A method and system for verifying and automatically shunting electricity meters through multi-task parallel processing.

Abstract

This invention discloses a multi-task parallel processing method and system for electricity meter verification and automatic load shedding, relating to the field of electricity meter verification technology. The method includes: zeroing a three-phase electricity meter; performing pre-action processing on the meter after zeroing; analyzing the condition of various parts of the meter to determine if there is excessive workload or potential faults; real-time reading of operating parameters; when there are no potential faults but the workload is excessive, constructing an intelligent data model to comprehensively evaluate the meter's performance and providing a corresponding verification plan; automatically shedding multiple tasks based on the verification results and workload excess, analyzing the correlation between network traffic trends and verification tasks, and reducing task queuing time. This invention enhances the real-time performance and data processing capabilities of the verification process, optimizes the verification plan, improves verification efficiency, enhances the overall performance and workload balance of the verification system, reduces the failure rate and misjudgment rate during the verification process, and provides flexible and personalized electricity meter verification services.

Description

Technical Field

[0001] This invention relates to the field of electricity meter calibration technology, specifically to a method and system for electricity meter calibration and automatic power diversion that involves multi-task parallel processing. Background Technology

[0002] In the field of electricity meter verification technology, with the rapid development of smart grids, the requirements for the accuracy, stability, and reliability of electricity meters are becoming increasingly stringent. Traditional electricity meter verification methods mainly rely on manual operation, which is not only inefficient but also difficult to meet the demands of large-scale verification tasks. In recent years, although some automated verification systems have been gradually applied to the electricity meter verification process, these systems still have many shortcomings when handling multiple tasks in parallel.

[0003] First, existing technologies for electricity meter verification often neglect pre-processing of various parts of the meter, potentially leading to task overload or potential malfunctions during task execution. These systems lack real-time monitoring and evaluation of the meter's status during verification, resulting in errors in the verification results. Second, when performing multi-task parallel processing, existing technologies fail to effectively analyze the correlation between network traffic trends and verification tasks, leading to excessively long task queuing times and low verification efficiency. Furthermore, existing technologies lack in-depth analysis and step-by-step verification for addressing performance deficiencies when evaluating electricity meter performance, failing to provide accurate verification solutions.

[0004] To address the aforementioned issues, we have invented a method and system for verifying and automatically shunting electricity meters through multi-task parallel processing. By monitoring the status of various parts of the electricity meter in real time, the system performs zeroing operations and pre-action processing on the meter, effectively identifying excessive workloads or potential faults. Compared to existing technologies, our invention has significant advantages in improving verification accuracy, increasing verification efficiency, and reducing the failure rate. Summary of the Invention

[0005] In view of the aforementioned existing problems, this invention aims to address the technical issues of low efficiency, large errors in verification results, long task queuing times, and lack of in-depth analysis and accurate verification schemes for performance deficiencies in existing electricity meter verification systems when handling multiple tasks in parallel. By providing a method and system capable of real-time monitoring of electricity meter status, intelligent evaluation of electricity meter performance, and automatic task distribution, this invention aims to improve the accuracy, efficiency, and reliability of electricity meter verification, while reducing the verification error rate caused by excessive tasks or potential faults.

[0006] To address the aforementioned technical problems, a multi-task parallel processing method for electricity meter verification and automatic current distribution is proposed, including:

[0007] The system performs a zeroing operation on three-phase energy meters. After zeroing, it performs pre-action processing on the energy meters, analyzes the condition of various parts of the energy meters, and determines whether there is an overload or potential fault. It reads the working parameters in real time. When there is no potential fault but the workload is excessive, it builds an intelligent data model to comprehensively evaluate the performance of the energy meters and provides corresponding verification solutions. Based on the verification results and the workload situation, it automatically distributes multiple tasks, analyzes the trend of network traffic changes and the correlation with verification tasks, reduces task queuing time, and optimizes automatic task distribution.

