Multitask parallel processing electric energy meter verification and automatic shunting method and system

Abstract

The invention discloses a multi-task parallel processing electric energy meter verification and automatic shunting method and system, and relates to the technical field of electric energy meter verification, and the method comprises the steps: carrying out the zero clearing operation of a three-phase line electric energy meter, carrying out the pre-action processing of the electric energy meter after zero clearing, analyzing the conditions of all parts of the electric energy meter, and carrying out the automatic shunting of the three-phase line electric energy meter. Judging whether the condition of task excess or potential fault occurs or not; reading working parameters in real time; when there is no potential fault and the task is excessive, constructing an intelligent data model to comprehensively evaluate the performance of the electric energy meter, and providing a corresponding verification scheme; according to a verification result and a task excess condition, multi-task automatic shunting is carried out, association between a network flow change trend and a verification task is analyzed, and task queuing time is reduced. The real-time performance and the data processing capacity of the verification process are enhanced, the verification scheme is optimized, the verification efficiency is improved, the overall performance and the working load balance of the verification system are improved, the fault rate and the misjudgment rate in the verification process are reduced, and flexible and personalized electric energy meter verification services are provided.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy meter calibration, and in particular to an electric energy meter calibration and automatic current diversion method and system for multi-task parallel processing. Background Art

[0002] In the field of electric energy meter calibration technology, with the rapid development of smart grids, the accuracy, stability and reliability of electric energy meters are increasingly required. Traditional electric energy meter calibration methods mainly rely on manual operations, which are not only inefficient but also difficult to cope with the needs of large-scale calibration tasks. In recent years, although some automated calibration systems have been gradually applied to the process of electric energy meter calibration, these systems still have many shortcomings when handling multiple tasks in parallel.

[0003] First, when calibrating electric energy meters, the existing technologies often ignore the pre-action processing of the conditions of various parts of the electric energy meter, which may lead to excessive tasks or potential failures during the execution of the task. These systems lack real-time monitoring and evaluation of the status of the electric energy meter during the calibration process, resulting in certain errors in the calibration results. Secondly, when performing multi-task parallel processing, the existing technologies fail to effectively analyze the relationship between the changing trend of network traffic and the calibration tasks, resulting in long task queuing time and low calibration efficiency. In addition, when evaluating the performance of electric energy meters, the existing technologies lack in-depth analysis and step-by-step verification of performance deficiencies, and cannot provide accurate calibration solutions for electric energy meters.

[0004] In response to the above problems, we have invented a multi-task parallel processing method and system for electric energy meter calibration and automatic diversion. By real-time monitoring of the conditions of various parts of the electric energy meter, the electric energy meter is cleared and pre-action processed, and the task overload or potential failure is effectively judged; compared with the existing technology, our invention has significant advantages in improving calibration accuracy, improving calibration efficiency and reducing failure rate. Summary of the invention

[0005] In view of the above existing problems, the present invention aims to solve the technical problems of low efficiency, large error in calibration results, long task queuing time, and lack of in-depth analysis and accurate calibration solutions for performance deficiencies in the existing electric energy meter calibration system when processing multiple tasks in parallel. By providing a method and system that can monitor the status of electric energy meters in real time, intelligently evaluate the performance of electric energy meters, and realize automatic multi-task diversion, the aim is to improve the accuracy, efficiency, and reliability of electric energy meter calibration, while reducing the calibration error rate caused by excessive tasks or potential failures.

[0006] In order to solve the above technical problems, a multi-task parallel processing method for electric energy meter verification and automatic diversion is proposed, including:

[0007] The three-phase electric energy meter is cleared, and then pre-action processing is performed on the electric energy meter after clearing, and the conditions of various parts of the electric energy meter are analyzed to determine whether there is a task overload or potential fault; the working parameters are read in real time; when there is no potential fault and the task is overloaded, an intelligent data model is constructed to conduct a comprehensive evaluation of the electric energy meter performance and provide corresponding calibration solutions; multiple tasks are automatically diverted according to the calibration results and the task overload situation, and the relationship between the network traffic change trend and the calibration tasks is analyzed to reduce the task queuing time and optimize the automatic diversion.

