Electromagnetic water meter high-precision measurement method adopting intelligent sensor

Abstract

The invention discloses an electromagnetic water meter high-precision measurement method adopting an intelligent sensor, and relates to the field of fluid metering cross technologies, and the method comprises the following steps: based on an electrode original signal generated by an excitation magnetic field, obtaining a uniformity index of a partition compensation excitation magnetic field, dynamically configuring a gate control voltage of an ion channel, and generating a purified voltage signal; constructing a joint correlation prediction model, and performing fusion analysis on the purified voltage signal and mechanical vibration data to obtain an initial water flow estimation value; and performing error correction on the initial water flow estimated value through dynamic temperature compensation and zero point online calibration to generate a high-confidence water flow value. According to the method, the magnetic field uniformity index of each sector area is calculated in real time through the space-time differential-frequency spectrum conjoint analysis method, the ion channel gating voltage is dynamically adjusted according to the magnetic field uniformity index, self-adaptive suppression of harmonic interference and time sequence errors in electrode signals is achieved, and the signal purity and the measurement stability are improved.

Description

Technical Field

[0001] The present invention relates to the field of fluid metering cross technology, in particular to a high-precision measurement method of an electromagnetic water meter using an intelligent sensor. Background Art

[0002] As an important device in the field of flow measurement, the core principle of electromagnetic water meters is based on Faraday's law of electromagnetic induction. It calculates flow by measuring the induced electromotive force generated by the conductive fluid cutting the magnetic flux lines. Traditional technologies usually adopt a solution that combines a uniform excitation magnetic field with electrode signal detection. The excitation system is mostly composed of Helmholtz coils or segmented excitation windings to generate a relatively stable working magnetic field. In terms of signal processing, conventional methods extract the fundamental component of the electrode signal through Fourier transform and use a fixed threshold to filter out high-frequency harmonic interference. In addition, temperature compensation mostly relies on a pre-calibrated linear temperature-flow curve, while zero-point calibration is achieved through periodic static calibration. This type of method can meet basic metering needs under stable working conditions.

[0003] Although traditional methods have the advantages of simple structure and controllable costs, their performance is limited by the bottleneck of insufficient spatial uniformity of the magnetic field and the ability to suppress dynamic interference. Specifically, the fixed excitation mode is difficult to adapt to the magnetic field distortion of the pipeline cross section (such as edge effects or interference from ferromagnetic materials), resulting in a decrease in the signal-to-noise ratio of the electrode signal; and the static temperature compensation curve cannot fully fit the nonlinear characteristics of the material's thermal expansion, especially under transient temperature change conditions, which is prone to introduce additional errors. In addition, the coupled interference of mechanical vibration and electromagnetic harmonics will further affect the accuracy of signal analysis. The adaptability of existing frequency domain filtering methods to time-varying interference needs to be improved. Summary of the Invention

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

[0005] Therefore, the present invention provides a high-precision measurement method for electromagnetic water meters using intelligent sensors to solve the problems of uneven spatial distribution of magnetic fields and inaccurate compensation of nonlinear temperature drift.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, the present invention provides a high-precision measurement method for electromagnetic water meters using intelligent sensors, which includes collecting quantum tunneling current signals circumferentially along the pipeline and extracting magnetic field distortion characteristics; based on the magnetic field distortion characteristics, using a compensation engine to calculate the compensation current intensity of each pipeline area, and combining the spatial distribution of the magnetic field distortion characteristics, generating a partitioned compensation excitation magnetic field through a space vector pulse width modulation algorithm; based on the original electrode signal generated by the excitation magnetic field, obtaining the uniformity index of the partitioned compensation excitation magnetic field, and dynamically configuring the gate voltage of the ion channel to generate a purified voltage signal; constructing a joint correlation prediction model, and performing a fusion analysis on the purified voltage signal and mechanical vibration data to obtain an initial water flow estimate; through dynamic temperature compensation and zero point online calibration, the initial water flow estimate is error corrected to generate a high-confidence water flow value.

[0008] As a preferred solution of the high-precision measurement method of electromagnetic water meters using intelligent sensors described in the present invention, the method of using a compensation engine to calculate the compensation current intensity of each pipeline area refers to defining a magnetic field gradient compensation threshold and identifying magnetic field distortion areas on the pipeline interface where the magnetic field intensity change rate exceeds the magnetic field gradient compensation threshold. At the same time, through the compensation engine, the compensation current intensity of the magnetic field distortion area is generated according to the ratio of the spatial distribution gradient of the magnetic field intensity to the magnetic field gradient compensation threshold.

