calibration and dispatching technology, the introduction of multi-agent large model algorithm is an innovative and effective method. The multi-agent system achieves a more flexible and intelligent dispatching strategy by simulating the interaction of multiple agents in a complex environment. Each agent can have autonomous decision-making capabilities and can dynamically adjust according to environmental changes and its own goals. This mechanism allows the system to respond quickly when faced with emergencies or changes in demand, thereby improving the flexibility and adaptability of calibration and dispatching. The core of the multi-agent large model algorithm is to use artificial intelligence and big data technology to comprehensively analyze various real-time and historical data to generate optimized dispatching plans. Through machine learning and deep learning technology, each agent can continuously learn and optimize its decision-making process, gradually improving the overall efficiency and effectiveness of the system. In addition, the algorithm can simultaneously handle multiple optimization objectives, such as calibration efficiency, cost control, and service quality, and use evolutionary computing and multi-objective optimization methods to find the best balance between these objectives. This algorithm also supports intelligent and customized dispatching solutions. Through personalized data analysis, the multi-agent system can provide tailored services for different users and devices, improving user experience and satisfaction. At the same time, this intelligent dispatching system can be integrated with other smart grid technologies to form an interconnected ecosystem, further improving the overall operating efficiency and reliability of the power system.

[0005] In summary, the existing electricity meter calibration and dispatching technology has significant deficiencies in flexibility, multi-objective optimization capabilities, and intelligent integration. Therefore, there is an urgent need for a new system based on a multi-agent large model algorithm to realize the intelligence, customization, and optimization of electricity meter calibration and dispatching, and to improve the overall operating efficiency and user satisfaction of the power system. By introducing a multi-agent system, the behaviors of different subjects in a complex environment can be better simulated, and a more flexible and efficient dispatching strategy can be achieved. This system can not only dynamically adjust the plan based on real-time data, but also find the best balance between different goals, ultimately improving resource utilization and service levels. Summary of the invention

[0006] The purpose of the present invention is to provide an energy meter calibration optimization scheduling system based on a multi-agent large model algorithm, which improves the efficiency, accuracy and automation level of the energy meter calibration process through technological innovation and intelligent means. It monitors and collects key data in real time through a data acquisition module to ensure the accuracy and consistency of the data, and provide reliable support for subsequent analysis and decision-making. At the same time, the system integrates a multi-agent optimization scheduling module to achieve intelligent management of the production process, reduce manual intervention, and thus improve resource utilization efficiency and production efficiency. The scheduling plan generation module is responsible for converting the analysis results into a dynamically adjustable production plan, flexibly responding to changes and emergencies in production, and ensuring the continuity and efficiency of production. The user interaction module provides users with a friendly interface and multiple input methods, so that they can adjust the production plan according to their own needs, and continuously optimize the system performance by collecting user feedback. Overall, the system aims to achieve efficient automation of the energy meter calibration process through intelligent solutions, reduce operating costs, and improve user experience and satisfaction.

[0007] To achieve the purpose of the present invention, the technical solution provided by the present invention is as follows:

[0008] An electric energy meter calibration optimization and dispatching system based on a multi-agent large model algorithm, the system comprising a data acquisition module, a multi-agent optimization and dispatching module, a dispatching plan generation module and a user interaction module;

[0009] The data acquisition module is used for data acquisition and monitoring, data preprocessing and cleaning, and data storage and management;

[0010] The multi-agent optimization scheduling module is used for agent collaboration mechanism, agent adaptive learning and agent management;

[0011] The scheduling plan generation module is used for forecasting analysis integration, scheduling plan generation, and self-optimization and learning;

[0012] The user interaction module is used for interface design and implementation, user-defined functions, user feedback and improvement.

[0013] The data acquisition module is specifically used to perform the following steps:

[0014] Step A1: Data collection and monitoring: The data collection module deploys high-precision sensors at important nodes of the calibration line to ensure that equipment operation data and environmental conditions can be captured in real time. The module uses high-frequency data collection technology to ensure the timeliness of data, and combines the anomaly detection mechanism to detect equipment anomalies in a timely manner through set thresholds and pattern recognition technology, and trigger alarms to prevent failures.

