Electric energy meter PCB function test method and device, medium and equipment

Abstract

The invention provides an electric energy meter PCB function test method and device, a medium and equipment, and the method comprises the steps: obtaining a multi-channel signal source, automatically adjusting the characteristics of an input signal based on a feedback signal of an electric energy meter through a self-adaptive algorithm, and determining the consistency of the working characteristics of the input signal and the working characteristics of the electric energy meter; a virtual power grid environment simulation model is constructed, through integrated power simulation software and dynamic interference simulation, simulation signal source automatic adjustment signal characteristics are input into the virtual power grid environment simulation model based on the integrated power simulation software to synthesize a composite signal, and load fluctuation of an electric energy meter in actual operation is simulated based on the composite signal. Determining an electric energy meter test data set; according to the invention, the fault mode of the electric energy meter can be automatically identified, the test data can be analyzed in real time, and fault diagnosis and optimization suggestions can be automatically given. The intelligent diagnosis mode is far better than a traditional manual detection method, and the speed and accuracy of fault diagnosis can be remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of PCB function testing, and in particular to a method, device, medium and equipment for testing the function of an electric energy meter PCB. Background Art

[0002] During the production and maintenance of electric energy meters, the various electronic components and circuits on the printed circuit board (PCB) are tested to ensure that the functions and performance of the electric energy meter meet the design requirements, thereby ensuring the accuracy and reliability of the electric energy meter. The PCB board of the electric energy meter usually contains functional modules such as processing, storage, and communication. Performing PCB board functional testing can detect the integrity and stability of its electronic circuits and whether various functions are normal.

[0003] Technical problems of the existing method for testing the function of the PCB board of the electric energy meter: 1. Low testing efficiency: Existing functional testing methods for electric energy meters often rely on manual testing and single-machine testing. In particular, the environmental adaptability and electromagnetic compatibility testing processes require a long period of manual operation, resulting in low testing efficiency.

[0004] Currently, most test steps are still manually controlled and result-reading is still not possible, making it impossible to achieve efficient testing on automated, mass-produced production lines.

[0005] 2. Poor test accuracy and consistency: During multi-batch production and complex testing processes, inconsistencies in test equipment or human operating errors may occur, resulting in inaccurate test data or inconsistent test results.

[0006] For electric energy meters with higher accuracy requirements (such as precision electric meters), test errors may have a significant impact on the final quality of the product.

[0007] 3. Unable to fully cover all operating conditions of complex power systems: Traditional testing methods often rely on a single analog test signal (such as simulated sinusoidal current, voltage, etc.), which cannot fully cover the various complex situations that occur in the power system, such as harmonics, voltage fluctuations, pulse interference, etc.

[0008] The standard signal used for testing may not fully conform to the actual power grid status, resulting in the test results not fully reflecting the performance of the energy meter in a complex power environment.

[0009] 4. Insufficient testing of the device’s response speed and long-term stability: Traditional testing methods mainly focus on static performance and transient response, ignoring the stability test of the electric energy meter under long-term continuous working conditions.

[0010] Important performance indicators such as long-term stability, anti-interference ability, and transient response may not be fully verified.

[0011] 5. Low degree of automation: Most tests rely on manual or semi-automated equipment, which cannot achieve fast, automated mass production testing.

[0012] Especially when conducting multimodal tests (such as power module, communication module, signal acquisition module, etc.), there is a lack of effective automation system to coordinate the testing of different modules.

[0013] To this end, a method, device, medium and equipment for functional testing of an electric energy meter PCB board are proposed. Summary of the invention

[0014] In view of this, the purpose of the present invention is to provide a method, device, medium and equipment for functional testing of PCB boards of electric energy meters, in terms of test accuracy, automation, environmental simulation and intelligent analysis. Through the introduction of these innovative technologies, the accuracy and efficiency of the test are greatly improved, and it is closer to practical application, so as to solve or alleviate the technical problems existing in the prior art, and at least provide one of the beneficial options.

[0015] In order to achieve the above-mentioned purpose, a first aspect of the embodiments of the present disclosure provides a method for testing the function of a PCB board of an electric energy meter; It includes: obtaining a multi-channel signal source, using an adaptive algorithm, automatically adjusting input signal characteristics based on feedback signals from the electric energy meter, and determining that the input signal is consistent with the working characteristics of the electric energy meter; By integrating multi-channel sampling, high-precision signal source and refined control, the test accuracy is improved and automated testing is achieved.

[0016] Using high-precision digital signal generator (DDS) and multi-channel data acquisition (such as up to 32 channels of high-speed sampling ADC, support sampling frequency) to generate and capture complex signals.

[0017] Digital signal synthesis technology is used to generate a variety of signal sources, including: sine waves, square waves, harmonics, voltage fluctuations, pulses and other composite signals.

[0018] The signal source can be dynamically adjusted in real time, including parameters such as frequency, amplitude, and phase, to simulate common grid fluctuations and interferences in electricity.

[0019] Construct a virtual power grid environment simulation model, and determine the simulation signal source as the input signal generated by the simulation dynamics by integrating power simulation software and dynamic interference simulation. Based on the integrated power simulation software, the simulation signal source automatically adjusts the signal characteristics and inputs them into the virtual power grid environment simulation model to synthesize a composite signal. Based on the composite signal, simulate the load fluctuation of the electric energy meter in actual operation and determine the electric energy meter test data set. Build a virtual power grid environment, and fully simulate the working performance of the energy meter in a complex power environment by integrating power simulation software and dynamic interference simulation; Based on real-time intelligent analysis of the electric energy meter test data set, the fault mode of the electric energy meter PCB board is automatically identified and a deep learning model is built, including: Step 301: obtaining multidimensional signal parameters of an electric energy meter, wherein the multidimensional signal parameters include current, voltage, frequency and power, and extracting time domain features and frequency domain features of the multidimensional signal parameters; Step 302: The deep learning model and fault diagnosis are trained in data collection and feature extraction to identify the difference between the electric energy meter in normal and fault states, and the performance of the deep learning model is evaluated through cross-validation and confusion matrix to optimize the deep learning model parameters; Step 303: Based on the completion of fault pattern recognition, the deep learning model automatically marks and analyzes the fault type; By integrating deep learning models, real-time intelligent analysis is performed based on the power meter test data set to automatically identify the failure mode of the power meter PCB board and provide optimization suggestions; Utilize multi-channel data acquisition to continuously collect real-time working data (including current, voltage, frequency, power, etc.) of the energy meter in various working environments.

[0020] Perform time-frequency domain analysis on the test data to extract time-domain features (such as the average value, maximum value, peak-to-peak value, etc. of the waveform) and frequency-domain features (such as harmonic components, noise level, signal amplitude, etc.); Based on historical data, a convolutional neural network (CNN) or long short-term memory network (LSTM) training model is used to identify the fault modes in the operation of the electricity meter.