[0009] As a preferred solution of the high-precision measurement method of electromagnetic water meters using intelligent sensors described in the present invention, wherein: the spatial distribution of the magnetic field distortion characteristics is combined to generate a partitioned compensation excitation magnetic field through a space vector pulse width modulation algorithm, the steps are as follows:

[0010] Real-time acquisition of the excitation coil driving signal around the pipeline;

[0011] According to the amplitude of the interfering harmonics, the reverse harmonic component is injected into the excitation coil drive signal. Combined with the spatial distribution of the magnetic field distortion characteristics, the phase lag and phase angle are compensated in a partitioned weighted manner to generate a spatially modulated excitation current waveform.

[0012] Based on the compensation current intensity and spatial modulation excitation current waveform in the magnetic field distortion area, three-phase current vector synthesis is performed through the space vector pulse width modulation algorithm to generate the partitioned compensation excitation magnetic field.

[0013] As an optimal solution for the high-precision measurement method of electromagnetic water meters using intelligent sensors described in the present invention, the uniformity index of the partitioned compensation excitation magnetic field is obtained by collecting the original electrode signal after the partitioned compensation excitation magnetic field acts, extracting the magnetic field intensity distribution characteristics of each sector area through STPA, and calculating the uniformity index of the partitioned compensation excitation magnetic field through the spatiotemporal differential-spectral joint analysis method.

[0014] As a preferred solution of the electromagnetic water meter high-precision measurement method using an intelligent sensor of the present invention, wherein: the gate voltage of the ion channel is dynamically configured to generate a purified voltage signal, the steps are as follows:

[0015] Define low and high uniformity thresholds, compare them with the uniformity index of the compensation excitation magnetic field of each partition, and dynamically configure the gating voltage of the ion channel based on the comparison results;

[0016] The original voltage signal is harmonically filtered and time-calibrated using the adjusted ion channel gating voltage to generate a purified voltage signal.

[0017] As a preferred solution of the high-precision measurement method of electromagnetic water meters using intelligent sensors described in the present invention, the steps of constructing a joint correlation prediction model and performing fusion analysis on the purified voltage signal and mechanical vibration data to obtain an initial water flow estimation value are as follows:

[0018] Based on the purified voltage signal and mechanical vibration data, the state variables and observation variables are defined, and the state space model is established through the particle filter algorithm;

[0019] The state space model is combined with the extended Kalman filter algorithm through variational Bayesian inference to generate a joint correlation prediction model;

[0020] Through the joint correlation prediction model, the purified voltage signal and mechanical vibration data are fused and analyzed to obtain the initial water flow estimation value.

[0021] As a preferred solution of the high-precision measurement method of electromagnetic water meters using intelligent sensors described in the present invention, wherein: the initial water flow estimation value is error corrected by dynamic temperature compensation and zero point online calibration to generate a high-confidence water flow value, the steps are as follows:

[0022] Collect real-time pipe wall temperature data and use linear interpolation compensation to compensate for temperature drift on the initial water flow estimate to generate a temperature-compensated water flow estimate.

[0023] In a no-flow state, a baseline drift correction algorithm is used to identify the zero point offset based on the temperature-compensated water flow estimate, and then corrected by exponentially weighted dynamic decay to generate a high-confidence water flow value.

[0024] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the high-precision measurement method of an electromagnetic water meter using an intelligent sensor as described in the first aspect of the present invention is implemented.

[0025] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the high-precision measurement method of an electromagnetic water meter using an intelligent sensor as described in the first aspect of the present invention is implemented.

[0026] The beneficial effects of the present invention are as follows: by adopting a compensation engine combined with a space vector pulse width modulation algorithm, a partitioned compensation excitation magnetic field that matches the magnetic field distortion characteristics is dynamically generated, thereby achieving precise control of the magnetic field distribution in the pipeline cross section, and effectively eliminating the magnetic field unevenness problem caused by edge effects and interference from ferromagnetic materials; the magnetic field uniformity index of each sector area is calculated in real time through the time-space differential-spectrum joint analysis method, and the ion channel gating voltage is dynamically adjusted accordingly, thereby achieving adaptive suppression of harmonic interference and timing errors in the electrode signal, and improving signal purity and measurement stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 This is a flow chart of a high-precision measurement method for electromagnetic water meters using intelligent sensors.

[0029] Figure 2 Flowchart for current intensity generation for magnetic field gradient compensation.

[0030] Figure 3 Flowchart for the synthesis of spatially modulated excitation magnetic field.

