Metering asset management system based on electric energy meter data

Abstract

The invention discloses a metering asset management system based on electric energy meter data. The system integrates functional modules of data acquisition, real-time data processing, intelligent scheduling decision, fault prediction, scheduling optimization, feedback monitoring and the like. The data acquisition module comprehensively collects multi-dimensional data such as an electric energy meter, a power load, an equipment state and an environment; the real-time data processing module performs primary processing on the data; the intelligent scheduling decision module automatically calculates an optimal metering asset distribution strategy through a multi-objective optimization model based on real-time and historical data; the fault prediction module identifies potential faults in advance and adjusts asset distribution; the scheduling optimization module automatically adjusts asset classification and equipment deployment according to the intelligent scheduling decision and fault prediction information; and the feedback monitoring module monitors the scheduling effect in real time and collects new data for feedback optimization. According to the invention, the management efficiency of the metering assets is improved, the distribution cost is reduced, and the stability and safety of the system are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of metering asset management, and particularly to a metering asset management system based on electric energy meter data. Background Art

[0002] In the power industry, metering asset management is a key link to ensure the stable operation and efficient operation of the power grid. Traditional metering asset management methods mainly rely on manual statistics and scheduling. This method is not only inefficient but also difficult to reflect the actual needs of the power grid in real time. With the development of smart grid technology, data-based automated management systems have gradually emerged. However, existing data-based metering asset management systems still have many deficiencies.

[0003] On the one hand, existing systems are often limited to single electric energy meter data in data collection, ignoring the impact of power load, equipment status, and environmental factors on metering asset management. This limitation makes it difficult for the system to comprehensively and accurately reflect the actual operating status of the power grid, thus affecting the accuracy of scheduling decisions.

[0004] On the other hand, existing systems lack intelligence in data processing and scheduling decisions. Most systems still use simple statistical and comparison methods and cannot perform deep learning and intelligent analysis based on real-time data and historical data, thus unable to formulate the optimal metering asset allocation strategy. In addition, existing systems also have obvious shortcomings in fault prediction and response. They can often only respond passively after a fault occurs and cannot identify potential faults in advance and actively adjust asset allocation to prevent the occurrence of faults; although some intelligent scheduling systems have begun to try to introduce fault prediction technology, the fault prediction models of these systems are often independent of scheduling decisions and lack effective collaborative work; usually, when facing sudden faults, these systems lack flexible response measures and are difficult to respond quickly, resulting in low allocation and scheduling efficiency of equipment, unreasonable allocation of equipment resources, and affecting the overall operating efficiency of the power grid.

[0005] Therefore, the prior art needs a metering asset management system that can comprehensively collect data, intelligently process data and make scheduling decisions, and has the ability of fault prediction and response to improve the management efficiency of metering assets and the stability of the power grid. Summary of the Invention

[0006] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this part, as well as in the abstract and title of the specification of this application, to avoid obscuring the purpose of this part, the abstract, and the title. However, such simplifications or omissions shall not be used to limit the scope of the present invention.

[0007] Therefore, to solve the above technical problems, the present invention provides the following technical solutions: A metering asset management system based on electricity meter data, including:

[0008] A data acquisition module for real-time collecting data from electricity meters, power load sensors, equipment status monitoring devices, and environmental monitoring devices in various places;

[0009] A real-time data processing module for preliminarily processing the real-time data collected by the data acquisition module to provide basic data support;

[0010] An intelligent scheduling decision-making module for making intelligent scheduling decisions based on real-time data and historical data, automatically calculating and generating an optimal metering asset allocation strategy;

[0011] A fault prediction module for identifying potential faults in advance based on electricity meter data, equipment operation status, and environmental data, and adjusting asset allocation accordingly to cope with faults;

[0012] A scheduling optimization module for automatically adjusting the classification strategy of metering assets and optimizing equipment deployment based on the results of the intelligent scheduling decision-making module and the information of the fault prediction module;

[0013] A feedback monitoring module for real-time monitoring the effect of asset allocation after scheduling and collecting new data for feedback to ensure the self-adjustment and optimization of the system.