[0015] Step A2: Data preprocessing and cleaning: To ensure the accuracy of the data, first filter out the noise and erroneous information in the collection process to ensure the purity of the data; use data correction technology to eliminate measurement errors and improve data reliability; and for the missing parts in the data, the module uses interpolation to reasonably fill in the missing parts to ensure the integrity and consistency of the data;

[0016] Step A3: Data storage and management: The module establishes a connection with the cloud database through a security protocol to achieve secure data transmission and centralized storage; creates an efficient index structure to support rapid data retrieval and ensure that production line data can be accessed and analyzed in a timely manner; during data storage, the module performs regular backups and has a complete data recovery mechanism to avoid data loss due to unexpected situations.

[0017] Wherein, the high-precision sensors include current sensors, temperature sensors and vibration sensors.

[0018] The multi-agent optimization scheduling module is specifically used to perform the following steps:

[0019] Step B1: Agent collaboration mechanism: This module uses multiple large language model agent systems. Each agent focuses on tasks in its own field. Efficient data synchronization is achieved through a designed information sharing protocol to ensure the effective use of resources and information. Agents resolve possible resource competition and conflicts through priority and arbitration mechanisms.

[0020] Step B2: Agent adaptive learning: These large language model agents can learn and optimize their own strategies during task execution by introducing reinforcement learning algorithms. The agents continuously perceive changes in the production line environment and use accumulated experience data to make adjustments and improvements. In addition, the agents share experience to improve the adaptability and optimization capabilities of the overall system, ensuring the accuracy and efficiency of scheduling.

[0021] Step B3: Management Agent: The management agent is responsible for the coordination and optimization of the overall scheduling system; it decomposes complex tasks into executable subtasks and reasonably allocates them to various professional agents; at the same time, the management agent evaluates the execution effect of each module, provides feedback to support its self-adjustment, and optimizes the global scheduling strategy based on real-time data and prediction results to ensure the best performance of the entire system.

[0022] Among them, the multiple large language model agent systems include an inspection and scheduling agent, an equipment management agent and an energy management agent.

[0023] The scheduling plan generation module is specifically used to perform the following steps:

[0024] Step C1: Prediction and analysis integration: Using the data analysis engine, the module deeply analyzes big data and model output to identify bottlenecks and optimization possibilities in the production process; by establishing a load prediction model, the module can predict future production loads and provide a scientific basis for scheduling plans; at the same time, resource demand assessment helps to reasonably allocate equipment and human resources to ensure efficient production operations;

[0025] Step C2: Scheduling plan generation: The module uses a priority sorting algorithm to analyze production tasks to ensure that high-priority tasks are processed in a timely manner; it formulates the optimal scheduling plan based on the current status of the equipment and resource allocation; it has dynamic adjustment capabilities, and the module can respond to production changes and emergencies in real time, adjust the scheduling plan, and ensure the continuity and efficiency of the production line;

[0026] Step C3: Self-optimization and learning: The scheduling plan generation module continuously improves the scheduling strategy through machine learning algorithms; by analyzing historical execution results, the module gradually optimizes the scheduling plan to improve the accuracy and adaptability of the plan; through self-learning, the module can not only meet current production needs, but also adapt to possible changes and challenges in the future.

[0027] The user interaction module is specifically used to perform the following steps:

[0028] Step D1: Interface design and implementation: The user interaction module provides an intuitive and friendly interface, supports clear navigation and function display; through information visualization tools, it displays production line status and scheduling in the form of charts and dashboards to help users quickly understand complex information; the module supports multi-language environments to ensure ease of use for international users;

[0029] Step D2: User customization function: Provides personalized setting options, allowing users to customize interface layout and information display according to personal needs; users can define and adjust scheduling rules and parameters to meet production needs of specific scenarios; the module has operation authority management function to ensure the security and flexibility of the system;

[0030] Step D3: User feedback and improvement: The module provides multiple feedback channels to collect user opinions and suggestions, help the multi-agent system understand user needs, and formulate continuous improvement plans based on feedback to improve the intelligence and efficiency of the overall scheduling system; the module supports text, voice and gesture operations to improve user experience and system availability.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] (A) The present invention has demonstrated many significant advantages in the field of production scheduling, especially in terms of optimizing the efficiency of the production scheduling process. It has achieved excellent performance improvement through the collaboration of a multi-agent system. The multi-agent system is composed of multiple agents that focus on different professional fields, including production scheduling, equipment management, and energy management. In this architecture, each agent uses adaptive learning algorithms such as reinforcement learning to continuously optimize its operating strategy, thereby greatly improving the functional performance in its professional field. The agents in the system achieve the optimal allocation of resources and information through efficient information sharing and priority management mechanisms, greatly reducing resource competition and conflicts. This mechanism not only makes resource utilization more reasonable, but also greatly improves the overall efficiency and reliability of the system, ensuring the efficient and continuous operation of the production line. In addition, the design of the system enables it to respond quickly to changes in the production environment, whether it is responding to fluctuations in production demand or handling emergencies, it can maintain a high degree of flexibility and adaptability.

