Method for detecting magnetic saturation during electricity meter replacement

Abstract

This invention discloses a method for detecting magnetic saturation during the replacement of electricity meters, relating to the field of electricity metering technology. The method includes: acquiring and preprocessing a current signal to obtain a filtered current signal; extracting current waveform features from the filtered current signal; performing harmonic analysis on the current waveform features to generate current signal feature data; dividing the current signal feature data into sliding time windows according to time length and inputting them into a long short-term memory network for real-time analysis to generate saturation detection results; checking wiring and tightening terminals based on the saturation assessment results to generate magnetic saturation alarm information; adjusting the electricity meter wiring and performing real-time analysis on the magnetic saturation alarm information to generate magnetic saturation recovery monitoring results; optimizing the reliability and intelligence level of magnetic saturation detection, ensuring the safe operation of the electricity meter.

Description

Technical Field

[0001] This invention relates to the field of electricity metering technology, and in particular to a method for detecting magnetic saturation during the replacement of electricity meters. Background Technology

[0002] The system acquires and preprocesses current signals, extracts current waveform features, and performs harmonic analysis. Real-time analysis is then performed using a long short-time memory network to accurately identify magnetic saturation phenomena in the current signal. Combined with contact resistance data from the electricity meter's terminals, the contact state is evaluated in real time, and a comprehensive saturation assessment result is generated through coupled analysis. This approach not only improves the response speed and accuracy of magnetic saturation detection but also comprehensively considers multiple factors such as current waveform characteristics and contact resistance, thus achieving comprehensive and accurate detection of the electricity meter's magnetic saturation state.

[0003] Traditional methods rely primarily on hardware sensors during the detection process and cannot adequately analyze and monitor potential contact problems at the electricity meter terminals in real time. These methods lack dynamic analysis based on current waveform characteristics and fail to efficiently and accurately identify and analyze current signals using deep learning technology, making them susceptible to environmental changes or fluctuations in power load. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for detecting magnetic saturation state during the replacement of electricity meters, which solves the problem of insufficient accuracy of current waveform and contact resistance data.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for detecting magnetic saturation during the replacement of an electricity meter, comprising: acquiring a current signal and preprocessing it to obtain a filtered current signal; extracting current waveform features from the filtered current signal; performing harmonic analysis on the current waveform features to generate current signal feature data; dividing the current signal feature data into sliding time windows according to time length and inputting them into a long short-term memory network for real-time analysis to generate a saturation detection result; combining the saturation detection result with the electricity meter terminals to perform real-time evaluation of the contact condition; obtaining contact resistance data by measuring the resistance value of the electricity meter terminals; and performing coupled analysis of the contact resistance data and the saturation detection result to generate a saturation evaluation result; performing wiring checks and terminal tightening on the saturation evaluation result to generate a magnetic saturation alarm message; and adjusting and analyzing the electricity meter wiring on the magnetic saturation alarm message in real time to generate a magnetic saturation recovery monitoring result.

[0007] As a preferred embodiment of the magnetic saturation state detection method during the replacement of the electricity meter according to the present invention, the step of obtaining the filtered current signal refers to performing bandpass filtering on the current signal and removing high-frequency noise.

[0008] As a preferred embodiment of the magnetic saturation state detection method during the replacement of the electricity meter according to the present invention, the specific steps of extracting current waveform features from the filtered current signal, performing harmonic analysis on the current waveform features, and generating current signal feature data are as follows: extracting current waveform features from the filtered current signal, performing a fast Fourier transform on the current waveform features, analyzing the fundamental and higher harmonic components in the current signal, and generating frequency domain signal data; combining the current waveform features with the time domain and frequency domain features in the frequency domain signal data through a weighted fusion method to generate current signal feature data.

[0009] As a preferred embodiment of the magnetic saturation state detection method during the replacement of the electricity meter according to the present invention, the specific steps of dividing the current signal feature data into sliding time windows according to the time length and inputting them into a long short-term memory network for real-time analysis are as follows: dividing the current signal feature data into multiple time sliding windows according to a fixed time length to generate window feature data; inputting the window feature data into a long short-term memory network, and learning the temporal characteristics of the current signal through the time dependency processing mechanism of the long short-term memory network to generate window classification results.