[0014] As a preferred solution of the metering asset management system based on electricity meter data of the present invention, wherein: The data collected by the data acquisition module includes but is not limited to electricity meter data, power load, equipment operation status, equipment health information, and environmental parameters; The electricity meter data includes historical electricity consumption, real-time electricity consumption, load demand, etc.; The power load includes real-time load demand data for each region, reflecting the power load fluctuation in that region; The equipment status information includes the operation status of the equipment (normal, faulty), remaining service life, fault prediction value, etc.; The environmental factors include factors affecting the electricity consumption pattern such as seasonal changes, climatic factors, holidays, etc.

[0015] As a preferred solution of the metering asset management system based on electricity meter data of the present invention, wherein: The data processing operations of the real-time data processing module include data cleaning, normalization processing, and supplementation of missing data.

[0016] As a preferred solution of the metering asset management system based on electricity meter data of the present invention, wherein: The intelligent scheduling decision-making module includes:

[0017] A scheduling model establishment unit for establishing an intelligent scheduling model based on input information such as electricity meter data, historical load data, environmental factors, and equipment operation status;

[0018] The asset allocation calculation unit uses a model to predict the real-time demand and load fluctuations of each region and device, calculates the optimal metering asset allocation plan, and at the same time considers avoiding asset concentration or resource waste;

[0019] The allocation decision output unit is used to convert the optimal allocation plan calculated by the asset allocation into specific allocation decisions, including equipment configuration, scheduling priorities, and scheduling plans, etc., for subsequent execution.

[0020] As a preferred solution of the metering asset management system based on electricity meter data according to the present invention, wherein: the intelligent scheduling model is a multi-objective optimization model, and the objectives include minimizing the allocation cost, maximizing the load matching degree, and maximizing the equipment utilization rate.

[0021] As a preferred solution of the metering asset management system based on electricity meter data according to the present invention, wherein: the fault prediction module includes:

[0022] The data collection and analysis unit collects historical fault data and real-time operation data, and analyzes the health status and fault risks of the equipment;

[0023] The fault warning information generation unit is used to identify potential faulty equipment or regions in advance, generate fault warning information and notify the scheduling system for adjustment;

[0024] The scheduling strategy adjustment unit is used to automatically adjust the scheduling strategy when a fault risk is detected to ensure the stability and security of the system.

[0025] As a preferred solution of the metering asset management system based on electricity meter data according to the present invention, wherein: the scheduling strategy includes: equipment priority adjustment, based on real-time load fluctuations and equipment fault prediction results, adjusting the priority of the equipment to ensure that high-demand regions obtain the required equipment first; load fluctuation handling, dynamically adjusting the load distribution between regions to avoid resource waste caused by excessive load fluctuations; fault response mechanism, when a faulty equipment is predicted, the system immediately adjusts the scheduling strategy to prevent the faulty equipment from continuing to work in regions with high loads and reduce risks.

[0026] As a preferred solution of the metering asset management system based on electricity meter data according to the present invention, wherein: the scheduling optimization module includes:

[0027] The equipment allocation execution unit automatically allocates the equipment to the target region according to the optimal allocation plan output by the intelligent scheduling decision module and executes the scheduling task;

[0028] The real-time adjustment and optimization unit continuously adjusts the asset allocation according to the real-time power load and equipment status, optimizes the allocation process, and avoids resource waste caused by regional load fluctuations or equipment failures;

[0029] The scheduling result verification unit implements the scheduling result and verifies it through the execution monitoring module to ensure that the scheduling plan meets the expected effect and prevent over - concentration of resources or unreasonable allocation.

[0030] As a preferred solution of the metering asset management system based on electricity meter data according to the present invention, wherein: the feedback monitoring module includes:

[0031] The data collection and tracking unit is used to track the asset scheduling effect in real - time and collect information such as new power load data, equipment operation status, and regional load changes.

[0032] The problem identification and optimization suggestion unit identifies potential problems and deficiencies in the scheduling process by analyzing the feedback data and optimizes and adjusts the scheduling algorithm using the model self - learning mechanism.