[0008] As a preferred embodiment of the multi-task parallel processing method for electricity meter verification and automatic current diversion described in this invention, the pre-action processing includes: after clearing the data of the three-phase lines, performing simulation processing according to a pre-set task running trajectory or task running direction after power-on, applying a known standard signal or load to the electricity meter, simulating real usage, and feeding back the simulation processing results and sending the information to the verification system. After confirming that the electricity meter is in normal and reliable condition, performing preliminary analysis on the acquired real-time data, checking whether the electricity meter output is consistent with the input signal, and considering that the output power should be consistent with the input signal when the error is within 5% and the current and voltage are not distorted.

[0009] Record the load status of each part of the electricity meter during the task execution. If the current load of any phase exceeds 80% of the rated value within 10 minutes, it is marked as task overload.

[0010] Using IF-THEN logic, when the current exceeds 110% of the rated current, it is marked as a potential fault; when the power factor is less than 0.85, it is marked as a performance degradation, and the energy meter is replaced.

[0011] After the judgment is completed, the judgment result is fed back to the verification system, and a report is generated including all test data, fault alarms, potential risks and recommended measures;

[0012] The information includes data on normal operating status, abnormal alarms, and historical records of various indicators of the electricity meter.

[0013] As a preferred embodiment of the multi-task parallel processing method for electricity meter verification and automatic current diversion described in this invention, the working parameters include: collecting three-phase voltage data, three-phase current data, power factor, active power, reactive power, total harmonic distortion, temperature changes of the electricity meter casing and internal components, grid frequency, electricity consumption changes within the metering cycle, status indication, sampling timestamp, and real-time network traffic data.

[0014] As a preferred embodiment of the multi-task parallel processing method for electricity meter verification and automatic power diversion described in this invention, the intelligent data model includes: constructing an intelligent data model when there are no potential faults and the workload is excessive. A comprehensive evaluation of the electricity meter's performance is conducted using both global evaluation coefficients and local performance feedback.

[0015]

[0016] Where X represents the overall performance evaluation of the electricity meter, N is the total number of features of the electricity meter, i is the variable index, and F i Let k represent the characteristic value of the i-th electricity meter. i This represents the dynamic weight used to adjust the current task load; P represents the overall performance index; α1 represents the global evaluation coefficient; 3 represents the total number of key performance indicators, including the overall performance impact index, stability function, and response time adjustment factor; j represents the variable index; β... j ξ represents the adjustment factor for the impact of the j-th performance indicator on the overall evaluation. j (X) represents the relative deviation based on the comprehensive feature X, measuring the gap between the feature and the target level; α2 represents the weight of the local performance feedback; H represents the feedback data extracted from historical data; δ j θ represents the historical performance feedback weight. j Specific numerical values ​​representing the historical performance of the electricity meter;

[0017] The key performance indicators (KPIs) used in the comprehensive evaluation are based on the adaptive range (minR) set for the KPIs. j maxR j ), where minR j MaxR represents the minimum value of the dynamic evaluation interval. j Indicates the maximum value of the dynamic evaluation interval;

[0018] Different performance levels are defined through dynamic evaluation of each performance metric:

[0019] When P > maxR j At that time, the system considers it to be a high-efficiency layer, and the overall evaluation result is qualified;

[0020] When minR j <P≤maxR j At that time, the system considers it to be in the medium-efficiency layer, and it needs to be directly evaluated as qualified after error correction;

[0021] When P≤minR j If the system considers it an inefficient layer, the overall evaluation result is deemed unqualified.

[0022] As a preferred embodiment of the multi-task parallel processing method for electricity meter verification and automatic current diversion described in this invention, wherein: the comprehensive evaluation result being unqualified includes performing in-depth performance analysis on the electricity meter, providing a corresponding verification plan, and determining the degree of performance deficiency S = P - min(P, R)j To address identified performance deficiencies, a step-by-step verification process was developed. Through a clear data recording and real-time processing mechanism, performance change data of the meter under various conditions was generated, and an optimized recalibration formula was established.