[0010] As a preferred embodiment of the magnetic saturation state detection method during the replacement of the electricity meter according to the present invention, the following steps are taken: The saturation detection result is combined with the electricity meter wiring terminals to evaluate the contact condition in real time. Contact resistance data is obtained by measuring the resistance value of the electricity meter wiring terminals. Specifically, saturation state information is extracted from the saturation detection result, wiring state information is extracted from the electricity meter wiring terminals, and the saturation state information and wiring state information are combined to generate associated data. Based on the associated data, the contact resistance value of the electricity meter wiring terminals is measured using a contact resistance measuring instrument and recorded as contact resistance data.

[0011] As a preferred embodiment of the magnetic saturation state detection method during the replacement of the electricity meter according to the present invention, the specific steps for coupling analysis of contact resistance data and saturation detection results to generate saturation evaluation results are as follows: Data filtering and noise reduction are performed on the contact resistance data to obtain a smoothed contact resistance signal, and the smoothed contact resistance signal is time-stamped to generate real-time contact resistance data; the real-time contact resistance data is combined with the saturation detection results, and the relationship between the contact resistance value and the saturation detection results is analyzed to generate a coupling analysis result; based on the coupling analysis result, the contact resistance value and the saturation detection results are comprehensively evaluated, and a comprehensive score is calculated using a multi-dimensional evaluation method to generate a saturation evaluation result.

[0012] As a preferred embodiment of the magnetic saturation state detection method during the replacement of the electricity meter according to the present invention, the specific steps for performing wiring checks and terminal tightening based on the saturation assessment results to generate magnetic saturation alarm information are as follows: Based on the saturation assessment results, the wiring terminals are checked, and the contact resistance of the wiring terminals is measured using a resistance testing instrument to generate wiring check results; based on the wiring check results, the wiring terminals are tightened using a torque meter to generate a terminal tightening state; the wiring check results are combined with the terminal tightening state, and when there is poor contact or ineffective tightening of the wiring terminals, an alarm is triggered, generating magnetic saturation alarm information.

[0013] As a preferred embodiment of the magnetic saturation state detection method during the replacement of the electricity meter according to the present invention, the specific steps for adjusting and analyzing the electricity meter wiring based on the magnetic saturation alarm information to generate a magnetic saturation recovery monitoring result are as follows: Based on the magnetic saturation alarm information, if the wiring terminals are loose, the terminals are tightened using a torque testing instrument to generate the wiring status after adjustment; the current signal and contact resistance value of the wiring terminals are monitored in real time, the current fluctuation and contact resistance value are recorded, and the real-time current signal data is compared with historical current signal data to generate a real-time monitoring result; the wiring status after adjustment and the real-time monitoring result are combined, and the contact resistance value and current signal stability are comprehensively analyzed to generate a magnetic saturation recovery monitoring result.

[0014] As a preferred embodiment of the magnetic saturation state detection method during the replacement of the electricity meter according to the present invention, the step of comprehensively evaluating the contact resistance value and the saturation detection result based on the coupling analysis results to generate a saturation evaluation result includes the following steps: combining the coupling analysis results, contact resistance value and saturation detection results, and calculating a comprehensive score through a multi-dimensional evaluation method to generate a comprehensive evaluation score; and comparing the comprehensive evaluation score with the normal recovery standard to generate a saturation evaluation result.

[0015] As a preferred embodiment of the magnetic saturation state detection method during the replacement of the electricity meter according to the present invention, the generation of saturation detection results refers to summarizing the window classification results, obtaining the category summary results, and calculating the category summary results by majority voting to select the category summary results with the highest frequency.