[0033] The periodic optimization plan generation unit automatically generates new optimization plans, performs periodic optimization according to the feedback, makes the metering asset allocation more reasonable and accurate, and avoids resource waste and improper scheduling.

[0034] As a preferred solution of the metering asset management system based on electricity meter data according to the present invention, wherein: the system further includes an artificial intervention module, which allows managers to manually intervene through the system interface to adjust the asset allocation strategy or execute emergency scheduling tasks when the system fails to allocate accurately or an anomaly occurs; managers can view historical data and prediction results through the interface to optimize future scheduling strategies and further improve the allocation efficiency of metering assets.

[0035] The beneficial effects of the present invention:

[0036] 1. The present invention comprehensively collects multi - dimensional data such as electricity meter data, power load, equipment status, and environmental factors through the data acquisition module, providing a rich and accurate information basis for the subsequent processing of the system. This comprehensive data acquisition method helps the system more accurately reflect the actual operation status of the power grid, thereby improving the accuracy of scheduling decisions.

[0037] 2. The present invention uses the real - time data processing module to preliminarily process the collected data, providing basic data support for subsequent intelligent scheduling decisions. The intelligent scheduling decision - making module establishes a multi - objective optimization model based on real - time data and historical data, automatically calculates and generates the optimal metering asset allocation strategy. This optimized decision not only takes into account the real - time load requirements of each region but also avoids asset concentration and resource waste through the intelligent scheduling model, thereby improving the utilization efficiency of metering assets; the above - mentioned intelligent data processing and scheduling decision - making methods can significantly improve the management efficiency of metering assets, reduce the allocation cost, and maximize the load matching degree and equipment utilization rate at the same time.

[0038] 3. The fault prediction module of the present invention analyzes the health status and fault risks of the equipment by collecting historical fault data and real-time operation data, and adjusts the asset allocation accordingly to cope with potential faults. This proactive fault prediction and response method ensures that the system can respond quickly and schedule when a fault occurs, reduces the losses caused by the fault, and improves the stability and security of the system.

[0039] 4. The scheduling optimization module of the present invention automatically adjusts the classification strategy of metering assets and equipment deployment according to the results of the intelligent scheduling decision module and the information of the fault prediction module. At the same time, by tracking the asset scheduling effect in real time and collecting new data for feedback, the system can continuously optimize the scheduling algorithm to make the metering asset allocation more reasonable and accurate. This real-time and accurate scheduling optimization method can further improve the management efficiency of metering assets and avoid resource waste and improper scheduling.

[0040] 5. The manual intervention module of the present invention allows managers to perform manual intervention when the system fails to allocate accurately or an abnormality occurs. This design enhances the flexibility and operability of the system, enabling managers to fine-tune the system according to the actual situation and further optimize future scheduling strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. Among them:

[0042] Figure 1 is the system architecture diagram of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0043] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be made in conjunction with the accompanying drawings of the specification.

[0044] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention. However, the present invention may be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0045] Second, the "one embodiment" or "embodiment" referred to herein means a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an individual or alternative embodiment that is mutually exclusive with other embodiments.

[0046] Embodiment 1

[0047] Referring to Figure 1 , for the first embodiment of the present invention, a metering asset management system based on electric energy meter data is provided. A metering asset management system based on electric energy meter data includes:

[0048] A data acquisition module for real-time collecting data from electric energy meters, power load sensors, equipment status monitoring devices, and environmental monitoring devices in various places; the data collected by the data acquisition module includes but is not limited to electric energy meter data, power load, equipment operation status, equipment health information, and environmental parameters; the electric energy meter data includes historical electricity consumption, real-time electricity consumption, load demand, etc.; the power load includes real-time load demand data for each region, reflecting the power load fluctuation in that region; the equipment status information includes the operation status of the equipment (normal, faulty), remaining service life, fault prediction value, etc.; the environmental factors include factors affecting the electricity consumption pattern such as seasonal changes, climatic factors, holidays, etc.