[0008] As a preferred solution of the multi-task parallel processing electric energy meter calibration and automatic shunting method described in the present invention, the pre-action processing includes: after the data is cleared on the three-phase line, after power is turned on, analog processing is performed according to the preset task running trajectory or task running direction, a known standard signal or load is applied to the electric energy meter, the actual usage is simulated and the result of the simulation processing is fed back and the information is sent to the calibration system, after confirming that the state of the electric energy meter is normal and reliable, a preliminary analysis is performed on the acquired real-time data to check whether the output of the electric energy meter 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;

[0009] Record the load conditions of each part of the energy meter during the task execution. When 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 is greater than 110% of the rated current, it is marked as a potential fault, and when the power factor is less than 0.85, it is marked as performance degradation and the energy meter needs to be replaced;

[0011] After the judgment is completed, the judgment results are 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 electric energy meter.

[0013] As a preferred solution of the multi-task parallel processing electric energy meter calibration and automatic diversion method described in the present 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 electric energy meter casing and internal components, grid frequency, changes in power consumption within the metering period, status indication, sampling timestamp, and real-time network traffic data.

[0014] As a preferred solution of the multi-task parallel processing electric energy meter verification and automatic diversion method described in the present invention, wherein: the intelligent data model includes, when there is no potential fault and the task is overloaded, constructing an intelligent data model Comprehensive evaluation of the energy meter performance using global evaluation coefficients and local performance feedback:

[0015]

[0016] Where X represents the overall evaluation of the performance of the energy meter, N is the total number of features of the energy meter, i is the variable index, and F i represents the characteristic value of the i-th electric energy meter, k i represents the dynamic weight for adjusting the current task load, P represents the comprehensive 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 represents the adjustment factor of the jth performance indicator on the overall evaluation, ξ j (X) represents the relative deviation based on the comprehensive feature X, which measures the gap between the feature and the target level; α2 represents the weight of local performance feedback, H represents the feedback data extracted from historical data, and δ j represents the historical performance feedback weight, θ j Specific values ​​indicating historical energy meter performance;

[0017] The key performance indicators in the comprehensive evaluation are set according to the key performance indicators. j , maxR j ), where minR j Indicates the minimum value of the dynamic evaluation interval, maxR j Indicates the maximum value of the dynamic evaluation interval;

[0018] Through dynamic evaluation of each performance indicator, different performance levels are defined:

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

[0020] When minR j <P≤maxR j When the system considers it as the middle efficiency layer, it needs to be directly corrected for errors before being evaluated as qualified;

[0021] When P≤minR j When , the system considers it to be an inefficient layer, and the system considers the comprehensive evaluation result to be unqualified.

[0022] As a preferred solution of the multi-task parallel processing electric energy meter verification and automatic diversion method described in the present invention, wherein: the comprehensive evaluation result is unqualified, including performing in-depth performance analysis on the electric energy meter, providing a corresponding verification plan, and determining the degree of insufficient performance S = P-min (P, Rj ), formulate step-by-step verification for the identified performance deficiencies, generate performance change data of the meter under various conditions through a clear data recording and real-time processing mechanism, and set the optimization formula for recalibration:

[0023]

[0024] Where C represents the final quantitative evaluation of the calibration scheme after recalibration, f1(X) represents the impact value of evaluating the current performance, f2 represents the function with historical output data, T represents the sum of the duration of the historical state, and T i Represents the state value at the i-th time point;

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

[0026] As a preferred solution of the multi-task parallel processing electric energy meter calibration and automatic diversion method described in the present invention, wherein: the multi-task automatic diversion includes, in the case of excessive tasks, performing a dynamic automatic diversion system, preliminarily classifying the calibration tasks of the electric energy meter, identifying the current load state, and prioritizing them according to importance and urgency, and using a model trained by an ant colony algorithm to adjust the priority of tasks under different network states and the load priority of electric energy meters of the same type, wherein the load priority of electric energy meters of the same type includes idle, low load, and high load;

[0027] Divert the paths according to the priority order, uniformly assign high-priority and excess parallel multi-tasks to matching idle energy meters of the same type, assign high-priority and excess non-parallel independent tasks to low-load energy meters of the same type, and readjust the load priority of energy meters of the same type after the high-load energy meter assigns the excess tasks. The original high-load energy meter with excess will not be prioritized, and will be prioritized again when the original high-load energy meter with excess tasks ends and is displayed as idle;

[0028] The real-time data stream of the performance parameters of the electricity meter is monitored through the coordination decision unit. When an overload of tasks is detected, the coordination decision unit is automatically activated, communicates with the back-end database through data interaction, analyzes the current status and extracts effective information. The coordination module makes current optimization decisions based on historical data and real-time monitoring indicators. After each task is completed, the system will feedback the results to the coordination module and update the task priority and diversion strategy in real time.