[0031] Figure 4 Flowchart for dynamic configuration of ion channel gating voltage. DETAILED DESCRIPTION

[0032] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0033] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0034] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0035] Reference Figures 1 to 4 , is an embodiment of the present invention, which provides a high-precision measurement method for an electromagnetic water meter using an intelligent sensor, comprising the following steps:

[0036] S1. Collect the quantum tunneling current signal around the pipeline and extract the magnetic field distortion characteristics;

[0037] The quantum tunneling current signal around the pipeline includes time-varying current data (including amplitude, phase and frequency components) detected in real time by the InAs quantum dot array, which reflects the distribution of magnetic field intensity in all directions on the cross section of the pipeline.

[0038] The magnetic field distortion characteristics include the spatial distribution gradient of the magnetic field intensity in the pipeline, the amplitude of the interference harmonics, and the phase lag. The extraction process is as follows:

[0039] Based on the quantum tunneling current signal, the spatial distribution gradient of the magnetic field intensity is extracted through spatial difference operation.

[0040] Furthermore, the quantum tunneling current signal contains information about the distribution of magnetic field intensity at all directions across the pipeline cross section. By performing spatial differentiation on the quantum tunneling current signals collected at adjacent measurement points, the rate of change of the current difference between adjacent points is calculated. The rate of change of the current difference is positively correlated with the magnetic field intensity gradient. Using a preset spatial differentiation algorithm, the radial and tangential magnetic field intensity changes along the pipeline are calculated, and a magnetic field intensity gradient distribution map is constructed across the pipeline cross section. This spatial differentiation algorithm utilizes the central difference method to ensure the accuracy and stability of the gradient calculation, ultimately outputting a quantized spatial distribution gradient of the magnetic field intensity.

[0041] Perform fast Fourier transform on the quantum tunneling current signal to extract the interference harmonic amplitude;

[0042] Furthermore, the time-domain waveform of the quantum tunneling current signal contains a fundamental component and multiple harmonic interference components. Fast Fourier transform (FFT) is used to convert the time-domain signal into a frequency-domain spectrum. In spectrum analysis, the main peak corresponding to the fundamental frequency is first identified, and then the amplitudes of the other frequency components in the spectrum are detected. A harmonic detection threshold is set based on the percentage of the fundamental amplitude. Harmonic components with amplitudes exceeding the threshold are screened out, and the frequency and corresponding amplitude of each harmonic are recorded. Fast Fourier transform (FFT) uses windowing to reduce spectral leakage and improve harmonic detection accuracy, ultimately outputting the amplitude data for each interfering harmonic.

[0043] The phase lag is extracted by cross-correlation analysis of the time domain waveform of the quantum tunneling current signal.

[0044] Furthermore, the quantum tunneling current signal exhibits time delays between different measurement points, and a cross-correlation algorithm is used to calculate the similarity of the signal waveforms. First, the signal waveform at a reference point is used as a reference for the quantum tunneling current signal waveform at that reference point. The cross-correlation function between the signals at other measurement points and the reference signal is calculated, and the time offset is determined by finding the peak of the cross-correlation function. The time offset is converted into a phase angle difference based on the ratio of the signal period to the sampling frequency. Combined with the normalized cross-correlation peak location, the phase lag at each measurement point is ultimately output.

[0045] The pipeline cross section is divided into several equiangular sector-shaped areas according to polar coordinates. The magnetic field distortion characteristics in each area are then identified through quantum tunneling current signal analysis, and finally the spatial distribution of the magnetic field distortion characteristics is generated.

[0046] Furthermore, the pipeline cross section is divided into several equiangular sectors using a polar coordinate system, each containing multiple measurement points. A multi-parameter spatial correlation analysis method is used to comprehensively analyze the quantum tunneling current signals within each sector, extracting the magnetic field distortion characteristics of the current sector. A spatial interpolation algorithm is then used to expand the magnetic field distortion characteristics of the discrete measurement points into a continuous regional distribution, creating a magnetic field distortion characteristic distribution map for each sector. The magnetic field distortion characteristics of each sector are then integrated to generate a complete spatial distribution of the magnetic field distortion characteristics of the pipeline cross section.

[0047] S2. Based on the magnetic field distortion characteristics, a compensation engine is used to calculate the compensation current intensity of each pipeline area. Combined with the spatial distribution of the magnetic field distortion characteristics, a partitioned compensation excitation magnetic field is generated using a space vector pulse width modulation algorithm.