[0023]

[0024] Where C represents the final quantitative evaluation of the recalibrated verification scheme, f1(X) represents the impact value of evaluating the current performance, f2 represents the function with historical output data, and T represents the sum of the durations of historical states. i This represents the state value at the i-th time point;

[0025] The effectiveness of the verification plan is confirmed by recalculating the comprehensive performance index and comparing it with the initial interval, where P' is the new comprehensive performance index after the verification plan is adjusted.

[0026] As a preferred embodiment of the multi-task parallel processing method for electricity meter verification and automatic load distribution described in this invention, the multi-task automatic load distribution includes: when the number of tasks exceeds the limit, performing a dynamic automatic load distribution system to initially classify the electricity meter verification tasks, identify the current load status, and prioritize them according to their importance and urgency; using a model trained by the ant colony algorithm to adjust the priority of tasks and the load priority of electricity meters of the same type under different network conditions, wherein the load priority of electricity meters of the same type includes idle, low load, and high load.

[0027] The traffic is distributed according to priority. High-priority and excessive parallel multitasking tasks are uniformly assigned to matching idle energy meters of the same type. High-priority and excessive non-parallel independent tasks are assigned to low-load energy meters of the same type. When high-load energy meters assign excess tasks, the load priority of energy meters of the same type is readjusted. The original excess high-load energy meters are not prioritized. When the original excess high-load energy meters finish their tasks and show that they are idle, the priority is re-sorted.

[0028] The system coordinates the real-time data stream of the electricity meter's performance parameters by monitoring the decision-making unit. When the system detects that the task is excessive, it automatically activates the decision-making unit and communicates with the backend database through data interaction to analyze the current status and extract effective information. Based on historical data and real-time monitoring indicators, the coordination module formulates the current optimization decision. After each task is completed, the system will feed back the results to the coordination module and update the task priority and diversion strategy in real time.

[0029] As a preferred embodiment of the multi-task parallel processing method for electricity meter verification and automatic traffic diversion described in this invention, the method for reducing task queuing time includes: real-time monitoring of network traffic changes, and traffic prediction based on traffic fluctuations through granular analysis.

[0030] Using the node association model in graph theory, an association network is established between electricity meter verification tasks. Nodes represent different verification tasks, and associations are modeled based on shared resources or time dependencies between tasks. Task priorities are adjusted according to the node association model, and the adjusted priority A is then used to... adj (a) Perform task scheduling to reduce task queuing time.

[0031] Another objective of this invention is to provide a multi-task parallel processing system for electricity meter verification and automatic load shedding. This invention aims to solve several problems in the electricity meter verification process, including improving verification efficiency, ensuring the accuracy and reliability of electricity meters, handling task overload situations, and rationally allocating tasks through intelligent data models and automatic load shedding strategies to avoid verification delays or errors. Simultaneously, the system collects data in real time and evaluates electricity meter performance, promptly detecting potential faults or performance degradation, and achieving fault warning and handling. Furthermore, the system optimizes resource allocation, prioritizing tasks according to their urgency and importance to improve overall operational efficiency, and reduces task queuing time through real-time monitoring and priority adjustment, ensuring efficient and timely processing of verification tasks. This comprehensively improves the automation, intelligence, and efficiency of the electricity meter verification process.

[0032] As a preferred embodiment of the multi-task parallel processing energy meter verification and automatic diversion system described in this invention, it is characterized by including a zeroing pre-action module, a data evaluation module, and an automatic diversion module;

[0033] The zeroing pre-action module includes a zeroing operation unit and a pre-action processing unit. The zeroing operation unit performs a zeroing operation on the energy meter, and the pre-action processing unit performs simulation processing on the energy meter and feeds the results back to the verification system, and transmits the energy meter status information to the data evaluation module.

