Field data verification device based on money-free clamp buckle type single-phase electric energy meter

Abstract

The invention provides a field data verification device based on a money-free clamp buckle type single-phase electric energy meter, and the device comprises a host which is used for starting a measurement process through the contact of a probe with a signal contact of the electric energy meter, receiving current data transmitted by a wireless clamp-shaped current transformer, and carrying out the filtering, calibration and error analysis of the measurement data; and the wireless split core type current transformer is used for being clamped on a current loop of the measured electric energy meter, starting to measure current data and sending the current data to the host in a wireless mode. According to the invention, a wireless communication technology, a field data processing algorithm and a verification function are integrated, and field real-time data acquisition, processing, analysis and verification can be realized, so that the efficiency and accuracy of verification work are improved.

Description

Technical Field

[0001] The invention relates to the technical field of electric digital data processing, and in particular to a field data verification device based on a moneyless clamp-type single-phase electric energy meter. Background Art

[0002] In the power system, the energy meter is an important device for measuring energy consumption. Its accuracy and reliability are directly related to the electricity costs of power users and the operating benefits of power companies. Traditional field calibration of single-phase energy meters usually requires professionals to carry bulky calibration equipment and measure and calibrate the energy meter through wired connections, which is not only cumbersome but also inefficient.

[0003] With the rapid development of wireless communication technology, wireless clamp-on single-phase energy meters are gradually being used in power systems. This type of energy meter can be easily clamped on the wire through a clamp-on structure, and current measurement can be performed without disconnecting the circuit, which greatly improves the convenience of installation and the safety of measurement. However, the existing wireless clamp-on single-phase energy meters still have some shortcomings in field data processing and verification.

[0004] On the one hand, existing calibration devices often rely on wired connections for data transmission, which not only limits the range of operators’ activities but is also difficult to implement in complex or narrow installation environments. On the other hand, field data processing and error calculation capabilities are limited, and measurement data usually needs to be brought back to the laboratory for subsequent analysis, which prolongs the calibration cycle and reduces work efficiency.

[0005] Therefore, there is an urgent need for an on-site data verification device based on a moneyless clamp-type single-phase electric energy meter. Summary of the invention

[0006] In order to solve the problems existing in the prior art, the present invention provides a field data verification device based on a cashless clamp-type single-phase electric energy meter, which integrates wireless communication technology, field data processing algorithm and verification function, and can realize field real-time data collection, processing, analysis and verification, and improve the efficiency and accuracy of verification work. At the same time, the device should also have the characteristics of simple operation, easy to carry, strong adaptability, etc., to meet the field verification needs of electric energy meters in different scenarios.

[0007] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0008] A field data verification device based on a cashless clamp-type single-phase electric energy meter, comprising:

[0009] The host fixed on the electric energy meter starts the measurement process by contacting the signal contact of the electric energy meter with the probe, receives the current data sent by the wireless clamp current transformer, and performs filtering, calibration and error analysis on the measurement data;

[0010] A wireless clamp-type current transformer with a clamp structure is used to clamp on the current loop of the electric energy meter to be measured, start measuring current data, and send it to the host computer wirelessly;

[0011] The host also includes:

[0012] The control chip is used to control the workflow of the entire calibration device, including the acquisition, filtering, calibration, error analysis and result display of measurement data;

[0013] The acquisition and measurement circuit is specifically configured to receive current data from the wireless clamp current transformer and voltage data from the electric energy meter, and perform analog-to-digital conversion on the received current data and voltage data to convert the analog signal into a digital signal;

[0014] The first wireless transmission module is used to establish a wireless communication protocol between the host and the wireless clamp current transformer to achieve wireless transmission of data.

[0015] According to a field data verification device based on a moneyless clamp-on single-phase electric energy meter provided by the present invention, the wireless clamp-on current transformer also includes:

[0016] The current measurement module is built into the wireless clamp current transformer and is used to measure the current data flowing through the electric energy meter wire;

[0017] A second wireless transmission module is connected to the current measurement module and is used to send the current data measured by the current measurement module to the host in a wireless manner;

[0018] The buckle structure is arranged on the shell of the wireless clamp-on current transformer and is used to clamp the wireless clamp-on current transformer on the wire of the electric energy meter.

[0019] According to a field data verification device based on a cashless clamp-type single-phase electric energy meter provided by the present invention, the host has a built-in filtering algorithm module for performing data filtering processing on noise and abnormal values ​​of different data types, specifically including:

[0020] Real-time noise estimation module, used to dynamically monitor and estimate the noise characteristics in the current data;

[0021] Adaptive filter design module, which automatically adjusts the filter parameters and structure according to the output of the real-time noise estimation module;

[0022] The outlier detection and processing module combines statistical methods and machine learning algorithms to detect outliers in data in real time and adopt correction strategies to eliminate or correct them;

[0023] The filtering effect evaluation and feedback module evaluates the quality of the filtered data and adjusts the parameters of the adaptive filter according to the evaluation results to form a closed-loop feedback mechanism;

[0024] The multi-mode filter selection module provides a variety of filter modes for users to choose from based on data characteristics and application requirements.

[0025] According to a field data verification device based on a cashless clamp-type single-phase electric energy meter provided by the present invention, the real-time noise estimation module specifically includes:

[0026] The time domain analysis unit is used to perform time domain analysis on the collected data and calculate the statistics of the data to preliminarily determine whether there is noise in the data;

[0027] A frequency domain analysis unit converts data from the time domain to the frequency domain by fast Fourier transform or other frequency domain transformation methods, analyzes the spectrum characteristics, and identifies the frequency components and distribution of noise and periodic noise;

[0028] Adaptive window adjustment unit, which automatically adjusts the size and position of the analysis window according to the dynamic changes of data to adapt to the changes of noise of different frequencies and amplitudes;

[0029] The noise model establishment and update unit establishes a mixed noise model based on the analysis results in the time domain and frequency domain, and updates the model parameters in real time as the data is continuously collected and analyzed to reflect the changes in noise characteristics;

[0030] The noise characteristic output unit outputs the noise estimation result to the adaptive filter design module.