[0033] (B) In addition to optimization in production scheduling, another major advantage of the present invention is its ability to support intelligent and customized scheduling solutions. Through personalized data analysis, the multi-agent system can accurately grasp the specific needs of different users and devices and provide them with tailor-made solutions. With the help of advanced algorithms, the system can accurately analyze and predict production needs, so as to formulate the best scheduling strategy to cope with diverse application scenarios. This customized service not only improves the accuracy and efficiency of scheduling, but also significantly enhances user experience and satisfaction. Through a deep understanding of user needs and rapid response, the system demonstrates excellent adaptability and can operate flexibly in various complex production environments to meet the unique needs of different users. In addition, the intelligent design of the system enables it to self-adjust and optimize in complex industrial environments, improving the flexibility and efficiency of the entire production process.

[0034] Overall, the present invention builds an efficient, reliable and flexible production scheduling system through efficient collaboration and information sharing between intelligent agents, combined with advanced learning algorithms and personalized scheduling strategies. It not only provides users with flexible and efficient production solutions, but also promotes progress in the field of intelligent manufacturing and provides new ideas and directions for future industrial production models. Whether in terms of production efficiency, reliability, or user experience, the present invention has set a new benchmark for modern industrial production. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Shown is a schematic diagram of the system structure of the present invention;

[0036] Figure 2 Shown is a structural diagram of the multi-agent scheduling and management system of the present invention. DETAILED DESCRIPTION

[0037] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the drawings 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.

[0038] It should be noted that the energy meter calibration optimization scheduling system based on the multi-agent large model algorithm of the present invention is composed of four modules: data acquisition module, multi-agent optimization scheduling module, scheduling plan generation module and user interaction module. The data acquisition module is the basic pillar of the system, responsible for real-time monitoring and collection of various key data on the energy meter calibration production line. Through advanced sensors and monitoring equipment, it tracks the equipment operation status and calibration status in detail, including equipment performance evaluation, fault prediction and energy consumption statistics, and connects with the cloud database to ensure data accuracy and consistency, and provide data support for subsequent analysis and optimization. The multi-agent optimization scheduling module constructs multiple agents focusing on different fields according to the characteristics of the calibration business, such as calibration scheduling, equipment management, energy management, etc. These agents achieve overall optimization of the calibration process through collaboration, and the management agent coordinates task decomposition and performance evaluation to ensure optimal system performance, and uses agents to replace manual dynamic optimization scheduling. The scheduling plan generation module converts the prediction and analysis results into actual operation plans, comprehensively considers equipment status, task priority and resource availability, generates the optimal scheduling plan, has dynamic adjustment capabilities and machine learning self-optimization functions, and improves the accuracy and adaptability of the plan. The user interaction module acts as a bridge between the system and the user, providing an intuitive and friendly interface, displaying scheduling and equipment status, allowing users to adjust tasks, enhancing system availability through multiple input methods, and collecting user feedback to improve the system. These four modules work together to form an efficient and intelligent electric energy meter calibration system, and each module works closely together to ensure that the overall performance and user satisfaction reach the best level.

[0039] This intelligent energy meter calibration system has significant advantages. First, the data acquisition module ensures real-time monitoring and high-precision data collection, providing a solid foundation for subsequent analysis and decision-making. Secondly, the multi-agent optimization scheduling module realizes the overall optimization of the energy meter calibration process through the collaborative work of multiple agents, and can dynamically respond to changes in production demand, reduce manual intervention and improve efficiency. The scheduling plan generation module formulates and adjusts the optimal scheduling plan according to the equipment status and task priority to ensure the continuity and efficiency of production. The user interaction module provides an intuitive and friendly interface and supports multiple input methods, which allows users to flexibly adjust production plans and collect feedback to achieve continuous improvement of the system. Combining these functions, the system significantly improves production efficiency, reduces operating costs, and improves the decision-making accuracy and adaptability of the system through continuous self-optimization, providing users with a flexible and efficient solution.