[0034] The data evaluation module includes a real-time data acquisition unit and an intelligent data model evaluation unit. The real-time data acquisition unit collects various parameters of the electricity meter based on the information provided by the zeroing pre-action module. The intelligent data model evaluation unit constructs an intelligent data model based on the real-time data, performs a comprehensive evaluation of the electricity meter's performance, and transmits the evaluation results to the automatic diversion module.

[0035] The automatic task allocation module includes a task classification and sorting unit and a task allocation path adjustment unit. The task classification and sorting unit classifies and prioritizes the verification tasks, and the task allocation path adjustment unit adjusts the allocation path according to the priority order to achieve automatic task allocation.

[0036] A computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of the multi-task parallel processing method for energy meter verification and automatic power diversion.

[0037] A computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the multi-task parallel processing method for energy meter verification and automatic power diversion.

[0038] The beneficial effects of this invention are as follows: The multi-task parallel processing method for electricity meter verification and automatic current distribution first performs a zeroing operation and pre-action processing on the electricity meter, achieving initialization and pre-diagnosis of the meter's state. This ensures that the electricity meter is in a standard state before verification, thereby reducing misjudgments during the verification process and improving verification efficiency. Real-time reading of operating parameters enables continuous monitoring of the electricity meter's operating status, collecting comprehensive electricity meter performance data, and providing real-time and accurate support for subsequent performance evaluation and verification schemes. The construction of an intelligent data model comprehensively evaluates the electricity meter's performance, providing personalized verification schemes and improving the relevance and effectiveness of the verification schemes. Finally, based on the verification results and the situation of excessive tasks, multiple tasks are automatically distributed, optimizing the allocation of verification tasks, balancing the workload of the verification system, reducing task queuing time, and improving the response speed and throughput of the verification system. This comprehensively improves the accuracy and reliability of electricity meter verification, enhances the real-time performance and data processing capabilities of the verification process, optimizes verification efficiency, and improves the overall performance and workload balance of the verification system through automatic task distribution, reducing the failure rate and misjudgment rate, and providing more flexible and personalized electricity meter verification services. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein:

[0040] Figure 1 This is a flowchart illustrating a multi-task parallel processing method for energy meter verification and automatic power diversion, provided as an embodiment of the present invention.

[0041] Figure 2 This is a system block diagram of a multi-task parallel processing power meter verification and automatic diversion system provided in one embodiment of the present invention. Detailed Implementation

[0042] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0043] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0044] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive, either alone or selectively, with other embodiments.

[0045] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0046] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0047] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0048] Example 1, referring to Figure 1This is the first embodiment of the present invention, which provides a method for multi-task parallel processing of electricity meter verification and automatic power diversion, including:

[0049] S1: Perform a zeroing operation on the three-phase energy meter. After zeroing, perform pre-action processing on the energy meter, analyze the condition of each part of the energy meter, and determine whether there is an overload or potential fault.

[0050] Furthermore, after clearing the data on the three-phase lines, the system performs simulation processing according to the pre-set task operation trajectory or direction after power-on. It applies known standard signals or loads to the energy meter to simulate real-world usage and feeds back the simulation results to the calibration system. After confirming that the energy meter is in normal and reliable condition, it performs preliminary analysis on the acquired real-time data to check whether the energy meter output is consistent with the input signal. When the error is within 5% and the current and voltage are not distorted, it is considered that the output power should be consistent with the input signal.

[0051] Record the load status of each part of the electricity meter during the task execution. If the current load of any phase exceeds 80% of the rated value within 10 minutes, it is marked as task overload.

[0052] Using IF-THEN logic, when the current exceeds 110% of the rated current, it is marked as a potential fault; when the power factor is less than 0.85, it is marked as a performance degradation, and the energy meter is replaced.

[0053] After the judgment is completed, the judgment result is fed back to the verification system, and a report is generated including all test data, fault alarms, potential risks and recommended measures;

[0054] The information includes data on normal operating status, abnormal alarms, and historical records of various indicators of the electricity meter.