[0016] The beneficial effects of this invention are as follows: filtering and harmonic analysis of the current signal effectively removes high-frequency noise, improves signal clarity, and provides accurate data input for subsequent saturation state detection; by coupling and analyzing the saturation detection results with contact resistance data, the state of the energy meter is comprehensively evaluated, avoiding misjudgments caused by poor contact, improving the accuracy and stability of the system, optimizing the reliability and intelligence level of magnetic saturation detection, and ensuring the safe operation of the energy meter. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the magnetic saturation detection method during the replacement of electricity meters.

[0019] Figure 2 Flowchart for generating current signal characteristic data.

[0020] Figure 3 This is a flowchart of a real-time magnetic saturation detection system based on a long short-term memory network.

[0021] Figure 4 This is a flowchart for magnetic saturation recovery monitoring. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides a method for detecting magnetic saturation state during the replacement of an electricity meter, including the following steps:

[0026] S1. Acquire current signals and perform preprocessing to obtain filtered current signals. Extract current waveform features from the filtered current signals, perform harmonic analysis on the current waveform features, and generate current signal feature data.

[0027] S1.1 Perform bandpass filtering on the current signal and remove high-frequency noise to obtain the filtered current signal.

[0028] It should be noted that, based on the characteristics of the current signal, the low-frequency cutoff and high-frequency cutoff values ​​of the filter are set to ensure that only frequency bands containing valid information are retained. After the current signal passes through the filter, the filter automatically removes high-frequency noise exceeding the upper limit and retains the meaningful low-frequency components of the signal to obtain the filtered current signal.

[0029] S1.2 Extract current waveform features from the filtered current signal, perform fast Fourier transform on the current waveform features, analyze the fundamental and higher harmonic components in the current signal, and generate frequency domain signal data.

[0030] It should be noted that time-domain analysis is performed on the filtered current signal to identify periodic changes and abrupt changes. By analyzing the waveform of the current signal, key time-domain features such as amplitude, period, rising edge, falling edge, and zero-crossing points can be extracted. A Fast Fourier Transform (FFT) is then performed on these time-domain features to convert the time-domain signal into a frequency-domain signal. In the frequency domain, the FFT decomposes the signal into multiple components of different frequencies and can accurately identify the fundamental frequency and higher harmonics. By observing the amplitudes of different frequency components in the spectrum, the fundamental frequency (the main frequency of the signal) and higher harmonics (integer multiples of the fundamental frequency) can be distinguished, and the influence of these harmonics on the current signal can be analyzed to generate frequency-domain signal data.

[0031] S1.3. By using a weighted fusion method, the current waveform features are combined with the time-domain and frequency-domain features in the frequency-domain signal data to generate current signal feature data.

[0032] It should be noted that the time-domain features (such as amplitude, period, rising edge, falling edge, etc.) and frequency-domain features (such as amplitude, phase, fundamental frequency, and amplitude of higher harmonics) of the current waveform and frequency-domain signal data are extracted separately. Appropriate weight values ​​are assigned to the time-domain and frequency-domain features based on the importance of each feature to the current signal analysis. The weighted time-domain and frequency-domain features are then fused to generate the current signal feature data.

[0033] S2. Divide the current signal characteristic data into sliding time windows according to the time length, and input them into the long short-term memory network for real-time analysis to generate saturation detection results.

[0034] It should be noted that existing methods typically rely on traditional signal processing techniques, such as Fourier transforms, filters, or threshold-based detection methods. Point-by-point analysis of current signals depends on fixed thresholds and rules for saturation detection. For example, setting a fixed current amplitude threshold to determine whether the signal is saturated. These methods cannot handle dynamic changes in signals, lack the ability to dynamically model time-series data, and struggle to cope with complex signal fluctuations, leading to misjudgments when the signal undergoes rapid changes or abrupt changes.

[0035] This invention utilizes a Long Short-Term Memory (LSTM) network for real-time analysis of current signal characteristic data. The signal is divided into sliding time windows, allowing for the capture of signal variations within each time period. By performing time-series modeling on the data within these time windows, the LSTM network dynamically captures the long-term dependencies of the current signal, thereby more accurately detecting saturation phenomena. This method offers greater flexibility and accuracy, better handling complex signal fluctuations and dynamic changes.

