Water flow testing method and system based on electromagnetic flowmeter

Abstract

The invention discloses a water flow test method and system based on an electromagnetic flowmeter, and relates to the technical field of automatic control, and the method comprises the following steps: obtaining historical impedance data of an electrode, and carrying out data analysis to obtain electrode conductivity fluctuation data; analyzing the water quality of the to-be-detected area, establishing a water quality conductivity characteristic model, and correcting the conductivity fluctuation data of the electrode; acquiring electrode pollution characteristics, and dynamically predicting the water quality pollution level of the to-be-detected area in combination with the corrected electrode conductivity fluctuation data; according to a pollution level prediction result, obtaining the influence intensity of pollution on an electrode signal, and dynamically adjusting the excitation frequency and amplitude; through the method based on electrode conductivity fluctuation data correction, water quality pollution level dynamic prediction and excitation frequency and amplitude self-adaptive adjustment, the problem that in the prior art, the measurement precision of an electromagnetic flowmeter is reduced in a complex water quality environment is solved.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and more particularly to a water flow testing method and system based on an electromagnetic flowmeter. Background Art

[0002] Electromagnetic flowmeters are widely used in water resource monitoring, municipal water supply and drainage, power cooling water, and other fields due to their simple structure, lack of moving parts, high measurement accuracy, and compatibility with various media. However, in practical applications, the complex and variable water quality of the water being measured, especially the presence of suspended particles, dissolved substances, and pollutants, can affect the working state and conductivity of the electrodes, causing fluctuations in the electrode impedance data and, in turn, affecting the flowmeter's measurement accuracy.

[0003] At present, most traditional flow rate testing methods only focus on the built-in excitation parameters and signal processing algorithms of the flow meter, but do not adequately consider compensation for electrode impedance fluctuations and changes in water conductivity, which makes the measurement results prone to errors in polluted environments or complex water quality conditions. In addition, after the electrode is contaminated, its conductivity fluctuations and impedance data change significantly, but the existing technology lacks real-time monitoring and dynamic correction mechanisms for electrode contamination characteristics, making it difficult to respond to water quality changes in a short period of time. Therefore, there is an urgent need for a new water flow test method that can comprehensively utilize historical impedance data, water conductivity characteristic models and electrode contamination characteristics to achieve dynamic analysis of the water quality of the test area, and dynamically adjust the excitation frequency and amplitude based on the pollution level prediction results, thereby improving flow measurement accuracy and system stability.

[0004] For example, the invention patent with announcement number CN115561284A discloses a frequency-adaptive square wave pulse water quality conductivity detection method, which includes: using a two-electrode conductivity meter to perform a first measurement on the measured solution to obtain the equivalent resistance of the measured solution; setting the voltage divider resistance to 1KΩ and the bipolar excitation frequency to 1KHz by default during the first measurement; determining the required optimal excitation frequency and corresponding voltage divider resistance based on the equivalent resistance of the measured solution; using IO control to select the corresponding multi-channel analog switch to adjust the state of the two-electrode conductivity meter to the optimal excitation frequency and voltage divider resistance corresponding to the equivalent resistance; using the adjusted two-electrode conductivity meter to perform a second measurement on the measured solution; and using the result of the second measurement as the optimal measurement result of the water quality conductivity of the measured solution. This method improves the accuracy of the measurement results of existing bipolar voltage pulse conductivity meters, especially for solutions with low conductivity, the measurement accuracy is significantly improved.

[0005] For example, the invention patent with announcement number: CN118244648B discloses a water inlet flow optimization method based on water outlet flow prediction, including: S1, obtaining input data, and inputting the input data into a trained water outlet flow prediction model to obtain a water outlet flow prediction value; the water outlet flow prediction model is a Transformer model, and the decoder in the Transformer model is a linear network module; the water outlet flow prediction model is pre-trained according to a source data set to obtain a trained water outlet flow prediction model; the source data set includes a sequence of water outlet flow and water outlet pressure at hourly intervals within a specified time period; S2, based on the water outlet flow prediction value and a preset total water outlet amount, a final water outlet flow is obtained; S3, based on the final water outlet flow, a preset range of change of the water pool liquid level and the water pool area, the optimal value of the water inlet flow is determined.