[0055] S2: Real-time reading of working parameters.

[0056] Furthermore, the operating parameters include the collection of three-phase voltage data, three-phase current data, power factor, active power, reactive power, total harmonic distortion, temperature changes of the meter casing and internal components, grid frequency, electricity consumption changes within the metering cycle, status indication, sampling timestamp, and real-time network traffic data.

[0057] S3: When there are no potential faults and the workload is excessive, build an intelligent data model to comprehensively evaluate the performance of the electricity meter and provide a corresponding verification plan.

[0058] Furthermore, when there are no potential faults and the workload is excessive, build an intelligent data model. A comprehensive evaluation of the electricity meter's performance is conducted using both global evaluation coefficients and local performance feedback.

[0059]

[0060] Where X represents the overall performance evaluation of the electricity meter, N is the total number of features of the electricity meter, i is the variable index, and F i Let k represent the characteristic value of the i-th electricity meter. i This represents the dynamic weight used to adjust the current task load; P represents the overall performance index; α1 represents the global evaluation coefficient; 3 represents the total number of key performance indicators, including the overall performance impact index, stability function, and response time adjustment factor; j represents the variable index; β... j ξ represents the adjustment factor for the impact of the j-th performance indicator on the overall evaluation. j (X) represents the relative deviation based on the comprehensive feature X, measuring the gap between the feature and the target level; α2 represents the weight of the local performance feedback; H represents the feedback data extracted from historical data; δ j θ represents the historical performance feedback weight. j Specific numerical values ​​representing the historical performance of the electricity meter;

[0061] The key performance indicators (KPIs) used in the comprehensive evaluation are set with adaptive ranges based on the KPIs:

[0062] (minR j maxR j )=(min(P j )·(1-η j ), max(P j )·(1+η j ))

[0063] Wherein, minR j MaxR represents the minimum value of the dynamic evaluation interval. j P represents the maximum value of the dynamic evaluation interval. j This represents the current evaluation value of the j-th performance indicator, where j is the index of the variable and has no actual meaning; η j This indicates a dynamic adjustment factor based on historical data to optimize the current assessment scope;

[0064] Different performance levels are defined through dynamic evaluation of each performance metric:

[0065] When P > maxR j At that time, the system considers it to be a high-efficiency layer, and the overall evaluation result is qualified;

[0066] When minR j <P≤maxR j At that time, the system considers it to be in the medium-efficiency layer, and it needs to be directly evaluated as qualified after error correction;

[0067] When P≤minR j If the system considers it an inefficient layer, the overall evaluation result is deemed unqualified.

[0068] Conduct in-depth performance analysis of the electricity meter, provide corresponding verification schemes, and determine the degree of performance deficiency: S = P - min(P, R) j To address identified performance deficiencies, a step-by-step verification process was developed. Through a clear data recording and real-time processing mechanism, performance change data of the meter under various conditions was generated, and an optimized recalibration formula was established.

[0069]

[0070] Where C represents the final quantitative evaluation of the recalibrated verification scheme, f1(X) represents the impact value of evaluating the current performance, f2 represents the function with historical output data, and T represents the sum of the durations of historical states. i This represents the state value at the i-th time point;

[0071] By recalculating the overall performance index and comparing it with the initial interval:

[0072]

[0073] Confirm the effectiveness of the verification plan, where P' is the new comprehensive performance index after the verification plan has been adjusted.

[0074] S4: Based on the verification results and the situation of excessive tasks, perform automatic multi-task distribution, analyze the correlation between network traffic change trends and verification tasks, reduce task queuing time, and optimize automatic distribution.

[0075] Furthermore, in the event of excessive tasks, a dynamic automatic task allocation system is implemented to initially classify the verification tasks of electricity meters, identify the current load status, and prioritize them according to their importance and urgency. The system uses a model trained with an ant colony algorithm to adjust the priority of tasks under different network conditions and the load priority of electricity meters of the same type. The load priority of electricity meters of the same type includes idle, low load, and high load.