[0049] A real-time data processing module for preliminarily processing the real-time data collected by the data acquisition module to provide basic data support; the data processing operations of the real-time data processing module include data cleaning, normalization processing, and supplementation of missing data.

[0050] An intelligent scheduling decision-making module for making intelligent scheduling decisions based on real-time data and historical data, automatically calculating and generating an optimal metering asset allocation strategy.

[0051] The intelligent scheduling decision-making module includes:

[0052] A scheduling model establishment unit for establishing an intelligent scheduling model based on input information such as electric energy meter data, historical load data, environmental factors, and equipment operation status; the intelligent scheduling model is a multi-objective optimization model, and the objectives include minimizing the allocation cost, maximizing the load matching degree, and maximizing the equipment utilization rate.

[0053] An asset allocation calculation unit for using the model to predict the real-time demand and load fluctuation of each region and equipment, calculating the optimal metering asset allocation plan, and at the same time considering avoiding asset concentration or resource waste.

[0054] An allocation decision output unit, which is used to convert the optimal allocation plan calculated by asset allocation into specific allocation decisions, including equipment configuration, scheduling priority, and scheduling plan, etc., for subsequent execution.

[0055] A fault prediction module, which identifies potential faults in advance based on electricity meter data, equipment operating status, and environmental data, and adjusts asset allocation accordingly to cope with faults;

[0056] The fault prediction module includes:

[0057] A data collection and analysis unit, which collects historical fault data and real-time operation data, and analyzes the health status and fault risk of equipment;

[0058] A fault warning information generation unit, which is used to identify potential faulty equipment or areas in advance, generate fault warning information and notify the scheduling system for adjustment;

[0059] A scheduling strategy adjustment unit, which is used to automatically adjust the scheduling strategy when detecting fault risks to ensure the stability and security of the system;

[0060] The scheduling strategy includes: equipment priority adjustment, based on real-time load fluctuations and equipment fault prediction results, adjusting the priority of equipment to ensure that high-demand areas obtain the required equipment first; load fluctuation handling, dynamically adjusting the load distribution between regions to avoid resource waste caused by excessive load fluctuations; fault response mechanism, when a faulty equipment is predicted, the system immediately adjusts the scheduling strategy to prevent the faulty equipment from continuing to work in areas with high loads and reduce risks;

[0061] A scheduling optimization module, which automatically adjusts the classification strategy of metering assets and optimizes equipment deployment based on the results of the intelligent scheduling decision module and the information of the fault prediction module;

[0062] The scheduling optimization module includes:

[0063] An equipment allocation execution unit, which automatically allocates equipment to the target area according to the optimal allocation plan output by the intelligent scheduling decision module and executes the scheduling task;

[0064] A real-time adjustment and optimization unit, which continuously adjusts asset allocation according to real-time power load and equipment status, optimizes the allocation process, and avoids resource waste caused by regional load fluctuations or equipment faults;

[0065] A scheduling result verification unit, which implements the scheduling result and verifies it through the execution monitoring module to ensure that the scheduling plan meets the expected effect and prevent over-concentration or unreasonable allocation of resources.

[0066] A feedback monitoring module, which is used to monitor the asset allocation effect after scheduling in real time and collect new data for feedback to ensure the self-adjustment and optimization of the system;

[0067] The feedback monitoring module includes:

[0068] A data collection and tracking unit, which is used to track the asset scheduling effect in real time and collect information such as new power load data, equipment operation status, and regional load changes;

[0069] A problem identification and optimization suggestion unit, which identifies potential problems and deficiencies in the scheduling process by analyzing feedback data and optimizes and adjusts the scheduling algorithm using the model self-learning mechanism;

[0070] A periodic optimization plan generation unit, which automatically generates new optimization plans, performs periodic optimization according to the feedback, makes the metering asset allocation more reasonable and accurate, and avoids resource waste and improper scheduling.