[0036] S2.1 Divide the current signal characteristic data into multiple time sliding windows according to a fixed time length to generate window characteristic data.

[0037] It should be noted that the duration of each time sliding window should be determined, and an appropriate time interval should be set according to actual needs. The current signal characteristic data is divided into multiple consecutive time sliding windows according to the time length, and each window contains a segment of current signal characteristic data. These sliding windows may overlap to some extent, and the overlap length can be adjusted according to the analysis needs. The current signal characteristic data within each time sliding window reflects the changes in the current signal within the time period; therefore, the data from each window will be used for further analysis and processing to generate window feature data.

[0038] S2.2 Input the window feature data into the Long Short-Term Memory (LSTM) network, and learn the temporal characteristics of the current signal through the time-dependent processing mechanism of the LSM network to generate window classification results.

[0039] It should be noted that the feature data within each time sliding window is sequentially input into the Long Short-Term Memory (LSTM) network. Through a time-dependent processing mechanism, the LTM network captures the temporal relationships in the current signal feature data and learns the patterns of current signal variation over time. The LTM network recursively updates its internal state and processes the input features at each time step to identify the temporal characteristics of the signal. These temporal characteristics reflect the dynamic change patterns of the current signal, helping to identify the current signal category within different time periods. Based on the learned temporal characteristics, the LTM network generates window classification results.

[0040] It should also be noted that Long Short-Term Memory (LSTM) networks are a special type of recurrent neural network used to process and predict temporal dependencies in sequential data. Compared to traditional recurrent neural networks, LSTM networks address long-term dependencies by introducing three gating mechanisms (input gate, forget gate, and output gate), enabling them to effectively capture long-term memories in sequential data.

[0041] Time-dependent processing mechanisms refer to using models to capture the patterns and dependencies in data over time. When processing time-series data, the system needs to remember past information and make predictions about future changes based on this historical data. Especially in recurrent neural networks such as Long Short-Term Memory (LSTM) networks, time-dependent mechanisms address long-term dependencies by introducing memory units.

[0042] The window classification results include state classification information of the current signal within each time window, primarily identifying whether the signal is in a normal, saturated, or abnormal state. Through the time-dependent processing mechanism of a Long Short-Term Memory (LSTM) network, the window classification results reflect the dynamic change pattern of the signal in the time series, capturing the fluctuations and long-term dependencies of the current signal over time. Functionally, the window classification results are used to assess the state changes of the current signal in real time, helping to identify abnormal situations or potential saturation phenomena, and determining whether the current signal is in a standard sinusoidal wave state or a fault current waveform state.

[0043] S2.3 Summarize the window classification results, obtain the category summary results, and calculate the category summary results using the majority voting method. Select the category summary results with the highest frequency and generate saturation detection results.

[0044] It should be noted that the classification results of all time-sliding windows are collected, and the category information corresponding to each window is organized. For all window classification results, the categories are summarized, and the frequency of each category appearing in all windows is calculated. A majority voting method is used to calculate the category summary results, comparing the frequency of each category, and selecting the category summary result with the highest frequency to generate the saturation detection result.

[0045] It should also be noted that majority voting is a common ensemble learning method. In majority voting, each classifier makes a prediction for the input data, all predictions are aggregated, and the class with the highest frequency is used as the final decision.

[0046] The saturation detection results include classification results of the current signal within each time window, reflecting whether the signal has saturated. The saturation detection results for each time window combine current signal characteristics such as amplitude, period, and frequency. Through analysis using a long short-term memory network, dynamic changes and long-term dependencies in the signal can be identified. Functionally, the saturation detection results are used to determine whether the current signal exceeds its normal operating range, helping to identify the saturation state of the current signal, thereby providing early warning of potential equipment failures or abnormalities, and ensuring that the equipment operates under stable conditions.

[0047] S3. Combine the saturation detection results with the electricity meter terminals to evaluate the contact situation in real time. Obtain contact resistance data by measuring the resistance value of the electricity meter terminals, and perform coupled analysis of the contact resistance data and saturation detection results to generate saturation evaluation results.

