An electromagnetic flowmeter online calibration system and acquisition and storage device

Abstract

The present invention relates to the field of electromagnetic flowmeter calibration technology, and discloses an electromagnetic flowmeter online calibration system and acquisition and storage device. The system includes signal acquisition, data processing, error analysis, parameter calibration and real-time monitoring modules. The signal acquisition module captures the original signal waveform through a multi-channel sensor array, collects flow signal data to generate a state set; the data processing module uses a wavelet packet transform and adaptive filtering combined algorithm to reduce noise and extract features; the error analysis module constructs a dynamic time-warping error matching model to obtain the error correction coefficient; the parameter calibration module adopts a fuzzy control and proportional-integral-differential composite strategy to adjust the excitation current and electrode sensitivity according to the error correction coefficient; the real-time monitoring module establishes a rolling optimization model with time-varying constraints, and generates calibration instructions in combination with signal stability and power consumption limit. This system realizes the online and precise calibration of electromagnetic flowmeters, improving measurement accuracy and equipment operation stability.

Description

Technical Field

[0001] The present invention relates to the technical field of electromagnetic flowmeter calibration, and in particular to an electromagnetic flowmeter online calibration system and a collection and storage device. Background Art

[0002] As a key device in the field of industrial flow measurement, electromagnetic flowmeters are widely used in numerous industries, including petrochemicals, water supply and drainage, and metallurgy, thanks to their high measurement accuracy, wide range of measurable media, and lack of mechanical moving parts. However, in actual use, electromagnetic flowmeters are affected by various factors, resulting in reduced measurement accuracy, which seriously restricts the efficient operation and quality control of industrial production.

[0003] From a measurement perspective, industrial sites are complex, with numerous sources of electromagnetic interference, such as alternating electromagnetic fields generated by high-power motors and frequency converters. These interferences can be superimposed on the electromagnetic flowmeter's measurement signal, distorting the original signal waveform. This, in turn, affects the accurate acquisition of flow signal amplitude, frequency, and phase difference data, leading to increased measurement errors. For example, in chemical production workshops, due to the intensive operation of various electrical equipment, electromagnetic flowmeter measurement errors can reach several times the normal level, affecting the precise control of material flow, resulting in unstable product quality and waste of raw materials.

[0004] Long-term operation also significantly impacts the accuracy of electromagnetic flowmeters. Over time, the sensor electrodes gradually corrode and scale due to the medium, altering their surface properties and sensitivity. Furthermore, the performance of the excitation system deteriorates due to component aging, leading to unstable excitation currents. These changes can cause the electromagnetic flowmeter's measurement characteristics to drift, making it unable to accurately reflect actual flow. According to statistics, approximately 30% of electromagnetic flowmeters that have been in continuous operation for more than one year experience measurement errors exceeding the allowable range, seriously impacting the reliability and economic benefits of the production process.

[0005] Traditional calibration methods have numerous limitations. Offline calibration requires removing the electromagnetic flowmeter from the piping system and transporting it to a specialized calibration laboratory for calibration. This process is not only labor-intensive, material-intensive, and time-consuming, leading to production interruptions and increased costs, but also potentially damaging the flowmeter during removal and reinstallation, further impacting its performance. For example, in large water supply systems, offline calibration of electromagnetic flowmeters can require service interruption for hours or even days, significantly disrupting the city's water supply and potentially leading to financial compensation issues.