[0006] The above disclosed technical solutions have at least the following technical problems: In conventional water flow measurement methods using electromagnetic flowmeters, electromagnetic flowmeters are subject to varying degrees of water contamination in complex water environments, resulting in reduced measurement accuracy. To address the above issues, the present invention proposes a solution. Summary of the Invention

[0007] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a water flow testing method and system based on an electromagnetic flowmeter. By correcting electrode conductivity fluctuation data, dynamically predicting water pollution levels, and adaptively adjusting the excitation frequency and amplitude, the method solves the problem of decreased measurement accuracy of electromagnetic flowmeters in complex water quality environments in current technology.

[0008] To achieve the above object, the present invention provides the following technical solutions: The water flow test method based on the electromagnetic flowmeter includes the following steps: obtaining historical impedance data of the electrode and performing data analysis to obtain electrode conductivity fluctuation data; analyzing the water quality of the test area, establishing a water quality conductivity characteristic model, and correcting the electrode conductivity fluctuation data; obtaining electrode contamination characteristics and dynamically predicting the water quality contamination level in the test area based on the corrected electrode conductivity fluctuation data; and according to the pollution level prediction result, obtaining the intensity of the pollution's impact on the electrode signal and dynamically adjusting the excitation frequency and amplitude.

[0009] In a preferred embodiment, the impedance data of the electrode is obtained and the electrode conductivity fluctuation data is obtained by performing data analysis, specifically as follows: the impedance data of the electrode of the device to be tested is obtained, and the temperature data and pressure data corresponding to the impedance data are obtained by a time alignment method, and the impedance data is dynamically corrected to obtain initial impedance data; and the conductivity fluctuation data is obtained by deducing based on the relationship formula between impedance and conductivity according to the initial impedance data.

[0010] In a preferred embodiment, the steps for obtaining the impedance correction term function are as follows: obtaining historical impedance data of the device under test and filtering out the original impedance data; obtaining historical temperature and pressure data of the device under test, and matching the impedance, temperature and pressure data at the same time point based on a timestamp alignment method; and obtaining a linear relationship model between impedance change and temperature and pressure based on a linear model.

[0011] In a preferred embodiment, the water quality of the area to be tested is analyzed, and the electrode conductivity fluctuation data is corrected based on a preset water quality conductivity characteristic model, specifically as follows: the water quality of the area to be tested is analyzed and first data is extracted; the correlation between the first data and the conductivity fluctuation is obtained based on Pearson correlation analysis, and the first data is screened according to the correlation to screen out the top N water quality characteristics; the screened water quality characteristics are decomposed at multiple scales to extract low-frequency components and high-frequency components; the decomposed multi-scale characteristics are spliced with the screened water quality characteristics to obtain a water quality characteristic vector; the water quality characteristic vector is input into a preset water quality conductivity characteristic model to output water quality conductivity; and the electrode conductivity fluctuation data is corrected according to the water quality conductivity.

[0012] In a preferred embodiment, the electrode pollution characteristics are obtained and the water quality pollution level of the test area is dynamically predicted in combination with the corrected electrode conductivity fluctuation data, specifically as follows: based on the corrected conductivity fluctuation data, the pollution characteristics are extracted; the extracted pollution characteristics are spliced to form a pollution characteristic vector; the pollution characteristic vector and the corrected electrode conductivity fluctuation data are input into the Markov chain model for training to establish a dynamic change model of the pollution state; the pollution characteristic vector at the latest moment is obtained, and the input is input into the dynamic change model of the pollution state to output the probability distribution of the pollution state; the pollution state at the previous moment and the current pollution characteristics are obtained, and the pollution state distribution at the next moment is predicted based on the state transition probability of the Markov chain; according to the pollution state distribution at the next moment, the pollution state corresponding to the maximum probability is selected, and the final prediction result of the pollution level is output.

[0013] In a preferred embodiment, the intensity of the impact of pollution on the electrode signal is obtained based on the pollution level prediction result, and the excitation frequency and amplitude are dynamically adjusted, specifically as follows: the electrode signal data to be measured is obtained, and the intensity of the impact of pollution on the electrode signal is evaluated in combination with the pollution level prediction result; the optimal excitation frequency and amplitude combination of the electromagnetic flowmeter signal under different impact intensities is obtained, and an excitation matching database is constructed; the real-time pollution level of the area to be measured is obtained, the corresponding intensity of the impact of the electrode signal is obtained, and the excitation matching database is matched, and the excitation frequency and amplitude are dynamically adjusted according to the matching results.

