Electric energy meter data interaction and management method and system based on measurement production scheduling

Abstract

The invention discloses an electric energy meter data interaction and management method and system based on metering production scheduling, and relates to the technical field of intelligent electric power metering, and the method comprises the steps: carrying out the appearance inspection, electrical test and function inspection of an electric energy meter, collecting the detection data of the electric energy meter in real time, analyzing the detection data through edge calculation, and carrying out the data interaction and management according to the analysis result. Feature extraction is carried out through a deep learning model, a fault mode and a trend are identified, early warning is carried out according to a fault prediction result, a fault log is recorded, abnormal conditions are classified, fault equipment is isolated, a detection model is trained, detection efficiency, a fault rate and an equipment utilization rate are evaluated, and an optimization suggestion is generated according to an analysis result; and adjusting detection equipment parameters. By integrating edge calculation, a deep learning model and real-time data preprocessing, comprehensive management of appearance inspection, electrical test and function inspection of the electric energy meter is realized, and meanwhile, powerful data support is provided for power dispatching management.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent power metering, and in particular to a method and system for interacting and managing electric energy meter data based on metering production scheduling. Background Art

[0002] With the gradual advancement of global smart grid construction, the technological development of electricity meters has entered a stage of rapid evolution. Traditional electricity meters mainly rely on mechanical principles for measurement, which are subject to many limitations in accuracy and efficiency. In recent years, the widespread application of electronic electricity meters has made major improvements from hardware to software, enabling them to achieve a qualitative leap in data collection and communication capabilities.

[0003] Modern energy meters can not only monitor electricity usage in real time, but also have remote communication functions, so that data can be quickly and accurately transmitted to the back-end management system. All of this makes energy meters play an increasingly important role in power company dispatch management, load forecasting and troubleshooting. However, although existing energy meters are gradually developing in the direction of intelligence and automation, there are still some deficiencies in fault detection and data management. For example, the current fault detection methods often rely on simple threshold settings and cannot effectively cope with the comprehensive analysis of multi-dimensional data, resulting in high delays in fault identification and false alarm rates. In addition, many systems lack intelligent learning and adaptability, making it difficult to capture gradually changing states. This results in insufficient detection and response capabilities when facing complex faults and emergencies. Summary of the invention

[0004] In view of the above existing problems, the present invention provides an electric energy meter data interaction and management method and system based on metering production scheduling, so as to solve the problems of high delay and false alarm rate of fault identification and insufficient detection and response capabilities in the prior art.

[0005] In order to solve the above technical problems, a method for electric energy meter data interaction and management based on metering production scheduling is proposed, including:

[0006] Perform visual inspection, electrical test and functional inspection on the electricity meter, collect the detection data of the electricity meter in real time, and analyze the detection data using edge computing; based on the analysis results, extract features through deep learning models, identify fault modes and trends, issue early warnings based on fault prediction results, record fault logs, classify abnormal situations, and isolate faulty equipment; train the detection model, evaluate the detection efficiency, failure rate and equipment utilization, generate optimization suggestions based on the analysis results, and adjust the detection equipment parameters.

[0007] As a preferred solution of the method for interacting and managing electric energy meter data based on metering and production scheduling described in the present invention, the collecting of detection data of the electric energy meter includes performing appearance inspection, electrical test and functional inspection on the electric energy meter, and collecting the detection data of the electric energy meter in real time.

[0008] The appearance inspection includes checking the integrity of the casing, the clarity of the display screen and the integrity of the identification information; the electrical test includes performing a voltage test, a current test and a withstand voltage test on the voltmeter; the functional inspection includes performing a reverse metering test, a communication function test and a fault alarm function test on the electric energy meter.

[0009] As a preferred solution of the method for interacting and managing electric energy meter data based on metering and production scheduling described in the present invention, the edge computing includes real-time collection of sensor and test data, and using edge computing to denoise, normalize and detect outliers on the detection data.

[0010] Among them, the collected data includes the current, voltage, power, power factor, functional status, temperature and historical detection data of the electricity meter.