[0006] While online calibration technology is gradually gaining adoption, most existing systems suffer from low accuracy and limited adaptability. Some systems use simple filtering algorithms to process measurement signals, which fail to effectively suppress complex interference and lead to inaccurate calibration results. Some systems also fail to adjust their calibration strategies in response to changing operating conditions, making it difficult to meet the diverse demands of industrial production. These issues limit the widespread adoption of online calibration technology for electromagnetic flowmeters. A more advanced, efficient, and accurate online calibration system is urgently needed to address these challenges. Summary of the Invention

[0007] The purpose of the present invention is to provide an electromagnetic flowmeter online calibration system and a data acquisition and storage device to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: an electromagnetic flowmeter online calibration system, the system comprising:

[0009] The signal acquisition module is used to synchronously capture the original signal waveform of the electromagnetic flowmeter through a multi-channel sensor array, and collect the amplitude, frequency and phase difference data of the flow signal in real time to generate a flow status data set;

[0010] A data processing module, configured to perform noise suppression and feature extraction on the flow state data set using a wavelet packet transform and adaptive filtering combined algorithm to generate a preprocessed signal sequence;

[0011] The error analysis module is used to build an error matching model based on dynamic time warping and generate a set of error correction coefficients according to the deviation between the preset standard flow curve and the measured signal sequence;

[0012] A parameter calibration module is used to design a composite regulation strategy based on fuzzy control and proportional-integral-differential, dynamically adjust the excitation current and electrode sensitivity parameters of the electromagnetic flowmeter according to the error correction coefficient set, and generate a calibrated parameter configuration;

[0013] The real-time monitoring module is used to establish a rolling optimization model with time-varying constraints and generate online calibration instructions based on signal stability thresholds and device power consumption limits.

[0014] Preferably, the wavelet packet transform and adaptive filtering joint algorithm includes:

[0015] Construct a multi-layer wavelet decomposition tree structure and select the optimal wavelet basis function through the energy entropy index;

[0016] Design the adaptive filter order adjustment equation to dynamically adjust the filter bandwidth according to the signal-to-noise ratio;

[0017] A sliding window mechanism is used to match the wavelet packet reconstruction and filter parameter update period, and the denoised signal components are output.

[0018] Preferably, the construction of the error matching model includes:

[0019] In the dynamic time warping stage, an elastic time window is set and the local similarity between the measured signal and the standard curve is calculated through the covariance matrix;

[0020] In the error correction stage, a weight distribution function is introduced to generate a priority list of correction coefficients based on the error amplitude classification;

[0021] The error matching path is iteratively optimized through the residual feedback mechanism, and an error correction coefficient convergence report is generated.

[0022] Preferably, the design of the composite regulation strategy includes:

[0023] The membership function of the fuzzy controller is defined as a triangular distribution, and a fuzzy rule base for excitation current deviation and correction is established.

[0024] Define the integral separation threshold of the proportional-integral-derivative controller and construct the saturation constraint condition of the electrode sensitivity adjustment amount;

[0025] The fuzzy control and proportional-integral-differential parameter combination is solved synchronously through a parallel computing framework to generate a piecewise continuous regulation curve.

[0026] Preferably, the establishment of the rolling optimization model includes:

[0027] The state variable is defined as a combination vector of signal fluctuation rate, calibration response time and power consumption level;

[0028] Construct a model predictive control framework with a rolling horizon and use a quadratic programming algorithm to solve multi-objective optimization problems;

[0029] The designed objective function is the normalized weighted sum of calibration accuracy and energy consumption cost, and the optimal calibration action sequence is output.

[0030] Preferably, the system further comprises: a signal tracing module for performing time-frequency characteristic analysis on the signals before and after calibration through Hilbert-Huang transform; inputting the analysis results into a historical database, and triggering a signal distortion warning signal through trend comparison.

[0031] Preferably, the generation of the flow state data set includes: synchronously collecting the instantaneous sampling rate, signal peak-to-peak value and cross-correlation data of the sensor array, and using an independent component analysis algorithm for multi-dimensional signal. Preferably, the system also includes: an equipment life assessment module for establishing a correlation model between the number of calibration times and sensor drift; using a genetic algorithm to dynamically optimize the calibration cycle and parameter adjustment amplitude to generate a maximum calibration interval threshold.