[0031] According to a field data verification device based on a cashless clamp-type single-phase electric energy meter provided by the present invention, the adaptive filter design module specifically includes:

[0032] A filter structure selection unit, which selects a filter structure according to the noise characteristics output by the real-time noise estimation module;

[0033] A parameter initialization and adjustment unit initializes the filter parameters according to the selected filter structure and adjusts these parameters in real time according to the change of noise characteristics;

[0034] An adaptive algorithm implementation unit adopts minimum mean square error, normalized minimum mean square error, recursive least squares or other adaptive algorithms to update the parameters of the filter in real time according to input data and expected output, so that the filter can adaptively track and suppress noise;

[0035] The performance evaluation and feedback unit evaluates the performance of the filter output, including calculating the signal-to-noise ratio (SNR), mean square error (MSE) or other performance indicators, and feeds the evaluation results back to the parameter initialization and adjustment unit;

[0036] The multi-mode switching unit provides a variety of filtering modes to choose from according to changes in noise characteristics or application requirements, and can switch between different modes according to preset conditions or user instructions.

[0037] According to a field data verification device based on a cashless clamp-type single-phase electric energy meter provided by the present invention, the abnormal value detection and processing module specifically includes:

[0038] An outlier detection unit uses statistical methods, machine learning algorithms, or rule-based methods to perform real-time or batch outlier detection on input data to identify data points that deviate significantly from the normal data distribution;

[0039] An abnormal value confirmation unit further confirms the detected abnormal value and determines the authenticity and reliability of the abnormal value;

[0040] The outlier processing strategy formulation unit formulates processing strategies based on the nature, impact and business needs of outliers, including deleting outliers, replacing outliers with averages or medians, and marking outliers;

[0041] The outlier processing execution unit automatically or manually processes the confirmed outliers according to the established processing strategy, and records the processing process and results;

[0042] The abnormal value alarm and notification unit will promptly issue an alarm signal or notification after detecting an abnormal value and confirming its authenticity.

[0043] According to a field data verification device based on a cashless clamp-type single-phase electric energy meter provided by the present invention, the filtering effect evaluation and feedback module specifically includes:

[0044] The filtering effect evaluation unit is used to evaluate the quality of the filtered data, quantify the filtering effect by calculating the signal-to-noise ratio (SNR), mean square error (MSE), peak signal-to-noise ratio (PSNR) or other related indicators, and measure the closeness between the filtered data and the original noise-free data or the expected data;

[0045] The data quality analysis subunit analyzes the statistical characteristics of the filtered data based on the filtering effect evaluation unit;

[0046] A feedback mechanism building unit builds a feedback mechanism based on the results of filtering effect evaluation and data quality analysis, and feeds back the evaluation results to the filtering processing module in real time or regularly;

[0047] The parameter optimization suggestion generation unit automatically generates optimization suggestions for the filtering algorithm parameters based on the feedback evaluation results and combined with machine learning or optimization algorithms.

[0048] According to a field data verification device based on a cashless clamp-type single-phase electric energy meter provided by the present invention, the host has a built-in calibration algorithm module for performing a calibration operation of the measurement data, which specifically includes:

[0049] The system error identification unit uses statistical analysis methods, machine learning models or pattern recognition technology to analyze the measurement data and automatically identify and extract the fixed error or periodic error characteristics in the device;

[0050] The temperature change impact assessment unit integrates temperature sensor data, combines the material thermal expansion coefficient and the temperature drift characteristics of electronic components, and evaluates the specific impact of temperature changes on measurement results in real time;

[0051] An intelligent calibration strategy formulation unit, which formulates a calibration strategy according to the outputs of the system error identification unit and the temperature change impact assessment unit, including calibration frequency, selection of calibration points and calibration method, wherein the calibration method includes two-point calibration, multi-point calibration or dynamic calibration;

[0052] The automatic compensation algorithm implementation unit implements the automatic compensation algorithm based on the established calibration strategy, and performs real-time or post-error compensation on the measurement data to eliminate or reduce the influence of device errors and temperature changes on the measurement results;

[0053] The adaptive learning and optimization subunit continuously collects new data during the measurement process and uses machine learning algorithms to adaptively learn and optimize the calibration model and compensation algorithm, so that the calibration algorithm can be automatically adjusted and improved as the device ages, the environment changes, or the measurement task changes.

[0054] According to a field data verification device based on a cashless clamp-type single-phase electric energy meter provided by the present invention, the host is equipped with an error analysis index evaluation module for performing error analysis of measurement data, which specifically includes:

[0055] Uncertainty analysis unit, based on measurement principle, sensor characteristics, environmental conditions and data processing algorithm, conducts comprehensive uncertainty analysis on measurement results, including evaluation of type A uncertainty and type B uncertainty, as well as calculation of combined uncertainty;

[0056] The confidence interval estimation unit uses statistical methods to estimate the confidence interval of the measurement results based on the results of uncertainty analysis, and determines the range of the measurement results at a certain confidence level to quantify the reliability of the measurement results;

[0057] The error source tracking subunit tracks and records various error sources in the measurement process, including sensor drift, environmental noise, circuit nonlinearity, data processing algorithm limitations, etc., providing basic data for uncertainty analysis and confidence interval estimation;

[0058] The reliability assessment report generation unit automatically generates a reliability assessment report of the measurement results based on the results of uncertainty analysis and confidence interval estimation, combined with the information of error source tracking.

