Automatic verification method and system for single-phase electric energy meter

Abstract

The invention relates to the technical field of electric energy metering verification, in particular to an automatic verification method and system for a single-phase electric energy meter, which integrates an RFID (Radio Frequency Identification Device) and bar code technology, a data matching algorithm, historical fault data analysis, process optimization and a real-time analysis and statistical process control method, and improves the efficiency and accuracy of a verification process in an automatic and intelligent manner. The electric energy meter verification process is high in automation degree, the actual operation state and the fault risk of equipment can be accurately and rapidly responded, verification parameters are adjusted and optimized in real time, human errors are reduced, and potential problems are effectively predicted and maintained; in addition, through automatic data collection and processing, high consistency and reliability of verification results are ensured, the production management efficiency of electric energy meter manufacturers is remarkably improved, and powerful support is provided for safe and reliable operation of an electric power system.

Description

Technical Field

[0001] The invention relates to the technical field of electric energy metering verification, and in particular to an automatic verification method and system for a single-phase electric energy meter. Background Art

[0002] With the rapid development of smart grids and smart metering equipment, higher requirements are placed on the accuracy and efficiency of the calibration of electric energy meters. Traditional manual or semi-automatic calibration methods can no longer meet the accuracy and real-time requirements of modern power systems for electric energy metering. Therefore, the development of an efficient and automated calibration system has become an inevitable trend in the development of the industry. The development of the single-phase electric energy meter automated calibration line system is precisely to meet the needs of large-scale and high-efficiency calibration of electric energy meters, while ensuring the accuracy and reliability of the calibration results.

[0003] The project is equipped with two automated production lines, each of which consists of multiple functional units, including docking units, loading and unloading units, pallet conveying units, identity binding units, pressure test units, appearance inspection units, accuracy and multi-function verification units, engraving units, labeling units and qualified / unqualified sorting units, etc. These units are designed to achieve fully automatic verification of electric energy meters from online to offline, including appearance structure inspection, electrical performance inspection, function inspection, communication performance inspection and fee control test, etc.

[0004] In addition, advanced data communication technology, sensor technology, intelligent embedding technology, terminal equipment interconnection and centralized control management technology are used to form a highly integrated automatic verification system. The system can not only automatically store verification data and calculate the pass rate, but also display the data and status of the verification process in real time on a large screen, greatly improving the transparency and interactivity of the verification work.

[0005] To ensure the accuracy and compliance of the verification work, the system design strictly follows international standards, national standards and power industry standards, such as GB / T, JJG and Q / CSG, etc. All standards used are the latest versions, ensuring the advancement of the system and the wide recognition of the verification results.

[0006] The entire supply scope covers manufacturing, installation and commissioning, transportation, storage, technical training, patents and papers, etc. The development and implementation of the system not only improves the verification efficiency, but also promotes the improvement of technical personnel's skills, laying a solid foundation for the long-term development of the electric energy measurement verification center.

[0007] By realizing the automation and intelligence of electricity meter calibration, the project not only meets the technical needs of electricity metering calibration, but also provides new directions and ideas for the future development of electricity metering technology. Summary of the invention

[0008] In view of the above problems existing in the prior art, the present invention is proposed.

[0009] Therefore, the technical problem to be solved by the present invention is: in order to improve the automation and efficiency of single-phase electricity meter calibration and ensure the accuracy and reliability of the calibration process, the present invention aims to meet the needs of large-scale electricity meter calibration by realizing a fully automated calibration pipeline system, while reducing human errors and increasing the calibration speed; in addition, the system can also realize automatic storage of calibration data, pass rate statistics, real-time monitoring and fault diagnosis through advanced technology and automation equipment, thereby improving the overall quality and efficiency of electricity meter calibration work.

[0010] In order to solve the above technical problems, the present invention provides the following technical solutions, a single-phase electric energy meter automated calibration method, comprising: using RFID and bar code technology to automatically identify the electric energy meter, and bind it to the calibration station, and use data matching algorithm to calculate; automatically executing the pre-inspection and pre-operation system, and using the historical fault data analysis algorithm to predict potential equipment problems; the electric energy meter passes through a multifunctional calibration unit, optimizes the process, and adjusts the calibration process; collects data during the calibration process, performs real-time analysis, and processes and analyzes the data; after the calibration, automatically adjusts the calibration parameters and analyzes the calibration parameters through a statistical process control method.