[0021] By comparing normal working data and fault data, the model automatically marks and analyzes possible faults in the energy meter (such as component damage, unstable power supply, signal noise, etc.); When a fault occurs, fault diagnosis is automatically performed and adjustment suggestions are fed back based on the model's analysis results. For example, if the harmonic interference of the energy meter is too large, it is recommended to add a filter module; if the voltage fluctuation is too large, it is recommended to check the power supply stability. Fault diagnosis reports are automatically generated and fed back to the operation interface in real time, prompting the operator to take corrective measures.

[0022] Deep learning algorithms can accurately identify fault modes, avoid human misjudgment, and improve fault detection efficiency and accuracy.

[0023] Automated fault diagnosis can reduce manual intervention and improve the intelligence of the detection process.

[0024] Based on the analysis of the test data set and deviation analysis of the electric energy meter, the deviations of several parameters of the electric energy meter are calculated and an automated report is generated.

[0025] As a further preferred embodiment of the present technical solution, the construction of the virtual power grid environment simulation model, by integrating power simulation software and dynamic interference simulation, determines that the simulation signal source is an input signal dynamically generated by simulation, specifically comprising: Step 201, power grid simulation and dynamic interference simulation, based on the simulated power environment, select integrated power simulation software; Step 202: Determine the dynamic power grid signal generated by simulating the power grid simulation environment using the integrated power simulation software as the simulation signal source; Step 203: Simulate the load change of the power grid by adjusting the parameters of the simulation signal source.

[0026] Use power simulation software (such as PSCAD, MATLAB / Simulink) to build a virtual power grid and simulate the working conditions of electric energy meters under various power environments.

[0027] Simulate dynamic changes in electricity, such as grid voltage fluctuations, transient current mutations, harmonic pollution, pulse interference, etc., and dynamically generate input signals through simulation signal sources.

[0028] Combined with the power operation model, it simulates high-frequency noise, transient response and other characteristics, and simulates the dynamic fluctuations of the power grid. The virtual power grid environment simulation model inputs the dynamic characteristics of the real power grid signal into the electric energy meter test, and simulates the load fluctuations of the electric energy meter in actual operation through the synthesized composite signal (such as voltage transients, current surges, etc.).

[0029] It can provide diversified power grid environment simulation and enhance the adaptability of electric energy meters in complex power environments.

[0030] The ability to simulate dynamic grid interference comprehensively covers various abnormal situations in the grid environment and improves the authenticity and accuracy of the test.

[0031] As a further preferred embodiment of the present technical solution, the simulation signal source includes a voltage fluctuation signal, a voltage mutation signal, a transient current mutation signal, a current surge and impact signal, a harmonic pollution signal, and a high-frequency noise and transient response signal.

[0032] As a further preferred embodiment of the present technical solution, the multi-channel signal source includes a sine wave signal, a square wave signal, a harmonic signal, a voltage fluctuation signal and a pulse signal.

[0033] As a further preferred embodiment of the present technical solution, the adaptive algorithm, the specific formula and calculation are as follows: Set the input signal to a composite waveform : ; in: is a sine wave signal, is a square wave signal, is a harmonic signal.

[0034] The signal is analyzed by fast Fourier transform (FFT) to calibrate the sampling module of the energy meter in real time: ; in, For signal frequency domain analysis, is the time domain signal, is a complex exponential function, is the frequency, i is an imaginary number, 2π is a constant, For time, is the integration variable.

[0035] Through the adaptive algorithm, the input signal characteristics are automatically adjusted according to the feedback signal of the electric energy meter to ensure that the input signal is consistent with the working characteristics of the electric energy meter and ensure the high accuracy of the test data.

[0036] Each test can provide real-time feedback and adjustments until the error range reaches the set standard; Through high-precision signal sources and data acquisition, various signal characteristics in the real power grid can be simulated to ensure the accuracy and comprehensiveness of the test.

[0037] The automated and adaptive adjustment functions can adjust the test signal based on real-time feedback, avoiding manual operation errors and improving test efficiency.

[0038] As a further preferred embodiment of the present technical solution, the calculation formula for calculating the deviation of several parameters of the electric energy meter is: ; in, is the actual measured value, is the ideal value.

[0039] As a further preferred embodiment of the present technical solution, the method further includes cloud-based real-time data analysis of 5G network and edge computing, and the specific steps include: Step 401: Upload data to the cloud; All data generated during the test, including real-time test results, sensor data, fault diagnosis information, etc., are uploaded to the cloud in real time through the 5G network for storage and analysis. With the high bandwidth and low latency characteristics of 5G, test data can be transmitted to the cloud in a very short time without affecting the real-time nature of on-site testing.

[0040] Step 402: edge computing and real-time analysis; Deploy edge computing nodes next to the test equipment to perform preliminary processing of real-time data locally, such as data cleaning and data aggregation, to reduce the pressure of cloud transmission and achieve real-time local analysis. For example, edge computing nodes can instantly detect an abnormality on the PCB board of the power meter and perform preliminary diagnosis directly through machine learning models.

[0041] Step 403: Cloud-based intelligent optimization and automatic feedback.

[0042] Big data analysis: The cloud platform aggregates, analyzes and learns massive amounts of data from multiple test points to generate a global view. Through big data analysis, the cloud platform can provide real-time feedback to the production line, automatically optimize the test process, reduce unnecessary test steps, and improve overall test efficiency.

[0043] Predictive maintenance: The cloud can identify the potential failure trend of the PCB board of the energy meter by analyzing the historical data accumulated over a long period of time, and perform predictive maintenance in advance to avoid unexpected failure and downtime during the production process; The combination of cloud and edge computing makes data processing more efficient, while providing real-time feedback and optimization for the testing process, ensuring that the electricity meter test is always in the best condition.

[0044] Second aspect: To solve the above technical problems, another technical solution adopted by the present application is: an electric energy meter PCB board function test device, comprising: The signal source acquisition module is used to acquire a multi-channel signal source, and use an adaptive algorithm to automatically adjust the input signal characteristics based on the feedback signal of the electric energy meter to ensure that the input signal is consistent with the working characteristics of the electric energy meter; A virtual power grid environment simulation module is used to construct a virtual power grid environment simulation model. By integrating power simulation software and dynamic interference simulation, the simulation signal source is determined to be an input signal dynamically generated by the simulation. Based on the integrated power simulation software, the simulation signal source automatically adjusts the signal characteristics and inputs them into the virtual power grid environment simulation model to synthesize a composite signal. Based on the composite signal, the load fluctuation of the electric energy meter in actual operation is simulated to determine the electric energy meter test data set. The deep learning module is used to perform real-time intelligent analysis based on the electric energy meter test data set, automatically identify the failure mode of the electric energy meter PCB board, and build a deep learning model.

[0045] The data analysis module is used to calculate the deviations of several parameters of the electric energy meter based on the analysis of the test data set and deviation analysis of the electric energy meter, and generate an automated report.

[0046] Improve the performance stability of electricity meters through intelligent analysis and automatic optimization, and generate detailed reports and suggestions.

[0047] The device will generate an automated report based on the data analysis results. The report content includes various test indicators, performance deviations, fault identification, optimization suggestions, etc.

[0048] When the energy meter shows abnormal performance, it will automatically suggest adjustment plans, such as adjusting the input signal, optimizing the power module, etc.