[0048] It should be noted that existing methods typically treat contact resistance and saturation detection results as independent parameters, and use simple threshold judgments or statistical analysis to evaluate the contact condition of the electricity meter terminals. Most methods ignore the complex coupling relationship between contact resistance and saturation state, resulting in inaccurate evaluation results, especially under dynamic load changes or large current fluctuations, failing to effectively reflect the actual impact of poor contact on saturation state.

[0049] This invention innovatively combines the dynamic relationship between contact resistance data and saturation detection results through coupled analysis. By measuring contact resistance values ​​in real time and jointly analyzing them with saturation detection results, the contact status of terminals can be assessed more accurately, especially under current fluctuation conditions. This coupled analysis method comprehensively considers the influence of contact resistance on saturation phenomena, improving the accuracy and reliability of saturation assessment results.

[0050] S3.1 Extract saturation state information from the saturation detection results, extract wiring state information from the electricity meter terminals, and combine the saturation state information with the wiring state information to generate associated data.

[0051] It should be noted that, based on the saturation level calibrated in the saturation detection results, the presence of saturation in the current signal is identified, and the corresponding saturation state data is extracted, reflecting the saturation status of the current signal. Wiring status information is extracted from the electricity meter terminals, and the working status of the terminals, including resistance value, contact condition, and connection quality, is analyzed to determine if there are any wiring problems. The extracted saturation state information is combined with the wiring status information to generate correlated data.

[0052] S3.2. Based on the associated data, measure the contact resistance value of the electricity meter terminals using a contact resistance measuring instrument and record it as contact resistance data.

[0053] It should be noted that you must ensure the contact resistance measuring instrument is correctly connected to the electricity meter terminals and that the instrument is functioning properly. The contact resistance measuring instrument measures the resistance between the terminals, and the measurement process reflects the quality of the electrical contact between the electricity meter terminals. The measuring instrument calculates the contact resistance value by applying current and detecting the voltage drop, and records the measured contact resistance value as contact resistance data.

[0054] The expression for calculating the contact resistance value is:

[0055] ;

[0056] in: This indicates the contact resistance value. This represents the voltage drop, which is the voltage difference across the terminals when a current is applied by the measuring instrument. This indicates the applied current value.

[0057] S3.3 Perform data filtering and noise reduction on the contact resistance data to obtain a smooth contact resistance signal, and synchronize the smooth contact resistance signal with a timestamp to generate real-time contact resistance data.

[0058] It should be noted that the contact resistance data undergoes preprocessing to remove outliers, which may be caused by measurement errors. Filtering algorithms, such as low-pass filtering or median filtering, are used to smooth the contact resistance data, removing high-frequency noise and obtaining a more stable contact resistance signal. The processed contact resistance signal is then synchronized using timestamps, aligning contact resistance data from different time points with their corresponding timestamps to ensure all data are compared on the same time scale, generating real-time contact resistance data.

[0059] S3.4 Combine the real-time contact resistance data with the saturation detection results, and analyze the relationship between the contact resistance value and the saturation detection results to generate coupling analysis results.

[0060] It should be noted that real-time contact resistance data should be aligned with corresponding saturation detection results to ensure that the contact resistance data at each time point matches the corresponding saturation state information. The relationship between contact resistance values ​​and saturation detection results should be analyzed to observe whether changes in contact resistance are correlated with the occurrence of saturation. For example, check whether an increase in contact resistance is temporally related to the occurrence of saturation. Through this analysis, the impact of contact resistance changes on saturation detection results can be identified, thereby helping to assess the effect of terminal condition on current signal saturation and generating coupling analysis results.

[0061] S3.5 Combine the coupling analysis results, contact resistance values, and saturation detection results, and calculate the comprehensive score using a multi-dimensional evaluation method to generate a comprehensive evaluation score.