[0011] As a preferred solution of the single-phase electric energy meter automated calibration method described in the present invention, the RFID and barcode technology includes key information for automatically identifying the electric energy meter to be inspected, and automatically binding the electric energy meter to a specific calibration station, specifically including installing an RFID tag and a barcode on the electric energy meter, capturing information through a reader at the entrance, and automatically entering it into a system database, and intelligently allocating the electric energy meter to the corresponding calibration station based on the acquired information.

[0012] As a preferred solution of the single-phase electric energy meter automatic verification method of the present invention, the data matching algorithm includes processing using the cosine similarity formula, which is expressed as:

[0013]

[0014] in, represents the attribute vector of the electric energy meter, represents the attribute vector of the calibration platform, and Represents the magnitude of a vector;

[0015] The cosine similarity formula is used to calculate the similarity of two vectors, and the similarity value is between (-1,1); the similarity threshold is set to 0.8 according to the actual application requirements. When the calculated similarity value is greater than or equal to 0.8, it indicates that the two vectors match successfully and the calibration work is carried out; when the calculated similarity value is less than 0.8, it indicates that the two vectors fail to match, and the calibration station and electric energy meter are reselected.

[0016] As a preferred solution of the single-phase electric energy meter automatic verification method of the present invention, the historical fault data analysis algorithm includes using the electric energy meter attribute vector associated with the linear regression model And the historical data of equipment failure, expressed as:

[0017]

[0018] in, represents the failure probability of the equipment, represents the parameter vector of the model, ∈ represents the model error term;

[0019] Using the failure probability output by the model To guide equipment maintenance decisions, if the predicted probability of failure is greater than or equal to the preset threshold of 0.8, emergency maintenance is performed; if the predicted probability of failure is greater than 0.4 and less than 0.8, it means that the equipment has not reached the emergency maintenance level.

[0020] As a preferred solution of the single-phase electric energy meter automatic verification method of the present invention, the process optimization includes judging the failure probability of the equipment based on the historical fault data analysis algorithm, when the failure probability When the probability of failure is greater than or equal to 0.8, the equipment is tested, including complete power system testing, stress testing of key components, and complete review of the software system. Priority is given to emergency problems that cause downtime during the testing process, including replacement and repair of components. When the probability of failure is greater than 0.4 and less than 0.8, the key data, including the temperature, voltage level, and vibration frequency of the equipment, are checked according to the established regular inspection process. The collected data are used for trend analysis to predict potential failures and performance degradation, and the verification and maintenance process plans are adjusted. When it is less than or equal to 0.4, carry out the standard equipment verification process, maintain regular verification intervals, continue to implement the preventive maintenance plan, evaluate equipment operating data, and optimize equipment performance, including adjusting operating parameters, reducing energy efficiency, and improving efficiency.

[0021] As a preferred solution of the single-phase electric energy meter automatic verification method of the present invention, the real-time analysis includes adjusting the detection and maintenance plan of the equipment in real time based on the real-time monitoring data, using the failure probability of the equipment in the historical fault data analysis algorithm to make judgments, predict the failure probability of the equipment and adjust the maintenance verification process;

[0022] When the probability of failure When it is less than or equal to 0.4, it is a low risk. The regular inspection and maintenance frequency is maintained. The equipment operates according to the preset performance standards. The equipment performance log is updated regularly. The performance comparison of historical operation data with equipment with the same similarity value is used to evaluate and adjust the calibration cycle and maintenance strategy. When the failure probability is When it is greater than 0.4 and less than 0.8, it is a medium risk. The key data of the equipment is closely monitored and condition monitoring technology is introduced, namely vibration analysis and thermal imaging analysis, to identify early equipment abnormalities. When the key data exceeds the warning threshold, the warning is automatically triggered and reported to the maintenance team for maintenance and tracking. When the failure probability is When it is greater than or equal to 0.8, it is a high risk, and the automated emergency response program is activated to immediately interrupt the operation of the equipment and report to the emergency maintenance team for a comprehensive inspection of the equipment. Key components are replaced based on the inspection results. After the emergency intervention, a comprehensive risk assessment of the equipment is conducted, and the failure risk level of the equipment is reassessed based on the equipment repair status and inspection results.

[0023] Collect and analyze the inspection and maintenance results obtained in the risk level and update the parameter vector and error term∈, and optimize and adjust the corresponding threshold according to the actual operation and maintenance history of the equipment.