[0040] like Figure 1-Figure 2 As shown in the figure, the flow chart of the energy meter calibration optimization scheduling system based on the multi-agent large model algorithm is shown. Specifically, the intelligent energy meter calibration system consists of four closely coordinated modules, which realizes efficient and intelligent operation. First, the data acquisition module is the basis of the system, which is responsible for real-time monitoring and collecting key data on the production line through high-precision sensors. It uses data preprocessing and cleaning technology to ensure the accuracy and consistency of the data, and transmits the data to the cloud database through a security protocol for centralized storage and management, providing solid data support for analysis and optimization scheduling. Secondly, the multi-agent optimization scheduling module uses multiple agents to handle tasks in different professional fields, such as calibration scheduling, equipment management, and energy management. The agents achieve efficient collaboration and self-optimization through information sharing protocols and reinforcement learning algorithms. A management agent is responsible for coordinating and optimizing the entire system to ensure reasonable resource allocation and optimal system performance. Then, the scheduling plan generation module converts the prediction and analysis results into specific operation plans, and uses big data analysis and priority sorting algorithms to generate scheduling plans that match production needs. This module has the ability to dynamically adjust, can respond to production changes in real time, and continuously self-optimizes through machine learning algorithms to improve the accuracy and adaptability of scheduling. Finally, the user interaction module provides users with an intuitive and friendly interface and supports multiple input methods, allowing users to understand and control the scheduling process. Users can customize the interface and scheduling tasks according to their personal needs, and help the system to continuously improve and optimize through feedback functions. These four modules ensure the optimization of the overall performance of the smart meter verification system and the improvement of user satisfaction through their respective responsibilities and close cooperation.

[0041] The data acquisition module is specifically used to perform the following steps:

[0042] Step 11: Data collection and detection. Install sensors at key locations on the production line. Assume that the distribution of current sensor I(t), temperature sensor T(t), and vibration sensor V(t) can be represented by the function S(x, y, z), where (x, y, z) are three-dimensional spatial coordinates. Each sensor has a certain accuracy and response time. The accuracy is defined as ∈ and the response time is τ; the frequency f = 100 Hz means that 100 data points are collected per second. The data stream is represented as D(t) = {I(t), T(t), V(t)}, where each data point has a timestamp t. Set the threshold θ I ,θ T ,θ V They correspond to current, temperature and vibration respectively. The anomaly detection function is used to determine whether it exceeds the normal range:

[0043]

[0044] Step 12: Data preprocessing and cleaning. Wavelet transform is used to decompose and reconstruct the signal to remove high-frequency noise components. Linear regression is used to correct data deviation. The least squares method is used to solve its parameters, and finally the Lagrange interpolation method is used to fill the missing data with interpolation polynomials.

[0045] Step 13: Data storage and management. Use SSL / TLS protocol for transmission encryption to ensure the confidentiality and integrity of data during transmission. Use relational databases (such as MySQL) or NoSQL databases (such as MongoDB) to store data. Build index structure: For relational databases, use B+ tree indexes; for NoSQL databases, use hash indexes. Database query optimization, improve query efficiency through execution plan analysis and index optimization. Adopt a strategy that combines periodic full backup with incremental backup. Incremental backup only records the changes since the last backup.

[0046] The multi-agent optimization scheduling module is specifically used to execute the following steps:.

[0047] Step 21: Agent collaboration mechanism. Through fine-tuning of different prompt statements, a large model of multiple agents is constructed: the inspection and scheduling agent is responsible for scheduling and task allocation, the equipment management agent monitors equipment status and maintenance, and the energy management agent optimizes energy use. Use message queues (such as RabbitMQ) to achieve asynchronous communication and ensure that each agent synchronizes data.

[0048] Step 22: Agent adaptive learning. Using Q-learning algorithm: Q value update formula is:

[0049]

[0050] Where α is the learning rate and r is the discount factor. The agent perceives the state of the environment S(t), selects the action A(t), and updates the strategy through feedback R(t). It uses the experience replay mechanism, shares the experience pool, and adopts a centralized learning and distributed execution structure.

[0051] Step 23: Manage agents. Use the divide-and-conquer method to split complex tasks into subtasks and assign them to professional agents through topological sorting. Use multi-criteria decision analysis (such as the analytic hierarchy process AHP) to evaluate the performance of the agents and provide a feedback mechanism for the agents to adjust themselves. Use genetic algorithms: individuals are represented as scheduling plans, fitness functions measure the quality of the plans, and selection, crossover, and mutation operations optimize the scheduling strategy.