[0029] As a preferred solution of the multi-task parallel processing electric energy meter verification and automatic diversion method described in the present invention, wherein: the reduction of task queuing time includes real-time monitoring of network traffic changes, and prediction of traffic through granularity analysis and traffic fluctuations;

[0030] The node association model in graph theory is used to establish an association network between the verification tasks of electric energy meters, where 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 used to determine the task priorities. adj (a) Perform task scheduling to reduce task queuing time.

[0031] Another object of the present invention is to provide a multi-task parallel processing electric energy meter calibration and automatic diversion system, which aims to solve multiple problems in the electric energy meter calibration process, including improving calibration efficiency, ensuring the accuracy and reliability of the electric energy meter, handling task overload, and reasonably allocating tasks through intelligent data models and automatic diversion strategies to avoid calibration delays or errors; at the same time, the system collects data in real time and evaluates the performance of the electric energy meter, promptly discovers potential faults or performance degradation, and realizes fault warning and processing; in addition, the system also optimizes resource allocation, prioritizes tasks according to their urgency and importance, improves overall operating efficiency, and reduces task queuing time through real-time monitoring and priority adjustment, ensuring efficient and timely processing of calibration tasks, thereby comprehensively improving the automation, intelligence and efficiency of the electric energy meter calibration process.

[0032] As a preferred solution of the multi-task parallel processing electric energy meter verification and automatic shunt system described in the present invention, it is characterized by comprising a zeroing pre-operation module, a data evaluation module, and an automatic shunt 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 electric energy meter. The pre-action processing unit performs a simulation process on the electric energy meter and feeds the result back to the verification system, and transmits the electric 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 electric energy meter according to the information provided by the zero reset pre-operation module. The intelligent data model evaluation unit builds an intelligent data model according to the real-time data, comprehensively evaluates the performance of the electric energy meter, and transmits the evaluation result to the automatic diversion module.

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

[0036] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the multi-task parallel processing electric energy meter calibration and automatic diversion method are implemented.

[0037] A computer-readable storage medium stores a computer program thereon, characterized in that when the computer program is executed by a processor, the steps of the multi-task parallel processing electric energy meter calibration and automatic diversion method are implemented.

[0038] Beneficial effects of the present invention: Through the multi-task parallel processing of the electric energy meter calibration and automatic diversion method of the present invention, the electric energy meter is firstly cleared and pre-action processed, so as to realize the initialization and pre-diagnosis of the electric energy meter state, and ensure that the electric energy meter is in a standard state before calibration, thereby reducing misjudgment in the calibration process and improving the calibration efficiency. The working parameters are read in real time, and the continuous monitoring of the operating status of the electric energy meter is realized, and comprehensive electric energy meter performance data is collected, which provides real-time and accurate support for subsequent performance evaluation and calibration schemes. An intelligent data model is constructed to comprehensively evaluate the performance of the electric energy meter, and a personalized calibration scheme is provided, which improves the pertinence and effectiveness of the calibration scheme. Finally, multiple tasks are automatically diverted according to the calibration results and task overload, which optimizes the distribution of calibration tasks, balances the workload of the calibration system, reduces task queuing time, and improves the response speed and throughput of the calibration system, thereby comprehensively improving the accuracy and reliability of electric energy meter calibration, enhancing the real-time and data processing capabilities of the calibration process, optimizing the calibration efficiency, and improving the overall performance and workload balance of the calibration system through automatic diversion, reducing the failure rate and misjudgment rate, and providing more flexible and personalized electric energy meter calibration services. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the 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 work, among which:

[0040] Figure 1 An overall flow chart of a multi-task parallel processing method for electric energy meter verification and automatic current diversion provided by an embodiment of the present invention.

[0041] Figure 2 A system solution module diagram of an electric energy meter calibration and automatic current diversion system with multi-task parallel processing provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0043] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0044] Secondly, the term "one 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 term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive with other embodiments, either individually or selectively.

[0045] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0046] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0047] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0048] Example 1, reference Figure 1, which is the first embodiment of the present invention, provides a multi-task parallel processing method for electric energy meter verification and automatic current diversion, comprising:

[0049] S1: Clear the three-phase electric energy meter, perform pre-action processing on the electric energy meter after clearing, analyze the conditions of various parts of the electric energy meter, and determine whether there is an overload or potential fault.

