An intelligent water meter flow measurement method with temperature compensation

Abstract

The present invention discloses an intelligent water meter flow measurement method with temperature compensation, which relates to the technical field of water meter measurement. The measurement method includes: collecting data: collecting the correlation data between the temperature of the liquid and the physical properties of the liquid, and collecting the performance test data of the sensor at different temperatures; expanding data: expanding the correlation data to obtain comprehensive correlation data. The present invention obtains the corresponding calculated physical property data from the temperature-physical property model through real-time temperature data, inputs the real-time temperature data into the temperature-parameter calibration model to obtain the calculated calibration parameter data, compares the calculated physical property data with the detected physical property data to obtain the comparison data, and substitutes the comparison data into the correction method to further realize the calculation of the flow value. This method can effectively reduce the influence brought by temperature changes on the sensor and water itself, and reduce the difference between the calculated flow value and the actual flow value.

Description

Technical Field

[0001] The invention relates to the technical field of water meter measurement, and in particular to a flow measurement method of an intelligent water meter with temperature compensation. Background Art

[0002] In the fields of water supply systems and industrial water management, accurate measurement of water flow is of great significance for resource management, cost control and environmental protection. Therefore, water meters are required to monitor the water flow inside the pipes.

[0003] For example, the ultrasonic water meter flow measurement method described in the patent publication number "CN114111929A" and the name "An ultrasonic water meter flow measurement method and ultrasonic water meter thereof" includes the following steps: S1, the ultrasonic transducer receives and / or sends ultrasonic pulses: the ultrasonic transducer includes a second transducer, a third transducer and at least one first transducer; the flight time of the first transducer respectively with the TDC scheme of the second transducer and the third transducer is obtained and saved. The technical solution of the present invention can eliminate the influence of interference factors such as fluid temperature and fluid nature, and can effectively improve the application range, accuracy and reliability of ultrasonic water meter measurement.

[0004] The above method can reduce the temperature and performance of the monitored fluid, reduce the influence of temperature parameters and the fluid's own performance parameters on the calculated data, so that the number of conditions to be calculated is not less than the number of unknowns required to confirm the calculation, and ensure the solvability of the results. Although the above method can solve the influence of the physical properties of water changing with temperature, the performance of flow measurement equipment (such as sensors, flow meters, etc.) may also change with temperature changes, especially in power plants and steel plants. The cooling water and circulating water have frequent changes in water temperature, and the degree of change is large, resulting in the sensitivity and response speed of the sensor may vary with temperature, thereby affecting the accuracy of the measurement results. The large degree of change in water temperature leads to a decrease in the measurement accuracy of the water meter flow. For this reason, a smart water meter flow measurement method with temperature compensation is invented. Summary of the invention

[0005] The object of the present invention is to provide a flow measurement method of an intelligent water meter with temperature compensation to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a flow measurement method of a smart water meter with temperature compensation, the measurement method comprising:

[0007] Collect data: collect the correlation data between the temperature of the liquid and the physical properties of the liquid, and collect the performance test data of the sensor at different temperatures;

[0008] Extended data: Perform data extension on associated data to obtain comprehensive associated data, and perform simulation calculations on performance test data to obtain comprehensive test data;

[0009] Establish a temperature - physical property model; Based on the comprehensive associated data, use the first modeling method to establish a mathematical model between temperature and the physical properties of the liquid;

[0010] Establish a temperature - parameter calibration model: Based on the comprehensive test data, use the second modeling method to establish a mathematical model between temperature and the calibration parameters of the sensor;

[0011] Real - time detection: Real - time detect the liquid data inside the pipeline to obtain real - time detection results. The real - time detection results include real - time temperature data and detected liquid property data. The detected liquid property data includes detected flow property data and detected physical property data;

[0012] Comparison: Input the real - time temperature data into the temperature - physical property model to obtain calculated physical property data. Compare the calculated physical property data with the detected physical property data to obtain comparison data;

[0013] Correction: Obtain adjustment parameters based on the comparison data. Input the real - time temperature data into the temperature - parameter calibration model to obtain calculated sensor calibration parameters. Use the adjustment parameters and the calculated sensor calibration parameters to correct the detected flow property data through a correction method to obtain calculated flow property data;

[0014] Calculation: Calculate the flow value by combining the flow calculation formula with the calculated flow property data.