[0048] Based on historical magnetic field gradient distribution data statistics, the magnetic field gradient compensation threshold is defined, and the magnetic field distortion area on the pipeline interface where the magnetic field intensity change rate exceeds the magnetic field gradient compensation threshold is identified;

[0049] Furthermore, based on the magnetic field gradient distribution data accumulated during historical operation, statistical analysis methods were used to calculate the typical distribution range of the magnetic field intensity change rate. The central tendency and dispersion of the magnetic field gradient distribution were determined by fitting a probability density function, and a specific percentile value was selected as the magnetic field gradient compensation threshold. The magnetic field intensity change rate at each location on the pipeline interface was monitored in real time, and the measured values ​​were compared point by point with the magnetic field gradient compensation threshold. Areas where the change rate exceeded the magnetic field gradient compensation threshold were marked as areas of magnetic field distortion.

[0050] The Hall sensor array is used to collect the excitation coil driving signal around the pipeline in real time;

[0051] It should be noted that the excitation coil driving signal in the circumferential direction of the pipeline includes a time-varying electrical signal of current amplitude, phase angle and harmonic components.

[0052] The compensation engine generates the compensation current intensity of the magnetic field distortion area according to the ratio of the magnetic field intensity spatial distribution gradient and the magnetic field gradient compensation threshold;

[0053] Furthermore, the compensation engine receives the spatial distribution gradient data of magnetic field intensity and the magnetic field gradient compensation threshold. By comparing the magnetic field gradient compensation threshold, it calculates the gradient excess multiple for each magnetic field distortion area. A compensation coefficient mapping relationship is set using linear interpolation. Based on this compensation coefficient mapping relationship, a linear proportional conversion is used to convert the gradient excess multiple into the corresponding compensation current intensity value.

[0054] According to the amplitude of the interfering harmonics, the reverse harmonic component is injected into the excitation coil drive signal. Combined with the spatial distribution of the magnetic field distortion characteristics, the phase lag and phase angle are compensated in a partitioned weighted manner to generate a spatially modulated excitation current waveform.

[0055] Furthermore, the collected excitation coil drive signal is first subjected to spectral analysis to accurately identify the amplitude and phase information of each interfering harmonic component contained therein. For each detected interfering harmonic component, an inverse harmonic signal with equal amplitude but a 180-degree phase difference is generated. After precise amplitude matching and phase calibration using a digital signal processor, the inverse harmonic signal is superimposed in real time onto the original excitation coil drive signal, effectively canceling out the harmonic components. Simultaneously, based on the spatial distribution of magnetic field distortion characteristics, the phase lag and phase angle data for each sector of the pipeline cross section are independently measured. Phase compensation weighting coefficients are defined based on the measured phase lag and phase angle data for each sector. The phase compensation weighting coefficients reflect the severity of magnetic field distortion in different regions and are used to determine the required phase compensation for each region. Using multi-channel digital signal synthesis technology, the precisely calibrated inverse harmonic components are combined with the weighted phase compensation values ​​for each sector. Programmable logic devices are used to achieve precise synchronization and superposition with the base excitation signal. The resulting spatially modulated excitation current waveform has dynamically adjustable spatial distribution characteristics, which can provide precisely matched harmonic cancellation and phase compensation effects for the specific magnetic field distortion characteristics of different areas of the pipeline.

[0056] Based on the compensation current intensity and spatial modulation excitation current waveform of the magnetic field distortion area, three-phase current vector synthesis is performed through the space vector pulse width modulation algorithm to generate the partitioned compensation excitation magnetic field.

[0057] Furthermore, the compensation current intensity data and the spatially modulated excitation current waveform are input into the space vector pulse width modulation algorithm. The space vector pulse width modulation algorithm establishes the three-phase current vector relationship through coordinate transformation and calculates the target current parameters and conduction time of each phase winding. The power electronic switching devices precisely switch according to the PWM control signal, synthesizing three-phase currents of specific amplitude and phase in the excitation coil. The three-phase current generates a differentiated magnetic field distribution in the pipeline space, whose intensity and phase are independently configured according to the distortion characteristics of each sector area. The compensation magnetic field forms an exact mirror image relationship with the original magnetic field distortion, and the inhomogeneous components are neutralized through vector superposition. The resulting partitioned compensation excitation magnetic field is finally generated.

[0058] S3. Based on the original electrode signal generated by the excitation magnetic field, obtain the uniformity index of the partition compensation excitation magnetic field, and dynamically configure the gate voltage of the ion channel to generate a purified voltage signal;

[0059] The original electrode signal after the partition compensation excitation magnetic field is collected by the quantum dot array, and the magnetic field intensity distribution characteristics of each sector area are extracted by STPA.