[0052] The scheduling plan generation module is specifically used to perform the following steps:

[0053] Step 31: Predictive analysis integration. Use ARIMA model for time series forecasting. Use association rule mining to analyze production process data, generate frequent item sets, and identify bottlenecks. Set up a linear programming model to solve the optimal allocation of equipment and manpower.

[0054] Step 32: Scheduling plan generation. Use dynamic programming to optimize the order of task execution to ensure maximum resource efficiency. Use event-driven architecture (such as complex event processing CEP) to monitor and adjust production plans in real time and dynamically adjust based on event patterns.

[0055] Step 33: Self-optimization and learning. Use support vector machines (SVM) or random forests to analyze historical execution data and improve scheduling strategies. Online learning mechanisms continuously update model parameters to adapt to environmental changes.

[0056] The user interaction module is specifically used to perform the following steps:

[0057] Step 41: Interface design and implementation. Use the D3.js library to design interactive charts to display production line status and scheduling data in real time. Implement multi-language interface through i18n technology and use tools such as gettext for language translation.

[0058] Step 42: User customization function. Provide draggable UI components, users can customize layout and information display. Use role-based access control (RBAC) to define different user roles and permissions to ensure system security.

[0059] Step 43: User feedback and improvement. Provide online feedback forms, emails, and instant messaging tools to collect user suggestions. Use natural language processing (NLP) technology to analyze text feedback, identify user needs, and promote system improvements. Through the above detailed implementation details, the system will be able to effectively collect, process, analyze, and schedule data, and achieve efficient and intelligent production management.

[0060] Through the above detailed steps and technical implementation, a multi-agent large-scale model of electric energy meter calibration and dispatching system is constructed, which realizes efficient and intelligent operation through high-precision data monitoring, intelligent dispatching and friendly user interface. Each module cooperates with each other to ensure data accuracy and optimal system performance, adapt to production changes, and improve user satisfaction.

[0061] In general, this invention is an intelligent production management system, which includes four modules: the first is the data acquisition module, which collects data by installing sensors on the production line, and uses a database to ensure the safe storage of data after preprocessing and cleaning; the next is the multi-agent optimization scheduling module, which builds multiple agents for collaboration, uses the Q-learning algorithm for adaptive learning and experience sharing, and optimizes the scheduling strategy through the genetic algorithm; the third is the scheduling plan generation module, which uses the ARIMA model for predictive analysis, combines dynamic programming and event-driven architecture to optimize task execution and resource allocation, and uses support vector machines and random forests to improve scheduling strategies; the last is the user interaction module, which provides a customizable multi-language interface, collects user feedback to improve the system, and ensures security through role-based access control. Through the integration of these modules, the system can efficiently perform data management and scheduling optimization, and achieve the goal of intelligent production management.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate and explain the present invention, and are not intended to limit the present invention to the scope of the described embodiments. In addition, those skilled in the art can understand that the present invention is not limited to the above embodiments, and more variations and modifications can be made according to the teachings of the present invention, and these variations and modifications all fall within the scope of the protection claimed by the present invention.

Claims

1. An energy meter calibration optimization and dispatching system based on a multi-agent large model algorithm, characterized in that: The system includes a data acquisition module, a multi-agent optimization scheduling module, a scheduling plan generation module and a user interaction module; The data acquisition module is used for data acquisition and monitoring, data preprocessing and cleaning, and data storage and management; The multi-agent optimization scheduling module is used for agent collaboration mechanism, agent adaptive learning and agent management; The scheduling plan generation module is used for forecasting analysis integration, scheduling plan generation, and self-optimization and learning; The user interaction module is used for interface design and implementation, user-defined functions, user feedback and improvement.

2. According to claim 1, an electric energy meter calibration optimization and dispatching system based on a multi-agent large model algorithm is characterized in that: The data acquisition module is specifically used to perform the following steps: Step A1: Data collection and monitoring: The data collection module deploys high-precision sensors at important nodes of the calibration line to ensure that equipment operation data and environmental conditions can be captured in real time. The module uses high-frequency data collection technology to ensure the timeliness of data, and combines the anomaly detection mechanism to detect equipment anomalies in a timely manner through set thresholds and pattern recognition technology, and trigger alarms to prevent failures. Step A2: Data preprocessing and cleaning: To ensure the accuracy of the data, first filter out the noise and erroneous information in the collection process to ensure the purity of the data; use data correction technology to eliminate measurement errors and improve data reliability; and for the missing parts in the data, the module uses interpolation to reasonably fill in the missing parts to ensure the integrity and consistency of the data; Step A3: Data storage and management: The module establishes a connection with the cloud database through a security protocol to achieve secure data transmission and centralized storage; creates an efficient index structure to support rapid data retrieval and ensure that production line data can be accessed and analyzed in a timely manner; during data storage, the module performs regular backups and has a complete data recovery mechanism to avoid data loss due to unexpected situations.