[0011] As a preferred solution of the method for interacting and managing electric energy meter data based on metering and production scheduling described in the present invention, the identification of fault modes and trends includes, based on the analysis results, extracting features through a deep learning model, setting anomaly detection thresholds, and performing anomaly detection. When a fault is determined, the fault is classified and measures are matched.

[0012] Set the abnormal threshold to H th , when HI <H th When the electric energy meter detects abnormal data, it is determined that the electric energy meter detects data abnormally and performs fault detection.

[0013] As a preferred solution of the method for interacting and managing electric energy meter data based on metering and production scheduling described in the present invention, the fault detection includes, when abnormal electric energy meter data is detected, making a fault judgment, grading the fault, and matching measures.

[0014] The fault judgment formula is:

[0015] F score =w1·I dev +w2·V dev +w3·PF dev +w4·T dev

[0016] Among them, F score Score the fault, I dev is the current deviation, V dev is the voltage deviation, PF dev is the power factor deviation, Tdev is the temperature deviation, w1 is the current weight, w2 is the voltage weight, w3 is the power factor weight, and w4 is the temperature weight.

[0017] The fault classification includes setting the light fault threshold to 2, the medium fault threshold to 4, and the severe fault threshold to 6. score <2, the fault is within the normal range, the status is recorded, and the function of the energy meter is checked regularly. score <4, the electric energy meter is judged to be a slight fault. The electric energy meter can measure normally, but there is an accuracy error. It needs to be adjusted and tested regularly. When 4≤F score <6, the meter is judged to be a moderate fault and needs to replace the meter parts. A fault report is generated and technicians are arranged to perform on-site repairs. score When the value is ≥6, the electric energy meter is judged to be in serious fault, which means that the electric energy meter has completely failed and cannot measure normally. An alarm is immediately issued and a comprehensive inspection and troubleshooting of the fault are carried out.

[0018] As a preferred solution of the method for interacting and managing electric energy meter data based on metering and production scheduling described in the present invention, the training of the detection model includes using the preprocessed historical detection data of the electric energy meter as a training set and a test set, and performing feature extraction, selecting a long short-term memory network to process time series data and high-dimensional features according to data characteristics and analysis objectives, and defining a loss function to measure the difference.

[0019] The detection efficiency formula is:

[0020]

[0021] Where DE is the detection efficiency, D t is the number of successfully detected energy meters, C d To detect the complexity adjustment factor, T t is the total detection time, T d is the fault detection time, T wr The sum of the waiting time and the repeated detection time.

[0022] The failure rate formula is:

[0023]

[0024] Where FR is the failure rate, F n is the number of faults found in the detection cycle, R d is the fault severity adjustment factor, T s is the total number of electric energy meters tested, C r is the usage response factor.

[0025] The equipment utilization formula is:

[0026]

[0027] Among them, UR is the equipment utilization rate, U a is the actual running time, F u is the fault impact adjustment factor, S u is the impact of planned maintenance time, T s is the total available time, T m is the total maintenance time, C u Adjust factors for fluctuations in user demand.

[0028] As a preferred solution of the method for interacting and managing electric energy meter data based on metering and production scheduling described in the present invention, the generation of optimization suggestions includes setting detection efficiency, failure rate and equipment utilization thresholds, generating optimization suggestions based on analysis results, and adjusting detection equipment parameters.

[0029] Set the detection efficiency threshold to 95%, the failure rate threshold to 2%, and the equipment utilization threshold to 80%. When DE<95%, FR≥2%, and UR<80%, determine that the detection system has defects, analyze the causes of the defects, increase the training frequency and automated detection steps, regularly maintain the equipment and replace wearing parts, perform intelligent scheduling, adjust equipment parameters, modify detection standards and processes, and optimize detection equipment.