[0060] Furthermore, after pre-processing, the electrode signals collected by the quantum dot array after the action of the partitioned compensation excitation magnetic field are subjected to the short-time phase analysis algorithm to extract the time-frequency domain features of the original electrode signals in each sector area. First, the electrode signal is windowed and segmented, the instantaneous phase information in each time window is calculated, and the rate of change of the magnetic field intensity is obtained by phase differential operation. At the same time, the spectral characteristics of each frequency band are analyzed in combination with the fast Fourier transform, and the amplitude and phase relationship of the fundamental and harmonic components are extracted. The instantaneous phase characteristics and the spectral characteristics are subjected to time-space correlation analysis, and the spatial gradient of the magnetic field intensity amplitude, phase difference and harmonic spectrum density of each sector area are calculated, which ultimately constitute the magnetic field intensity distribution characteristics of each sector area.

[0061] It should be noted that the preprocessing includes noise filtering, baseline correction and signal normalization of the electrode signals collected by the quantum dot array. The specific process is: first, a digital filter is used to eliminate high-frequency noise and power frequency interference, then the signal baseline drift is removed through the sliding average algorithm, and finally the signal amplitude of each channel is normalized and calibrated according to a unified benchmark.

[0062] The magnetic field intensity distribution characteristics include the magnetic field intensity amplitude spatial gradient, phase difference and harmonic spectrum density;

[0063] Based on the magnetic field intensity distribution characteristics of each sector area, the uniformity index of the compensation excitation magnetic field of each partition is calculated by the spatiotemporal differential-spectral joint analysis method. The expression is:

[0064]

[0065] Among them, Γ iis the uniformity index of the compensation excitation magnetic field in the i-th sector area, is the spatial gradient of the magnetic field intensity amplitude in the i-th sector area, A is the average value of the magnetic field intensity amplitude in all sector areas, N is the total number of sector areas divided by the pipeline cross section, α is the weight coefficient of the phase difference (the range is: 0.1-0.5), β is the weight coefficient of the harmonic spectrum density (the range is: 0.2-0.8), Δφ ij is the phase lag between the i-th and j-th sector regions, H i (f1) is the density of frequency f1 in the harmonic spectrum of the i-th sector area, H(f1) is the density reference of frequency f1 in the harmonic spectrum (the value range is: 0.01-1.0), and f1 represents the frequency in the harmonic spectrum;

[0066] Furthermore, first obtain the spatial gradient of the magnetic field intensity amplitude in each sector area And the average amplitude A, calculate the normalized squared gradient Then determine the maximum phase lag between each sector area max(|Δφ ij |, multiplied by the phase difference weight coefficient α; then extract the harmonic spectrum density H of each region at the characteristic frequency f1 i (f1) and the reference density H i The normalized deviation of (f1) is multiplied by the harmonic weight coefficient β; the sum of the three results is divided by the total number of fan-shaped areas N, and the square root is taken to obtain Γ i During the calculation process, vector operations are used to process spatial gradient data, the maximum phase difference is determined by extreme value search, the L2 norm is used to calculate the spectral density deviation, and finally the uniformity index of the compensation excitation magnetic field of each partition is output.

[0067] Based on the statistical distribution of historical magnetic field distortion data, a low uniformity threshold Γ1 and a high uniformity threshold Γ2 are defined;

[0068] When Γ i ≤Γ1, reduce the current ion channel gating voltage; when Γ1<Γ i When ≤Γ2, the current ion channel gating voltage is maintained; when Γ i When ≥Γ2, increase the current ion channel gating voltage.

[0069] It should be noted that the process of defining the low uniformity threshold Γ1 and the high uniformity threshold Γ2 is as follows: the uniformity index Γ of each sector area accumulated in the historical operation is calculated. i The data were statistically analyzed and the probability density distribution fitting method was used to determine Γ iThe typical value range of Γ is Γ1. By calculating the cumulative distribution function, the 30th percentile value is selected as the uniformity threshold Γ1, and the 80th percentile value is selected as the high uniformity threshold Γ2. The low uniformity threshold Γ1 ranges from 0.15 to 0.35, and the high uniformity threshold Γ2 ranges from 0.45 to 0.75.

[0070] The original voltage signal is harmonically filtered and time-calibrated using the adjusted ion channel gating voltage to generate a purified voltage signal.

[0071] Furthermore, according to the uniformity index Γ i The ion channel gating voltage is dynamically adjusted based on the judgment results, and an adaptive filtering algorithm is used to perform harmonic elimination and timing calibration on the original voltage signal. First, the bandpass filter parameters are set based on the adjusted gating voltage, and the harmonic interference components in the specified frequency band are eliminated through a digital filter bank. Then, a timing calibration circuit is used to phase compensate the filtered signal to correct for signal delays caused by magnetic field inhomogeneities. Finally, a signal reconstruction unit integrates the processed multi-channel data into a continuous voltage waveform, outputting the purified voltage signal.