[0050] Furthermore, after the data of the three-phase line is cleared, simulation processing is performed according to the preset task running track or task running direction after power is turned on, and a known standard signal or load is applied to the electric energy meter to simulate the actual usage and feedback the result of the simulation processing and send the information to the verification system. After confirming that the state of the electric energy meter is normal and reliable, a preliminary analysis is performed on the acquired real-time data to check whether the output of the electric energy meter 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 conditions of each part of the energy meter during the task execution. When 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 is greater than 110% of the rated current, it is marked as a potential fault, and when the power factor is less than 0.85, it is marked as performance degradation and the energy meter needs to be replaced;

[0053] After the judgment is completed, the judgment results are 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 electric energy meter.

[0055] S2: Read working parameters in real time.

[0056] Furthermore, the working 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 housing and internal components, grid frequency, changes in power consumption during the metering period, status indication, sampling timestamp, and real-time data of network traffic.

[0057] S3: When there are no potential faults and the task is overloaded, an intelligent data model is built to comprehensively evaluate the performance of the electricity meter and provide corresponding calibration solutions.

[0058] Going a step further, building intelligent data models when there are no potential failures and the task is overloaded Comprehensive evaluation of the energy meter performance using global evaluation coefficients and local performance feedback:

[0059]

[0060] Where X represents the overall evaluation of the performance of the energy meter, N is the total number of features of the energy meter, i is the variable index, and F i represents the characteristic value of the i-th electric energy meter, k i represents the dynamic weight for adjusting the current task load, P represents the comprehensive 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 represents the adjustment factor of the jth performance indicator on the overall evaluation, ξ j (X) represents the relative deviation based on the comprehensive feature X, which measures the gap between the feature and the target level; α2 represents the weight of local performance feedback, H represents the feedback data extracted from historical data, and δ j represents the historical performance feedback weight, θ j Specific values ​​indicating historical energy meter performance;

[0061] Key performance indicators in comprehensive evaluation Set the adaptive range of performance indicators according to the key performance indicators:

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

[0063] Among them, minR j Indicates the minimum value of the dynamic evaluation interval, maxR j Indicates the maximum value of the dynamic evaluation interval, P j Represents the current evaluation value of the jth performance indicator, j is the index of the variable and has no actual meaning; η j Represents a dynamic adjustment factor based on historical data to optimize the current valuation range;

[0064] Through dynamic evaluation of each performance indicator, different performance levels are defined:

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

[0066] When minR j <P≤maxR j When the system considers it as the middle efficiency layer, it needs to be directly corrected for errors before being evaluated as qualified;

[0067] When P≤minR j When , the system considers it to be an inefficient layer, and the system considers the comprehensive evaluation result to be unqualified.

[0068] Conduct in-depth performance analysis on the energy meter, provide corresponding verification solutions, and determine the degree of performance deficiency S = P-min (P, R j ), formulate step-by-step verification for the identified performance deficiencies, generate performance change data of the meter under various conditions through a clear data recording and real-time processing mechanism, and set the optimization formula for recalibration:

[0069]

[0070] Where C represents the final quantitative evaluation of the calibration scheme after recalibration, f1(X) represents the impact value of evaluating the current performance, f2 represents the function with historical output data, T represents the sum of the duration of the historical state, and T i Represents the state value at the i-th time point;

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

[0072]

[0073] Confirm whether the verification scheme is effective, where P' is the new comprehensive performance index after adjustment of the verification scheme.

[0074] S4: Automatically divert multiple tasks based on the verification results and task overload, analyze the correlation between network traffic change trends and verification tasks, reduce task queuing time, and optimize automatic diversion.

[0075] Furthermore, in the case of overload of tasks, a dynamic automatic diversion system is implemented to preliminarily classify the verification tasks of the electric energy meter, identify the current load status, and prioritize them according to importance and urgency. The model trained by the ant colony algorithm is used to adjust the priority of tasks under different network states and the load priority of the same type of electric energy meters, wherein the load priority of the same type of electric energy meters includes idle, low load, and high load.