[0032] Preferably, the generation of the online calibration instruction includes:

[0033] Construct a state transition model with a Markov decision process, with signal stability and device life as boundary conditions;

[0034] A dynamic programming algorithm is used to solve the multi-stage calibration strategy and generate a feasible solution set of excitation current and electrode sensitivity.

[0035] The comprehensive utility value of the solution set is evaluated by the entropy weight method, and a real-time calibration operation queue is output.

[0036] Preferably, the present invention further includes a collection and storage device for an electromagnetic flowmeter online calibration system, the device comprising:

[0037] a memory configured to store instructions; and

[0038] The processor is configured to call the instructions from the memory and implement the functions of the above-mentioned electromagnetic flowmeter online calibration system when executing the instructions.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] To enhance measurement accuracy, the signal acquisition module uses a multi-channel sensor array to synchronously capture raw signal waveforms, collecting rich flow signal data and laying the foundation for precise measurement. The data processing module utilizes a combined wavelet packet transform and adaptive filtering algorithm to effectively suppress noise and accurately extract features, improving signal quality. The error analysis module constructs an error matching model based on dynamic time warping, accurately calculating deviations from the standard flow curve and generating a reliable set of error correction coefficients, enabling more precise calibration.

[0041] In terms of intelligent and automated calibration, the parameter calibration module uses a composite adjustment strategy based on fuzzy control and proportional-integral-differential (PID) to dynamically adjust the electromagnetic flowmeter's excitation current and electrode sensitivity parameters according to the error correction coefficient. Fuzzy control can handle complex nonlinear relationships, while PID ensures system stability and rapid responsiveness. The combination of these two enables intelligent and automated parameter adjustment, reduces manual intervention, and improves calibration efficiency. The real-time monitoring module establishes a rolling optimization model with time-varying constraints. This combines signal stability thresholds with device power consumption limits to generate online calibration instructions. This can trigger calibration operations promptly and automatically based on actual operating conditions, ensuring the device is always in optimal working condition.

[0042] To ensure operational stability and extend equipment life, the signal traceability module uses the Hilbert-Huang transform to analyze the time-frequency characteristics of pre- and post-calibration signals. Trend comparison triggers signal distortion warnings, enabling timely identification of potential issues and proactive action to prevent measurement errors and equipment failures caused by signal distortion, thus ensuring operational stability. The equipment life assessment module establishes a correlation model between calibration times and sensor drift. It uses a genetic algorithm to dynamically optimize the calibration cycle and parameter adjustment range, generating a maximum calibration interval threshold. A sound calibration strategy helps mitigate sensor drift, extend equipment life, and reduce maintenance costs.

[0043] In terms of energy consumption management, the rolling optimization model incorporates calibration accuracy and energy consumption cost into the objective function, and solves the multi-objective optimization problem through the quadratic programming algorithm, thereby reducing the energy consumption of the equipment while ensuring calibration accuracy.

[0044] The system also boasts excellent adaptability and versatility. Whether dealing with fluids with varying properties or complex, ever-changing environmental conditions, the collaborative working of its various modules enables effective calibration, meeting the application requirements of a wide range of industrial scenarios. Furthermore, the design of its data acquisition and storage devices ensures stable system functionality, ensuring the reliability and scalability of the entire online calibration system, and providing strong support for the efficient and stable operation of industrial production. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a working principle diagram of an electromagnetic flowmeter online calibration system according to the present invention;

[0046] Figure 2 Workflow diagram constructed for the error matching model;

[0047] Figure 3 A working principle diagram for the rolling optimization model;

[0048] Figure 4 This is the working principle diagram of the signal tracing module. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 making creative efforts are within the scope of protection of the present invention.

[0050] See also Figures 1-4 The present invention provides a technical solution: an electromagnetic flowmeter online calibration system, the overall implementation scheme of which is as follows:

[0051] The system mainly consists of signal acquisition module, data processing module, error analysis module, parameter calibration module and real-time monitoring module. In actual operation, each module works together.