[0024] As a preferred solution of the single-phase electric energy meter automatic verification method of the present invention, the statistical process control method includes real-time collection of key data of equipment operation, and obtaining current equipment performance indicators using historical fault data analysis algorithms;

[0025] Use data matching algorithms to calculate the similarity between the current equipment status and the ideal operating status, and evaluate the gap between the current status and the ideal status;

[0026] like When the calculation results show that the performance is lower than the preset threshold and the similarity value is less than the preset threshold, the system will trigger an alarm; based on the results of the failure probability and cosine similarity, it is decided to adjust the verification parameters, including machine speed, temperature control, pressure setting, recipe and mixing ratio, and calibrate and maintain the equipment; using the data collected during the verification cycle, update ∈ to adapt to changes in the verification process and optimize performance indicators.

[0027] Another object of the present invention is to provide an automated calibration system for single-phase electric energy meters, which can integrate the functions of various modules and automatically perform the identification, fault prediction, process optimization and quality control of electric energy meters to improve the efficiency and accuracy of calibration, reduce the need for manual intervention, and optimize the calibration process through real-time data analysis and feedback to ensure that the electric energy meters meet the highest quality standards before being put on the market; this system can provide a comprehensive solution for power supply companies or electric energy meter manufacturers to effectively manage and perform calibration of electric energy meters, thereby reducing costs, improving output quality and accelerating the calibration cycle.

[0028] To solve the above technical problems, the present invention provides the following technical solutions: a single-phase electric energy meter automatic verification system, comprising: an identity recognition module, a pre-inspection analysis module, a process optimization module, a data analysis module and a quality control module;

[0029] The identification module uses RFID and barcode technology to automatically identify the electric energy meter, binds to the calibration station, and calculates using a data matching algorithm;

[0030] The pre-check analysis module automatically performs pre-check and pre-operation system, and uses historical fault data analysis algorithm to predict potential equipment problems;

[0031] The process optimization module, the electric energy meter passes through the multifunctional verification unit, performs process optimization and adjusts the verification process;

[0032] The data analysis module collects data during the verification process, performs real-time analysis, processes and analyzes the data, and automatically adjusts the verification parameters;

[0033] After verification, the adjustment control module analyzes the verification data and identifies the quality trend through statistical process control methods.

[0034] 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 single-phase electric energy meter automatic calibration method as described above are implemented.

[0035] A computer-readable storage medium stores a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned single-phase electric energy meter automatic calibration method.

[0036] Beneficial effects of the present invention: The single-phase electric energy meter automated calibration method and system provided by the present invention significantly improves the automation level and efficiency of electric energy meter calibration by comprehensively utilizing RFID and bar code technology, data matching algorithms, historical fault data analysis, process optimization, and real-time analysis and statistical process control methods. This method makes the calibration process of the electric energy meter more accurate and efficient, and can respond to the actual operating status and failure risks of the equipment in real time, and make necessary adjustments and maintenance in a timely manner. In addition, the method minimizes manpower requirements and operational errors through intelligent data processing and resource allocation, while ensuring the reliability and consistency of the calibration results. Therefore, the present invention not only improves the production management efficiency of electric energy meter manufacturers, but also provides strong technical support for the safe and reliable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0038] Figure 1 An overall flow chart of a single-phase electric energy meter automated calibration method provided by one embodiment of the present invention.

[0039] Figure 2 This is a comparison chart of various indicators between the experimental group and the control group of the single-phase electric energy meter automatic calibration method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] 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.

[0041] Example 1, reference Figure 1 , is an embodiment of the present invention, which provides a single-phase electric energy meter automatic verification method, comprising:

[0042] S1: Use RFID and barcode technology to automatically identify the electricity meter, bind it to the calibration station, and use data matching algorithm for calculation.

[0043] It should be noted that if Figure 1As shown in S1, the RFID and barcode technology includes key information for automatically identifying the electric energy meter to be inspected, and automatically binding the electric energy meter to a specific calibration station, which specifically includes installing an RFID tag and a barcode on the electric energy meter, capturing information through a reader at the entrance, and automatically entering the information into the system database, and intelligently allocating the electric energy meter to the corresponding calibration station based on the acquired information; by reading the RFID and barcode information, the system can automatically enter the model, specification, factory number and other key attributes of each electric energy meter; after the entry is completed, the information is compared with the characteristic parameters of the calibration station (such as load capacity, detection accuracy, etc.).