[0059] According to a field data verification device based on a cashless clamp-type single-phase electric energy meter provided by the present invention, the wireless communication protocol between the wireless current clamp and the host is optimized, including:

[0060] Design a dynamic data transmission rate adjustment mechanism to automatically select the transmission rate based on the quality of the current wireless channel and the priority of data transmission to ensure that the packet loss rate can be kept low even when the channel quality is poor;

[0061] Implement a retransmission mechanism. For data packets that are not successfully received, the host sends a retransmission request to the wireless clamp current transformer. After receiving the request, the wireless clamp current transformer resends the data packet until it is successfully received or the preset retransmission limit is reached;

[0062] Introducing the confirmation and negative confirmation mechanism, after each data packet transmission, the receiver must send a confirmation or negative confirmation message to the sender;

[0063] Optimize the frame structure of the data packet, including adding fields such as checksum and sequence number, to improve the anti-interference ability of the data packet and the reliability of transmission;

[0064] Implement an adaptive channel selection mechanism to dynamically select the channel with the least interference for data transmission based on the interference situation in the current wireless environment, so as to reduce the impact of external interference on communication quality.

[0065] It can be seen that compared with the prior art, the present invention has the following beneficial effects:

[0066] 1. The device integrates wireless communication technology, so that the wireless clamp-type single-phase energy meter can transmit the measured data to the host wirelessly in real time. In this way, the operator does not need to make complicated wired connections on site, which greatly saves time and energy. At the same time, the device can process and analyze the data in real time on site, quickly obtain the verification results, and further improve the verification efficiency.

[0067] 2. The wireless clamp-on single-phase energy meter adopts a clamp-on structure, which can be easily clamped on the wire and can measure the current without disconnecting the circuit. This design makes it easier and more convenient for operators to calibrate on site, without carrying heavy tools and equipment, thus reducing the workload.

[0068] 3. The device has built-in high-precision data processing algorithms and verification functions, which can process and analyze the measured data in real time and accurately calculate the error of the electric energy meter. This on-site real-time processing method avoids possible interference and loss of data during transmission, and improves the accuracy and reliability of the data.

[0069] 4. The device is suitable for various complex or narrow installation environments without the limitation of wired connection. Whether it is a residential area, commercial building or industrial plant, operators can easily use the device to perform on-site calibration of electric energy meters.

[0070] 5. Through real-time data processing and verification, the device can promptly detect problems and faults in the electricity meter, and provide maintenance personnel with accurate fault location and repair suggestions, which helps to reduce power losses and maintenance costs caused by electricity meter failures and improve the stability and economy of the power system.

[0071] In summary, the present invention not only improves verification efficiency and operational convenience, but also improves data accuracy and adaptability, reduces maintenance costs, and provides strong guarantees for the stable operation of the power system and the power consumption experience of power users.

[0072] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 The present invention is a schematic diagram of an embodiment of a field data verification device based on a moneyless clamp-type single-phase electric energy meter.

[0074] Figure 2 It is a principle diagram of a filtering algorithm module in an embodiment of a field data verification device based on a moneyless clamp-type single-phase electric energy meter of the present invention.

[0075] Figure 3 It is a principle diagram of a calibration algorithm module in an embodiment of a field data verification device based on a moneyless clamp-type single-phase electric energy meter of the present invention.

[0076] Figure 4 It is a principle diagram of an error analysis index evaluation module in an embodiment of a field data verification device based on a moneyless clamp-type single-phase electric energy meter of the present invention. DETAILED DESCRIPTION

[0077] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0078] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0079] See also Figures 1 to 4 This embodiment provides a field data verification device based on a cashless clamp-type single-phase electric energy meter, comprising:

[0080] The host 10 fixed on the electric energy meter starts the measurement process by contacting the signal contacts of the electric energy meter with a probe, and is used to obtain electrical parameters, read data on the electric energy meter, process pulse data, receive current data sent by the wireless clamp current transformer, filter, calibrate and analyze errors of the measured data, display the measurement results, and store data.

[0081] The wireless clamp-type current transformer 20 with a clamp structure is used to clamp on the current loop of the electric energy meter to be measured, start measuring current data, and send it to the host 10 in a wireless manner.

[0082] In this embodiment, the host 10 further includes:

[0083] The control chip is used to control the workflow of the entire calibration device, including measurement data collection, filtering, calibration, error analysis and result display.

[0084] The acquisition and measurement circuit is specifically configured to receive current data from the wireless clamp-on current transformer 20 and voltage data from the electric energy meter, and perform analog-to-digital conversion on the received current data and voltage data to convert the analog signal into a digital signal.

[0085] The first wireless transmission module is used to establish a wireless communication protocol between the host 10 and the wireless clamp current transformer 20 to achieve wireless transmission of data.

[0086] In this embodiment, the wireless clamp current transformer 20 further includes:

[0087] A current measurement module, built into the wireless clamp current transformer 20, is used to measure the current data flowing through the electric energy meter wire;

[0088] A second wireless transmission module, connected to the current measurement module, is used to send the current data measured by the current measurement module to the host 10 in a wireless manner;

[0089] The buckle structure is arranged on the housing of the wireless clamp-on current transformer 20 and is used to clamp the wireless clamp-on current transformer 20 onto the conductor of the electric energy meter.

[0090] Specifically, the host 10 uses a high-performance microprocessor with powerful computing capabilities, and can quickly process a large amount of measurement data transmitted from the wireless current clamp meter. The built-in data processing algorithm filters, calibrates and analyzes the measurement data to ensure the accuracy and reliability of the calculation results. By comparing with the built-in standard electric energy meter, the device can calculate the error of the measured electric energy meter in real time, providing an accurate basis for the calibration work.

[0091] Further, regarding the intuitive user interface and real-time data display: the device of this embodiment is equipped with a high-definition screen display, which displays the measurement data and calculation results in real time, so that the user can intuitively understand the operating status and calibration results of the electric energy meter. The user interface design is concise and clear, which is convenient for users to perform parameter setting, data viewing and calibration operations, and improves the ease of use of the device.