[0052] Signal Acquisition Module: This module uses a multi-channel sensor array to synchronously capture the electromagnetic flowmeter's raw signal waveform. During the acquisition process, the multi-channel sensor array acquires signals from various angles and positions to ensure signal integrity and accuracy. Simultaneously, it collects the flow signal's amplitude, frequency, and phase difference data in real time. For example, amplitude reflects signal strength, frequency indicates the speed of signal change, and phase difference is used to analyze the temporal relationship between different signals. By collecting and processing this data, a flow status dataset is generated, providing foundational data for subsequent analysis and calibration.

[0053] Data Processing Module: A combined wavelet packet transform and adaptive filtering algorithm is used to process traffic status datasets. This algorithm first constructs a multi-layer wavelet decomposition tree structure and selects the optimal wavelet basis functions using the energy entropy metric to achieve effective signal decomposition and feature extraction. An adaptive filter order adjustment equation is then designed to dynamically adjust the filter bandwidth based on the signal-to-noise ratio, effectively suppressing noise in varying noise environments. Finally, a sliding window mechanism is used to match the wavelet packet reconstruction and filter parameter update cycles. The noise-reduced signal components are output, resulting in a preprocessed signal sequence, providing more accurate data for subsequent error analysis.

[0054] Error Analysis Module: Builds an error matching model based on dynamic time warping. During the dynamic time warping phase, a flexible time window is set. The local similarity between the measured signal and the standard curve is calculated using the covariance matrix to measure the difference between the two. During the error correction phase, a weight allocation function is introduced to generate a priority list of correction coefficients based on the error amplitude level, determining the correction priority for different error scenarios. A residual feedback mechanism is used to iteratively optimize the error matching path, generate an error correction coefficient convergence report, and obtain a set of error correction coefficients, providing a basis for subsequent parameter calibration.

[0055] Parameter calibration module: Design a composite regulation strategy based on fuzzy control and proportional-integral-differential (PID). Define the fuzzy controller's membership function as a triangular distribution, establish a fuzzy rule base for excitation current deviation and correction, and perform fuzzy regulation of the excitation current based on a set of error correction coefficients. Define the integral separation threshold of the PID controller, establish a saturation constraint for the electrode sensitivity adjustment, and precisely adjust the electrode sensitivity. A parallel computing framework is used to synchronously solve the fuzzy control and PID parameter combinations, generate a piecewise continuous regulation curve, and dynamically adjust the electromagnetic flowmeter's excitation current and electrode sensitivity parameters based on the set of error correction coefficients to generate a calibrated parameter configuration.

[0056] Real-time monitoring module: Establish a rolling optimization model with time-varying constraints. Define the state variables as a combination vector of signal volatility, calibration response time, and power consumption level, and comprehensively consider multiple performance indicators of the system. Construct a model predictive control framework with a rolling time domain, use a quadratic programming algorithm to solve the multi-objective optimization problem, and find the optimal solution under different time points and conditions. Design the objective function as the normalized weighted sum of calibration accuracy and energy consumption cost, and balance calibration accuracy and energy consumption by adjusting the weights. According to the above steps, output the optimal calibration action sequence, combine the signal stability threshold and the device power consumption limit to generate online calibration instructions, and realize real-time calibration of the electromagnetic flowmeter.

[0057] The specific implementation of the present invention is further described in detail below through five examples.

[0058] Example 1: This example details the application of a combined wavelet packet transform and adaptive filtering algorithm in a data processing module. In practical applications, flow signals collected by electromagnetic flowmeters are often subject to various noise interferences, affecting measurement accuracy. To address this issue, this example employs a combined wavelet packet transform and adaptive filtering algorithm to suppress noise and extract features from flow state datasets.