[0014] In a preferred embodiment, the electrode signal data to be measured is obtained, combined with the pollution level prediction result, to evaluate the impact intensity of pollution on the electrode signal, specifically as follows: the electrode signal data to be measured is obtained, and the electrode signal features are extracted, the electrode signal features including time domain features and frequency domain features; the time domain features and the frequency domain features are combined to construct a feature vector; the feature vector and the pollution level prediction result are input into a preset electrode signal impact intensity evaluation model, and the electrode signal impact intensity evaluation result is output.

[0015] In a preferred embodiment, the method of obtaining the optimal excitation frequency and amplitude combination of the electromagnetic flowmeter signal under different influence intensities is as follows: obtaining a first interval of excitation frequency and amplitude according to the influence intensity of the electrode signal; obtaining signal quality data of different excitation frequency and amplitude combinations under the influence intensity of the electrode signal, re-evaluating the influence intensity of the electrode signal under different excitation frequency and amplitude combinations, and obtaining the optimization level of the electrode signal; and obtaining the optimal combination of excitation frequency and amplitude according to the optimization level of the electrode signal.

[0016] A system for a water flow test method based on an electromagnetic flowmeter includes a data analysis module, a conductivity data correction module, a water pollution level prediction module and an excitation regulation module, and there are connections between the modules; the data analysis module is used to obtain historical impedance data of the electrode and perform data analysis to obtain electrode conductivity fluctuation data; the conductivity data correction module is used to analyze the water quality of the test area, establish a water quality conductivity characteristic model, and correct the electrode conductivity fluctuation data; the water pollution level prediction module is used to obtain electrode pollution characteristics and dynamically predict the water pollution level of the test area based on the corrected electrode conductivity fluctuation data; the excitation regulation module is used to obtain the intensity of the impact of pollution on the electrode signal based on the pollution level prediction result, and dynamically adjust the excitation frequency and amplitude.

[0017] The technical effects and advantages of the water flow measurement method and system based on electromagnetic flowmeter of the present invention are as follows: 1. This method acquires historical electrode impedance data and performs detailed data analysis to accurately capture fluctuations in electrode conductivity. Furthermore, based on the water quality characteristics of the measured area, a preset water quality conductivity characteristic model is used to dynamically correct the electrode conductivity fluctuation data, effectively eliminating the impact of water quality changes on measurement results. This method significantly improves the measurement accuracy of electromagnetic flowmeters under varying water quality conditions, ensuring data accuracy and reliability.

[0018] 2. This invention dynamically predicts water pollution levels by extracting electrode contamination characteristics, combining them with corrected electrode conductivity fluctuation data, and utilizing advanced algorithms such as Markov chain models. This capability provides a scientific basis for timely response measures and helps prevent potential water quality issues. Furthermore, based on the predicted pollution levels, this invention intelligently adjusts the excitation frequency and amplitude, further optimizing the electromagnetic flowmeter's operating state and ensuring high-precision measurements even under complex water quality conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The figure is a flow chart of a water flow testing method based on an electromagnetic flowmeter according to the present invention.

[0020] Figure 2 The diagram is a schematic diagram of the system structure of a water flow rate testing method based on an electromagnetic flowmeter according to the present invention.

[0021] Figure 3 It is a graph of conductivity fluctuation data. DETAILED DESCRIPTION

[0022] The following will provide a clear and complete description of 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. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0023] Example 1, Figure 1 The present invention provides a water flow measurement method based on an electromagnetic flowmeter, comprising the following steps: S1, obtain the impedance data of the electrode and perform data analysis to obtain the electrode conductivity fluctuation data.

[0024] In this embodiment, the impedance data of the electrode is obtained, and the data analysis is performed to obtain the electrode conductivity fluctuation data, as follows: Obtain impedance data from the electrodes of the device under test, preprocess the impedance data to remove abnormal data points, such as extreme values caused by signal disconnection or noise interference, and smooth the data using median filtering or mean filtering; The temperature data and pressure data corresponding to the impedance data are obtained by time alignment method, and the impedance data are dynamically corrected to obtain the initial impedance data; The conductivity fluctuation data was obtained by deducing the relationship between impedance and conductivity based on the initial impedance data.

[0025] The calculation formula for impedance correction is as follows:

[0026] Where: is the corrected impedance data, is the initial measured impedance data, Is the impedance correction function, which represents the error or offset caused by changes in temperature and pressure on the impedance measurement. is the temperature data at time t, is the pressure data at time t.

[0027] In this embodiment, the steps for obtaining the impedance correction term function are as follows: Obtain historical impedance data of the device under test and filter out the original impedance data; Obtain historical temperature and pressure data of the device under test, and match the impedance, temperature, and pressure data at the same time point based on the timestamp alignment method; Outliers are detected and removed using the box plot method, and impedance, temperature, and pressure data are denoised using methods such as Kalman filtering or wavelet transform. A linear relationship model between impedance change and temperature and pressure is obtained based on a linear model.