[0015] Furthermore, the data extension includes: Establish a first association between the temperature of the liquid and the physical properties of the liquid based on the associated data. For the missing data points in the associated data, use data interpolation to fill them, supplement the data in the associated data, and obtain comprehensive associated data.

[0016] Furthermore, the first modeling method includes: Extract features from the comprehensive change data to obtain first - order feature data between temperature and flow characteristics in the data, and use physical modeling methods, statistical analysis techniques, and the first - order feature data to establish a mathematical model between temperature and flow characteristics.

[0017] Furthermore, the second modeling method includes:

[0018] Extract features from the comprehensive test data to extract second - order feature data between temperature and sensor parameters, and use statistical analysis techniques and the second - order feature data to establish a mathematical model between temperature and flow characteristics.

[0019] Furthermore, the method for simulating and calculating performance test data includes: establishing a simulation model of the data, analyzing the secondary correlation between the temperature in the performance test data and the performance test data through mathematical mining methods, simulating the performance test data through the simulation model to obtain simulated performance test data, and combining the simulated performance test data and the performance test data to obtain comprehensive test data.

[0020] Furthermore, the method for data interpolation includes: obtaining the correlation between the temperature of the liquid and the physical properties of the liquid based on the primary correlation, inputting the specific temperature and obtaining the specific data of the corresponding physical properties of the liquid based on the correlation, and supplementing the correlation data based on the specific temperature and the specific data of the corresponding physical properties of the liquid to achieve data interpolation.

[0021] Furthermore, the method for obtaining the adjusted parameters includes: the comparison data includes calculating the difference between the calculated physical property data and the detected physical property data. The detected physical property data is obtained by multiplying the electrical signal data collected by the sensor by the sensor parameters. By adjusting the specific data of the sensor parameters, the difference between the two is controlled to be zero, and the specific data of the adjusted sensor parameters is the adjusted parameter.

[0022] Furthermore, the correction method includes: the detected flow characteristic data is obtained by combining the detected electrical signal and the flow characteristic parameters. Based on the primary difference between the adjusted parameter and the calculated sensor calibration parameter, the primary difference is divided by the calculated sensor calibration parameter to obtain the adjustment reference value. The flow characteristic parameters are adjusted through the adjustment reference value to obtain the adjusted flow characteristic parameters, and the calculated flow characteristic data is obtained by combining the detected electrical signal and the adjusted flow characteristic parameters.

[0023] Compared with the prior art, the beneficial effects of the present invention are:

[0024] This intelligent water meter flow measurement method with temperature compensation designs through a temperature - physical property model and a temperature - parameter calibration model. The corresponding calculated physical property data is obtained from the temperature - physical property model through real - time temperature data. The real - time temperature data is input into the temperature - parameter calibration model to obtain the calculated calibration parameter data. The calculated physical property data and the detected physical property data are compared to obtain the comparison data, and the comparison data is substituted into the correction method to further calculate the flow value. This method can effectively reduce the influence brought by temperature changes on the sensor and water itself, and reduce the difference between the calculated flow value and the actual flow value.

[0025] Meanwhile, the application of data expansion and simulation calculation enables the method to obtain and utilize data more comprehensively, improving the reliability and integrity of the data. Comprehensive correlation data and comprehensive test data are obtained through data expansion and simulation calculation. The existence of comprehensive correlation data and comprehensive test data helps to establish subsequent temperature-physical property models and temperature-parameter calibration models. At the same time, comprehensive correlation data and comprehensive test data provide a theoretical basis for subsequent data reference and calculation, ensuring the reliability of subsequent flow value calculation.

[0026] By setting the correction method, adjust the parameters and calculate the sensor calibration parameters. Correct the detected flow characteristic data through the correction method. There is a difference between the calculated physical property data and the detected physical property data. Taking the change of parameters in the sensor of the detected liquid's physical properties as a reference provides a basis for adjusting the parameters in the sensor of the subsequent detected liquid's flow characteristic data. Brief Description of the Drawings

[0027] Figure 1 It is a schematic diagram of the measurement method of the present invention;

[0028] Figure 2 It is a flowchart of the measurement method of the present invention;

[0029] Figure 3 It is a schematic diagram of the correction method of the present invention. Detailed Embodiment