[0049] To solve the above technical problems, another technical solution adopted in the present application is: an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the program, the steps of the electric energy meter PCB board functional testing method as described in any one of the above are implemented.

[0050] To solve the above technical problems, another technical solution adopted in the present application is: a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the electric energy meter PCB board functional testing method as described in any one of the above are implemented.

[0051] The embodiment of the present invention has the following advantages due to the adoption of the above technical solution: 1. The present invention improves the test accuracy by adopting multi-channel high-speed data acquisition and digital signal synthesis technology, and greatly improves the test efficiency by combining automatic control. This high-precision automatic test can support a large number of electric energy meter function tests and avoid errors caused by manual operation.

[0052] 2. The present invention integrates power simulation software (such as PSCAD, MATLAB / Simulink) to make the test environment more diversified and dynamic, and can simulate complex power grid characteristics in the power grid (such as transient voltage fluctuations, current surges, harmonic pollution, etc.). This multi-modal environment simulation capability greatly improves the complexity of the test scenario of the electric energy meter based on deep learning models (such as CNN, LSTM); 3. The present invention can automatically identify the fault mode of the electric energy meter, analyze the test data in real time, and automatically give fault diagnosis and optimization suggestions. This intelligent diagnosis method far exceeds the traditional manual detection method and can significantly improve the speed and accuracy of fault diagnosis.

[0053] 4. The present invention can automatically generate an optimization report based on the test data and give relevant adjustment suggestions to ensure that the electric energy meter is always in the best working state under different test conditions. Through mathematical tools such as regression analysis and least squares method, the deviation of various parameters can be accurately calculated, and the test data can be automatically adjusted according to the deviation value.

[0054] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 It is a flow chart of the method for testing the function of the PCB board of an electric energy meter of the present invention; Figure 2 A flow chart for constructing a virtual power grid environment simulation model for the present invention; Figure 3 A flowchart for constructing a deep learning model for the present invention; Figure 4 It is a flow chart of the cloud-based real-time data analysis method of 5G network and edge computing of the present invention; Figure 5 It is a structural schematic diagram of the electric energy meter PCB board function test device of the present invention; Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0058] It should be clear that the following embodiments of the present disclosure are described by specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other in the absence of conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0059] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0060] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0061] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0062] Embodiment 1 Figure 1 1 is a flow chart of a method for testing the function of a PCB board of an electric energy meter according to an embodiment of the present invention. It should be noted that if there are substantially the same results, the method of the present application is not limited to the method of the present invention. Figure 1 The process sequence shown is limited. Figure 1-Figure 3 As shown: The function test method of the electric energy meter PCB board includes the following steps: Step 10: Obtain a multi-channel signal source, and use an adaptive algorithm to automatically adjust the input signal characteristics based on the feedback signal of the electric energy meter to ensure that the input signal is consistent with the working characteristics of the electric energy meter; By integrating multi-channel sampling, high-precision signal source and refined control, the test accuracy is improved and automated testing is achieved.

[0063] Using high-precision digital signal generator (DDS) and multi-channel data acquisition (such as up to 32 channels of high-speed sampling ADC, support sampling frequency) to generate and capture complex signals.

[0064] Digital signal synthesis technology is used to generate a variety of signal sources, including: sine waves, square waves, harmonics, voltage fluctuations, pulses and other composite signals.

[0065] The signal source can be dynamically adjusted in real time, including parameters such as frequency, amplitude, and phase, to simulate common grid fluctuations and interferences in electricity.

[0066] The specific formula and calculation of the adaptive algorithm are as follows: Set the input signal to a composite waveform : ; in: is a sine wave signal, is a square wave signal, is a harmonic signal.

[0067] The signal is analyzed by fast Fourier transform (FFT) to calibrate the sampling module of the energy meter in real time: ; in, For signal frequency domain analysis, is the time domain signal, is a complex exponential function, is the frequency, i is an imaginary number, 2π is a constant, For time, is the integration variable.

[0068] Through the adaptive algorithm, the input signal characteristics are automatically adjusted according to the feedback signal of the electric energy meter to ensure that the input signal is consistent with the working characteristics of the electric energy meter and ensure the high accuracy of the test data.

[0069] Each test can provide real-time feedback and adjustments until the error range reaches the set standard; Through high-precision signal sources and data acquisition, various signal characteristics in the real power grid can be simulated to ensure the accuracy and comprehensiveness of the test.

[0070] The automated and adaptive adjustment functions can adjust the test signal based on real-time feedback, avoiding manual operation errors and improving test efficiency.

[0071] Step 20, construct a virtual power grid environment simulation model, determine the simulation signal source as an input signal dynamically generated by the simulation by integrating power simulation software and dynamic interference simulation, automatically adjust the signal characteristics of the simulation signal source based on the integrated power simulation software and input it into the virtual power grid environment simulation model to synthesize a composite signal, simulate the load fluctuation of the electric energy meter in actual operation based on the composite signal, and determine the electric energy meter test data set; Build a virtual power grid environment, and fully simulate the working performance of the energy meter in a complex power environment by integrating power simulation software and dynamic interference simulation; The virtual power grid environment simulation model is constructed, by integrating power simulation software and dynamic interference simulation, to determine that the simulation signal source is an input signal dynamically generated by the simulation, specifically including: Step 201, power grid simulation and dynamic interference simulation, based on the simulated power environment, select integrated power simulation software; Step 202: Determine the dynamic power grid signal generated by simulating the power grid simulation environment using the integrated power simulation software as the simulation signal source; Step 203: Simulate the load change of the power grid by adjusting the parameters of the simulation signal source.

[0072] Use power simulation software (such as PSCAD, MATLAB / Simulink) to build a virtual power grid and simulate the working conditions of electric energy meters under various power environments.

[0073] Simulate dynamic changes in electricity, such as grid voltage fluctuations, transient current mutations, harmonic pollution, pulse interference, etc., and dynamically generate input signals through simulation signal sources.

[0074] Combined with the power operation model, it simulates high-frequency noise, transient response and other characteristics, and simulates the dynamic fluctuations of the power grid. The virtual power grid environment simulation model inputs the dynamic characteristics of the real power grid signal into the electric energy meter test, and simulates the load fluctuations of the electric energy meter in actual operation through the synthesized composite signal (such as voltage transients, current surges, etc.).

[0075] It can provide diversified power grid environment simulation and enhance the adaptability of electric energy meters in complex power environments.

[0076] The ability to simulate dynamic grid interference comprehensively covers various abnormal situations in the grid environment and improves the authenticity and accuracy of the test.

[0077] Through virtual grid and dynamic environment simulation technology, the performance of the energy meter in complex power is fully simulated and tested, especially in the face of dynamic fluctuations and interference under different grid conditions. This method can ensure that the energy meter can work stably in a complex real-world power environment, while reducing the interference of uncontrollable factors in the real environment on the test: In order to accurately simulate the power environment, it is crucial to choose the right power simulation software. Commonly used software includes: PSCAD (Power Systems Computer Aided Design): This is a professional power dynamic simulation software suitable for transient response analysis of power. PSCAD can provide high-precision power dynamic simulation and support simulation of various power grid fluctuations (such as voltage sag, overload, short circuit, etc.) and disturbance conditions.