[0092] Further, regarding wireless transmission and low power consumption design: the device of this embodiment integrates a wireless transmission module to realize data transmission between the host 10 and the wireless current clamp meter without wired connection, thereby improving the convenience of operation. The wireless communication module adopts a low power consumption design to ensure that the instrument can work for a long time and reduce the trouble of frequent battery replacement or charging.

[0093] Furthermore, regarding the safe and reliable hardware design: the insulating shell is made of high-strength, good insulating material to ensure safety during use. The snap-on structure enables the host 10 to be firmly fixed on the junction box without causing damage to the electric energy meter, ensuring the stability of the measurement. The elastic probe is set at the bottom of the shell and matches the contact in the electric energy meter to achieve accurate collection of electric energy parameters.

[0094] Further, regarding the design of a high-precision wireless current clamp meter: the wireless current clamp meter adopts a clamp-shaped design, which can be easily clamped on the current loop of the measured electric energy meter, and can be measured without disconnecting the circuit. The built-in high-precision current measurement module ensures the accuracy of the measurement results and provides a reliable basis for data processing and error calculation. The integrated wireless transmission module sends the measurement data to the host 10 in real time, realizing instant synchronization and processing of data.

[0095] Furthermore, regarding the data storage and export function: the device implements the data storage function, which is convenient for users to view historical measurement data and calibration results at any time. It supports the data export function, and users can export the stored data to a computer or other device for subsequent analysis and processing, meeting the needs of different users.

[0096] Therefore, the device achieves efficient data processing, accurate error calculation, intuitive user interface, convenient operation and powerful data storage and export functions through high-performance microprocessors, wireless transmission technology, safe and reliable hardware design and high-precision wireless current clamp meter design. These beneficial effects jointly improve the efficiency and accuracy of field verification of electric energy meters, and provide strong guarantees for the stable operation of the power system and the power consumption experience of power users.

[0097] In this embodiment, the host 10 has a built-in filtering algorithm module for performing data filtering processing on noise and abnormal values ​​of different data types to improve data quality. Specifically, an adaptive filtering algorithm is used, and the algorithm specifically includes:

[0098] The real-time noise estimation module is used to dynamically monitor and estimate the noise characteristics in the current data, including the noise type, frequency distribution and amplitude.

[0099] The adaptive filter design module automatically adjusts the parameters and structure of the filter according to the output of the noise estimation module to adapt to different types of noise environments and ensure the optimization of the filtering effect.

[0100] The outlier detection and processing module combines statistical methods and machine learning algorithms to detect outliers in the data in real time and adopts correction strategies to eliminate or correct them to reduce the impact of outliers on data analysis.

[0101] The filtering effect evaluation and feedback module evaluates the quality of the filtered data and adjusts the parameters of the adaptive filter according to the evaluation results to form a closed-loop feedback mechanism to continuously improve the performance and adaptability of the filtering algorithm.

[0102] The multi-mode filter selection module provides a variety of filter modes for users to choose from, such as low-pass filtering, high-pass filtering, band-pass filtering, and adaptive filtering, according to data characteristics and application requirements, to meet data processing requirements in different scenarios.

[0103] Among them, the real-time noise estimation module specifically includes:

[0104] The time domain analysis unit is used to perform time domain analysis on the collected data and calculate the mean, variance, peak and other statistical quantities of the data to preliminarily determine whether there is noise in the data and its approximate characteristics.

[0105] The frequency domain analysis unit converts data from the time domain to the frequency domain through fast Fourier transform (FFT) or other frequency domain transformation methods, analyzes the spectrum characteristics, and identifies the frequency components and distribution of noise and periodic noise.

[0106] The adaptive window adjustment unit automatically adjusts the size and position of the analysis window according to the dynamic changes of the data to adapt to the changes of noise of different frequencies and amplitudes, ensuring the accuracy and real-time performance of noise estimation.

[0107] The noise model establishment and update unit establishes a mixed noise model based on the analysis results in the time domain and frequency domain, and updates the model parameters in real time as the data is continuously collected and analyzed to reflect changes in noise characteristics.

[0108] The noise characteristic output unit outputs the noise estimation results to the adaptive filter design module, including noise type, frequency distribution, amplitude, and noise model parameters, etc., to provide a basis for subsequent filtering processing and data analysis.

[0109] Through the above design, the real-time noise estimation module can dynamically monitor and accurately estimate the noise characteristics in the current data, providing key noise information for the adaptive filtering algorithm, thereby improving the filtering effect and data quality.

[0110] Among them, the adaptive filter design module specifically includes:

[0111] The filter structure selection unit selects a suitable filter structure, such as a linear filter, a nonlinear filter, a recursive filter or a non-recursive filter, according to the noise characteristics output by the real-time noise estimation module.

[0112] The parameter initialization and adjustment unit initializes the filter parameters, such as weight coefficient, threshold, order, etc., according to the selected filter structure, and adjusts these parameters in real time according to the changes in noise characteristics to maintain the optimal performance of the filter.

[0113] The adaptive algorithm implementation unit adopts the least mean square error (LMS), normalized least mean square error (NLMS), recursive least square (RLS) or other adaptive algorithms to update the parameters of the filter in real time according to the input data and the expected output, so that the filter can adaptively track and suppress the noise.

[0114] The performance evaluation and feedback unit performs performance evaluation on the output of the filter, including calculating the signal-to-noise ratio (SNR), mean square error (MSE) or other performance indicators, and feeds the evaluation results back to the parameter initialization and adjustment unit.

[0115] The multi-mode switching unit provides a variety of filtering modes to choose from according to changes in noise characteristics or application requirements, such as low-pass filtering mode, high-pass filtering mode, band-pass filtering mode and adaptive filtering mode, and can switch between different modes according to preset conditions or user instructions.

[0116] Through the above design, the adaptive filter design module can dynamically select and adjust the structure and parameters of the filter according to the results of real-time noise estimation, so that the filter can adapt to different noise environments and improve the filtering effect and data quality.