[0059] Construct a multi-layer wavelet decomposition tree structure and select the optimal wavelet basis function through the energy entropy index. Assume that the number of layers of wavelet decomposition is , in each layer of decomposition, there are multiple wavelet basis functions to choose from. Energy entropy index It is used to measure the uniformity of energy distribution of the signal under different wavelet basis functions. The calculation formula is: .

[0060] in, Indicates that the signal The ratio of the energy of each frequency band to the total energy, is the number of frequency bands. By calculating the energy entropy under different wavelet basis functions, the wavelet basis function with the smallest energy entropy is selected as the optimal wavelet basis function. This can better concentrate the signal energy on a few frequency bands, facilitating subsequent processing.

[0061] Design the adaptive filter order adjustment equation and dynamically adjust the filter bandwidth according to the signal-to-noise ratio. Assume that the signal-to-noise ratio of the signal is , the order of the adaptive filter is , and its adjustment equation is:

[0062] in, is the initial order, is the adjustment coefficient. When the signal-to-noise ratio When it is higher, it indicates that the signal quality is good, and the order of the filter can be appropriately reduced. , reduce the filtering bandwidth to avoid excessive filtering leading to loss of signal characteristics; when the signal-to-noise ratio When it is lower, increase the filter order , increase the filtering bandwidth to better suppress noise.

[0063] The sliding window mechanism is used to match the wavelet packet reconstruction and the filter parameter update period. The size of the sliding window is set to Within each window, wavelet packet reconstruction is first performed to obtain a preliminarily denoised signal. The adaptive filter parameters are then updated based on the characteristics of the signal within that window. By continuously sliding the window, the signal is continuously processed, and the denoised signal components are output, generating a preprocessed signal sequence. This allows the processing parameters to be adjusted at different time points based on real-time signal changes, improving the algorithm's adaptability and processing effectiveness.

[0064] Example 2: This example focuses on the construction of the error matching model in the error analysis module. In the calibration of electromagnetic flowmeters, accurately measuring the deviation between the measured signal and the standard flow curve is a key step in calibration.

[0065] In the dynamic time warping stage, an elastic time window is set to calculate the local similarity between the measured signal and the standard curve. Assume that the measured signal sequence is , the standard curve sequence is , set the elastic time window size to For each point in the measured signal , search for a matching point in the standard curve When , the search range is limited to When calculating the similarity between the two, a simple distance formula is used, namely By traversing all points in the window, find The smallest distance , this minimum distance represents the local similarity of the current point. For example, when , in the window The value is When , calculate the distance , , , then the minimum distance is 1, and the matching point found is or (The unique matching point can be further determined based on other rules).

[0066] Entering the error correction stage, the weight distribution function is introduced to classify the errors according to their magnitudes, and then generate a priority list of correction coefficients. , the weight distribution function is , specifically defined as: ,in, It is the error amplitude threshold value set according to actual calibration requirements and experience. Greater than or equal to When , it indicates that the error has a greater impact on the measurement results, and the weight is given , which means that the correction coefficient corresponding to this error has a higher priority and needs to be corrected first; and when the error amplitude Less than When the weight , indicating that the error is relatively small and can be processed later. In this way, all error points are sorted according to the error magnitude to generate a priority list of correction coefficients.

[0067] Finally, the error matching path is iteratively optimized with the help of the residual feedback mechanism to generate an error correction coefficient convergence report. , residual The calculation method is ,here is the measured signal value after correction. Feedback to the next matching process, according to the size and trend of the residual, the matching path is adjusted. For example, if the residual After multiple iterations, it gradually decreases, indicating that the current matching path adjustment direction is correct; if the residual If the residual increases, the matching strategy needs to be reviewed. Repeat this iterative process until the residual If the error is less than a preset threshold, it is considered that the error matching path has converged, and an error correction coefficient convergence report is generated to obtain an accurate set of error correction coefficients, providing a reliable basis for subsequent parameter calibration.