[0078] MATLAB / Simulink: When used in combination with Simulink, MATLAB can provide powerful mathematical modeling and simulation capabilities, especially in dynamic modeling and control. Simulink's power modules include generators, transformers, loads, etc., which can fully simulate various situations of the power grid; The simulation signal source includes voltage fluctuation signal, voltage mutation signal, transient current mutation signal, current surge and impact signal, harmonic pollution signal and high-frequency noise and transient response signal; The power grid dynamic simulation signal sources include: 1. Voltage fluctuations: Simulates voltage transients that occur in the grid during load fluctuations or fault recovery. These voltage fluctuations may be caused by high-power equipment switching operations, load changes, etc. Through simulation, the response of the energy meter to a sharp change in voltage can be observed.

[0079] Voltage Sudden Change: By simulating the voltage drop and recovery process of the power grid, the performance of the energy meter when the voltage drops suddenly (such as a drop to 10%-20%) is simulated to test its impact on the energy meter.

[0080] 2. Transient current mutation: simulate the impact of current fluctuations or sudden increases (such as equipment switching, electrical failure, etc.) on the power meter to evaluate its response capability.

[0081] Current surge and impact: By simulating current surges (such as arcs, switching power supply startup) and transient mutations, the energy meter is tested to see how it handles sudden current impacts to ensure accurate measurement.

[0082] 3. Harmonic pollution: Harmonics caused by common nonlinear loads in the power grid (such as motors, power electronic equipment, etc.) will affect the measurement accuracy of the energy meter. Simulation can simulate harmonic signals of different frequencies and amplitudes to evaluate the performance of the energy meter under harmonic interference.

[0083] Harmonic source simulation: Generate common harmonics in the power grid (such as 5th, 7th, and 11th harmonics) through specific algorithms to simulate the harmonic filtering capabilities of different electricity meters.

[0084] 4. High-frequency noise and transient response: Simulate the impact of high-frequency noise in the power grid (such as high-frequency interference generated by electrical equipment) and transient response of the power grid (such as short-circuit transient, ground fault, etc.) on the performance of the energy meter to ensure that the energy meter can work stably in a complex environment.

[0085] Signal mixing and real-time simulation: Software is used to simulate high-frequency noise, transient response and other signals, and combine them with basic grid voltage and current signals to create a "mixed signal" to test the meter's ability to withstand a variety of interferences.

[0086] The dynamic grid signals generated by the grid simulation environment are input into the working characteristics and testing of the energy meter. The specific operation steps are as follows: The dynamic change signals of the power grid (such as voltage transients, current surges, frequency fluctuations, etc.) are synthesized with signals such as harmonic pollution and high-frequency noise to generate composite test signals. These signals will simulate the operation of the energy meter in the actual working environment.

[0087] These simulation signals are input into the electric energy meter test through the signal generator and data acquisition equipment. The amplitude, frequency, phase and other parameters of the simulation signal can be flexibly adjusted according to the test needs.

[0088] During the test, the parameters of the signal source are adjusted to simulate load changes, current surges, transient disturbances, etc. in the power grid, and the response of the energy meter under different conditions is monitored, including display accuracy, power consumption changes, fault detection, etc.

[0089] Through high-precision data acquisition, the output data of the energy meter is monitored in real time to capture the performance of the energy meter under various interference conditions. Special attention is paid to problems such as accuracy loss, output instability, and crash.

[0090] Compare the test results of the energy meter with the standard specifications to evaluate whether it can work properly in a complex power grid environment. For any deviations or failures that occur, find the root cause through data analysis and evaluate the impact of dynamic fluctuations or interference in the power grid on the energy meter.

[0091] By simulating various grid disturbances, such as voltage fluctuations, current mutations, harmonic pollution, etc., it is possible to comprehensively evaluate the working performance of the energy meter in a complex power environment and enhance the reliability and adaptability of the energy meter.

[0092] Dynamic simulation technology can test the response of the energy meter under various power grid fluctuations, covering various power disturbances that may occur in the actual power grid environment, ensuring that the energy meter can work normally even in the most extreme environment.

[0093] Dynamic interference simulation in a virtual grid environment can comprehensively and accurately test the various functions of the energy meter in a short time. Compared with traditional physical environment testing, it is not only more efficient, but also can repeatedly simulate various interference conditions to ensure the comprehensiveness and accuracy of the test.

[0094] The virtual grid environment can be quickly adjusted and optimized as needed, and can provide customized test solutions for different specifications and types of electric energy meters. At the same time, large-scale batch testing can be carried out to ensure that each electric energy meter undergoes strict quality inspection.

[0095] Specifically, the dynamic environment simulation based on the virtual power grid can provide a highly simulated and complex test environment for the functional test of the energy meter, which not only covers various dynamic changes and interferences that may be encountered in the power grid, but also can efficiently simulate and test multiple signals. Through this method, the test of the energy meter will be more comprehensive and accurate, and at the same time, it can identify and solve various problems in the power grid environment in advance, and improve the reliability and stability of the energy meter in actual operation.

[0096] Step 30: Based on the real-time intelligent analysis of the electric energy meter test data set, the failure mode of the electric energy meter PCB board is automatically identified, and a deep learning model is constructed, which specifically includes: Step 301: obtaining multidimensional signal parameters of an electric energy meter, wherein the multidimensional signal parameters include current, voltage, frequency and power, and extracting time domain features and frequency domain features of the multidimensional signal parameters; In the first stage of intelligent fault diagnosis, the goal is to ensure that complete and accurate data is obtained from the test process of the energy meter, providing high-quality data input for subsequent deep learning training; In order to accurately capture the performance of the energy meter in the working environment, multi-channel data acquisition is used to monitor multiple parameters (current, voltage, frequency, power, etc.) simultaneously, including: Voltage: Test the electric energy meter input voltage waveform.

[0097] Current: Tests the fluctuation of load current.

[0098] Frequency: Monitor grid frequency changes.

[0099] Power: The change in power recorded by the energy meter.

[0100] Temperature and humidity: The impact of changes in ambient temperature and humidity on the electricity meter.

[0101] Equipment and tools: Use high-precision oscilloscopes, data acquisition cards, smart sensors and other equipment to ensure that the multi-dimensional signals of the electricity meter can be captured.

[0102] Time domain feature extraction: Through time domain analysis, basic features related to the performance of the energy meter are extracted: Average value of waveform: The mean value of current and voltage waveforms, reflecting the stable working state.

[0103] Peak values ​​(maximum, minimum): Helps identify abnormal fluctuations in the signal, especially when sudden events (such as sudden current changes and voltage drops) occur.

[0104] Peak-to-peak value of the waveform: used to evaluate the amplitude of signal fluctuations and determine whether the electric energy meter is subject to significant interference.

[0105] Frequency domain feature extraction: The signal is analyzed in the frequency domain through Fast Fourier Transform (FFT) to extract the frequency components that may appear when the electric energy meter is working: Harmonic components: Identify whether the electric energy meter is affected by harmonic interference and evaluate its harmonic filtering capability.