[0028] S2, analyzes the water quality of the area to be tested, and corrects the electrode conductivity fluctuation data based on a preset water quality conductivity characteristic model.

[0029] In this embodiment, the water quality of the area to be measured is analyzed, and based on a preset water quality conductivity characteristic model, the electrode conductivity fluctuation data is corrected as follows: Analyze the water quality of the area to be tested, extract first data, obtain the correlation between the first data and the conductivity fluctuation based on Pearson correlation analysis, and screen the first data based on the correlation to screen out the top N water quality features; Using median filtering and Kalman filtering to process water quality characteristic data, remove noise data, and convert data of different dimensions into comparable characteristics through normalization or standardization, the first data including TDS, ion concentration, and pH value; The filtered water quality characteristics are decomposed into multiple scales based on wavelet transform to extract low-frequency components and high-frequency components, namely multi-scale features; The decomposed multi-scale features are combined with the filtered water quality features to obtain the water quality feature vector; Input the water quality characteristic vector into the preset water quality conductivity characteristic model and output the water quality conductivity; Correct the electrode conductivity fluctuation data according to the water conductivity.

[0030] The calculation formula for correcting the electrode conductivity fluctuation data using water conductivity is as follows:

[0031] Where: is the corrected conductivity, is the conductivity measured in real time, is the error correction coefficient, It is the conductivity predicted by the water conductivity characteristic model.

[0032] S3, obtain the electrode pollution characteristics, and combine the corrected electrode conductivity fluctuation data to dynamically predict the water pollution level in the test area.

[0033] In this embodiment, the electrode pollution characteristics are obtained and combined with the corrected electrode conductivity fluctuation data to dynamically predict the water pollution level in the test area, as follows: Extracting pollution features based on the corrected conductivity fluctuation data, wherein the pollution features include polarization effect features, resistance change features, and noise features; The polarization effect characteristic is a phenomenon produced when the electrode is covered by pollutants, which is usually manifested as a change in electrode surface impedance and an increase in nonlinear characteristics. The polarization effect characteristics include electrode impedance amplitude, phase angle change, and polarization curve steepness; The noise characteristics are additional noise signals introduced by pollutants, which increase signal instability. The noise characteristics include noise variance and signal-to-noise ratio; The extracted pollution features are spliced to form a pollution feature vector; The pollution feature vector and the corrected electrode conductivity fluctuation data are input into the Markov chain model for training to establish a dynamic change model of the pollution state; Obtain the pollution feature vector at the latest moment, input it into the dynamic change model of the pollution state, and output the probability distribution of the pollution state; Obtain the pollution status at the previous moment and the current pollution characteristics, and predict the pollution status distribution at the next moment based on the state transition probability of the Markov chain; Output the predicted result of pollution level according to the pollution state distribution at the next moment; According to the pollution level prediction results, the state with the largest probability value is selected as the pollution level prediction result.

[0034] The calculation formula for the pollution level prediction is as follows:

[0035]

[0036]

[0037] Where: is the probability distribution of the previous contamination state, In the polluted state The eigenvector is observed when The conditional probability of is the probability of pollution state at time t, is the pollution level at time t, is the observed eigenvector The marginal probability of is the pollution feature vector at the current time t, is the probability of pollution state at time t+1, is the state transition probability from time t to time t+1, is the pollution level prediction result at time t.

[0038] It should be noted that the continuity and stability of the model's prediction results in time series are ensured through step-by-step connection, while the pollution change trend is captured through state updates, thereby achieving more accurate dynamic predictions.

[0039] S4, based on the pollution level prediction results, obtains the impact intensity of pollution on the electrode signal and dynamically adjusts the excitation frequency and amplitude.

[0040] In this embodiment, based on the pollution level prediction result, the impact of pollution on the electrode signal is obtained, and the excitation frequency and amplitude are dynamically adjusted, as follows: Obtain the signal data of the electrode to be tested, and combine it with the pollution level prediction results to evaluate the impact of pollution on the electrode signal; Obtain the optimal excitation frequency and amplitude combination of the electromagnetic flowmeter signal under different impact intensities and build an excitation matching database; The real-time pollution level of the area to be measured is obtained, the impact strength of the corresponding electrode signal is obtained, and the excitation matching database is matched. The excitation frequency and amplitude are dynamically adjusted according to the matching results.