[0068] Example 3: This example mainly focuses on the design and application of a composite adjustment strategy in a parameter calibration module. To achieve precise adjustment of the electromagnetic flowmeter excitation current and electrode sensitivity parameters, a composite adjustment strategy based on fuzzy control and proportional-integral-differential is adopted.

[0069] The membership function of the fuzzy controller is defined as a triangular distribution, and a fuzzy rule base of the excitation current deviation and correction amount is constructed. Assume that the excitation current deviation is , whose domain is set to , the fuzzy subset contains , representing negative large, negative medium, negative small, zero, positive small, positive medium, and positive large respectively. Taking the positive small (PS) fuzzy subset as an example, its membership function for:

[0070] .

[0071] in, and It is a parameter determined according to actual conditions and experimental data. Determines the position where the membership function starts to rise from 0, Determines the position where the membership function drops to 0. Based on a large number of experiments and professional experience, the excitation current deviation and correction amount are established. For example, when the excitation current deviation is detected When it is negative (NB), the correction amount is determined according to the fuzzy rule base. is positive (PB), thereby achieving preliminary regulation of the excitation current.

[0072] Define the integral separation threshold of the proportional integral differential controller and construct the saturation constraint of the electrode sensitivity adjustment. Assume that the electrode sensitivity is , the adjustment amount is , the input of the PID controller is the error , set the integral separation threshold to When the error The absolute value of the integral separation threshold is greater than In order to prevent the occurrence of integral saturation, the integral effect is temporarily canceled; when When , the integral link works normally. To ensure the stable operation of the system, the saturation constraint condition of the electrode sensitivity adjustment is constructed as follows: ,

[0073] in, and These are the minimum and maximum values for electrode sensitivity adjustment, set according to equipment performance and safety requirements. This prevents system instability or even equipment damage caused by overly large or undersized adjustments.

[0074] A parallel computing framework is used to simultaneously solve the fuzzy control and PID parameter combinations, generating a piecewise continuous regulation curve. Leveraging the powerful computing power of the parallel computing framework, the fuzzy control and PID parameter combinations can be calculated simultaneously, significantly improving computational efficiency. Under different error conditions, corresponding regulation curve segments are generated based on the calculated parameter combinations. These curve segments are interconnected to form a piecewise continuous regulation curve. Using this curve, the electromagnetic flowmeter's excitation current and electrode sensitivity parameters are dynamically and precisely adjusted based on a set of error correction coefficients. Ultimately, the calibrated parameter configuration is generated, completing the high-precision calibration of the electromagnetic flowmeter.

[0075] Example 4: This example describes in detail the establishment and application of a rolling optimization model in a real-time monitoring module. In order to ensure calibration accuracy while taking into account the power consumption and response time of the device, this example establishes a rolling optimization model with time-varying constraints.

[0076] Define the state variable as a combination vector of signal fluctuation rate, calibration response time and power consumption level. Suppose the signal fluctuation rate is , the calibration response time is , the power consumption level is , the state variable vector Signal volatility Reflects the stability of the signal, which is obtained by calculating the standard deviation of the signal over a period of time; calibration response time Indicates the time from issuing a calibration command to completing calibration; power consumption level It reflects the energy consumption of the equipment during the calibration process.

[0077] A model predictive control framework with a rolling horizon is constructed, and a quadratic programming algorithm is used to solve the multi-objective optimization problem. Assume that the rolling horizon is , at each time step , according to the current state variables Predicting the future The objective function of the multi-objective optimization problem is the calibration accuracy and energy consumption costs The normalized weighted sum of , that is: .

[0078] in, and is the weight coefficient, and . Calibration accuracy The energy consumption cost can be measured by the error between the measured flow rate and the standard flow rate. The power consumption level The quadratic programming algorithm is used to solve the multi-objective optimization problem and obtain the optimal control input at the current time step.