[0044] Furthermore, the data matching algorithm includes processing using the cosine similarity formula, which is expressed as:

[0045]

[0046] in, represents the attribute vector of the electric energy meter, represents the attribute vector of the calibration platform, and Represents the magnitude of a vector;

[0047] The cosine similarity formula is used to calculate the similarity of two vectors, and the similarity value is between (-1,1); the similarity threshold is set to 0.8 according to the actual application requirements. When the calculated similarity value is greater than or equal to 0.8, it indicates that the two vectors match successfully and the calibration work is carried out; when the calculated similarity value is less than 0.8, it indicates that the two vectors fail to match, then reselect the calibration station and the electric energy meter, and the system will automatically reselect the appropriate calibration station and electric energy meter until the match is successful; after the match fails, the system not only simply reselects the calibration station, but also makes dynamic adjustments based on historical matching data and fault records; for example, a calibration device has a high success rate in processing a certain type of electric energy meter in the past, and the system will give priority to assigning it to electric energy meters with similar parameters.

[0048] Furthermore, after the electricity meters arrive at the calibration workshop in large quantities, the entrance reader quickly collects RFID and barcode information to complete the information entry; the data is quickly matched to the calibration station through the cosine similarity algorithm. After the matching is completed, the electricity meters are sent to the corresponding calibration station according to the allocation results; during the calibration process, equipment data is collected in real time to further optimize the matching logic.

[0049] S2: Automatically execute pre-check and pre-action system, using historical fault data analysis algorithm to predict potential equipment problems.

[0050] It should be noted that if Figure 1 As shown in S2, the historical fault data analysis algorithm includes using the linear regression model to associate the electric energy meter attribute vector And the historical data of equipment failures, expressed as:

[0051]

[0052] in, represents the failure probability of the equipment, represents the parameter vector of the model, ∈ represents the model error term;

[0053] Real-time collection of electric energy meter operation data: including equipment operating environment (temperature, humidity), equipment usage time, electrical parameters (current, voltage), mechanical vibration, etc.

[0054] Extract historical fault data: such as the time of equipment failure, fault type, maintenance records, etc.

[0055] Furthermore, the failure probability output by the model is used To guide equipment maintenance decisions, if the predicted probability of failure is greater than or equal to the preset threshold of 0.8, emergency maintenance is performed; if the predicted probability of failure is greater than 0.4 and less than 0.8, it means that the equipment has not reached the emergency maintenance level and regular inspection operations are sufficient.

[0056] Furthermore, emergency maintenance operations: the system automatically generates maintenance notifications, arranges engineers to inspect the equipment, and troubleshoots possible fault points. After the inspection is completed, the maintenance results are entered into the system and the equipment status data is updated;

[0057] Regular inspection operations: The system generates inspection tasks and includes them in the engineers’ daily inspection plan. After the inspection is completed, the equipment operating status is recorded as data for subsequent model updates.

[0058] S3: The electric energy meter passes through the multifunctional calibration unit to optimize the process and adjust the calibration process.

[0059] It should be noted that if Figure 1 S3 shows that process optimization includes judging the failure probability of equipment based on historical failure data analysis algorithms. When the value is greater than or equal to 0.8, the equipment is inspected, including complete inspection of the power system, stress testing of key components, and complete review of the software system. Priority is given to emergency problems that cause downtime during the inspection process, including replacement and repair of parts.

[0060] Specifically, functional testing, system debugging, component replacement and re-inspection are carried out; among which, functional testing includes hardware testing and software testing. Hardware testing: including circuit testing of metering chips, power modules, and communication modules to determine whether there are problems such as short circuits and aging; software testing: updating device firmware and repairing potential communication and metering logic vulnerabilities; among which, system debugging: calibrating errors (such as power factor correction and voltage drift) for the metering data of the calibration equipment; among which, component replacement and re-inspection: replacing key damaged components (such as current transformers or voltage sensors) and performing multiple calibrations.

[0061] Furthermore, when the failure probability When it is greater than 0.4 and less than 0.8, the established regular inspection process is followed to check key data, including the operating temperature, voltage level, and vibration frequency of the equipment. The collected data is used for trend analysis to predict potential failures and performance degradation, and to adjust the verification and maintenance process plans.

[0062] Specifically, if the output voltage of a certain device is found to be lower than the set threshold for a long time, it is speculated that there may be an aging problem with the voltage acquisition module; for equipment with risk trends, the regular inspection cycle is adjusted in advance (for example, shortened from the original 6 months to 3 months); during the inspection process, focus on testing the components or functional modules involved in the abnormal trend; correct the parameter configuration of the equipment, such as resetting the collection interval time and optimizing the data storage and transmission mechanism.

[0063] Furthermore, when the failure probability When it is less than or equal to 0.4, carry out the standard equipment verification process, maintain regular verification intervals, continue to implement the preventive maintenance plan, evaluate equipment operating data, and optimize equipment performance, including adjusting operating parameters, reducing energy efficiency, and improving efficiency.