[0076] The traffic is distributed according to priority. High-priority and excessive parallel multitasking tasks are uniformly assigned to matching idle energy meters of the same type. High-priority and excessive non-parallel independent tasks are assigned to low-load energy meters of the same type. When high-load energy meters assign excess tasks, the load priority of energy meters of the same type is readjusted. The original excess high-load energy meters are not prioritized. When the original excess high-load energy meters finish their tasks and show that they are idle, the priority is re-sorted.

[0077] The system coordinates the real-time data stream of the electricity meter's performance parameters by monitoring the decision-making unit. When the system detects that the task is excessive, it automatically activates the decision-making unit and communicates with the backend database through data interaction to analyze the current status and extract effective information. Based on historical data and real-time monitoring indicators, the coordination module formulates the current optimization decision. After each task is completed, the system will feed back the results to the coordination module and update the task priority and diversion strategy in real time.

[0078] It should be noted that real-time monitoring of network traffic changes, through granular analysis, allows for traffic prediction based on traffic fluctuations.

[0079] F for (t)=F for (t-1)+ε×(F real (t)-F for (t-1))×e -v(t)

[0080] Among them, F for (t) represents the predicted flow rate at time t, F for (t-1) The predicted flow rate at time t-1, i.e., the prediction from the previous time step; F real (t) Instantaneous real-time flow rate at time t, ε is the adjustment coefficient for the impact of instantaneous flow rate changes on the prediction, and v is the adjustment factor for convergence speed.

[0081] Using the node association model in graph theory, an association network is established between electricity meter verification tasks. Nodes represent different verification tasks, and associations are modeled based on shared resources or time dependencies between tasks. Task priorities are adjusted according to the node association model.

[0082]

[0083] Based on the adjusted priority A adj (a) Perform task scheduling to reduce task queuing time;

[0084] Among them, A adj (a) represents the adjusted priority of task a, A(i) represents the original priority of task a, Y is a factor dynamically adjusted based on real-time traffic, and t max The observed maximum completion time, To weigh the impact of task waiting time on priority adjustment, t(a) is the waiting time of task a, reflecting the delay of the task in the current queue.

[0085] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0086] Example 2, the second embodiment of the present invention, differs from the previous two embodiments in that:

[0087] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0088] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0089] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0090] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0091] Example 3, referring to Figure 2 This is the third embodiment of the present invention. This embodiment provides a multi-task parallel processing electricity meter verification and automatic diversion system, including a zeroing pre-action module 10, a data evaluation module 20, and an automatic diversion module 30.

[0092] The zeroing pre-action module 10 includes a zeroing operation unit 101 and a pre-action processing unit 102. The zeroing operation unit 101 performs a zeroing operation on the energy meter, and the pre-action processing unit 102 performs simulation processing on the energy meter and feeds the results back to the verification system, and transmits the energy meter status information to the data evaluation module 20.

[0093] The data evaluation module 20 includes a real-time data acquisition unit 201 and an intelligent data model evaluation unit 202. The real-time data acquisition unit 201 collects various parameters of the electricity meter based on the information provided by the zeroing pre-action module 10. The intelligent data model evaluation unit 202 constructs an intelligent data model based on the real-time data, performs a comprehensive evaluation of the electricity meter performance, and transmits the evaluation results to the automatic diversion module 30.

[0094] The automatic task allocation module 30 includes a task classification and sorting unit 301 and a task allocation path adjustment unit 302. The task classification and sorting unit 301 classifies and prioritizes the verification tasks, and the task allocation path adjustment unit 302 adjusts the allocation path according to the priority order to realize automatic task allocation.