[0117] Among them, the outlier detection and processing module specifically includes:

[0118] The outlier detection unit uses statistical methods, machine learning algorithms or rule-based methods to perform real-time or batch outlier detection on input data to identify data points that deviate significantly from the normal data distribution.

[0119] The outlier confirmation unit further confirms the detected outliers by comparing historical data, reference data or using domain knowledge to determine the authenticity and reliability of the outliers and avoid false positives and negative negatives.

[0120] The outlier processing strategy formulation unit formulates processing strategies based on the nature, impact and business needs of the outliers, including deleting outliers, replacing outliers with mean values ​​or medians, marking outliers for subsequent analysis, or taking other corrective measures.

[0121] The outlier processing execution unit automatically or manually processes the confirmed outliers according to the established processing strategy, and records the processing process and results for traceability and verification.

[0122] The abnormal value alarm and notification unit will promptly issue an alarm signal or notification after detecting an abnormal value and confirming its authenticity, reminding relevant personnel to pay attention to and deal with the abnormal situation, so as to prevent decision-making errors or device failures caused by abnormal values.

[0123] Through the above design, the outlier detection and processing module can efficiently identify and process outliers in the data, improve the accuracy and reliability of the data, and provide strong support for subsequent data analysis and decision-making.

[0124] Among them, the filtering effect evaluation and feedback module specifically includes:

[0125] The filtering effect evaluation unit is used to evaluate the quality of the filtered data, quantify the filtering effect by calculating the signal-to-noise ratio (SNR), mean square error (MSE), peak signal-to-noise ratio (PSNR) or other related indicators, and measure the closeness between the filtered data and the original noise-free data or the expected data;

[0126] The data quality analysis subunit analyzes the statistical characteristics of the filtered data, such as mean, variance, skewness, kurtosis, etc., as well as the continuity, smoothness and consistency of the data on the basis of the filtering effect evaluation unit, so as to comprehensively evaluate the quality of the data.

[0127] The feedback mechanism construction unit constructs a feedback mechanism according to the results of filtering effect evaluation and data quality analysis, and feeds back the evaluation results to the filtering processing module in real time or regularly, providing a basis for parameter adjustment, structure optimization or algorithm selection of the filtering algorithm.

[0128] The parameter optimization suggestion generation unit automatically generates optimization suggestions for the filtering algorithm parameters based on the feedback evaluation results and combined with machine learning or optimization algorithms, such as adjusting the filter order, changing the weight coefficient, selecting a more appropriate filter window size, etc., to improve the filtering effect.

[0129] Through the above design, the filtering effect evaluation and feedback module can comprehensively and objectively evaluate the quality of the data after filtering, provide strong support for the continuous optimization of the filtering algorithm, and ensure that the data after filtering can meet the requirements of subsequent analysis and decision-making.

[0130] In this embodiment, the host 10 has a built-in calibration algorithm module for performing a calibration operation of the measurement data, which specifically includes:

[0131] The system error identification unit uses statistical analysis methods, machine learning models or pattern recognition technology to analyze the measurement data, automatically identify and extract fixed errors or periodic error characteristics in the device, such as offset, gain error, nonlinear error, etc.

[0132] The temperature change impact assessment unit integrates temperature sensor data and combines prior knowledge such as material thermal expansion coefficient and electronic component temperature drift characteristics to evaluate in real time the specific impact of temperature changes on measurement results, including temperature-induced drift, noise increase, or response speed changes.

[0133] The intelligent calibration strategy formulation unit formulates a calibration strategy according to the output of the system error identification unit and the temperature change impact assessment unit, including calibration frequency, selection of calibration points and calibration methods, wherein the calibration methods include two-point calibration, multi-point calibration or dynamic calibration.

[0134] The automatic compensation algorithm implementation unit implements the automatic compensation algorithm based on the formulated calibration strategy, and performs real-time or post-error compensation on the measurement data to eliminate or reduce the influence of device errors and temperature changes on the measurement results and improve the measurement accuracy.

[0135] The adaptive learning and optimization subunit continuously collects new data during the measurement process and uses machine learning algorithms to adaptively learn and optimize the calibration model and compensation algorithm, so that the calibration algorithm can be automatically adjusted and improved as the device ages, the environment changes, or the measurement task changes.

[0136] Specifically, the system error identification unit may analyze the measurement data using a statistical analysis method, a machine learning model, or a pattern recognition technology to improve the accuracy and reliability of the data. The following is a specific implementation of the statistical analysis method in this embodiment:

[0137] Use statistical analysis methods: reflect the central tendency of the data by calculating indicators such as the mean, median, and mode; measure the degree of dispersion of the data by calculating the variance and standard deviation. These methods can help identify whether there are outliers or values ​​that deviate from the normal range in the data. For example, a t-test can be used to compare whether there are significant differences between two sets of measurement data. If the difference is significant, it may indicate the presence of device errors. By calculating the confidence interval of the overall parameter, the reliability and accuracy of the measurement results can be evaluated. If the confidence interval is too wide, it may indicate that there is a large uncertainty in the measurement data, which may be related to device errors.

[0138] Of course, when utilizing a machine learning model, supervised learning methods (such as logistic regression, support vector machines, etc.) can be used to train the model to identify device errors in new measurement data. The measurement data can be grouped into different clusters to identify data points with similar characteristics. If the data points in a cluster deviate significantly from other clusters, it may indicate the presence of device errors. The machine learning model can learn the normal distribution pattern of the measurement data and identify outliers that deviate from the normal pattern. These outliers may be manifestations of device errors.

[0139] When using pattern recognition technology, features that help identify device errors, such as frequency, amplitude, phase, etc., are extracted from the measurement data. The extracted features are matched with known device error patterns to identify device errors in new measurement data. A classifier is designed using pattern recognition technology to classify the measurement data into normal data and data containing device errors.