[0062] It should be noted that the coupling analysis results, contact resistance values, and saturation detection results are integrated into a comprehensive dataset, ensuring that each data point is matched with its corresponding counterparts. A multidimensional evaluation method is applied to analyze the integrated data. This process involves calculating the importance weights of each data dimension and weighting each data point accordingly, ensuring that the influence of contact resistance values, saturation detection results, and coupling analysis results is fully considered in the evaluation. Using the multidimensional evaluation method, a comprehensive score is calculated for each data point, taking into account the relationship between contact resistance values ​​and saturation detection results, thereby generating a comprehensive evaluation score.

[0063] The expression for calculating the overall score is:

[0064] ;

[0065] in: This is a comprehensive score. Parameters for each evaluation dimension. The weights assigned to each dimension reflect its importance in the overall evaluation. The total number of dimensions to be evaluated is usually a weighted sum of multiple variables or factors.

[0066] It should also be noted that the multidimensional evaluation method is an evaluation method that comprehensively considers multiple different dimensions for analysis. In this method, evaluation is mainly conducted through three dimensions: contact resistance, saturation detection results, and coupling analysis results. Contact resistance reflects the electrical contact quality between the electricity meter terminals and the circuit; saturation detection results reflect whether the current signal has saturated; and coupling analysis results analyze the relationship between changes in contact resistance and the saturation state. The evaluation results of each dimension are assigned different weights according to their importance, and a weighted sum is used to obtain the final comprehensive evaluation score, reflecting the relative importance of each dimension in the entire system, thereby enabling a more accurate assessment of the equipment's operating status.

[0067] The contact resistance, saturation detection results, and coupling analysis results are converted into dimensionless values ​​using a standardization method. Specifically: contact resistance can be standardized using the Z-score to convert it into a dimensionless value. Saturation detection results, typically values ​​between 0 and 1, are already dimensionless and can be used directly. Coupling analysis results can be obtained by calculating the correlation between contact resistance and the saturation state, yielding a dimensionless correlation coefficient (such as the Pearson correlation coefficient, ranging from -1 to 1). This standardization process ensures that contact resistance, saturation detection results, and coupling analysis results can be evaluated under a unified dimension, making the evaluation results more consistent and accurate.

[0068] S3.6. Based on the comparison between the comprehensive assessment score and the normal recovery standard, generate the saturation assessment result.

[0069] It should be noted that the comprehensive evaluation score is compared with the normal recovery standard, which reflects the contact resistance value and saturation state that the electricity meter terminals should achieve under normal operating conditions. The comparison determines whether the comprehensive evaluation score exceeds these standards. If the comprehensive evaluation score meets the normal recovery standard, it indicates that the terminals are in normal condition. If the comprehensive evaluation score exceeds the range of the normal recovery standard, it indicates a possible poor contact, resulting in a saturation evaluation result.

[0070] S4. Perform wiring checks and terminal tightening on the saturation assessment results, and generate magnetic saturation alarm information.

[0071] Based on the saturation assessment results, the wiring terminals are inspected, and the contact resistance of the wiring terminals is measured using a resistance testing instrument to generate wiring inspection results.

[0072] S4.1 Based on the wiring inspection results, a torque meter is used to tighten the wiring terminals to generate a terminal tightening status.

[0073] It should be noted that the wiring inspection results should be used to determine if the terminals are loose. If the wiring inspection indicates that the terminals are loose or have poor contact, a torque meter should be used to tighten them. The torque meter can accurately measure the torque applied to the terminals and tighten them according to the set standard torque value. By applying appropriate torque, the connection between the terminals and the circuit is ensured to be secure, avoiding poor contact. After tightening, the tightening status of the terminals should be recorded to generate a terminal tightening status record.

[0074] S4.2 Combine the wiring inspection results with the terminal tightness status. If there is poor contact or failure to tighten the wiring terminals effectively, trigger an alarm and generate a magnetic saturation alarm message.

[0075] It should be noted that the wiring inspection results should be compared and analyzed with the terminal tightening status data to confirm whether there is poor contact or ineffective tightening of the wiring terminals. If the wiring inspection results indicate poor contact of the wiring terminals, i.e., ineffective tightening, this information will trigger the alarm mechanism, indicating a possible magnetic saturation problem and generating a magnetic saturation alarm message.