[0030] Another object of the present invention is to provide an electric energy meter data interaction and management system based on metering production scheduling. The present invention significantly improves work efficiency and reduces manual operations by integrating automated detection, real-time monitoring and data analysis. With the help of a deep learning model, the system of the present invention can identify the failure mode of the electric energy meter in advance and trigger an intelligent alarm mechanism to achieve rapid response and fault handling. In addition, the accumulated historical detection data provides a solid foundation for decision support and optimizes resource allocation and operational efficiency. Through continuous monitoring, users can effectively manage power loss, control maintenance costs, and extend the service life of equipment.

[0031] As a preferred solution of the electric energy meter data interaction and management system based on metering production scheduling described in the present invention, it is characterized by including a detection and data acquisition module, a data preprocessing module, a fault mode recognition and early warning module, a detection model training and evaluation module and a parameter optimization module.

[0032] The detection and data acquisition module is used to perform a comprehensive appearance inspection, electrical test and function inspection on the electric energy meter, and to collect detection data in real time.

[0033] The data preprocessing module is used to process the data collected in real time using edge computing technology.

[0034] The fault pattern recognition and early warning module is used to extract important features of historical data using a deep learning algorithm, establish a fault indication model, set an abnormality detection threshold, determine the state of the electric energy meter by calculating the abnormality index, classify the faults according to the abnormality detection results, and trigger the early warning mechanism.

[0035] The detection model training and evaluation module is used to use the pre-processed historical detection data as a training set, apply a deep learning model to process time series data, and optimize the detection model by evaluating detection efficiency and failure rate indicators.

[0036] The parameter optimization module is used to generate optimization suggestions based on real-time data analysis results and model evaluation, improve detection efficiency, and perform equipment management.

[0037] 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 method described in the method for interacting and managing electric energy meter data based on metering production scheduling are implemented.

[0038] A computer-readable storage medium stores a computer program thereon, wherein when the computer program is executed by a processor, the steps of the method described in the method for interacting and managing electric energy meter data based on metering production scheduling are implemented.

[0039] Beneficial effects of the present invention: The present invention realizes all-round management of appearance inspection, electrical testing and functional inspection of electric energy meters by integrating edge computing, deep learning models and real-time data preprocessing; by real-time collection of detection data from multiple sensors and application of deep learning for fault pattern recognition, early warning can be achieved before a fault occurs, significantly improving the efficiency of fault detection; in addition, by recording fault logs and classifying abnormal conditions, the maintenance and repair process becomes more systematic and intelligent. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] 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:

[0041] Figure 1 An overall flow chart of an electric energy meter data interaction and management method based on metering production scheduling provided by an embodiment of the present invention.

[0042] Figure 2A system solution flow chart of an electric energy meter data interaction and management system based on metering production scheduling provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides an electric energy meter data interaction and management method based on metering production scheduling, including:

[0050] S1: Perform appearance inspection, electrical test and functional inspection on the electric energy meter, collect the detection data of the electric energy meter in real time, and use edge computing to analyze the detection data.

[0051] The collecting of the detection data of the electric energy meter includes performing appearance inspection, electrical test and function inspection on the electric energy meter, and collecting the detection data of the electric energy meter in real time.

[0052] The appearance inspection includes checking the integrity of the casing, the clarity of the display screen and the integrity of the identification information; the electrical test includes performing a voltage test, a current test and a withstand voltage test on the voltmeter; the functional inspection includes performing a reverse metering test, a communication function test and a fault alarm function test on the electric energy meter.

[0053] It should be noted that the edge computing includes real-time collection of sensor and test data, and the use of edge computing to denoise, normalize and detect outliers on the detection data.

[0054] Among them, the collected data includes the current, voltage, power, power factor, functional status, temperature and historical detection data of the electricity meter.

[0055] The de-noising includes calculating the sliding average value within the data window using a mean filter, which is expressed as:

[0056]

[0057] Among them, N is the window size, x(t) is the original data, Z is the filtered data, and i is the variable index.

[0058] The normalization formula is:

[0059]

[0060] Among them, x is the original data, x' is the normalized data, min(X) is the minimum value of the data set, and max(X) is the maximum value of the data set.