[0071] The system also includes a manual intervention module, which allows managers to manually intervene through the system interface to adjust the asset allocation strategy or execute emergency scheduling tasks when the system fails to accurately allocate or abnormal situations occur; managers can view historical data and prediction results through the interface to optimize future scheduling strategies and further improve the allocation efficiency of metering assets.

[0072] Most current smart grid scheduling systems focus on the optimization of a single objective, such as minimizing cost or maximizing equipment utilization, while this embodiment introduces a multi-objective optimization model, which not only considers the allocation cost, but also considers the load matching degree and equipment utilization. This comprehensive optimization strategy can balance multiple objectives during the resource scheduling process, thereby achieving optimal asset allocation;

[0073] The fault prediction module of the present invention is closely combined with the intelligent scheduling decision module, and can adjust the scheduling strategy in time when potential faults are identified to ensure that the equipment can be reasonably allocated. This collaborative mechanism can effectively reduce the impact of faults on the operation of the power grid and avoid resource waste and operation interruption caused by equipment failures;

[0074] Through the feedback monitoring module and the self-learning mechanism, the present invention can continuously optimize the scheduling strategy during the operation of the system, making the metering asset allocation plan more and more accurate over time. This dynamic optimization ability cannot be achieved by traditional systems, which greatly improves the adaptive ability and long-term operation benefit of the system;

[0075] In summary, the metering asset management system based on electric energy meter data proposed by the present invention shows significant technical advantages in data collection, data processing and scheduling decision-making, fault prediction and response, scheduling optimization, and manual intervention. This system can more comprehensively and accurately reflect the actual operation status of the power grid, improve the management efficiency of metering assets and the stability of the power grid, and has broad application prospects and important social value.

[0076] Example 2

[0077] This is the second embodiment of the present invention. The difference between this embodiment and the first embodiment is that this embodiment aims to verify the innovation of a metering asset management system based on electricity meter data and its effects in actual applications. The system conducts real-time intelligent scheduling on electricity meter data, power load, equipment operation status, fault prediction, scheduling optimization, etc., and optimizes the metering asset allocation strategy to improve resource utilization efficiency, reduce costs, and enhance system stability. For this purpose, this embodiment simulates the application of the system in a typical area with complex power loads for testing and compares it with traditional manual scheduling and single intelligent scheduling methods.

[0078] 1. System Configuration and Implementation Process

[0079] Data Acquisition Module: Real-time collect data from electricity meters, power load sensors, equipment status monitoring devices, and environmental monitoring devices in three different regions; the data content includes historical power load, real-time power load, the operation status of equipment (such as faults, repairs, etc.), the remaining service life of equipment, and external environmental data (such as weather, seasonal changes, holidays, etc.);

[0080] Real-time Data Processing Module: Clean, normalize, and supplement missing data for the above-collected raw data to ensure data integrity and availability; the processed data is used as the input for the intelligent scheduling decision-making module;

[0081] Intelligent Scheduling Decision-making Module: Based on real-time data and historical load data, combined with information such as equipment status and environmental factors, through a multi-objective optimization model (such as minimizing allocation costs, maximizing load matching degree, and maximizing equipment utilization rate), automatically calculate and generate the optimal metering asset allocation strategy; this strategy optimally allocates electricity meters, sensors, and equipment among different regions and generates a specific equipment deployment plan according to the predetermined scheduling priority and scheduling plan.

[0082] Fault Prediction Module: Analyze electricity meter data and equipment operation status, and combine with a fault warning model to predict potential equipment fault risks; according to the prediction results, timely adjust the equipment allocation plan to reduce the impact of fault occurrence; for example, when a fault is predicted for a certain piece of equipment, the system will automatically transfer the load of this equipment to other equipment to ensure normal power supply in the area.

[0083] Scheduling Optimization Module: Real-time monitor the effect of asset allocation and adjust and optimize unreasonable allocations; when the system discovers that there is an over-concentration of equipment resources or large load fluctuations in certain regions, the system will adjust the equipment allocation according to real-time data and optimize the scheduling process to avoid resource waste or power supply shortages.