[0106] Noise Level: Detects unusual noise in the energy meter, which may indicate power supply or circuit instability.

[0107] Signal Amplitude: Evaluates whether the signal strength is within the normal range to ensure that the signal received by the energy meter is not affected by clipping or attenuation.

[0108] Step 302: The deep learning model and fault diagnosis are trained in data collection and feature extraction to identify the difference between the electric energy meter in normal and fault states, and the performance of the deep learning model is evaluated through cross-validation and confusion matrix to optimize the deep learning model parameters; Convolutional Neural Networks (CNN): CNN is suitable for processing time-frequency domain feature maps, and can automatically identify and extract complex patterns in data. CNN is suitable for inputting two-dimensional feature matrices (such as time-frequency maps), and can capture local features and spatial hierarchical relationships in signals.

[0109] CNN model structure: Convolutional layer: Automatically extract local patterns in time and frequency domain features.

[0110] Pooling layer: Reduces the dimension of the feature map and retains the most important features.

[0111] Fully connected layer: Pass the extracted feature vector to the output layer for fault classification.

[0112] Long Short-Term Memory (LSTM) LSTM is a deep learning model suitable for processing time series data. It can capture the dynamic patterns of electric energy meters over time. It is especially effective in processing timing correlations in waveforms (such as frequency fluctuations, voltage drops, etc.).

[0113] LSTM model structure: Input layer: The time domain data and frequency domain data (such as power waveform and frequency change) of the electric energy meter are taken as input.

[0114] LSTM layer: Captures temporal dependencies through long short-term memory units in recurrent neural networks (RNNs).

[0115] Output layer: classification output, identification of normal state and failure mode; Step 303: Based on the completion of fault pattern recognition, the deep learning model automatically marks and analyzes the fault type; Use historical fault data for model training and create a classification model to classify the working status of the energy meter into multiple categories: Normal working mode: the performance of the energy meter in the absence of faults or abnormal interference.

[0116] Harmonic interference failure: The electric energy meter is affected by the harmonics of the power grid, resulting in measurement deviation.

[0117] Voltage fluctuation failure: The electric energy meter is affected by the voltage fluctuation of the power grid, resulting in inaccurate measurement.

[0118] Signal noise failure: The electric energy meter is interfered by high-frequency noise, which may affect the measurement accuracy.

[0119] Component damage and failure: Hardware failure of the electricity meter leads to inaccurate measurement.

[0120] The performance of the model is evaluated through cross-validation, confusion matrix and other methods, and the model parameters are optimized to ensure its accuracy and reliability in different failure scenarios.

[0121] Once the fault mode identification is completed, the model will provide automatic feedback and optimization suggestions to help testers respond quickly; In one embodiment, specifically, when the model detects that there is a fault in the energy meter, the deep learning model will automatically mark the fault type and perform a detailed analysis. For example, if the model detects that the harmonic interference is too large, it will be marked as a "harmonic interference fault"; Based on the failure mode, optimization suggestions are automatically generated. For example: Excessive harmonic interference: It is recommended to add a filtering module and use a low-pass filter to reduce high-frequency noise.

[0122] Excessive voltage fluctuations: It is recommended to check the power supply stability or use a voltage regulator.

[0123] Hardware damage: prompts the user to check the components of the energy meter and identifies the components that may be damaged.

[0124] A detailed diagnostic report is automatically generated when a fault occurs, including: Failure type and cause.

[0125] The time and environmental conditions when the failure occurred.

[0126] Optimization suggestions and corrective measures.

[0127] The report will be pushed to the operator interface in real time for the operator to refer to and take corresponding actions.

[0128] By integrating deep learning models, real-time intelligent analysis is performed based on the power meter test data set to automatically identify the failure mode of the power meter PCB board and provide optimization suggestions; Utilize multi-channel data acquisition to continuously collect real-time working data (including current, voltage, frequency, power, etc.) of the energy meter in various working environments.

[0129] Perform time-frequency domain analysis on the test data to extract time-domain features (such as the average value, maximum value, peak-to-peak value, etc. of the waveform) and frequency-domain features (such as harmonic components, noise level, signal amplitude, etc.); Based on historical data, a convolutional neural network (CNN) or long short-term memory network (LSTM) training model is used to identify the fault modes in the operation of the electricity meter.

[0130] By comparing normal working data and fault data, the model automatically marks and analyzes possible faults in the energy meter (such as component damage, unstable power supply, signal noise, etc.); When a fault occurs, fault diagnosis is automatically performed and adjustment suggestions are fed back based on the model's analysis results. For example, if the harmonic interference of the energy meter is too large, it is recommended to add a filter module; if the voltage fluctuation is too large, it is recommended to check the power supply stability. Fault diagnosis reports are automatically generated and fed back to the operation interface in real time, prompting the operator to take corrective measures.

[0131] Deep learning algorithms can accurately identify fault modes, avoid human misjudgment, and improve fault detection efficiency and accuracy.

[0132] Automated fault diagnosis can reduce manual intervention and improve the intelligence of the detection process; Through deep learning technology, the fault mode of the electricity meter can be identified efficiently and accurately, avoiding errors in manual diagnosis and significantly improving the accuracy of fault diagnosis.

[0133] It can automatically analyze faults in real time and provide optimization suggestions, reducing human intervention and improving detection efficiency.

[0134] Through dynamic detection and real-time feedback, problems with the energy meter can be identified and resolved in a timely manner, reducing the occurrence of failures and improving the stability and reliability of the energy meter.

[0135] Through deep learning-driven intelligent fault diagnosis, the functional testing of electricity meters has not only achieved intelligence and automation, but also improved testing accuracy and work efficiency, providing strong technical support for the quality control and optimization of electricity meters.

[0136] Step 40: Based on the analysis of the electric energy meter test data set and the deviation analysis, the deviations of several parameters of the electric energy meter are calculated and an automated report is generated.

[0137] The calculation formula for calculating the deviation of several parameters of the electric energy meter is: ; in, is the actual measured value, is the ideal value.

[0138] In one embodiment, specifically, an automated report is generated based on the data analysis results, and the report content includes various test indicators, performance deviations, fault identification, optimization suggestions, etc.

[0139] When the energy meter shows abnormal performance, it will automatically suggest adjustment plans, such as adjusting the input signal, optimizing the power module, etc.

[0140] Automatically generated optimization reports and optimization suggestions make the entire testing process more intelligent and efficient, reducing the probability of manual intervention and errors.

[0141] Ability to timely adjust test strategies and electric energy meter working parameters to ensure that the electric energy meter is always in the best working condition.

[0142] In one embodiment, multi-channel high-speed data acquisition and digital signal synthesis technology are used to improve test accuracy, and combined with automatic control, the test efficiency is greatly improved. This high-precision automated test can support a large number of electric energy meter function tests and avoid errors caused by manual operation.