[0076] Divert the paths according to the priority order, uniformly assign high-priority and excess parallel multi-tasks to matching idle energy meters of the same type, assign high-priority and excess non-parallel independent tasks to low-load energy meters of the same type, and readjust the load priority of energy meters of the same type after the high-load energy meter assigns the excess tasks. The original high-load energy meter with excess will not be prioritized, and will be prioritized again when the original high-load energy meter with excess tasks ends and is displayed as idle;

[0077] The real-time data stream of the performance parameters of the electricity meter is monitored through the coordination decision unit. When an overload of tasks is detected, the coordination decision unit is automatically activated, communicates with the back-end database through data interaction, analyzes the current status and extracts effective information. The coordination module makes current optimization decisions based on historical data and real-time monitoring indicators. After each task is completed, the system will feedback 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, granular analysis, and 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) is the flow prediction value at time t, F for (t-1) The traffic forecast value at time t-1, that is, the forecast at the previous moment; F real (t) is the instantaneous real-time flow at time t, ε is the adjustment coefficient of the impact of instantaneous flow changes on the prediction, and v is the adjustment factor of the convergence speed;

[0081] The node association model in graph theory is used to establish an association network between the electric energy meter verification tasks, where nodes represent different verification tasks, and the association is modeled based on shared resources or time dependencies between tasks. The task priority is 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) is the adjusted priority of task a, A(i) is the original priority of task a, Y is the factor dynamically adjusted according to real-time traffic, t max The maximum observed completion time, In order to weigh the impact coefficient of task waiting time in 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 are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0086] Embodiment 2, the second embodiment of the present invention, is different from the first two embodiments in that:

[0087] If the functions are implemented in the form of 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 the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

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

[0089] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0090] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated 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, reference Figure 2 , which is the third embodiment of the present invention, and provides a multi-task parallel processing electric energy meter verification and automatic shunt system, including a zeroing pre-operation module 10, a data evaluation module 20, and an automatic shunt 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 electric energy meter, and the pre-action processing unit 102 performs a simulation process on the electric energy meter and feeds the result back to the verification system, and transmits the electric 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 electric energy meter according to the information provided by the zero reset pre-operation module 10. The intelligent data model evaluation unit 202 constructs an intelligent data model according to the real-time data, comprehensively evaluates the performance of the electric energy meter, and transmits the evaluation result to the automatic diversion module 30.

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

[0095] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-task parallel processing method for electric energy meter verification and automatic current diversion, characterized in that: include, Perform zeroing operation on the three-phase electric energy meter, perform pre-action processing on the electric energy meter after zeroing, analyze the conditions of each part of the electric energy meter, and determine whether there is an overload or potential fault; Read working parameters in real time; When there are no potential faults and the task is overloaded, an intelligent data model is built to comprehensively evaluate the performance of the electric energy meter and provide corresponding verification solutions; Automatically divert multiple tasks based on the verification results and task overload, analyze the relationship between network traffic change trends and verification tasks, reduce task queuing time, and optimize automatic diversion.

2. The multi-task parallel processing method for electric energy meter verification and automatic current diversion according to claim 1, characterized in that: The pre-action processing includes, after the data of the three-phase line is cleared, after power is turned on, analog processing is performed according to the preset task running track or task running direction, a known standard signal or load is applied to the electric energy meter, the actual usage is simulated, and the result of the analog processing is fed back and the information is sent to the verification system, after confirming that the state of the electric energy meter is normal and reliable, a preliminary analysis is performed on the acquired real-time data to check whether the output of the electric energy meter 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 conditions of each part of the energy meter during the task execution. When 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 is greater than 110% of the rated current, it is marked as a potential fault, and when the power factor is less than 0.85, it is marked as performance degradation and the energy meter needs to be replaced; After the judgment is completed, the judgment results are 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 electric energy meter.

3. The multi-task parallel processing method for electric energy meter verification and automatic current diversion as claimed in claim 2, characterized in that: 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 electric energy meter casing and internal components, grid frequency, changes in power consumption within the metering cycle, status indication, sampling timestamp, and real-time network traffic data.

4. The method for calibrating and automatically shunting electric energy meters with multi-task parallel processing as claimed in claim 3, characterized in that: The intelligent data model includes building an intelligent data model when there is no potential failure and the task is overloaded. Comprehensive evaluation of the energy meter performance using global evaluation coefficients and local performance feedback: Where X represents the overall evaluation of the performance of the energy meter, N is the total number of features of the energy meter, i is the variable index, and F i represents the characteristic value of the i-th electric energy meter, k i represents the dynamic weight for adjusting the current task load, P represents the comprehensive 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 represents the adjustment factor of the jth performance indicator on the overall evaluation, ξ 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 local performance feedback, H represents the feedback data extracted from historical data, and δ j represents the historical performance feedback weight, θ j Specific values ​​indicating historical energy meter performance; The key performance indicators in the comprehensive evaluation are set according to the adaptive interval of the performance indicators (minR j ,maxR j ), where minR j Indicates the minimum value of the dynamic evaluation interval, maxR j Indicates the maximum value of the dynamic evaluation interval; Through dynamic evaluation of each performance indicator, different performance levels are defined: When P>maxR j When , the system considers it to be a high-efficiency layer, and the comprehensive evaluation result is qualified; When minR j <P≤maxR j When the system considers it as the middle efficiency layer, it needs to be directly corrected for errors before being evaluated as qualified; When P≤minR j When , the system considers it to be an inefficient layer, and the system considers the comprehensive evaluation result to be unqualified.