[0084] Feedback Monitoring Module: The system, through the data collection and tracking unit, real-time tracks the asset scheduling effect and analyzes the scheduling effect based on real-time data; the problem identification and optimization suggestion unit can automatically identify problems in scheduling and provide optimization suggestions. Through the periodic optimization plan generation unit, a new optimization plan is automatically generated to further improve the accuracy and rationality of asset allocation.

[0085] 2. Experimental Process

[0086] During the implementation process, first, the power load data, equipment status information, and external environment data of the experimental area were set; then, the data was input into the system, and the intelligent scheduling decision module was started to allocate metering assets. Compared with the traditional manual scheduling method and the single intelligent scheduling method, this system can automatically optimize resource allocation, and when a fault prediction occurs for a device, the system can make timely adjustments to transfer the load of the faulty device to other devices to ensure the stability of regional power supply.

[0087] 3. Data Recording and Table Display

[0088] During the experiment, this embodiment collected data such as power load data, equipment status data, allocation costs, load matching degrees, and equipment utilization rates in different time periods and different regions for comparative analysis; the specific data records are shown in the following table:

[0089]

[0090] By comparing the data in the table, it can be seen that the metering asset management system of the present invention shows obvious advantages in many aspects.

[0091] Allocation Cost: The allocation cost of the method of the present invention is the lowest, which is 75,000 yuan, a 40% reduction compared with 125,000 yuan of the traditional manual scheduling. This shows that the intelligent scheduling decision module of the present invention can reduce unnecessary allocation and transportation costs through a multi-objective optimization algorithm and improve resource utilization efficiency.

[0092] Load Matching Degree: The load matching degree of the method of the present invention reaches 95%, which is 10% and 5% higher than 85% and 90% of the traditional manual scheduling and the single intelligent scheduling methods respectively. The increase in the load matching degree indicates that the present invention can more accurately meet the power load requirements of each region and avoid resource waste or insufficient power supply.

[0093] Equipment Utilization Rate: The equipment utilization rate of the method of the present invention is 90%, higher than 75% of the traditional manual scheduling and 85% of the single intelligent scheduling. This shows that the system can more reasonably allocate metering equipment, avoid equipment idleness and overloading, and improve the overall utilization efficiency of the equipment.

[0094] Number of failures: The number of failures is an important indicator to measure the reliability of the system. The number of failures of the method of the present invention is only 1 time, significantly lower than 5 times of traditional manual scheduling and 3 times of single intelligent scheduling. This indicates that the failure prediction module in the present invention can effectively identify potential failures and take measures in advance for adjustment, significantly improving the stability and reliability of the system.

[0095] Resource waste after adjustment: The resource waste of the method of the present invention is only 5%, significantly lower than 15% of traditional manual scheduling and 10% of single intelligent scheduling. This result shows that by adjusting and optimizing the scheduling strategy in real time, the waste of resources can be effectively reduced.

[0096] Total load demand satisfaction rate: The total load demand satisfaction rate of the method of the present invention is 98%, much higher than 92% of traditional manual scheduling and 95% of single intelligent scheduling. This further proves that the present invention can more accurately meet the regional power load demand, improving the reliability and stability of the power system.

[0097] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A metering asset management system based on electric energy meter data, characterized in that, Including: A data acquisition module, which is used to collect data in real time from electricity meters, power load sensors, equipment status monitoring devices, and environmental monitoring devices in various places; A real-time data processing module, which preliminarily processes the real-time data collected by the data acquisition module to provide basic data support; An intelligent scheduling decision-making module, which makes intelligent scheduling decisions based on real-time data and historical data, and automatically calculates and generates an optimal metering asset allocation strategy; A fault prediction module, which identifies potential faults in advance based on electricity meter data, equipment operating status, and environmental data, and adjusts asset allocation accordingly to cope with faults; A scheduling optimization module, which automatically adjusts the classification strategy of metering assets and optimizes equipment deployment based on the results of the intelligent scheduling decision-making module and the information of the fault prediction module; A feedback monitoring module, which is used to monitor the asset allocation effect after scheduling in real time, and collect new data for feedback to ensure the self-adjustment and optimization of the system.