[0143] By integrating power simulation software (such as PSCAD, MATLAB / Simulink), the test environment is made more diversified and dynamic, and can simulate complex power grid characteristics in the power grid (such as transient voltage fluctuations, current surges, harmonic pollution, etc.). This multimodal environment simulation capability greatly improves the complexity of the test scenarios of the energy meter. Based on deep learning models (such as CNN, LSTM), the deep learning model can automatically identify the fault mode of the energy meter, analyze the test data in real time, and automatically provide fault diagnosis and optimization suggestions. This intelligent diagnosis method far exceeds the traditional manual detection method and can significantly improve the speed and accuracy of fault diagnosis.

[0144] The deep learning model can automatically generate optimization reports based on test data and give relevant adjustment suggestions to ensure that the energy meter is always in the best working state under different test conditions. Through mathematical tools such as regression analysis and least squares method, the deep learning model can accurately calculate the deviation of various parameters and automatically adjust the test strategy according to the deviation value.

[0145] Embodiment 2 Traditional PCB functional testing of electric energy meters usually relies on manual operation and simple hardware equipment, with limited test accuracy, efficiency and repeatability. By introducing an automated test system, the entire test process is fully automated from test data collection to fault diagnosis, greatly improving test accuracy and efficiency. The automated system can run continuously under multiple test conditions, significantly reducing manual intervention and improving the accuracy and repeatability of the detection process.

[0146] Traditional testing methods mainly rely on a single signal input and cannot simulate the complex signal interference that may occur in the real environment. However, through virtual grid simulation and multimodal signal fusion technology, multiple influencing factors such as voltage, current, temperature, humidity, etc. can be introduced simultaneously during the test process to simulate the complex conditions in the actual operating environment. This method greatly improves the comprehensiveness and authenticity of the electric energy meter function test, so that the test results can more accurately reflect the performance in actual applications.

[0147] To solve the above technical problems, based on the first embodiment, another technical solution adopted by the present application is: a method for testing the function of a PCB board of an electric energy meter, comprising the following steps: Step 1: Obtain a multi-channel data acquisition system with a high-speed sampling rate (such as using a 16-channel ADC). Each channel collects real-time data from different modules of the energy meter (such as current, voltage, power factor, etc.), and uses a robot arm to automatically complete the connection and plug-in operations of the energy meter interface to reduce human errors and improve the speed and accuracy of the test. The system automatically adjusts the amplitude, frequency and other parameters of the input signal in each test cycle, and records the test data output by the energy meter; Set the input signal to a sine wave current: ; in, is the amplitude of the signal, is the frequency, is the phase, 2π is a constant, For time.

[0148] The test system will automatically change , and , simulate the changes in the actual power system; Specifically, the robot arm and automation platform are used to quickly test multiple electric energy meters. High-precision data acquisition: multi-channel ADC and synchronous sampling ensure the accuracy and consistency of the test.

[0149] Step 2: The signal generator system can output multiple analog signals at the same time, including sine waves, square waves, pulse signals, random noise, etc., to simulate various working conditions in the power grid.

[0150] Combined with power system simulation software (such as PSCAD) to simulate various complex interferences and dynamic changes in the actual power grid, by integrating multiple signal sources, simulate the complex working environment in the power system, covering harmonics, transient voltage fluctuations, noise interference and other factors; Multiple analog output signals are synthesized by addition to form a composite signal which is input to the energy meter: ; in, is a sine wave signal, is a pulse signal, is a random noise signal; Specifically, multi-signal source simulation can better simulate the performance of the energy meter in a complex power grid environment and improve the practical applicability of the test. The environmental simulation software can accurately restore various complex situations to ensure the stability of the energy meter in different power environments.

[0151] Step 3: Analyze the working status and failure mode of the electric energy meter through a deep neural network (such as convolutional neural network CNN or long short-term memory network LSTM), and train the deep learning model by annotating a large amount of test data (including data in normal and fault states) to achieve automatic fault diagnosis; The input features are the working parameters of the electric energy meter (such as current, voltage, power factor, etc.). The deep learning model extracts effective features through layer-by-layer convolution and pooling operations: ; in, is the activation function output, is the weight matrix, is the input feature, is the bias term, is the activation function (such as ReLU, Sigmoid, etc.).

[0152] Specifically, automated fault diagnosis through deep learning algorithms reduces errors in manual judgment and improves the accuracy and speed of fault detection.

[0153] It can detect various potential failure modes of the energy meter in real time, provide timely feedback and take corrective measures.

[0154] Step 4: The system uses data visualization tools to analyze the test results, generate charts and reports, and display the deviations and error sources of various working parameters; After fault diagnosis, the system will adjust the working parameters of the energy meter (such as input current, calculation algorithm, etc.) according to the diagnosis results to optimize its performance.

[0155] Assume that the calculation error of the energy meter is The system adjusts the amplitude and frequency of the input signal to make the error Minimize: ; in, is the actual test value, is the theoretical ideal value.

[0156] Specifically, automatically generated reports and adaptive adjustments can quickly reflect the performance status of the electricity meter and automatically adjust it to the optimal working state.

[0157] The reliability and stability of the electric energy meter are improved, and the probability of manual intervention and failure is reduced.

[0158] In summary, the combination of innovative technologies such as automated test systems, multimodal signal environment simulation, deep learning fault diagnosis and adaptive regulation has overcome the summary and innovation; Through the combination of innovative technologies such as automated test system, multi-modal signal environment simulation, deep learning fault diagnosis and adaptive adjustment, several technical bottlenecks in the traditional electric energy meter PCB board functional test method have been overcome, which is of significant innovation and substantial progress; By using deep learning models (such as CNN and LSTM), not only can the fault type be automatically identified based on the operating data of the energy meter, but fault prediction and warning can also be performed in real time, greatly improving the accuracy and timeliness of fault identification. Unlike traditional rule-based diagnostic methods, deep learning can automatically learn fault modes from a large amount of historical data and has greater adaptability and versatility. Through the trained model, potential faults of the energy meter PCB board can be quickly identified and located, greatly improving the diagnostic efficiency and accuracy during the test process; It has intelligent adaptive adjustment function, which can adjust the test conditions and methods according to the real-time changes in the test environment. For example, when abnormal high temperature or frequency fluctuation is identified, the system can automatically adjust the test parameters and reconfigure the test equipment to avoid test distortion caused by environmental interference. The introduction of this adaptive function makes the test process more flexible and has higher fault tolerance; After the test is completed, the system can not only automatically generate a detailed diagnostic report, but also automatically generate optimization suggestions based on the failure mode to guide operators to make effective corrections. Different from the manual analysis and report generation commonly seen in traditional methods, intelligent reports can quickly and accurately convert test results into actionable guidance, providing important reference for subsequent production and maintenance work; Through the combination of multimodal signal environment simulation and deep learning models, every link in the testing process has been processed more finely, which can effectively deal with the impact of different environmental factors on the accuracy of the electricity meter and avoid errors caused by manual operation or a single test environment in traditional methods.

[0159] The introduction of automated testing systems reduces manual intervention and improves the continuity and efficiency of testing. Deep learning models can also perform real-time data analysis, automatically identify faults, and reduce the time for manual inspection and feedback, thereby greatly shortening the test cycle and improving the efficiency of the production line.