[0041] In this embodiment, the signal data of the electrode to be tested is obtained, and the impact of the pollution on the electrode signal is evaluated in combination with the pollution level prediction result, as follows: Acquire the electrode signal data to be tested and extract the electrode signal features, including time domain features (mean, variance, root mean square, and signal fluctuation amplitude of the electrode signal) and frequency domain features (noise frequency band, main frequency component, and signal-to-noise ratio); Combine time domain features and frequency domain features to construct feature vectors; The characteristic vector and the pollution level prediction result are input into a preset electrode signal impact intensity assessment model, and the electrode signal impact intensity assessment result is output.

[0042]

[0043] Where: is the intensity of the impact of pollution on the electrode signal, 、 and is the weight coefficient, is the total number of features in the feature vector, is the i-th feature in the feature vector, is the pollution level prediction result.

[0044] In this embodiment, the optimal excitation frequency and amplitude combination of the electromagnetic flowmeter signal under different impact intensities is obtained as follows: Obtaining a first interval of excitation frequency and amplitude according to the electrode signal influence strength; Obtain signal quality data for different excitation frequency and amplitude combinations under electrode signal influence strength, re-evaluate the electrode signal influence strength under different excitation frequency and amplitude combinations, and obtain the optimization level of the electrode signal; The best combination of excitation frequency and amplitude is obtained according to the optimization level of the electrode signal.

[0045] Example 2, Figure 2 The present invention provides a system for a water flow measurement method based on an electromagnetic flowmeter, comprising a data analysis module, a conductivity data correction module, a water pollution level prediction module, and an excitation regulation module, wherein the modules are connected; The data analysis module is used to obtain the historical impedance data of the electrode and perform data analysis to obtain the electrode conductivity fluctuation data; Conductivity data correction module, used to analyze the water quality of the test area, establish a water quality conductivity characteristic model, and correct the electrode conductivity fluctuation data; The water pollution level prediction module is used to obtain the electrode pollution characteristics and dynamically predict the water pollution level in the test area based on the corrected electrode conductivity fluctuation data; The excitation regulation module is used to obtain the impact of pollution on electrode signals based on the pollution level prediction results and dynamically adjust the excitation frequency and amplitude.

[0046] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0047] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0048] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0049] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0050] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0051] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A water flow test method based on an electromagnetic flowmeter, characterized in that: The steps include: Obtain historical impedance data of the electrode and perform data analysis to obtain electrode conductivity fluctuation data; Analyze the water quality of the area to be tested, establish a water conductivity characteristic model, and correct the electrode conductivity fluctuation data; Obtain electrode pollution characteristics and dynamically predict the water pollution level in the test area based on the corrected electrode conductivity fluctuation data; According to the pollution level prediction results, the impact of pollution on the electrode signal is obtained, and the excitation frequency and amplitude are dynamically adjusted.

2. The water flow rate testing method based on electromagnetic flowmeter according to claim 1, characterized in that: The impedance data of the electrode is obtained and the data is analyzed to obtain the electrode conductivity fluctuation data, as follows: Obtain the impedance data of the electrode of the device under test, and obtain the temperature data and pressure data corresponding to the impedance data through the time alignment method, dynamically correct the impedance data, and obtain the initial impedance data; Conductivity fluctuation data is obtained by deducing the relationship between impedance and conductivity based on the initial impedance data; The calculation formula for impedance data correction is as follows: Where: is the corrected impedance data, is the initial measured impedance data, Is the impedance correction function, which represents the error or offset caused by changes in temperature and pressure on the impedance measurement. is the temperature data at time t, is the pressure data at time t.

3. The water flow rate testing method based on the electromagnetic flowmeter according to claim 2, characterized in that: The steps for obtaining the impedance correction function are as follows: Obtain historical impedance data of the device under test and filter out the original impedance data; Obtain historical temperature and pressure data of the device under test, and match the impedance, temperature, and pressure data at the same time point based on the timestamp alignment method; A linear relationship model between impedance change and temperature and pressure is obtained based on a linear model.

4. The water flow rate testing method based on electromagnetic flowmeter according to claim 3, characterized in that: The water quality of the test area is analyzed, and based on the preset water quality conductivity characteristic model, the electrode conductivity fluctuation data is corrected as follows: Analyze the water quality of the area to be tested and extract first data; The correlation between the first data and the conductivity fluctuation is obtained based on the Pearson correlation analysis, and the first data is screened according to the correlation to screen out the top N water quality characteristics; Perform multi-scale decomposition on the filtered water quality characteristics to extract low-frequency components and high-frequency components; The decomposed multi-scale features are combined with the filtered water quality features to obtain the water quality feature vector; Input the water quality characteristic vector into the preset water quality conductivity characteristic model and output the water quality conductivity; Correct the electrode conductivity fluctuation data according to the water conductivity.