[0095] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for verifying and automatically shunting electricity meters through multi-task parallel processing, characterized in that: include, The three-phase energy meter is reset. After resetting, the energy meter is pre-operated. The condition of each part of the energy meter is analyzed to determine whether there is an overload or potential fault. Real-time reading of operating parameters; When there are no potential faults and the workload is excessive, a smart data model is built to comprehensively evaluate the performance of the electricity meter and provide a corresponding verification plan. Based on the verification results and the situation of excessive tasks, multiple tasks are automatically distributed, and the correlation between network traffic change trends and verification tasks is analyzed to reduce task queuing time and optimize automatic distribution. The intelligent data model includes constructing an intelligent data model when there are no potential faults and the workload is excessive. A comprehensive evaluation of the electricity meter's performance is conducted using both global evaluation coefficients and local performance feedback. Where X represents the overall performance evaluation of the electricity meter, N is the total number of features of the electricity meter, i is the variable index, and F i Let k represent the characteristic value of the i-th electricity meter. i This represents the dynamic weight used to adjust the current task load; P represents the overall performance index; α1 represents the global evaluation coefficient; 3 represents the total number of key performance indicators, including the overall performance impact index, stability function, and response time adjustment factor; j represents the variable index; β... j ξ represents the adjustment factor for the impact of the j-th performance indicator on the overall evaluation. j (X) represents the relative deviation based on the comprehensive feature X, measuring the gap between the feature and the target level; α2 represents the weight of the local performance feedback; H represents the feedback data extracted from historical data; δ j θ represents the historical performance feedback weight. j Specific numerical values ​​representing the historical performance of the electricity meter; The key performance indicators (KPIs) used in the comprehensive evaluation are based on the adaptive range (minR) set for the KPIs. j ,maxR j ), where minR j MaxR represents the minimum value of the dynamic evaluation interval. j Indicates the maximum value of the dynamic evaluation interval; Different performance levels are defined through dynamic evaluation of each performance metric: When P>maxR j At that time, the system considers it to be a high-efficiency layer, and the overall evaluation result is qualified; When minR j <P≤maxR j At that time, the system considers it to be in the medium-efficiency layer, and it needs to be directly evaluated as qualified after error correction; When P≤minR j If the system considers it an inefficient layer, the overall evaluation result is deemed unqualified.

2. The method for multi-task parallel processing of electricity meter verification and automatic current distribution as described in claim 1, characterized in that: The pre-action processing includes: after clearing the data on the three-phase lines, performing simulation processing according to the pre-set task operation trajectory or task operation direction after power-on; applying known standard signals or loads to the energy meter; simulating real usage conditions; feeding back the simulation processing results and sending the information to the verification system; after confirming that the energy meter is in normal and reliable condition; performing preliminary analysis on the acquired real-time data; checking whether the energy meter output is consistent with the input signal; when the error is within 5% and the current and voltage are not distorted, it is considered that the output power should be consistent with the input signal. Record the load status of each part of the electricity meter during the task execution. If the current load of any phase exceeds 80% of the rated value within 10 minutes, it is marked as task overload. Using IF-THEN logic, when the current exceeds 110% of the rated current, it is marked as a potential fault; when the power factor is less than 0.85, it is marked as a performance degradation, and the energy meter is replaced. After the judgment is completed, the judgment result is fed back to the verification system, and a report is generated including all test data, fault alarms, potential risks and recommended measures; The information includes data on normal operating status, abnormal alarms, and historical records of various indicators of the electricity meter.

3. The method for multi-task parallel processing of electricity meter verification and automatic current distribution as described in claim 2, characterized in that: The operating parameters include the collection of three-phase voltage data, three-phase current data, power factor, active power, reactive power, total harmonic distortion, temperature changes of the electricity meter casing and internal components, grid frequency, electricity consumption changes within the metering cycle, status indication, sampling timestamp, and real-time network traffic data.