[0160] The intelligent diagnosis system based on deep learning has strong fault prediction capabilities. It can identify potential faults in advance and issue early warnings based on historical test data and real-time monitoring data, helping operators perform maintenance in a timely manner and avoiding downtime and economic losses caused by equipment failure.

[0161] Not only can it accurately identify the fault type of the electric energy meter during the test, but it can also generate optimization suggestions based on the fault mode, further improving the intelligence level of the production line. Traditional methods usually rely on manual experience, but this solution provides a more accurate and effective optimization solution through automation and intelligent means.

[0162] The system can dynamically adjust the test strategy according to environmental changes, test requirements and the specific usage of the electric energy meter, ensuring the stability and reliability of the test method under changing operating environments. This flexibility and adaptability significantly enhances the applicability of the test system in different application scenarios.

[0163] Embodiment 3 With the advancement of intelligent manufacturing, the functional testing of PCB boards of electric energy meters is also facing the need to develop from traditional manual testing to automation, efficiency and intelligence. Although the existing testing methods have a certain degree of accuracy and efficiency, as the scale and complexity of production continue to increase, how to improve the intelligence and automation level of testing while ensuring accuracy has become an urgent problem to be solved in the industry.

[0164] As shown in FIG4 , in order to solve the above technical problems, based on the second embodiment, another technical solution adopted by the present application is: a method for testing the function of a PCB board of an electric energy meter, comprising the following steps: Step 1: Introduce high-precision robot arms (such as ABB and KUKA robots) to automatically carry and install the PCB board of the energy meter. The robot arm can accurately install the PCB board of the energy meter on the test platform, avoiding the errors caused by manual installation; Connect the robot system to automated test equipment (such as multi-channel data acquisition instrument, frequency response analyzer, power analyzer, etc.). The robot will automatically start the test equipment to test various electric energy meter functions according to the preset test process. Realize automatic switching of test equipment to ensure that different test methods (such as accuracy test, power consumption test, harmonic test, etc.) can be run automatically without manual intervention; The automated testing system can monitor the test progress in real time. If an abnormality occurs at a certain stage (such as a large deviation in the test results), the system will automatically adjust the test parameters or switch to other test methods; A high degree of automation reduces manual intervention and improves the stability and accuracy of the testing process.

[0165] Step 2: Through multi-channel high-speed data acquisition equipment, various performance data (such as current, voltage, power, power consumption, etc.) of the electric energy meter PCB board under different test conditions are obtained. Important features such as voltage fluctuation, frequency response, distortion, etc. are extracted through specific algorithms; Use data preprocessing techniques such as standardization and normalization to process the collected data to ensure that the deep learning model can be trained efficiently; A combination of convolutional neural network (CNN) and long short-term memory network (LSTM) is used. CNN is used to extract spatial features from sensor data, while LSTM is used to capture long-term dependencies in time series. Through transfer learning, we use the existing electric energy meter test data set for pre-training, and migrate the training results to the new data set to improve the generalization ability of the model and ensure accurate diagnosis even when facing different types of electric energy meter PCBs; Use the trained deep learning model for fault detection and diagnosis, automatically identify the possible fault types that may occur in the functional test of the PCB board of the energy meter (such as excessive voltage fluctuation, abnormal power consumption, excessive harmonics, etc.), and provide possible fault causes and repair suggestions; The application of deep learning can automatically identify anomalies during the testing process of electricity meters, quickly give fault predictions, reduce the pressure of manual inspections, and improve the accuracy of fault detection.

[0166] Step 3: All data generated during the test, including real-time test results, sensor data, fault diagnosis information, etc., are uploaded to the cloud in real time through the 5G network for storage and analysis. With the high bandwidth and low latency characteristics of 5G, test data can be transmitted to the cloud in a very short time without affecting the real-time nature of on-site testing; Deploy edge computing nodes next to the test equipment to perform preliminary processing of real-time data locally, such as data cleaning and data aggregation, to reduce the pressure of cloud transmission and achieve real-time local analysis. For example, the edge computing node can instantly detect an abnormality on the PCB board of the power meter and make a preliminary diagnosis directly through the machine learning model; The cloud platform aggregates, analyzes and learns massive amounts of data from multiple test points to generate a global view. Through big data analysis, the cloud platform can provide real-time feedback to the production line, automatically optimize the test process, reduce unnecessary test steps, and improve overall test efficiency; By analyzing historical data accumulated over a long period of time, the cloud system can identify potential failure trends of the PCB board of the energy meter, perform predictive maintenance in advance, and avoid unexpected failure downtime during the production process; The combination of cloud and edge computing makes data processing more efficient, while providing real-time feedback and optimization for the test process, ensuring that the meter test is always in the best condition; Specific: 1. Highly automated and intelligent: Through the combination of automated robots and deep learning, the test process is more efficient and intelligent, and fault diagnosis can be performed automatically. This not only reduces manual operations, but also significantly improves test precision and accuracy.

[0167] 2. Real-time data analysis and feedback: Using 5G networks and edge computing technology, test data can be uploaded to the cloud in real time for big data analysis and intelligent optimization, ensuring that every link in the testing process is fully optimized and adjusted.

[0168] 3. Intelligent fault prediction and maintenance: The cloud platform based on deep learning and big data analysis can detect and predict potential problems of the PCB board of the power meter in real time, take measures in advance to avoid failures, and improve product reliability and production line stability.

[0169] 4. Scalability and flexibility: This solution can not only be applied to the existing PCB functional test of electric energy meters, but can also be gradually expanded and upgraded with production requirements and technological development. Through software upgrades and modular design, the solution can adapt to different types and specifications of electric energy meters.

[0170] In summary, by combining the latest technologies such as automated robots, deep learning, 5G networks and edge computing, this solution provides a new intelligent, efficient and precise solution for the functional testing of electric energy meter PCB boards. It has obvious innovation and can significantly improve the efficiency and accuracy of the test. It can also cope with more complex electric energy meter testing needs and adapt to the rapid changes and development of the modern production environment.

[0171] Embodiment 4 Figure 5 Schematic diagram of the structure of the electric energy meter PCB board function test device of the embodiment of the present application, such as Figure 5 As shown, the electric energy meter PCB board function test device includes: The signal source acquisition module is used to acquire a multi-channel signal source, and use an adaptive algorithm to automatically adjust the input signal characteristics based on the feedback signal of the electric energy meter to ensure that the input signal is consistent with the working characteristics of the electric energy meter; A virtual power grid environment simulation module is used to construct a virtual power grid environment simulation model. By integrating power simulation software and dynamic interference simulation, the simulation signal source is determined to be an input signal dynamically generated by the simulation. Based on the integrated power simulation software, the simulation signal source automatically adjusts the signal characteristics and inputs them into the virtual power grid environment simulation model to synthesize a composite signal. Based on the composite signal, the load fluctuation of the electric energy meter in actual operation is simulated to determine the electric energy meter test data set. The deep learning module is used to perform real-time intelligent analysis based on the electric energy meter test data set, automatically identify the failure mode of the electric energy meter PCB board, and build a deep learning model.