5. The water flow rate testing method based on electromagnetic flowmeter according to claim 4, characterized in that: The electrode pollution characteristics are obtained and the water pollution level in the test area is dynamically predicted in combination with the corrected electrode conductivity fluctuation data, as follows: Extract pollution characteristics based on the corrected conductivity fluctuation data; The extracted pollution features are spliced to form a pollution feature vector; The pollution feature vector and the corrected electrode conductivity fluctuation data are input into the Markov chain model for training to establish a dynamic change model of the pollution state; Obtain the pollution feature vector at the latest moment, input it into the dynamic change model of the pollution state, and output the probability distribution of the pollution state; Obtain the pollution status at the previous moment and the current pollution characteristics, and predict the pollution status distribution at the next moment based on the state transition probability of the Markov chain; According to the pollution state distribution at the next moment, the pollution state corresponding to the maximum probability is selected, and the final prediction result of the pollution level is output.

6. The water flow rate testing method based on electromagnetic flowmeter according to claim 5, characterized in that: According to the pollution level prediction result, the influence of pollution on the electrode signal is obtained, and the excitation frequency and amplitude are dynamically adjusted, as follows: Obtain the signal data of the electrode to be tested, and combine it with the pollution level prediction results to evaluate the impact of pollution on the electrode signal; Obtain the optimal excitation frequency and amplitude combination of the electromagnetic flowmeter signal under different impact intensities and build an excitation matching database; The real-time pollution level of the area to be measured is obtained, the impact strength of the corresponding electrode signal is obtained, and the excitation matching database is matched. The excitation frequency and amplitude are dynamically adjusted according to the matching results.

7. The water flow rate testing method based on electromagnetic flowmeter according to claim 6, characterized in that: The acquisition of the electrode signal data to be tested and the evaluation of the impact of pollution on the electrode signal in combination with the pollution level prediction result are as follows: Acquire the electrode signal data to be measured and extract the electrode signal features, wherein the electrode signal features include time domain features and frequency domain features; Combine time domain features and frequency domain features to construct feature vectors; The characteristic vector and the pollution level prediction result are input into a preset electrode signal impact intensity assessment model, and the electrode signal impact intensity assessment result is output.

8. The water flow rate testing method based on electromagnetic flowmeter according to claim 7, characterized in that: The optimal excitation frequency and amplitude combination of the electromagnetic flowmeter signal under different impact intensities is obtained as follows: Obtaining a first interval of excitation frequency and amplitude according to the electrode signal influence strength; Obtain signal quality data for different excitation frequency and amplitude combinations under electrode signal influence strength, re-evaluate the electrode signal influence strength under different excitation frequency and amplitude combinations, and obtain the optimization level of the electrode signal; The best combination of excitation frequency and amplitude is obtained according to the optimization level of the electrode signal.

9. The water flow rate testing method based on electromagnetic flowmeter according to claim 8, characterized in that: The calculation formula for the pollution level prediction is as follows: Where: is the probability distribution of the previous contamination state, In the polluted state The eigenvector is observed when The conditional probability of is the probability of pollution state at time t, is the pollution level at time t, is the observed eigenvector The marginal probability of is the pollution feature vector at the current time t, is the probability of pollution state at time t+1, is the state transition probability from time t to time t+1, is the pollution level prediction result at time t.

10. A system using the water flow measurement method based on an electromagnetic flowmeter according to any one of claims 1 to 9, characterized in that: It includes a data analysis module, a conductivity data correction module, a water pollution level prediction module and an excitation regulation module, and there are connections between the modules; The data analysis module is used to obtain the historical impedance data of the electrode and perform data analysis to obtain the electrode conductivity fluctuation data; Conductivity data correction module, used to analyze the water quality of the test area, establish a water quality conductivity characteristic model, and correct the electrode conductivity fluctuation data; The water pollution level prediction module is used to obtain the electrode pollution characteristics and dynamically predict the water pollution level in the test area based on the corrected electrode conductivity fluctuation data; The excitation regulation module is used to obtain the impact of pollution on electrode signals based on the pollution level prediction results and dynamically adjust the excitation frequency and amplitude.

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