[0064] Specifically, the calibration content includes measurement accuracy verification, clock synchronization check, and other recording of calibration results, and updating of equipment files; adjusting operating parameters, such as reducing the acquisition frequency in power consumption mode, and regularly clearing the storage space of the electricity meter to prevent storage overflow problems caused by long-term operation; based on equipment operation data, regularly conduct health assessments of the equipment, mark risk levels and optimize subsequent calibration plans.

[0065] S4: Collect data during the verification process, conduct real-time analysis, and process and analyze the data.

[0066] It should be noted that if Figure 1 As shown in S4, the real-time analysis includes adjusting the detection and maintenance plan of the equipment in real time based on the real-time monitoring data, using the failure probability of the equipment in the historical failure data analysis algorithm to make judgments, predict the failure probability of the equipment and adjust the maintenance verification process.

[0067] Furthermore, when the failure probability When it is less than or equal to 0.4, it is low risk, the equipment status is stable, and it is suitable for routine inspections and long-term trend analysis. The routine inspection and maintenance frequency is maintained, the equipment operates according to the preset performance standards, and the equipment performance log is updated regularly. The historical operation data is compared with the performance of the equipment with the same similarity value to evaluate and adjust the calibration cycle and maintenance strategy;

[0068] Specifically, a monthly equipment health report is generated to evaluate operating performance indicators (such as voltage fluctuations and current stability) and to adjust the equipment operating mode to optimize performance; for example, optimizing the collection interval to reduce power consumption; and adjusting the operating load to avoid aging caused by long-term full-load operation.

[0069] When the probability of failure When it is greater than 0.4 and less than 0.8, it is a medium risk. The equipment has already shown preliminary abnormalities. It is necessary to strengthen monitoring and optimize the calibration cycle. The key data of the equipment should be intensively monitored. Condition monitoring technology, namely vibration analysis and thermal imaging analysis, should be introduced to identify early equipment abnormalities. When the key data falls below the warning threshold, the warning will be automatically triggered and reported to the maintenance team for maintenance and tracking.

[0070] Specifically, intensive monitoring and dynamic analysis: real-time calculation of trend indicators during operation (such as whether the vibration frequency increases or the temperature rises) and the introduction of image analysis technology to detect surface damage of equipment, such as thermal imaging to monitor overheated parts; adjustment of maintenance plans: execution of scheduled inspection procedures in advance, including testing accuracy and cleaning of key components (such as terminal connection points); dynamic tuning: use feedback control algorithms to adjust the operating parameters of the equipment; for example, reducing heat problems by reducing the current load.

[0071] When the probability of failure When it is greater than or equal to 0.8, it is a high risk. The equipment has a major failure risk and needs to be shut down for maintenance immediately. The automated emergency response program is activated to immediately interrupt the equipment operation and report to the emergency maintenance team for a comprehensive inspection of the equipment. Key components are replaced based on the inspection results. After the emergency intervention, a comprehensive risk assessment is conducted on the equipment. Based on the equipment repair status and inspection results, the equipment failure risk level is reassessed.

[0072] Specifically, fault warning and alarm response: when the fault probability exceeds 0.8, the system automatically sends an alarm to the operation and maintenance team via SMS or email; automatically triggers the emergency shutdown procedure to avoid further damage; in-depth inspection and maintenance: focus on checking whether key components (such as current transformers, voltage acquisition modules) are short-circuited, aged or damaged; replace damaged components and perform comprehensive calibration; resume operation and risk reassessment: recalculate after maintenance Confirm that the probability of equipment failure has been reduced to a safe range.

[0073] Furthermore, the inspection and maintenance results obtained in the risk level are collected and analyzed to update the model parameters. and error term∈, optimize and adjust the corresponding threshold according to the actual operation and maintenance history of the equipment; if the equipment is in a high-risk range and frequently fails, the high-risk threshold is dynamically lowered; if it is in a low-risk range and no failure occurs for a long time, the low-risk threshold is appropriately raised to reduce excessive maintenance; for example: if an electric energy meter is running under high load and often has a high probability of failure, When problems occur, the high-risk threshold is lowered from 0.8 to 0.7, and the inspection frequency of related equipment is increased (e.g. once a week); if a certain electric energy meter has no fault records for one year, the fault probability If it is less than 0.2 for a long time, the low-risk threshold will be raised from 0.4 to 0.5 to reduce the number of inspections of low-risk equipment.