[0061] The outlier detection formula is:

[0062]

[0063] Among them, A is the standard score, y is the new measurement value, μ is the mean of the data set, and σ is the standard deviation. When |Z|>3, it is judged as abnormal data and needs to be eliminated.

[0064] S2: Based on the analysis results, feature extraction is performed through deep learning models to identify fault modes and trends, early warnings are issued based on fault prediction results, fault logs are recorded, abnormal conditions are classified, and faulty equipment is isolated.

[0065] Furthermore, the identification of fault modes and trends includes, based on the analysis results, extracting features through a deep learning model, setting anomaly detection thresholds, and performing anomaly detection. When a fault is determined, the fault is classified and measures are matched.

[0066] The anomaly detection formula is expressed as:

[0067]

[0068] Among them, HI is the abnormality index, m is the total number of features, and Y j is the measured value of the jth feature, μ j is the historical mean, σ j is the historical standard deviation, and j is the variable index.

[0069] Set the abnormal threshold to H th , when HI <H th When the electric energy meter detects abnormal data, it is determined that the electric energy meter detects data abnormally and performs fault detection.

[0070] Furthermore, the fault detection includes, when abnormal data of the electric energy meter is detected, making a fault judgment, grading the fault, and matching measures.

[0071] The fault judgment formula is:

[0072] F score =w1·I dev +w2·V dev +w3·PF dev +w4·T dev

[0073] Among them, F score Score the fault, I dev is the current deviation, V dev is the voltage deviation, PF dev is the power factor deviation, T dev is the temperature deviation, w1 is the current weight, w2 is the voltage weight, w3 is the power factor weight, and w4 is the temperature weight.

[0074] The fault classification includes setting the light fault threshold to 2, the medium fault threshold to 4, and the severe fault threshold to 6. score <2, the fault is within the normal range, the status is recorded, and the function of the energy meter is checked regularly.score <4, the electric energy meter is judged to be a slight fault. The electric energy meter can measure normally, but there is an accuracy error. It needs to be adjusted and tested regularly. When 4≤F score <6, the meter is judged to be a moderate fault and needs to replace the meter parts. A fault report is generated and technicians are arranged to perform on-site repairs. score When the value is ≥6, the electric energy meter is judged to be in serious fault, which means that the electric energy meter has completely failed and cannot measure normally. An alarm is immediately issued and a comprehensive inspection and troubleshooting of the fault are carried out.

[0075] S3: Train the detection model, evaluate the detection efficiency, failure rate and equipment utilization, generate optimization suggestions based on the analysis results, and adjust the detection equipment parameters.

[0076] Furthermore, the training of the detection model includes using the preprocessed historical detection data of the electric energy meter as a training set and a test set, and performing feature extraction, selecting a long short-term memory network to process time series data and high-dimensional features according to data characteristics and analysis objectives, and defining a loss function for difference measurement.

[0077] The detection efficiency formula is:

[0078]

[0079] Where DE is the detection efficiency, D t is the number of successfully detected energy meters, C d To detect the complexity adjustment factor, T t is the total detection time, T d is the fault detection time, T wr The sum of the waiting time and the repeated detection time.

[0080] The failure rate formula is:

[0081]

[0082] Where FR is the failure rate, F n is the number of faults found in the detection cycle, R d is the fault severity adjustment factor, T s is the total number of electric energy meters tested, C r is the usage response factor.

[0083] The equipment utilization formula is:

[0084]

[0085] Among them, UR is the equipment utilization rate, U a is the actual running time, F u is the fault impact adjustment factor, Su is the impact of planned maintenance time, T s is the total available time, T m is the total maintenance time, C u Adjust factors for fluctuations in user demand.

[0086] Furthermore, the generating of optimization suggestions includes setting thresholds of detection efficiency, failure rate and equipment utilization, generating optimization suggestions based on analysis results, and adjusting detection equipment parameters.