4. The method for multi-task parallel processing of electricity meter verification and automatic current distribution as described in claim 3, characterized in that: The comprehensive evaluation result of being unqualified includes conducting in-depth performance analysis of the electricity meter, providing a corresponding verification plan, and determining the degree of performance deficiency S = P - min(P, R) j To address identified performance deficiencies, a step-by-step verification process was developed. Through a clear data recording and real-time processing mechanism, performance change data of the meter under various conditions was generated, and an optimized recalibration formula was established. Where C represents the final quantitative evaluation of the recalibrated verification scheme, f1(X) represents the impact value of evaluating the current performance, f2 represents the function with historical output data, and T represents the sum of the durations of historical states. i This represents the state value at the i-th time point; By recalculating the overall performance index and comparing it with the initial interval: Confirm the effectiveness of the verification plan, where P' is the new comprehensive performance index after the verification plan has been adjusted.

5. The method for multi-task parallel processing of electricity meter verification and automatic current distribution as described in claim 4, characterized in that: The automatic multi-task distribution includes a dynamic automatic distribution system in the event of excessive tasks. This system performs preliminary classification of the electricity meter verification tasks, identifies the current load status, and prioritizes them according to their importance and urgency. It also uses a model trained by the ant colony algorithm to adjust the priority of tasks under different network conditions and the load priority of electricity meters of the same type. The load priority of electricity meters of the same type includes idle, low load, and high load. The traffic is distributed according to priority. High-priority and excessive parallel multitasking tasks are uniformly assigned to matching idle energy meters of the same type. High-priority and excessive non-parallel independent tasks are assigned to low-load energy meters of the same type. When high-load energy meters assign excess tasks, the load priority of energy meters of the same type is readjusted. The original excess high-load energy meters are not prioritized. When the original excess high-load energy meters finish their tasks and show that they are idle, the priority is re-sorted. The system coordinates the real-time data stream of the electricity meter's performance parameters by monitoring the decision-making unit. When the system detects that the task is excessive, it automatically activates the decision-making unit and communicates with the backend database through data interaction to analyze the current status and extract effective information. Based on historical data and real-time monitoring indicators, the coordination module formulates the current optimization decision. After each task is completed, the system will feed back the results to the coordination module and update the task priority and diversion strategy in real time.

6. The method for multi-task parallel processing of electricity meter verification and automatic current distribution as described in claim 5, characterized in that: The reduction of task queuing time includes real-time monitoring of network traffic changes and traffic prediction based on traffic fluctuations through granular analysis. Using the node association model in graph theory, an association network is established between electricity meter verification tasks. Nodes represent different verification tasks, and associations are modeled based on shared resources or time dependencies between tasks. Task priorities are adjusted according to the node association model, and the adjusted priority A is then used to... adj (a) Perform task scheduling to reduce task queuing time.

7. A system employing a multi-task parallel processing method for energy meter verification and automatic current distribution as described in any one of claims 1 to 6, characterized in that: Includes a zeroing pre-action module, a data evaluation module, and an automatic traffic distribution module; The zeroing pre-action module includes a zeroing operation unit and a pre-action processing unit. The zeroing operation unit performs a zeroing operation on the energy meter, and the pre-action processing unit performs simulation processing on the energy meter and feeds the results back to the verification system, and transmits the energy meter status information to the data evaluation module. The data evaluation module includes a real-time data acquisition unit and an intelligent data model evaluation unit. The real-time data acquisition unit collects various parameters of the electricity meter based on the information provided by the zeroing pre-action module. The intelligent data model evaluation unit constructs an intelligent data model based on the real-time data, performs a comprehensive evaluation of the electricity meter's performance, and transmits the evaluation results to the automatic diversion module. The automatic task allocation module includes a task classification and sorting unit and a task allocation path adjustment unit. The task classification and sorting unit classifies and prioritizes the verification tasks, and the task allocation path adjustment unit adjusts the allocation path according to the priority order to achieve automatic task allocation.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the multi-task parallel processing method for energy meter verification and automatic power diversion as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-task parallel processing method for energy meter verification and automatic power diversion as described in any one of claims 1 to 6.

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