[0072] S4. Build a joint correlation prediction model and perform fusion analysis on the purified voltage signal and mechanical vibration data to obtain an initial water flow estimation value;

[0073] Mechanical vibration data is collected through acceleration sensors; the mechanical vibration data includes the time domain waveform of the pipeline surface vibration and the vibration energy intensity in each frequency band.

[0074] Based on the purified voltage signal and mechanical vibration data, state variables and observation variables are defined;

[0075] Furthermore, the amplitude parameters of the purified voltage signal after normalization are used as the main observation variables, including two sub-items: the fundamental voltage amplitude and the third harmonic voltage amplitude ratio. After feature extraction, the mechanical vibration data is converted into vibration energy observation variables, including two sub-items: the integrated energy in the low-frequency band (0-100Hz) and the peak energy in the resonant frequency band (100-300Hz). The state variables are defined as dynamic parameters related to water flow rate, including three dimensions: instantaneous flow rate, flow rate change rate, and fluid-solid coupling coefficient. A mapping relationship is established between the observation variables and the state variables through the measurement equation.

[0076] Based on state variables and observation variables, a state space model is established through the particle filter algorithm;

[0077] Furthermore, a nonlinear state-space model was constructed using a particle filter algorithm. 300-600 particles were initialized to characterize the probability distribution of the state variables. The state transition equation was based on a simplified form of the Navier-Stokes equations, describing the coupling relationship between the fluid inertia effect and the pipeline damping characteristics via a second-order differential equation. The specific expression includes the time derivative and spatial gradient terms of the flow velocity. During the observation update phase, the predicted observed variables corresponding to each particle were calculated, including the amplitude of the purified fundamental voltage signal and the predicted vibration energy intensity at each frequency band. The predicted values ​​were then matched with the time-domain waveform of the pipeline surface vibration measured by the accelerometer and the vibration energy intensity at each frequency band extracted by fast Fourier transform. A systematic resampling strategy was then used to update the particle weight distribution, retaining high-weighted particles and eliminating low-weighted ones. The output of the state-space model consists of the mean vector and covariance matrix of the estimated state variables. The mean vector is obtained by taking the arithmetic mean of the weighted particle set, while the covariance matrix is ​​calculated based on the particle dispersion. This process, through iterative operations, gradually approximates the particle set to the true state distribution, ultimately forming a complete state-space model.

[0078] The state space model is combined with the extended Kalman filter algorithm through variational Bayesian inference to generate a joint correlation prediction model;

[0079] Furthermore, the state-space model output of the particle filter algorithm serves as the input to the extended Kalman filter algorithm, coupling the two algorithms through variational Bayesian inference. Within the variational Bayesian framework, the posterior distribution of the state variables is approximately decomposed into multiple independent factors, and state estimation and parameter estimation are alternately optimized. The extended Kalman filter algorithm processes the Gaussian portion of the state variables, while the particle filter algorithm processes the non-Gaussian portion. By maximizing the variational lower bound, the model parameters are jointly optimized, ultimately generating a joint correlation prediction model.

[0080] It should be noted that state estimation focuses on the dynamic characteristic parameters of water flow, including real-time numerical calculations of three dimensions: instantaneous flow velocity, flow velocity change rate, and fluid-structure coupling coefficient. Parameter estimation focuses on the fixed characteristic parameters in the state-space model, including elements of the measurement equation coefficient matrix, the fluid inertia coefficient and pipeline damping coefficient in the state transfer equation, and the components of the process noise covariance matrix and the observation noise covariance matrix. These two types of estimation are alternately iteratively optimized within the variational Bayesian inference framework, forming a complete parameter system for the joint correlation prediction model.

[0081] The purified voltage signal and mechanical vibration data are fused and analyzed by the joint correlation prediction model to obtain the initial water flow estimation value, which is expressed as follows:

[0082] Q=g1·V+g2·∫E(f2)df+γ;

[0083] Where Q is the estimated initial water flow rate in the pipeline, g1 is the voltage signal weight coefficient (range: 0.5-1.2), V is the purified voltage signal, g2 is the vibration energy intensity weight coefficient (range: 0.1-0.5), E(f2) is the vibration energy spectrum density value at frequency f2, and γ is the deviation compensation constant of the water flow rate (range: 0.3 to +0.3 (unit: m 3 / h)).