[0172] The data analysis module is used to calculate the deviations of several parameters of the electric energy meter based on the analysis of the test data set and deviation analysis of the electric energy meter, and generate an automated report.

[0173] Improve the performance stability of electricity meters through intelligent analysis and automatic optimization, and generate detailed reports and suggestions.

[0174] The device will generate an automated report based on the data analysis results. The report content includes various test indicators, performance deviations, fault identification, optimization suggestions, etc.

[0175] When the energy meter has abnormal performance, the method will automatically suggest adjustment plans, such as adjusting the input signal, optimizing the power module, etc.

[0176] For other details about the technical solutions for implementing each module in the device of the above embodiment, please refer to the description of the electric energy meter PCB board function testing method in the above embodiment, which will not be repeated here.

[0177] It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For this method embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0178] Embodiment 5 Figure 6 is a schematic diagram of the structure of an electronic device according to an embodiment of the present application, such as Figure 6 As shown, an electronic device includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus. The processor can call the logic instructions in the memory to execute the electric energy meter PCB board function test method, the method comprising: controlling the electric energy meter PCB board function test device to perform the test and receive the test result; displaying the test result on the display screen, such as the cold start current value, temperature data, etc.; providing a user interface to allow the user to set the test parameters, view the historical data, etc.; supporting data transmission with a computer-readable storage medium, so as to export the test result for further analysis.

[0179] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0180] Embodiment 6 An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for performing the electric energy meter PCB board function test provided by the above methods is implemented. The method includes: controlling an electric energy meter PCB board function test device to perform a test and receiving the test result; displaying the test result on a display screen, such as a cold start current value, temperature data, etc.; providing a user interface to allow the user to set test parameters, view historical data, etc.; supporting data transmission with a computer-readable storage medium so as to export the test result for further analysis.

[0181] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).

[0182] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0183] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0184] In the present disclosure, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. The devices, apparatuses, equipment, and block diagrams of the method involved in the present disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, apparatuses, and equipment can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The word "such as" used here refers to the phrase "such as but not limited to", and can be used interchangeably with it.

[0185] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0186] It should also be noted that in the method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0187] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of the present disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions of events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or actions within their scope.

[0188] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0189] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. The method for testing the function of the PCB board of an electric energy meter is characterized in that: The method comprises the following steps: Obtain a multi-channel signal source, and use an adaptive algorithm to automatically adjust the input signal characteristics based on the feedback signal of the electric energy meter to ensure that the input signal is consistent with the working characteristics of the electric energy meter; Construct a virtual power grid environment simulation model, and determine the simulation signal source as the input signal generated by the simulation dynamics by integrating power simulation software and dynamic interference simulation. Based on the integrated power simulation software, the simulation signal source automatically adjusts the signal characteristics and inputs them into the virtual power grid environment simulation model to synthesize a composite signal. Based on the composite signal, simulate the load fluctuation of the electric energy meter in actual operation and determine the electric energy meter test data set. Based on real-time intelligent analysis of the electric energy meter test data set, the fault mode of the electric energy meter PCB board is automatically identified and a deep learning model is built, including: Step 301: obtaining multidimensional signal parameters of an electric energy meter, wherein the multidimensional signal parameters include current, voltage, frequency and power, and extracting time domain features and frequency domain features of the multidimensional signal parameters; Step 302: The deep learning model and fault diagnosis are trained in data collection and feature extraction to identify the difference between the electric energy meter in normal and fault states, and the performance of the deep learning model is evaluated through cross-validation and confusion matrix to optimize the deep learning model parameters; Step 303: Based on the completion of fault pattern recognition, the deep learning model automatically marks and analyzes the fault type; Based on the analysis of the test data set and deviation analysis of the electric energy meter, the deviations of several parameters of the electric energy meter are calculated and an automated report is generated.

2. The method for testing the function of the PCB board of an electric energy meter according to claim 1, characterized in that: The virtual power grid environment simulation model is constructed, by integrating power simulation software and dynamic interference simulation, to determine that the simulation signal source is an input signal dynamically generated by the simulation, specifically including: Step 201, power grid simulation and dynamic interference simulation, based on the simulated power environment, select integrated power simulation software; Step 202: Determine the dynamic power grid signal generated by simulating the power grid simulation environment using the integrated power simulation software as the simulation signal source; Step 203: Simulate the load change of the power grid by adjusting the parameters of the simulation signal source.

3. The method for testing the function of the PCB board of an electric energy meter according to claim 2, characterized in that: The simulation signal source includes voltage fluctuation signal, voltage mutation signal, transient current mutation signal, current surge and impact signal, harmonic pollution signal and high-frequency noise and transient response signal.

4. The method for testing the function of a PCB board of an electric energy meter according to claim 1, characterized in that: The multi-channel signal source includes a sine wave signal, a square wave signal, a harmonic signal, a voltage fluctuation signal and a pulse signal.

5. The method for testing the function of the PCB board of an electric energy meter according to claim 4, characterized in that: The specific formula and calculation of the adaptive algorithm are as follows: Set the input signal to a composite waveform : ； in: is a sine wave signal, is a square wave signal, is a harmonic signal.

6. The method for testing the function of a PCB board of an electric energy meter according to claim 1, characterized in that: The calculation formula for calculating the deviation of several parameters of the electric energy meter is: ； in, is the actual measured value, is the ideal value.

7. The method for testing the function of a PCB board of an electric energy meter according to claim 1, characterized in that: The method also includes real-time data analysis of 5G network and edge computing in the cloud, and the specific steps include: Step 401: Upload data to the cloud; Step 402: edge computing and real-time analysis; Step 403: Cloud-based intelligent optimization and automatic feedback.

8. An electric energy meter PCB function test device, applied to an electric energy meter PCB function test method as claimed in any one of claims 1 to 7, characterized in that: include: The signal source acquisition module is used to acquire a multi-channel signal source, and use an adaptive algorithm to automatically adjust the input signal characteristics based on the feedback signal of the electric energy meter to ensure that the input signal is consistent with the working characteristics of the electric energy meter; A virtual power grid environment simulation module is used to construct a virtual power grid environment simulation model. By integrating power simulation software and dynamic interference simulation, the simulation signal source is determined to be an input signal dynamically generated by the simulation. Based on the integrated power simulation software, the simulation signal source automatically adjusts the signal characteristics and inputs them into the virtual power grid environment simulation model to synthesize a composite signal. Based on the composite signal, the load fluctuation of the electric energy meter in actual operation is simulated to determine the electric energy meter test data set. Deep learning module, which is used to conduct real-time intelligent analysis based on the electric energy meter test data set, automatically identify the failure mode of the electric energy meter PCB board, and build a deep learning model; The data analysis module is used to calculate the deviations of several parameters of the electric energy meter based on the analysis of the test data set and deviation analysis of the electric energy meter, and generate an automated report.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the electric energy meter PCB board function testing method 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 electric energy meter PCB board function testing method as described in any one of claims 1 to 7 are implemented.

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