[0087] Set the detection efficiency threshold to 95%, the failure rate threshold to 2%, and the equipment utilization threshold to 80%. When DE<95%, FR≥2%, and UR<80%, determine that the detection system has defects, analyze the causes of the defects, increase the training frequency and automated detection steps, regularly maintain the equipment and replace wearing parts, perform intelligent scheduling, adjust equipment parameters, modify detection standards and processes, and optimize detection equipment.

[0088] 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.

[0089] Example 2, reference Figure 2 , which is the second embodiment of the present invention, provides an electric energy meter data interaction and management system based on metering production scheduling, including a detection and data acquisition module 100, a data preprocessing module 200, a fault mode recognition and early warning module 300, a detection model training and evaluation module 400 and a parameter optimization module 500.

[0090] The detection and data acquisition module 100 is used to perform a comprehensive appearance inspection, electrical test and function inspection on the electric energy meter, and to collect detection data in real time.

[0091] The data preprocessing module 200 is used to process the data collected in real time using edge computing technology.

[0092] The fault pattern recognition and early warning module 300 is used to extract important features of historical data using a deep learning algorithm, establish a fault indication model, set an abnormality detection threshold, determine the state of the electric energy meter by calculating the abnormality index, classify the faults according to the abnormality detection results, and trigger an early warning mechanism.

[0093] The detection model training and evaluation module 400 is used to use the pre-processed historical detection data as a training set, apply a deep learning model to process time series data, and optimize the detection model by evaluating detection efficiency and failure rate indicators.

[0094] The parameter optimization module 500 is used to generate optimization suggestions based on real-time data analysis results and model evaluation, improve detection efficiency, and perform equipment management.

[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.

[0096] Embodiment 3, the third embodiment of the present invention, is different from the first two embodiments in that:

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

Claims

1. The electric energy meter data interaction and management method based on metering production scheduling is characterized by: include, Perform appearance inspection, electrical test and functional inspection on the electric energy meter, collect the test data of the electric energy meter in real time, and use edge computing to analyze the test data; Based on the analysis results, feature extraction is performed through deep learning models to identify fault modes and trends, early warnings are issued based on fault prediction results, fault logs are recorded, abnormal conditions are classified, and faulty equipment is isolated; Train the detection model, evaluate the detection efficiency, failure rate and equipment utilization, generate optimization suggestions based on the analysis results, and adjust the detection equipment parameters.

2. The method for electric energy meter data interaction and management based on metering production scheduling according to claim 1, characterized in that: The collecting of the detection data of the electric energy meter includes performing an appearance inspection, an electrical test and a functional inspection on the electric energy meter, and collecting the detection data of the electric energy meter in real time; The appearance inspection includes checking the integrity of the casing, the clarity of the display screen and the integrity of the identification information; the electrical test includes performing a voltage test, a current test and a withstand voltage test on the voltmeter; the functional inspection includes performing a reverse metering test, a communication function test and a fault alarm function test on the electric energy meter.

3. The method for electric energy meter data interaction and management based on metering production scheduling according to claim 2 is characterized in that: The edge computing includes real-time collection of sensor and test data, and using edge computing to perform noise removal, normalization, and outlier detection on the test data; Among them, the collected data includes the current, voltage, power, power factor, functional status, temperature and historical detection data of the electricity meter.

4. The method for electric energy meter data interaction and management based on metering production scheduling according to claim 3 is characterized in that: The identification of fault modes and trends includes, based on the analysis results, extracting features through a deep learning model, setting anomaly detection thresholds, and performing anomaly detection, and when a fault is determined, grading the fault and matching measures; Set the abnormal threshold to H th , when HI <H th When the electric energy meter detects abnormal data, it is determined that the electric energy meter detects data abnormally and performs fault detection.