[0079] The objective function is designed to be the normalized weighted sum of calibration accuracy and energy consumption cost, and output the optimal calibration action sequence. and You can balance the calibration accuracy and energy consumption according to actual needs. For example, when the calibration accuracy is high, increase ; When you are more sensitive to energy consumption, increase Based on the optimal control input obtained, the optimal calibration action sequence is output, and online calibration instructions are generated by combining the signal stability threshold and the device power consumption limit to achieve real-time calibration of the electromagnetic flowmeter.

[0080] Example 5: This example covers the signal tracing module, the equipment life assessment module, and the related contents of online calibration instruction generation.

[0081] The signal tracing module uses Hilbert-Huang transform to analyze the time-frequency characteristics of the signal before and after calibration. Assume that the signal before calibration is , the calibrated signal is The Hilbert-Huang transform first performs empirical mode decomposition on the signal, decomposing the signal into multiple intrinsic mode functions ,Right now: .

[0082] Then perform Hilbert transform on each intrinsic mode function to obtain its instantaneous frequency and amplitude The calibration effect can be determined by analyzing changes in the signal's time-frequency characteristics before and after calibration, such as changes in frequency components and amplitude adjustments. The analysis results are entered into a historical database, and trend comparisons are used to trigger a signal distortion warning. If the time-frequency characteristics of the calibrated signal show abnormal changes compared to historical data, an alert is triggered, indicating a possible signal distortion issue.

[0083] Equipment Life Assessment Module Establishes Calibration Times With sensor drift The association model of . Let the association model be , through the analysis and fitting of a large amount of historical data, the function is determined The specific form of the calibration cycle is dynamically optimized using genetic algorithms. and parameter adjustment amplitude Generate the maximum calibration interval threshold. The genetic algorithm searches for the optimal calibration period within a certain range by simulating the process of natural selection and genetic variation. and parameter adjustment amplitude The objective function can be set as a function that comprehensively considers factors such as equipment life, calibration cost, and measurement accuracy, such as: .

[0084] in, Indicates the remaining life of the device. represents the calibration cost, Indicates the measurement accuracy, is the weight coefficient. Through iterative optimization of genetic algorithm, the optimal calibration period is obtained and parameter adjustment amplitude , which in turn generates the maximum calibration interval threshold.

[0085] The process of generating online calibration instructions is as follows: a state transition model with a Markov decision process is constructed, with signal stability and equipment life as boundary conditions. Assume that the state of the system is , the action set is , the state transition probability is ,in To perform an action The new state of the system is obtained. Under the constraints of signal stability and equipment life, the rules of state transition are determined. A dynamic programming algorithm is used to solve the multi-stage calibration strategy and generate a feasible solution set of excitation current and electrode sensitivity. The dynamic programming algorithm decomposes the multi-stage decision-making problem into a series of sub-problems and gradually solves the optimal strategy. The comprehensive utility value of the solution set is evaluated by the entropy weight method, and a real-time calibration operation queue is output. The entropy weight method determines the weight of each indicator according to the degree of variation of each solution indicator, thereby evaluating the comprehensive utility value of different feasible solutions, and selecting the solution with the highest comprehensive utility value as the real-time calibration operation queue to achieve efficient and accurate online calibration of the electromagnetic flowmeter.