[0076] S5. Adjust the wiring of the energy meter and perform real-time analysis on the magnetic saturation alarm information to generate magnetic saturation recovery monitoring results.

[0077] S5.1. Based on the magnetic saturation alarm information, if the wiring terminal is loose, tighten the wiring terminal using a torque testing instrument to generate the wiring status after adjustment.

[0078] It should be noted that the looseness of the wiring terminals is confirmed by analyzing the magnetic saturation alarm information. If the alarm information indicates that the wiring terminals are loose, a torque tester is used to tighten them. The torque tester applies a specific torque to ensure that the connection between the wiring terminals and the circuit meets the specified tightening standards. Through precise torque control, the wiring terminals are tightened to a suitable state to ensure stable and safe current transmission. After tightening, the state of the wiring terminals after tightening is recorded, and wiring status data after adjustment is generated.

[0079] S5.2 Real-time monitoring of the current signal and contact resistance value of the terminal block, recording the current fluctuation and contact resistance value, and comparing the real-time current signal data with the historical current signal data to generate real-time monitoring results.

[0080] It should be noted that continuous monitoring of the current signal and contact resistance value at the terminal block is essential, with current fluctuations and contact resistance values ​​recorded at each point in time. This provides a real-time reflection of the terminal block's operating status and lays the foundation for subsequent analysis. Comparing the real-time current signal data with historical current signal data allows for the identification of abnormal changes in current fluctuations, thereby determining whether there are potential problems with the terminal block and generating real-time monitoring results.

[0081] S5.3 Combine the wiring status after wiring adjustment with the real-time monitoring results, and conduct a comprehensive analysis of the contact resistance value and current signal stability to generate magnetic saturation recovery monitoring results.

[0082] It should be noted that the wiring status after adjustment should be compared with the real-time monitoring results to ensure that the terminals have been restored to a normal connection state and to eliminate any potential loosening issues. A comprehensive analysis should be conducted on the stability of the contact resistance value and current signal to assess whether the contact resistance value remains within the normal range and to check whether the current signal is stable without abnormal fluctuations. Through this comprehensive analysis, it can be determined whether the working state of the terminals has returned to normal, ensuring that no new saturation phenomena or contact problems have occurred, and generating magnetic saturation recovery monitoring results.

[0083] In summary, this invention effectively removes high-frequency noise and improves signal clarity by filtering and harmonic analysis of the current signal, providing accurate data input for subsequent saturation state detection. By coupling the saturation detection results with contact resistance data, the state of the energy meter is comprehensively evaluated, avoiding misjudgments caused by poor contact, improving the accuracy and stability of the system, optimizing the reliability and intelligence level of magnetic saturation detection, and ensuring the safe operation of the energy meter.

[0084] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for detecting a magnetic saturation state in an electricity meter replacement process, characterized by, include: The current signal is collected and preprocessed to obtain a filtered current signal. Current waveform features are extracted from the filtered current signal, and harmonic analysis is performed on the current waveform features to generate current signal feature data. The current signal characteristic data is divided into sliding time windows according to the time length, and then input into a long short-term memory network for real-time analysis to generate saturation detection results; By combining the saturation detection results with the electricity meter terminals, the contact condition can be evaluated in real time. Contact resistance data can be obtained by measuring the resistance value of the electricity meter terminals. The contact resistance data and saturation detection results are coupled and analyzed to generate saturation assessment results. The specific steps are as follows: Data filtering and noise reduction are performed on the contact resistance data to obtain a smooth contact resistance signal. The smooth contact resistance signal is then timestamped to generate real-time contact resistance data. By combining real-time contact resistance data with saturation detection results and analyzing the relationship between contact resistance values ​​and saturation detection results, coupled analysis results are generated. Based on the coupling analysis results, the contact resistance value and saturation detection results are comprehensively evaluated, and a comprehensive score is calculated using a multi-dimensional evaluation method to generate the saturation evaluation result. The wiring is checked and the terminals are tightened based on the saturation assessment results, and a magnetic saturation alarm message is generated. The following steps are taken to adjust the wiring of the energy meter and perform real-time analysis on the magnetic saturation alarm information to generate magnetic saturation recovery monitoring results: Based on the magnetic saturation alarm information, if the wiring terminals are loose, the wiring terminals are tightened using a torque testing instrument to generate the wiring status after adjustment; The system monitors the current signal and contact resistance value of the terminals in real time, records the current fluctuations and contact resistance values, and compares the real-time current signal data with historical current signal data to generate real-time monitoring results. By combining the wiring status after adjustment with real-time monitoring results, and by comprehensively analyzing the contact resistance value and current signal stability, magnetic saturation recovery monitoring results are generated.