2. The metering asset management system based on electric energy meter data according to claim 1, wherein: The data collected by the data acquisition module includes electricity meter data, power load, equipment operating status, equipment health information, and environmental parameters.

3. The metering asset management system based on the watt-hour meter data according to claim 2, characterized in that: The data processing operations of the real-time data processing module include data cleaning, normalization processing, and supplementation of missing data.

4. The metering asset management system based on electricity meter data according to claim 3, characterized in that: The intelligent scheduling decision-making module includes: A scheduling model establishment unit, which establishes an intelligent scheduling model based on input information such as electricity meter data, historical load data, environmental factors, and equipment operating status; An asset configuration calculation unit, which uses the model to predict the real-time demand and load fluctuations of each region and equipment, calculates the optimal metering asset configuration plan, and at the same time considers avoiding asset concentration or resource waste; An allocation decision output unit, which is used to convert the optimal configuration plan calculated by the asset configuration into specific allocation decisions, including equipment configuration, scheduling priority, and scheduling plan, for subsequent execution.

5. The metering asset management system based on electric energy meter data according to claim 4, characterized in that: The intelligent scheduling model is a multi-objective optimization model, and the objectives include minimizing the allocation cost, maximizing the load matching degree, and maximizing the equipment utilization rate.

6. The metering asset management system based on the electric energy meter data according to claim 5, characterized in that: The fault prediction module includes: A data collection and analysis unit, which collects historical fault data and real-time operation data, and analyzes the health status and fault risk of equipment; A fault warning information generation unit, which is used to identify potential faulty equipment or regions in advance, generate fault warning information, and notify the scheduling system to make adjustments; A scheduling strategy adjustment unit, which is used to automatically adjust the scheduling strategy when a fault risk is detected to ensure the stability and security of the system.

7. The metering asset management system based on the electric energy meter data according to claim 6, characterized in that: The scheduling strategies include: Equipment priority adjustment, which adjusts the priority of equipment based on real-time load fluctuations and equipment fault prediction results to ensure that high-demand regions obtain the required equipment first; Load fluctuation processing, which dynamically adjusts the load distribution between regions to avoid resource waste caused by excessive load fluctuations; A fault response mechanism, when a faulty equipment is predicted, the system immediately adjusts the scheduling strategy to avoid the faulty equipment from continuing to work in a region with a higher load, reducing risks.

8. The metering asset management system based on the electric energy meter data according to claim 7, wherein: The scheduling optimization module includes: An equipment allocation execution unit, which automatically allocates equipment to the target region according to the optimal allocation plan output by the intelligent scheduling decision-making module, and executes the scheduling task; The real-time adjustment and optimization unit continuously adjusts asset allocation according to real-time power load and equipment status, optimizes the allocation process, and avoids resource waste caused by regional load fluctuations or equipment failures; The scheduling result verification unit implements the scheduling result and verifies it through the execution monitoring module to ensure that the scheduling plan meets the expected effect and prevent over-concentration or unreasonable allocation of resources.

9. The metering asset management system based on the electric energy meter data according to claim 8, characterized in that: The feedback monitoring module includes: The data collection and tracking unit is used to track the asset scheduling effect in real time and collect new power load data, equipment operation status, and regional load change information; The problem identification and optimization suggestion unit identifies potential problems and deficiencies in the scheduling process by analyzing the feedback data and optimizes and adjusts the scheduling algorithm using the model self-learning mechanism; The periodic optimization scheme generation unit automatically generates a new optimization scheme, performs periodic optimization based on the feedback, makes the metering asset allocation more reasonable and accurate, and avoids resource waste and improper scheduling.

10. The metering asset management system based on electric energy meter data according to claim 9, characterized in that: The system also includes a manual intervention module, which allows managers to manually intervene through the system interface to adjust the asset allocation strategy or execute emergency scheduling tasks in case the system fails to allocate accurately or an abnormality occurs; managers view historical data and prediction results through the interface to optimize future scheduling strategies and further improve the allocation efficiency of metering assets.

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