[0074] S5: After verification, the verification parameters are automatically adjusted and the verification data are analyzed through statistical process control methods.

[0075] It should be noted that if Figure 1 As shown in S5, the statistical process control method includes collecting key data of equipment operation in real time and using historical fault data analysis algorithm to obtain current equipment performance indicators.

[0076] Furthermore, a data matching algorithm is used to calculate the similarity between the current device state and the ideal operating state, and to evaluate the gap between the current state and the ideal state, wherein the parameter vector and the electric energy meter attribute vector are mainly used;

[0077] The value range is: 1 means complete consistency, 0 means complete dissimilarity, and the preset threshold is 0.7;

[0078] When the calculated similarity result is less than the preset threshold, it means that the current device state deviates significantly from the ideal state; at the same time, combined with the fault probability To make a comprehensive judgment;

[0079] Furthermore, if When the calculation results show that the performance is lower than the preset threshold and the similarity value is less than the preset threshold, the system will trigger an alarm; based on the results of the failure probability and cosine similarity, it is decided to adjust the calibration parameters, including machine speed, temperature control, pressure setting, and recipe and mixing ratio, to calibrate and maintain the equipment; using the data collected during the calibration cycle, the parameter vector is updated and error term∈, adapting to changes in the verification process and optimizing performance indicators.

[0080] Specifically, for high temperature and high humidity environments, optimize the set values ​​of temperature control equipment, and adjust the supporting structure or foundation when the vibration value deviates from the normal range; calibration machine speed: reduce the excessive equipment calibration speed in real time to avoid false detection; pressure value: adjust the applied calibration load to match the actual operating conditions; perform adaptability updates based on the calibrated equipment performance data; for example: if the vibration frequency of a certain equipment (measured value = 60Hz) is significantly different from the ideal state (historical average = 50Hz), and the similarity is lower than the set threshold; then reduce the load pressure during calibration, reduce the vibration intensity, correct the supporting structure, and eliminate foundation abnormalities; if the equipment is calibrated in a high humidity environment, the real-time humidity = 85%, which is much higher than the normal operating humidity = 60%, then enhance humidity control measures (such as adding dehumidification equipment) to reduce the impact of humidity on equipment performance.

[0081] The above is a schematic scheme of a single-phase electric energy meter automatic calibration method of this embodiment. It should be noted that the technical scheme of the system of the single-phase electric energy meter automatic calibration method and the technical scheme of the above-mentioned single-phase electric energy meter automatic calibration method belong to the same concept, and the details of the technical scheme of the single-phase electric energy meter automatic calibration system in this embodiment that are not described in detail can all be referred to the description of the technical scheme of the above-mentioned single-phase electric energy meter automatic calibration method.

[0082] The single-phase electric energy meter automatic verification system in this embodiment is characterized by comprising: an identity recognition module, a pre-inspection analysis module, a process optimization module, a data analysis module and a quality control module;

[0083] The identification module uses RFID and barcode technology to automatically identify the electric energy meter, binds to the calibration station, and calculates using a data matching algorithm;

[0084] The pre-check analysis module automatically performs pre-check and pre-operation system, and uses historical fault data analysis algorithm to predict potential equipment problems;

[0085] The process optimization module, the electric energy meter passes through the multifunctional verification unit, performs process optimization and adjusts the verification process;

[0086] The data analysis module collects data during the verification process, performs real-time analysis, processes and analyzes the data, and automatically adjusts the verification parameters;

[0087] After verification, the adjustment control module analyzes the verification data and identifies the quality trend through statistical process control methods.

[0088] This embodiment further provides a computing device, which is applicable to the case of the single-phase electric energy meter automatic verification method, including:

[0089] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the single-phase electric energy meter automatic calibration method proposed in the above embodiment.

[0090] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the single-phase electric energy meter automatic calibration method proposed in the above embodiment is implemented.

[0091] The storage medium proposed in this embodiment and the single-phase electric energy meter automatic calibration method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0092] 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, etc., which can store program codes.

[0093] 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.

[0094] 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.

[0095] Example 2, reference Figure 2 , which is an embodiment of the present invention, provides an automated calibration method for a single-phase electric energy meter. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0096] 1. Experimental and control group settings:

[0097] Experimental group: An automated verification system was used, including an identity recognition module, a pre-inspection analysis module, a process optimization module, a data analysis module, and a quality control module.

[0098] Control group: Use traditional manual or semi-automatic detection methods,

[0099] 2. Data generation and collection:

[0100] The data covers verification efficiency, fault prediction accuracy, verification parameter adjustment effect, etc. The collection parameters are as follows:

[0101] Each group operates 200 electricity meters.