[0140] Specifically, based on the established calibration strategy, an automatic compensation algorithm can be implemented to perform real-time or post-error compensation on the measured data. The following are the key steps in the implementation process:

[0141] According to the calibration data, the error model of the measuring equipment is established, which involves regression analysis, neural network and other methods to fit the error relationship between the actual measurement value and the theoretical value. The error model should be able to accurately reflect the error characteristics of the measuring equipment under different conditions. Based on the error model, an automatic compensation algorithm is designed, such as proportional integral control (PID control), fuzzy control, and adaptive control method based on neural network. The algorithm should be able to automatically calculate and apply compensation values ​​according to real-time or post-measurement data to correct the measurement error. The design of the algorithm should take into account factors such as computational efficiency, real-time and accuracy.

[0142] Therefore, through the above design, a smarter calibration algorithm module can automatically identify and compensate for the impact of device errors and temperature changes on measurement results, improve the accuracy and stability of the measuring device, reduce the frequency and difficulty of manual calibration, reduce maintenance costs, and improve measurement efficiency.

[0143] In this embodiment, the host 10 has a built-in error analysis index evaluation module for performing error analysis of measurement data, which specifically includes:

[0144] The uncertainty analysis unit performs a comprehensive uncertainty analysis of the measurement results based on the measurement principle, sensor characteristics, environmental conditions and data processing algorithms, including the evaluation of Type A uncertainty (caused by random errors) and Type B uncertainty (caused by device errors), as well as the calculation of combined uncertainty.

[0145] The confidence interval estimation unit, based on the results of uncertainty analysis, uses statistical methods (such as the standard error method under the normal distribution assumption, the Bootstrap method, etc.) to estimate the confidence interval of the measurement results, determine the range of the measurement results at a certain confidence level, and quantify the reliability of the measurement results.

[0146] The error source tracking subunit tracks and records various error sources in the measurement process, including sensor drift, environmental noise, circuit nonlinearity, data processing algorithm limitations, etc., providing basic data for uncertainty analysis and confidence interval estimation.

[0147] The reliability assessment report generation unit automatically generates a reliability assessment report for the measurement results based on the results of uncertainty analysis and confidence interval estimation, combined with information on error source tracking. The report content includes measurement values, uncertainty, confidence intervals, main error sources and degree of influence, etc., to help users fully understand the reliability and accuracy of the measurement results.

[0148] Through the above design, the evaluation module using more comprehensive error analysis indicators can more accurately evaluate the reliability of measurement results, provide users with detailed error analysis and reliability evaluation reports, help users better understand the accuracy and limitations of measurement results, and provide more reliable data support for decision-making.

[0149] In this embodiment, the wireless communication protocol between the wireless current clamp and the host 10 is optimized, including:

[0150] Data compression algorithms are used to pre-process the transmission data to reduce the size of the data packet, thereby reducing transmission time and improving transmission efficiency.

[0151] A dynamically adjusted data transmission rate mechanism is designed to automatically select the most appropriate transmission rate based on the quality of the current wireless channel and the priority of data transmission to ensure a low packet loss rate when the channel quality is poor.

[0152] A retransmission mechanism is implemented. For data packets that are not successfully received, the host 10 sends a retransmission request to the wireless clamp current transformer 20. After receiving the request, the wireless clamp current transformer 20 retransmits the data packet until it is successfully received or the preset retransmission limit is reached.

[0153] The ACK / NACK mechanism is introduced. After each data packet transmission, the receiver needs to send a confirmation or negative confirmation message to the sender so that the sender can understand the data packet transmission status in time and adjust the transmission strategy accordingly.

[0154] Optimize the frame structure of the data packet, including adding fields such as checksum and sequence number, to improve the anti-interference ability of the data packet and the reliability of transmission.

[0155] Implement an adaptive channel selection mechanism to dynamically select the channel with the least interference for data transmission based on the interference situation in the current wireless environment, so as to reduce the impact of external interference on communication quality.

[0156] Through the above optimization measures, the data transmission delay and packet loss rate between the wireless traffic clamp and the host 10 are significantly reduced, and the real-time performance and reliability of the device are improved.

[0157] The adaptive SPIHT compression algorithm is used to compress the transmission data, including: segmenting the continuous current data, each segment of which can be a sampling data of an industrial frequency cycle (such as 20ms or 16.67ms at 50Hz or 60Hz). Discrete wavelet transform is performed on each segment of current data to convert the time domain signal into a frequency domain signal; the coefficients obtained after the wavelet transform are organized into sets, which are used for subsequent encoding processing in the adaptive SPIHT algorithm. Wavelet transform can extract different frequency components in the signal, providing a basis for subsequent compression processing.

[0158] The core of the adaptive SPIHT algorithm is to adaptively divide the set according to the significance of the wavelet coefficient set. First, the probability of whether all the coefficients in each set are 0 is estimated in advance, and then the decision on whether to further divide the set is made based on this probability. This adaptive division strategy can reduce unnecessary coding overhead and improve compression efficiency.

[0159] The coefficient set after division is encoded. The adaptive SPIHT compression algorithm uses a combination of bit plane coding and binary tree coding to gradually encode and quantize the coefficients to achieve data compression. For example, each bit plane is first encoded to reduce the redundant information in the bit plane. The data after bit plane coding is binary tree encoded. Since bit plane coding has reduced redundancy, binary tree coding will be able to further utilize the statistical characteristics of the coefficients to generate an efficient compressed code stream.

[0160] When performing bit plane coding, each coefficient is represented in binary form, and different bit planes are created according to each bit of its binary representation. For example, for an 8-bit coefficient, 8 bit planes can be created, each bit plane corresponding to a bit in the binary representation of the coefficient. Each bit plane is encoded. Bit plane coding can use a variety of methods, such as run-length coding, two-dimensional run-length coding, etc., to further reduce redundant information.