2. The method of claim 1, wherein the method further comprises: determining whether the magnetic saturation state is detected based on the comparison result. The acquisition of the filtered current signal refers to performing bandpass filtering on the current signal and removing high-frequency noise.

3. The method of claim 2, wherein the magnetic saturation state is detected by a magnetic sensor. The specific steps for extracting current waveform features from the filtered current signal, performing harmonic analysis on the current waveform features, and generating current signal feature data are as follows: The current waveform features are extracted from the filtered current signal, and a fast Fourier transform is performed on the current waveform features to analyze the fundamental and higher harmonic components in the current signal and generate frequency domain signal data. By using a weighted fusion method, the current waveform features are combined with the time and frequency domain features in the frequency domain signal data to generate current signal feature data.

4. The method of claim 3, wherein the magnetic saturation state is detected by a magnetic sensor. The specific steps for dividing the current signal characteristic data into sliding time windows according to time length and inputting them into a long short-term memory network for real-time analysis are as follows: The current signal feature data is divided into multiple time sliding windows according to a fixed time length to generate window feature data; The window feature data is input into the Long Short-Term Memory (LSTM) network, and the temporal characteristics of the current signal are learned through the time-dependent processing mechanism of the LSM network to generate window classification results.

5. The method of claim 4, wherein the magnetic saturation state is detected by a magnetic sensor. The process of combining the saturation detection results with the electricity meter terminals to evaluate the contact condition in real time, and obtaining contact resistance data by measuring the resistance value of the electricity meter terminals, is as follows: Saturation state information is extracted from the saturation detection results, wiring state information is extracted from the electricity meter terminals, and the saturation state information and wiring state information are combined to generate associated data. Based on the associated data, the contact resistance value of the electricity meter terminals is measured using a contact resistance measuring instrument and recorded as contact resistance data.

6. The method of claim 1, wherein the magnetic saturation state is detected by a magnetic sensor. The specific steps for checking the wiring and tightening the terminals based on the saturation assessment results, and generating magnetic saturation alarm information, are as follows: Based on the saturation assessment results, the wiring terminals are inspected, and the contact resistance of the wiring terminals is measured using a resistance testing instrument to generate wiring inspection results; Based on the wiring inspection results, a torque meter was used to tighten the wiring terminals to generate a terminal tightening status. By combining the wiring inspection results with the terminal tightness status, an alarm is triggered and a magnetic saturation alarm message is generated when there is poor contact or ineffective tightening of the wiring terminals.

7. The method of claim 1, wherein the magnetic saturation state is detected by a magnetic sensor. Based on the coupling analysis results, the contact resistance value and saturation detection results are comprehensively evaluated to generate a saturation evaluation result. The specific steps are as follows: Based on the results of coupling analysis, contact resistance value and saturation detection, and by combining them with a multi-dimensional evaluation method, a comprehensive evaluation score is generated. A saturation assessment result is generated by comparing the comprehensive assessment score with the normal recovery standard.

8. The method for detecting magnetic saturation during the replacement of an electricity meter as described in claim 1, characterized in that: The process of generating saturation detection results refers to summarizing the window classification results, obtaining the category summary results, and then calculating the category summary results using a majority voting method to select the category summary results that appear most frequently.

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