5. The method for calibrating and automatically shunting electric energy meters with multi-task parallel processing as claimed in claim 4, characterized in that: The comprehensive evaluation result is unqualified, including conducting in-depth performance analysis on the electric energy meter, providing a corresponding verification plan, and determining the degree of performance deficiency S = P-min (P, R j ), formulate step-by-step verification for the identified performance deficiencies, generate performance change data of the meter under various conditions through a clear data recording and real-time processing mechanism, and set the optimization formula for recalibration: Where C represents the final quantitative evaluation of the calibration scheme after recalibration, f1(X) represents the impact value of evaluating the current performance, f2 represents the function with historical output data, T represents the sum of the duration of the historical state, and T i Represents the state value at the i-th time point; The effectiveness of the verification scheme is confirmed by recalculating the comprehensive performance index and comparing it with the initial interval, where P' is the new comprehensive performance index after adjustment of the verification scheme.

6. The method for calibrating and automatically shunting electric energy meters with multi-task parallel processing as claimed in claim 5, characterized in that: The multi-task automatic diversion includes, in the case of excessive tasks, a dynamic automatic diversion system, preliminary classification of the verification tasks of the electric energy meter, identification of the current load state, and priority sorting according to importance and urgency, and using the model trained by the ant colony algorithm to adjust the priority of tasks under different network states and the load priority of the same type of electric energy meters, wherein the load priority of the same type of electric energy meters includes idle, low load, and high load; Divert the paths according to the priority order, uniformly assign high-priority and excess parallel multi-tasks to matching idle energy meters of the same type, assign high-priority and excess non-parallel independent tasks to low-load energy meters of the same type, and readjust the load priority of energy meters of the same type after the high-load energy meter assigns the excess tasks. The original high-load energy meter with excess will not be prioritized, and will be prioritized again when the original high-load energy meter with excess tasks ends and is displayed as idle; The real-time data stream of the performance parameters of the electricity meter is monitored through the coordination decision unit. When an overload of tasks is detected, the coordination decision unit is automatically activated, communicates with the back-end database through data interaction, analyzes the current status and extracts effective information. The coordination module makes current optimization decisions based on historical data and real-time monitoring indicators. After each task is completed, the system will feedback the results to the coordination module and update the task priority and diversion strategy in real time.

7. The method for calibrating and automatically shunting electric energy meters with multi-task parallel processing as claimed in claim 6, characterized in that: The reducing task queuing time includes monitoring network traffic changes in real time, and performing traffic forecasting based on traffic fluctuations through granular analysis; The node association model in graph theory is used to establish an association network between the verification tasks of electric energy meters, where 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 used to determine the task priorities. adj (a) Perform task scheduling to reduce task queuing time.

8. A system using a multi-task parallel processing method for electric energy meter verification and automatic current diversion as claimed in any one of claims 1 to 7, characterized in that: Including zero reset pre-operation module, data evaluation module, automatic diversion 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 electric energy meter. The pre-action processing unit performs a simulation process on the electric energy meter and feeds the result back to the verification system, and transmits the electric 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 electric energy meter according to the information provided by the zero reset pre-operation module. The intelligent data model evaluation unit builds an intelligent data model according to the real-time data, comprehensively evaluates the performance of the electric energy meter, and transmits the evaluation result to the automatic diversion module. The automatic diversion module includes a task classification and sorting unit and a diversion path adjustment unit. The task classification and sorting unit classifies and prioritizes the verification tasks, and the diversion path adjustment unit adjusts the diversion path according to the priority order to achieve automatic task diversion.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a multi-task parallel processing electric energy meter calibration and automatic diversion method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a multi-task parallel processing electric energy meter calibration and automatic diversion method described in any one of claims 1 to 7 are implemented.

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