[0161] When performing binary tree coding, the frequency statistics of the symbols (0 and 1) in each bit plane are performed to determine the probability of each symbol appearing. The probability information obtained by statistics is used to construct a Huffman tree. In the Huffman tree, symbols with higher probabilities will be assigned shorter codes, while symbols with lower probabilities will be assigned longer codes. A coding table is generated from the Huffman tree to map each symbol to its corresponding Huffman code.

[0162] During the encoding process, the coefficients can be quantized to map the values ​​of the coefficients into a discrete range of values. Quantization can further reduce the storage and transmission costs of data. In this embodiment, the compressed current data is transmitted to the host 10 by wireless means, or the compressed current data can be stored in an appropriate medium, such as a memory, a hard disk, or a cloud storage. Since the data has been compressed, the transmission efficiency will be significantly improved.

[0163] At the receiving end, such as the host 10, the received compressed data is decompressed using a decompression algorithm corresponding to the sending end, and the decompressed data is restored to the form of original current data through operations such as inverse wavelet transform for subsequent analysis and processing.

[0164] In summary, this embodiment not only improves verification efficiency and operational convenience, but also improves data accuracy and adaptability, reduces maintenance costs, and provides strong guarantees for the stable operation of the power system and the power consumption experience of power users.

[0165] Furthermore, the present embodiment integrates wireless communication technology, so that the wireless clamp-type single-phase energy meter can transmit the measured data to the host 10 in real time by wireless means. In this way, the operator does not need to make complicated wired connections on site, which greatly saves time and energy. At the same time, the device can process and analyze the data in real time on site, quickly obtain the verification results, and further improve the verification efficiency.

[0166] Furthermore, the wireless clamp-on single-phase energy meter adopts a clamp-on structure, which can be easily clamped on the wire and can measure the current without disconnecting the circuit. This design makes it easier and more convenient for operators to perform on-site calibration, without having to carry heavy tools and equipment, thus reducing the workload.

[0167] Furthermore, the device has built-in high-precision data processing algorithms and verification functions, which can process and analyze measurement data in real time and accurately calculate the error of the electricity meter. This on-site real-time processing method avoids possible interference and loss of data during transmission, and improves the accuracy and reliability of the data.

[0168] Furthermore, the device is suitable for various complex or narrow installation environments without the limitation of wired connection. Whether it is a residential area, commercial building or industrial plant, operators can easily use the device to perform on-site calibration of electric energy meters.

[0169] Furthermore, through real-time data processing and verification, the device can promptly detect problems and faults in the electricity meter, and provide maintenance personnel with accurate fault location and repair suggestions, which helps reduce power losses and maintenance costs caused by electricity meter failures and improves the stability and economy of the power system.

[0170] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0171] The above-mentioned embodiments are only preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantial changes and substitutions made by technicians in this field on the basis of the present invention shall fall within the scope of protection required by the present invention.

Claims

1. A field data verification device based on a cashless clamp-type single-phase electric energy meter, characterized in that: include: The host fixed on the electric energy meter starts the measurement process by contacting the signal contact of the electric energy meter with the probe, receives the current data sent by the wireless clamp current transformer, and performs filtering, calibration and error analysis on the measurement data; A wireless clamp-type current transformer with a clamp structure is used to clamp on the current loop of the electric energy meter to be measured, start measuring current data, and send it to the host computer wirelessly; The host also includes: The control chip is used to control the workflow of the entire calibration device, including the acquisition, filtering, calibration, error analysis and result display of measurement data; The acquisition and measurement circuit is specifically configured to receive current data from the wireless clamp current transformer and voltage data from the electric energy meter, and perform analog-to-digital conversion on the received current data and voltage data to convert the analog signal into a digital signal; The first wireless transmission module is used to establish a wireless communication protocol between the host and the wireless clamp current transformer to achieve wireless transmission of data.

2. The device according to claim 1, characterized in that: The wireless clamp current transformer also includes: The current measurement module is built into the wireless clamp current transformer and is used to measure the current data flowing through the electric energy meter wire; A second wireless transmission module is connected to the current measurement module and is used to send the current data measured by the current measurement module to the host in a wireless manner; The buckle structure is arranged on the shell of the wireless clamp-on current transformer and is used to clamp the wireless clamp-on current transformer on the wire of the electric energy meter.

3. The device according to claim 1, characterized in that: The host is equipped with a built-in filtering algorithm module for performing data filtering processing on noise and outliers of different data types, specifically including: a real-time noise estimation module for dynamically monitoring and estimating the noise characteristics in the current data; an adaptive filter design module for automatically adjusting the parameters and structure of the filter according to the output of the real-time noise estimation module; an outlier detection and processing module for combining statistical methods and machine learning algorithms to detect outliers in the data in real time and adopt correction strategies to eliminate or correct them; a filtering effect evaluation and feedback module for performing quality evaluation on the filtered data and adjusting the parameters of the adaptive filter according to the evaluation results to form a closed-loop feedback mechanism; and a multi-mode filtering selection module for providing a variety of filtering modes for users to choose from according to data characteristics and application requirements.

4. The device according to claim 3, characterized in that: The real-time noise estimation module specifically includes: The time domain analysis unit is used to perform time domain analysis on the collected data and calculate the statistics of the data to preliminarily determine whether there is noise in the data; A frequency domain analysis unit converts data from the time domain to the frequency domain by fast Fourier transform or other frequency domain transformation methods, analyzes the spectrum characteristics, and identifies the frequency components and distribution of noise and periodic noise; Adaptive window adjustment unit, which automatically adjusts the size and position of the analysis window according to the dynamic changes of data to adapt to the changes of noise of different frequencies and amplitudes; The noise model establishment and update unit establishes a mixed noise model based on the analysis results in the time domain and frequency domain, and updates the model parameters in real time as the data is continuously collected and analyzed to reflect the changes in noise characteristics; The noise characteristic output unit outputs the noise estimation result to the adaptive filter design module.