[0084] Furthermore, the purified voltage signal V output by the joint correlation prediction model is first obtained and amplitude demodulated to obtain standardized voltage parameters. Simultaneously, mechanical vibration data collected by the accelerometer is extracted, and the integral of the vibration energy spectral density E(f2) at the characteristic frequency f2 within a specified frequency band is calculated through frequency domain integration. The voltage signal weight coefficient g1 is multiplied by the purified voltage signal V to obtain the voltage component, and the vibration energy intensity weight coefficient g2 is multiplied by the vibration energy integral to obtain the vibration component. These two components are summed and then added with the water flow deviation compensation constant γ to form the initial water flow estimate Q. During the calculation process, the voltage signal weight coefficient g1 and the vibration energy intensity weight coefficient g2 are trained by the joint correlation prediction model based on historical data, and the water flow deviation compensation constant γ is determined through zero-point calibration under no-flow conditions. The final output, the initial water flow estimate Q, incorporates the fusion of the voltage signal and mechanical vibration data, enabling collaborative measurement of multiple physical quantities.

[0085] S5. Through dynamic temperature compensation and zero point online calibration, the error of the initial water flow estimation value is corrected to generate a high confidence water flow value.

[0086] The real-time pipe wall temperature data is collected through the PT100 temperature sensor, and the temperature drift of the initial water flow estimate is compensated by the linear interpolation compensation method to generate the temperature-compensated water flow estimate;

[0087] Furthermore, a PT100 temperature sensor collects pipe outer wall temperature data at a fixed sampling period and converts it into a digital temperature value via a signal conditioning circuit. Based on a pre-stored standard temperature-flow characteristic curve, a linear interpolation compensation method is used to calculate the flow compensation corresponding to the current temperature. The initial water flow estimate Q is added to the temperature compensation value to obtain the temperature-compensated water flow estimate. The linear interpolation compensation method establishes a local linear relationship between adjacent temperature calibration points and calculates the flow reading offset caused by temperature changes in real time. The temperature compensation process takes into account the sensor's thermal response time constant and uses digital filtering to eliminate temperature measurement noise, ensuring compensation accuracy. The final output, the temperature-compensated water flow estimate, eliminates the effects of ambient temperature changes on the measurement results.

[0088] In a no-flow state (the water flow in the pipe is completely still), based on the temperature-compensated water flow estimate, a baseline drift correction algorithm is used to identify the zero point offset, and then corrected through exponentially weighted dynamic attenuation to generate a high-confidence water flow value.

[0089] Furthermore, when the water flow in the pipe is completely still, the temperature-compensated water flow estimate is recorded as the zero-point offset. The baseline drift correction algorithm uses a sliding time window to calculate the mean and standard deviation of the zero-point offset and establish a dynamic baseline for the drift. An exponentially weighted dynamic decay method is used to process historical drift data, assigning a higher weight to recent data and calculating a real-time drift correction value. The temperature-compensated water flow estimate is subtracted from the drift correction value to obtain a high-confidence water flow value that eliminates the zero-point offset. During the correction process, the exponential weighting coefficient is adaptively adjusted based on the drift change rate, ensuring a rapid response to sudden drift events while maintaining long-term stability. The resulting high-confidence water flow value exhibits optimized zero-point stability and measurement repeatability.

[0090] This embodiment also provides a computer device suitable for the high-precision measurement method of electromagnetic water meters using smart sensors, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the high-precision measurement method of electromagnetic water meters using smart sensors proposed in the above embodiment.

[0091] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0092] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the high-precision measurement method of an electromagnetic water meter using an intelligent sensor as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0093] In summary, the present invention achieves precise control of the magnetic field distribution in the pipeline cross section by adopting a compensation engine combined with a space vector pulse width modulation algorithm to dynamically generate a partitioned compensation excitation magnetic field that matches the magnetic field distortion characteristics, effectively eliminating the magnetic field unevenness problem caused by edge effects and interference from ferromagnetic materials; calculates the magnetic field uniformity index of each sector area in real time through a spatiotemporal differential-spectral joint analysis method, and dynamically adjusts the ion channel gating voltage accordingly to achieve adaptive suppression of harmonic interference and timing errors in the electrode signal, thereby improving signal purity and measurement stability.