5. The method for electric energy meter data interaction and management based on metering production scheduling according to claim 4 is characterized in that: The fault detection includes, when abnormal data of the electric energy meter is detected, fault judgment is performed, and fault classification and matching measures are performed; The fault judgment formula is: F score =w1·I dev +w2·V dev +w3·PF dev +w4·T dev Among them, F score Score the fault, I dev is the current deviation, V dev is the voltage deviation, PF dev is the power factor deviation, T dev is the temperature deviation, w1 is the current weight, w2 is the voltage weight, w3 is the power factor weight, and w4 is the temperature weight; The fault classification includes setting the light fault threshold to 2, the medium fault threshold to 4, and the severe fault threshold to 6. score <2, the fault is within the normal range, the status is recorded, and the function of the energy meter is checked regularly. score <4, the electric energy meter is judged to be a slight fault. The electric energy meter can measure normally, but there is an accuracy error. It needs to be adjusted and tested regularly. When 4≤F score <6, the meter is judged to be a moderate fault and needs to replace the meter parts. A fault report is generated and technicians are arranged to perform on-site repairs. score When the value is ≥6, the electric energy meter is judged to be in serious fault, which means that the electric energy meter has completely failed and cannot measure normally. An alarm is immediately issued and a comprehensive inspection and troubleshooting of the fault are carried out.

6. The method for electric energy meter data interaction and management based on metering production scheduling according to claim 5 is characterized in that: The training of the detection model includes using the preprocessed historical detection data of the electric energy meter as a training set and a test set, and extracting features, selecting a long short-term memory network to process time series data and high-dimensional features according to data characteristics and analysis objectives, and defining a loss function to measure differences; The detection efficiency formula is: Where DE is the detection efficiency, D t is the number of successfully detected energy meters, C d To detect the complexity adjustment factor, T t is the total detection time, T d is the fault detection time, T wr The sum of waiting time and repeated detection time; The failure rate formula is: Where FR is the failure rate, F n is the number of faults found in the detection cycle, R d is the fault severity adjustment factor, T s is the total number of electric energy meters tested, C r is the usage response degree factor; The equipment utilization formula is: Among them, UR is the equipment utilization rate, U a is the actual running time, F u is the fault impact adjustment factor, S u is the planned maintenance time impact, T s is the total available time, T m is the total maintenance time, C u Adjust factors for fluctuations in user demand.

7. The method for electric energy meter data interaction and management based on metering production scheduling according to claim 6 is characterized in that: The generating of optimization suggestions comprises: Set thresholds for detection efficiency, failure rate, and equipment utilization, generate optimization suggestions based on analysis results, and adjust detection equipment parameters; Set the detection efficiency threshold to 95%, the failure rate threshold to 2%, and the equipment utilization threshold to 80%. When DE<95%, FR≥2%, and UR<80%, determine that the detection system has defects, analyze the causes of the defects, increase the training frequency and automated detection steps, regularly maintain the equipment and replace wearing parts, perform intelligent scheduling, adjust equipment parameters, modify detection standards and processes, and optimize detection equipment.

8. A system using the electric energy meter data interaction and management method based on metering production scheduling as claimed in any one of claims 1 to 7, characterized in that: It includes detection and data acquisition module, data preprocessing module, fault mode recognition and early warning module, detection model training and evaluation module and parameter optimization module; The detection and data acquisition module is used to perform a comprehensive appearance inspection, electrical test and function inspection on the electric energy meter, and collect detection data in real time; The data preprocessing module is used to process the data collected in real time using edge computing technology; The fault pattern recognition and early warning module is used to extract important features of historical data using a deep learning algorithm, establish a fault indication model, set an abnormality detection threshold, determine the state of the electric energy meter by calculating the abnormality index, classify the fault according to the abnormality detection result, and trigger the early warning mechanism; The detection model training and evaluation module is used to use the pre-processed historical detection data as a training set, apply the deep learning model to process the time series data, and optimize the detection model by evaluating the detection efficiency and failure rate indicators; The parameter optimization module is used to generate optimization suggestions based on real-time data analysis results and model evaluation, improve detection efficiency, and perform equipment management.

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 the method for interacting and managing electric energy meter data based on metering production scheduling as 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 the method for interacting and managing electric energy meter data based on metering production scheduling described in any one of claims 1 to 7 are implemented.

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