5. The device according to claim 3, characterized in that: The adaptive filter design module specifically includes: A filter structure selection unit, which selects a filter structure according to the noise characteristics output by the real-time noise estimation module; A parameter initialization and adjustment unit initializes the filter parameters according to the selected filter structure and adjusts these parameters in real time according to the change of noise characteristics; An adaptive algorithm implementation unit adopts minimum mean square error, normalized minimum mean square error, recursive least squares or other adaptive algorithms to update the parameters of the filter in real time according to input data and expected output, so that the filter can adaptively track and suppress noise; The performance evaluation and feedback unit evaluates the performance of the filter output, including calculating the signal-to-noise ratio (SNR), mean square error (MSE) or other performance indicators, and feeds the evaluation results back to the parameter initialization and adjustment unit; The multi-mode switching unit provides a variety of filtering modes to choose from according to changes in noise characteristics or application requirements, and can switch between different modes according to preset conditions or user instructions.

6. The device according to claim 5, characterized in that: The outlier detection and processing module specifically includes: An outlier detection unit uses statistical methods, machine learning algorithms, or rule-based methods to perform real-time or batch outlier detection on input data to identify data points that deviate significantly from the normal data distribution; An abnormal value confirmation unit further confirms the detected abnormal value and determines the authenticity and reliability of the abnormal value; The outlier processing strategy formulation unit formulates processing strategies based on the nature, impact and business needs of outliers, including deleting outliers, replacing outliers with averages or medians, and marking outliers; The outlier processing execution unit automatically or manually processes the confirmed outliers according to the established processing strategy, and records the processing process and results; The abnormal value alarm and notification unit will promptly issue an alarm signal or notification after detecting an abnormal value and confirming its authenticity.

7. The device according to claim 6, characterized in that: The filtering effect evaluation and feedback module specifically includes: The filtering effect evaluation unit is used to evaluate the quality of the filtered data, quantify the filtering effect by calculating the signal-to-noise ratio (SNR), mean square error (MSE), peak signal-to-noise ratio (PSNR) or other related indicators, and measure the closeness between the filtered data and the original noise-free data or the expected data; The data quality analysis subunit analyzes the statistical characteristics of the filtered data based on the filtering effect evaluation unit; A feedback mechanism building unit builds a feedback mechanism based on the results of filtering effect evaluation and data quality analysis, and feeds back the evaluation results to the filtering processing module in real time or regularly; The parameter optimization suggestion generation unit automatically generates optimization suggestions for the filtering algorithm parameters based on the feedback evaluation results and combined with machine learning or optimization algorithms.

8. The device according to claim 1, characterized in that: The host has a built-in calibration algorithm module for performing calibration operations on measurement data, which specifically includes: The system error identification unit uses statistical analysis methods, machine learning models or pattern recognition technology to analyze the measurement data and automatically identify and extract the fixed error or periodic error characteristics in the device; The temperature change impact assessment unit integrates temperature sensor data, combines the material thermal expansion coefficient and the temperature drift characteristics of electronic components, and evaluates the specific impact of temperature changes on measurement results in real time; An intelligent calibration strategy formulation unit, which formulates a calibration strategy according to the outputs of the system error identification unit and the temperature change impact assessment unit, including calibration frequency, selection of calibration points and calibration method, wherein the calibration method includes two-point calibration, multi-point calibration or dynamic calibration; The automatic compensation algorithm implementation unit implements the automatic compensation algorithm based on the established calibration strategy, and performs real-time or post-error compensation on the measurement data to eliminate or reduce the influence of device errors and temperature changes on the measurement results; The adaptive learning and optimization subunit continuously collects new data during the measurement process and uses machine learning algorithms to adaptively learn and optimize the calibration model and compensation algorithm, so that the calibration algorithm can be automatically adjusted and improved as the device ages, the environment changes, or the measurement task changes.

9. The device according to claim 1, characterized in that: The host is equipped with an error analysis index evaluation module for performing error analysis of measurement data, which specifically includes: Uncertainty analysis unit, based on measurement principle, sensor characteristics, environmental conditions and data processing algorithm, conducts comprehensive uncertainty analysis on measurement results, including evaluation of type A uncertainty and type B uncertainty, as well as calculation of combined uncertainty; The confidence interval estimation unit uses statistical methods to estimate the confidence interval of the measurement results based on the results of uncertainty analysis, and determines the range of the measurement results at a certain confidence level to quantify the reliability of the measurement results; The error source tracking subunit tracks and records various error sources in the measurement process, including sensor drift, environmental noise, circuit nonlinearity, and data processing algorithm limitations, providing basic data for uncertainty analysis and confidence interval estimation; The reliability assessment report generation unit automatically generates a reliability assessment report of the measurement results based on the results of uncertainty analysis and confidence interval estimation, combined with the information of error source tracking.

10. The device according to claim 1, characterized in that Optimize the wireless communication protocol between the wireless current clamp and the host, including: Design a dynamic data transmission rate adjustment mechanism to automatically select the transmission rate based on the quality of the current wireless channel and the priority of data transmission to ensure that the packet loss rate can be kept low even when the channel quality is poor; Implement a retransmission mechanism. For data packets that are not successfully received, the host sends a retransmission request to the wireless clamp current transformer. After receiving the request, the wireless clamp current transformer resends the data packet until it is successfully received or the preset retransmission limit is reached; Introducing the confirmation and negative confirmation mechanism, after each data packet transmission, the receiver must send a confirmation or negative confirmation message to the sender; Optimize the frame structure of the data packet, including adding checksum and sequence number fields, to improve the anti-interference ability of the data packet and the reliability of transmission; Implement an adaptive channel selection mechanism to dynamically select the channel with the least interference for data transmission based on the interference situation in the current wireless environment, so as to reduce the impact of external interference on communication quality.

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