[0094] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A high-precision measurement method for electromagnetic water meters using intelligent sensors, characterized by: include, Collect the quantum tunneling current signal around the pipeline and extract the magnetic field distortion characteristics; Based on the magnetic field distortion characteristics, a compensation engine is used to calculate the compensation current intensity of each pipeline area. Combined with the spatial distribution of the magnetic field distortion characteristics, a partitioned compensation excitation magnetic field is generated using a space vector pulse width modulation algorithm. Based on the original electrode signal generated by the excitation magnetic field, the uniformity index of the partition compensation excitation magnetic field is obtained, and the gate voltage of the ion channel is dynamically configured to generate a purified voltage signal; A joint correlation prediction model is constructed, and the purified voltage signal and mechanical vibration data are fused and analyzed to obtain an initial water flow estimate. Through dynamic temperature compensation and zero point online calibration, the initial water flow estimation value is corrected and a high confidence water flow value is generated.

2. The high-precision electromagnetic water meter measurement method using an intelligent sensor according to claim 1, characterized in that: The use of a compensation engine to calculate the compensation current intensity of each pipeline area refers to defining a magnetic field gradient compensation threshold and identifying a magnetic field distortion area on the pipeline interface where the magnetic field intensity change rate exceeds the magnetic field gradient compensation threshold. At the same time, the compensation engine generates the compensation current intensity of the magnetic field distortion area according to the ratio of the spatial distribution gradient of the magnetic field intensity to the magnetic field gradient compensation threshold.

3. The high-precision electromagnetic water meter measurement method using an intelligent sensor according to claim 2, characterized in that: The spatial distribution of the magnetic field distortion characteristics is combined to generate a partitioned compensation excitation magnetic field through a space vector pulse width modulation algorithm. The steps are as follows: Real-time acquisition of the excitation coil driving signal around the pipeline; According to the amplitude of the interfering harmonics, the reverse harmonic component is injected into the excitation coil drive signal. Combined with the spatial distribution of the magnetic field distortion characteristics, the phase lag and phase angle are compensated in a partitioned weighted manner to generate a spatially modulated excitation current waveform. Based on the compensation current intensity and spatial modulation excitation current waveform in the magnetic field distortion area, three-phase current vector synthesis is performed through the space vector pulse width modulation algorithm to generate the partitioned compensation excitation magnetic field.

4. The high-precision electromagnetic water meter measurement method using an intelligent sensor according to claim 1, characterized in that: The obtaining of the uniformity index of the partitioned compensating excitation magnetic field refers to collecting the original electrode signal after the partitioned compensating excitation magnetic field acts, extracting the magnetic field intensity distribution characteristics of each sector area through STPA, and calculating the uniformity index of the partitioned compensating excitation magnetic field through the spatiotemporal differential-spectral joint analysis method.

5. The high-precision electromagnetic water meter measurement method using an intelligent sensor according to claim 4, characterized in that: The gate voltage of the dynamic configuration ion channel is used to generate a purified voltage signal, and the steps are as follows: Define low and high uniformity thresholds, compare them with the uniformity index of the compensation excitation magnetic field of each partition, and dynamically configure the gating voltage of the ion channel based on the comparison results; The original voltage signal is harmonically filtered and time-calibrated using the adjusted ion channel gating voltage to generate a purified voltage signal.

6. The high-precision electromagnetic water meter measurement method using an intelligent sensor according to claim 1, characterized in that: The steps of constructing a joint correlation prediction model and performing fusion analysis on the purified voltage signal and mechanical vibration data to obtain the initial water flow estimation value are as follows: Based on the purified voltage signal and mechanical vibration data, the state variables and observation variables are defined, and the state space model is established through the particle filter algorithm; The state space model is combined with the extended Kalman filter algorithm through variational Bayesian inference to generate a joint correlation prediction model; Through the joint correlation prediction model, the purified voltage signal and mechanical vibration data are fused and analyzed to obtain the initial water flow estimation value.

7. The high-precision electromagnetic water meter measurement method using an intelligent sensor according to claim 1, characterized in that: The initial water flow estimation value is corrected by dynamic temperature compensation and zero point online calibration to generate a high confidence water flow value. The steps are as follows: Collect real-time pipe wall temperature data and use linear interpolation compensation to compensate for temperature drift on the initial water flow estimate to generate a temperature-compensated water flow estimate. In a no-flow state, a baseline drift correction algorithm is used to identify the zero point offset based on the temperature-compensated water flow estimate, and then corrected by exponentially weighted dynamic decay to generate a high-confidence water flow value.

8. The high-precision electromagnetic water meter measurement method using an intelligent sensor according to claim 1, characterized in that: The magnetic field distortion characteristics include the spatial distribution gradient of the magnetic field intensity in the pipeline, the interference harmonic amplitude and the phase lag.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the high-precision measurement method of an electromagnetic water meter using an intelligent sensor according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the high-precision measurement method of an electromagnetic water meter using an intelligent sensor according to any one of claims 1 to 8 are implemented.

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