[0086] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An electromagnetic flowmeter online calibration system, characterized in that: The system comprises: The signal acquisition module is used to synchronously capture the original signal waveform of the electromagnetic flowmeter through a multi-channel sensor array, and collect the amplitude, frequency and phase difference data of the flow signal in real time to generate a flow status data set; A data processing module, configured to perform noise suppression and feature extraction on the flow state data set using a wavelet packet transform and adaptive filtering combined algorithm to generate a preprocessed signal sequence; The error analysis module is used to build an error matching model based on dynamic time warping and generate a set of error correction coefficients according to the deviation between the preset standard flow curve and the measured signal sequence; A parameter calibration module is used to design a composite regulation strategy based on fuzzy control and proportional-integral-differential, dynamically adjust the excitation current and electrode sensitivity parameters of the electromagnetic flowmeter according to the error correction coefficient set, and generate a calibrated parameter configuration; A real-time monitoring module is used to establish a rolling optimization model with time-varying constraints and generate online calibration instructions based on signal stability thresholds and device power consumption limits. The establishment of the rolling optimization model includes: The state variable is defined as a combination vector of signal fluctuation rate, calibration response time and power consumption level; Construct a model predictive control framework with a rolling horizon and use a quadratic programming algorithm to solve multi-objective optimization problems; The designed objective function is the normalized weighted sum of calibration accuracy and energy consumption cost, and the optimal calibration action sequence is output.

2. The electromagnetic flowmeter online calibration system according to claim 1, characterized in that: The wavelet packet transform and adaptive filtering joint algorithm includes: Construct a multi-layer wavelet decomposition tree structure and select the optimal wavelet basis function through the energy entropy index; Design the adaptive filter order adjustment equation to dynamically adjust the filter bandwidth according to the signal-to-noise ratio; A sliding window mechanism is used to match the wavelet packet reconstruction and filter parameter update period, and the denoised signal components are output.

3. The electromagnetic flowmeter online calibration system according to claim 1, characterized in that: The construction of the error matching model includes: In the dynamic time warping stage, an elastic time window is set and the local similarity between the measured signal and the standard curve is calculated through the covariance matrix; In the error correction stage, a weight distribution function is introduced to generate a priority list of correction coefficients based on the error amplitude classification; The error matching path is iteratively optimized through the residual feedback mechanism, and an error correction coefficient convergence report is generated.

4. The electromagnetic flowmeter online calibration system according to claim 3, characterized in that: The design of the composite regulation strategy includes: The membership function of the fuzzy controller is defined as a triangular distribution, and a fuzzy rule base for excitation current deviation and correction is established. Define the integral separation threshold of the proportional-integral-derivative controller and construct the saturation constraint condition of the electrode sensitivity adjustment amount; The fuzzy control and proportional-integral-differential parameter combination is solved synchronously through a parallel computing framework to generate a piecewise continuous regulation curve.

5. The electromagnetic flowmeter online calibration system according to claim 1, characterized in that: The system also includes: a signal tracing module for performing time-frequency characteristic analysis on the signals before and after calibration through Hilbert-Huang transform; inputting the analysis results into a historical database, and triggering a signal distortion warning signal through trend comparison.

6. The electromagnetic flowmeter online calibration system according to claim 1, characterized in that: The generation of the flow state data set includes: synchronously collecting the instantaneous sampling rate, signal peak-to-peak value and inter-channel cross-correlation data of the sensor array, using the independent component analysis algorithm to perform blind source separation on the multidimensional signal, and constructing a characteristic signal subspace.

7. The electromagnetic flowmeter online calibration system according to claim 1, characterized in that: The system also includes: an equipment life assessment module for establishing a correlation model between the number of calibrations and sensor drift; and a genetic algorithm for dynamically optimizing the calibration cycle and parameter adjustment range to generate a maximum calibration interval threshold.

8. The electromagnetic flowmeter online calibration system according to claim 1, characterized in that: The generation of the online calibration instruction includes: Construct a state transition model with a Markov decision process, with signal stability and device life as boundary conditions; A dynamic programming algorithm is used to solve the multi-stage calibration strategy and generate a feasible solution set of excitation current and electrode sensitivity. The comprehensive utility value of the solution set is evaluated by the entropy weight method, and a real-time calibration operation queue is output.

9. An acquisition and storage device for an electromagnetic flowmeter online calibration system, characterized in that: The device comprises: a memory configured to store instructions; and The processor is configured to call the instructions from the memory and implement the functions of the electromagnetic flowmeter online calibration system according to any one of claims 1 to 8 when executing the instructions.

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