3. The electric energy meter calibration optimization and dispatching system based on multi-agent large model algorithm according to claim 2 is characterized in that: The high-precision sensors include current sensors, temperature sensors and vibration sensors.

4. The electric energy meter calibration optimization and dispatching system based on multi-agent large model algorithm according to claim 1 is characterized in that: The multi-agent optimization scheduling module is specifically used to perform the following steps: Step B1: Agent collaboration mechanism: This module uses multiple large language model agent systems. Each agent focuses on tasks in its own field. Efficient data synchronization is achieved through a designed information sharing protocol to ensure the effective use of resources and information. Agents resolve possible resource competition and conflicts through priority and arbitration mechanisms. Step B2: Agent adaptive learning: These large language model agents can learn and optimize their own strategies during task execution by introducing reinforcement learning algorithms. The agents continuously perceive changes in the production line environment and use accumulated experience data to make adjustments and improvements. In addition, the agents share experience to improve the adaptability and optimization capabilities of the overall system, ensuring the accuracy and efficiency of scheduling. Step B3: Management Agent: The management agent is responsible for the coordination and optimization of the overall scheduling system; it decomposes complex tasks into executable subtasks and reasonably allocates them to various professional agents; at the same time, the management agent evaluates the execution effect of each module, provides feedback to support its self-adjustment, and optimizes the global scheduling strategy based on real-time data and prediction results to ensure the best performance of the entire system.

5. The electric energy meter calibration optimization and dispatching system based on multi-agent large model algorithm according to claim 4 is characterized in that: The multiple large language model agent systems include an inspection and scheduling agent, an equipment management agent, and an energy management agent.

6. The electric energy meter calibration optimization and dispatching system based on multi-agent large model algorithm according to claim 1 is characterized in that: The scheduling plan generation module is specifically used to perform the following steps: Step C1: Prediction and analysis integration: Using the data analysis engine, the module deeply analyzes big data and model output to identify bottlenecks and optimization possibilities in the production process; by establishing a load prediction model, the module can predict future production loads and provide a scientific basis for scheduling plans; at the same time, resource demand assessment helps to reasonably allocate equipment and human resources to ensure efficient production operations; Step C2: Scheduling plan generation: The module uses a priority sorting algorithm to analyze production tasks to ensure that high-priority tasks are processed in a timely manner; it formulates the optimal scheduling plan based on the current status of the equipment and resource allocation; it has dynamic adjustment capabilities, and the module can respond to production changes and emergencies in real time, adjust the scheduling plan, and ensure the continuity and efficiency of the production line; Step C3: Self-optimization and learning: The scheduling plan generation module continuously improves the scheduling strategy through machine learning algorithms; by analyzing historical execution results, the module gradually optimizes the scheduling plan to improve the accuracy and adaptability of the plan; through self-learning, the module can not only meet current production needs, but also adapt to possible changes and challenges in the future.

7. The electric energy meter calibration optimization and dispatching system based on multi-agent large model algorithm according to claim 1 is characterized in that: The user interaction module is specifically used to perform the following steps: Step D1: Interface design and implementation: The user interaction module provides an intuitive and friendly interface, supports clear navigation and function display; through information visualization tools, it displays production line status and scheduling in the form of charts and dashboards to help users quickly understand complex information; the module supports multi-language environments to ensure ease of use for international users; Step D2: User customization function: Provides personalized setting options, allowing users to customize interface layout and information display according to personal needs; users can define and adjust scheduling rules and parameters to meet production needs of specific scenarios; the module has operation authority management function to ensure the security and flexibility of the system; Step D3: User feedback and improvement: The module provides multiple feedback channels to collect user opinions and suggestions, help the multi-agent system understand user needs, and formulate continuous improvement plans based on feedback to improve the intelligence and efficiency of the overall scheduling system; the module supports text, voice and gesture operations to improve user experience and system availability.

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