[0102] The time required for calibration of each electric energy meter (unit: minutes).

[0103] Verify fault prediction accuracy (based on historical data).

[0104] The inspection pass rate after process optimization.

[0105] 3. Experimental process:

[0106] 1. Energy meter binding:

[0107] Experimental group: RFID and barcode technology are used for automatic identification, and the system automatically allocates the calibration station.

[0108] Control group: manual recording and allocation.

[0109] 2. Fault prediction:

[0110] Experimental group: The historical data analysis algorithm is combined with the linear regression model to predict the failure probability.

[0111] Control group: Relying on technicians to manually analyze the equipment operation history.

[0112] 3. Process optimization:

[0113] Experimental group: Dynamically adjust the verification process based on the fault prediction results.

[0114] Control group: A fixed assay procedure was used.

[0115] 4. Real-time data analysis:

[0116] Experimental group: Real-time analysis using cosine similarity and statistical process control methods.

[0117] Control group: Manually check key data regularly.

[0118] 5.Quality Control:

[0119] Experimental group: The system dynamically adjusts the test parameters based on historical data.

[0120] Control group: manually adjusted or fixed parameters.

[0121] The experimental data comparison is shown in Table 1:

[0122] Table 1 Comparison of experimental data

[0123]

[0124]

[0125] Analysis of experimental results: Efficiency improvement: The single-unit calibration time of the experimental group was reduced by 59% compared with the control group, which significantly improved the calibration efficiency. Fault prediction: The prediction accuracy of the experimental group based on the historical data analysis algorithm was significantly higher than that of the control group. Process optimization effect: The experimental group improved the calibration pass rate and accuracy by dynamically adjusting the process. Error rate reduction: The experimental group relied on automated data analysis and statistical process control to reduce data processing errors.

[0126] The experimental data are as follows Figure 2 As shown:

[0127] Figure 2 : The experimental group and the control group were compared on five key performance indicators:

[0128] 1. Prediction accuracy: The prediction accuracy of the experimental group was significantly higher than that of the control group, indicating that it is more reliable in predicting failures.

[0129] 2. Test pass rate: The pass rate of the experimental group was higher than that of the control group, which shows the accuracy of automation.

[0130] 3. Error rate: The error rate of the experimental group was much lower than that of the control group, indicating the reliability of data processing.

[0131] The figure shows that the experimental group (automated detection system) is significantly better than the control group (traditional detection method) in multiple dimensions, especially in terms of efficiency, accuracy and flexibility.

[0132] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. 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. The single-phase electric energy meter automatic verification method is characterized by: include: Use RFID and barcode technology to automatically identify the energy meter, bind it to the calibration station, and use data matching algorithm to calculate; Automatically execute pre-check and pre-operation system, using historical fault data analysis algorithm to predict potential equipment problems; The electric energy meter passes through the multifunctional verification unit to optimize the process and adjust the verification process; Collect data during the verification process, conduct real-time analysis, and process and analyze the data; After verification, the verification parameters are automatically adjusted and the verification data are analyzed through statistical process control methods.

2. The single-phase electric energy meter automatic verification method according to claim 1, characterized in that: The RFID and barcode technology includes key information for automatically identifying the electric energy meter to be inspected and automatically binding the electric energy meter to a specific calibration station. Specifically, it includes installing RFID tags and barcodes on the electric energy meter, capturing information through a reader at the entrance, and automatically entering it into the system database, and intelligently allocating the electric energy meter to the corresponding calibration station based on the acquired information.

3. The single-phase electric energy meter automatic verification method according to claim 2, characterized in that: The data matching algorithm includes processing using the cosine similarity formula, expressed as: in, represents the attribute vector of the electric energy meter, represents the attribute vector of the calibration platform, and Represents the magnitude of a vector; The cosine similarity formula is used to calculate the similarity of two vectors, and the similarity value is between (-1,1); the similarity threshold is set to 0.8 according to the actual application requirements. When the calculated similarity value is greater than or equal to 0.8, it indicates that the two vectors match successfully and the calibration work is carried out; when the calculated similarity value is less than 0.8, it indicates that the two vectors fail to match, and the calibration station and electric energy meter are reselected.

4. The single-phase electric energy meter automatic verification method according to claim 3, characterized in that: The historical fault data analysis algorithm includes using the electric energy meter attribute vector associated with the linear regression model And the historical data of equipment failure, expressed as: in, represents the failure probability of the equipment, represents the parameter vector of the model, ∈ represents the model error term; Using the failure probability output by the model To guide equipment maintenance decisions, if the predicted probability of failure is greater than or equal to the preset threshold of 0.8, emergency maintenance is performed; if the predicted probability of failure is greater than 0.4 and less than 0.8, it means that the equipment has not reached the emergency maintenance level.

5. The single-phase electric energy meter automatic verification method according to claim 4, characterized in that: The process optimization includes judging the failure probability of the equipment in the historical failure data analysis algorithm, and when the failure probability When the probability of failure is greater than or equal to 0.8, the equipment is tested, including complete power system testing, stress testing of key components, and complete review of the software system. Priority is given to emergency problems that cause downtime during the testing process, including replacement and repair of components. When the probability of failure is greater than 0.4 and less than 0.8, the key data, including the temperature, voltage level, and vibration frequency of the equipment, are checked according to the established regular inspection process. The collected data are used for trend analysis to predict potential failures and performance degradation, and the verification and maintenance process plans are adjusted. When it is less than or equal to 0.4, carry out the standard equipment verification process, maintain regular verification intervals, continue to implement the preventive maintenance plan, evaluate equipment operating data, and optimize equipment performance, including adjusting operating parameters, reducing energy efficiency, and improving efficiency.

6. The single-phase electric energy meter automatic verification method according to claim 5, characterized in that: The real-time analysis includes adjusting the equipment inspection and maintenance plan in real time based on the real-time monitoring data, using the equipment failure probability in the historical failure data analysis algorithm to make judgments, predict the equipment failure probability and adjust the maintenance verification process; When the probability of failure When it is less than or equal to 0.4, it is a low risk. The regular inspection and maintenance frequency is maintained. The equipment operates according to the preset performance standards. The equipment performance log is updated regularly. The performance comparison of historical operation data with equipment with the same similarity value is used to evaluate and adjust the calibration cycle and maintenance strategy. When the failure probability is When it is greater than 0.4 and less than 0.8, it is a medium risk. The key data of the equipment is closely monitored and condition monitoring technology is introduced, namely vibration analysis and thermal imaging analysis, to identify early equipment abnormalities. When the key data exceeds the warning threshold, the warning is automatically triggered and reported to the maintenance team for maintenance and tracking. When the failure probability is When it is greater than or equal to 0.8, it is a high risk, and the automated emergency response program is activated to immediately interrupt the operation of the equipment and report to the emergency maintenance team for a comprehensive inspection of the equipment. Key components are replaced based on the inspection results. After the emergency intervention, a comprehensive risk assessment of the equipment is conducted, and the failure risk level of the equipment is reassessed based on the equipment repair status and inspection results. Collect and analyze the inspection and maintenance results obtained in the risk level and update the parameter vector and error term∈, and optimize and adjust the corresponding threshold according to the actual operation and maintenance history of the equipment.

7. The single-phase electric energy meter automatic verification method according to claim 6, characterized in that: The statistical process control method includes collecting key data of equipment operation in real time and obtaining current equipment performance indicators using historical fault data analysis algorithms; Use data matching algorithms to calculate the similarity between the current equipment status and the ideal operating status, and evaluate the gap between the current status and the ideal status; like When the calculation results show that the performance is lower than the preset threshold and the similarity value is less than the preset threshold, the system will trigger an alarm; based on the results of the failure probability and cosine similarity, it is decided to adjust the verification parameters, including machine speed, temperature control, pressure setting, recipe and mixing ratio, and calibrate and maintain the equipment; using the data collected during the verification cycle, update ∈ to adapt to changes in the verification process and optimize performance indicators.

8. A system for automatic verification of a single-phase electric energy meter according to any one of claims 1 to 7, characterized in that: include: Identity recognition module, pre-inspection analysis module, process optimization module, data analysis module and quality control module; The identification module uses RFID and barcode technology to automatically identify the electric energy meter, binds to the calibration station, and calculates using a data matching algorithm; The pre-check analysis module automatically performs pre-check and pre-operation system, and uses historical fault data analysis algorithm to predict potential equipment problems; The process optimization module, the electric energy meter passes through the multifunctional verification unit, performs process optimization and adjusts the verification process; The data analysis module collects data during the verification process, performs real-time analysis, and processes and analyzes the data; After verification, the adjustment control module automatically adjusts verification parameters and analyzes verification data through a statistical process control method.

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 single-phase electric energy meter automatic calibration 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 the single-phase electric energy meter automatic calibration method described in any one of claims 1 to 7 are implemented.

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