[0030] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0031] As Figure 1 — Figure 3 shown, the present invention provides a technical solution: an intelligent water meter flow measurement method with temperature compensation. The measurement method includes:

[0032] Collect data: Collect the correlation data between the temperature of the liquid and the physical properties of the liquid, and collect the performance test data of the sensor at different temperatures;

[0033] Expand data: Expand the correlation data to obtain comprehensive correlation data, and perform simulation calculations on the performance test data to obtain comprehensive test data;

[0034] Establish a temperature-physical property model; Based on the comprehensive correlation data, use the first modeling method to establish a mathematical model between the temperature and the physical properties of the liquid;

[0035] Establish a temperature - parameter calibration model: Based on comprehensive test data, use the second modeling method to establish a mathematical model between temperature and sensor calibration parameters;

[0036] Real - time detection: Real - time detect the liquid data inside the pipeline to obtain real - time detection results. The real - time detection results include real - time temperature data and detected liquid characteristic data. The detected liquid characteristic data includes detected flow characteristic data and detected physical characteristic data;

[0037] Comparison: Input the real - time temperature data into the temperature - physical characteristic model to obtain calculated physical characteristic data. Compare the calculated physical characteristic data with the detected physical characteristic data to obtain comparison data;

[0038] Correction: Obtain adjustment parameters based on the comparison data. Input the real - time temperature data into the temperature - parameter calibration model to obtain calculated sensor calibration parameters. The adjustment parameters and the calculated sensor calibration parameters correct the detected flow characteristic data through a correction method to obtain calculated flow characteristic data;

[0039] Calculation: Calculate the flow value by combining the flow calculation formula with the calculated flow characteristic data.

[0040] Data expansion includes: Establish a first - order association between the temperature of the liquid and the physical characteristics of the liquid based on associated data. For the missing data points in the associated data, use data interpolation to fill them and supplement the data in the associated data to obtain comprehensive associated data.

[0041] The first modeling method includes: Extract features from comprehensive variable data to obtain first - order feature data between temperature and flow characteristics in the data. Use physical modeling methods, statistical analysis techniques, and the first - order feature data to establish a mathematical model between temperature and flow characteristics.

[0042] The second modeling method includes:

[0043] Extract features from comprehensive test data to extract second - order feature data between temperature and sensor parameters. Use statistical analysis techniques and the second - order feature data to establish a mathematical model between temperature and flow characteristics.

[0044] The method for simulating and calculating performance test data includes: Establish a simulation model of the data. Analyze the second - order association between temperature and performance test data in the performance test data through mathematical mining methods. Simulate the performance test data through the simulation model to obtain simulated performance test data. Combine the simulated performance test data and the performance test data to obtain comprehensive test data.

[0045] The data interpolation method includes: obtaining the correlation between the temperature of the liquid and the physical properties of the liquid based on the first correlation, inputting the specific temperature, and obtaining the specific data of the corresponding physical properties of the liquid based on the correlation. Based on the specific temperature and the specific data of the corresponding physical properties of the liquid, the correlation data is supplemented to achieve data interpolation.

[0046] The method for obtaining the adjusted parameter includes: the comparison data includes calculating the difference between the physical property data and the detected physical property data. The detected physical property data is regarded as the electrical signal data collected by the sensor multiplied by the sensor parameter. By adjusting the specific data of the sensor parameter, the difference between the two is controlled to be zero, and the specific data of the adjusted sensor parameter is the adjusted parameter.

[0047] The correction method includes: the detected flow characteristic data is regarded as obtained by combining the detected electrical signal and the flow characteristic parameter. Based on the first difference between the adjusted parameter and the calculated sensor calibration parameter, the first difference is divided by the calculated sensor calibration parameter to obtain the adjustment reference value. The flow characteristic parameter is adjusted through the adjustment reference value to obtain the adjusted flow characteristic parameter, and the calculated flow characteristic data is obtained by combining the detected electrical signal and the adjusted flow characteristic parameter.

[0048] The change in temperature will cause a change in the physical properties of the liquid. In this measurement method, there are a temperature sensor, a sensor for detecting the physical properties of the liquid, and a sensor for detecting the flow characteristic data of the liquid. The temperature sensor can detect the temperature data in real time, and the sensor for detecting the physical properties of the liquid detects the physical properties of the liquid in real time. Through the real-time temperature data and the temperature-physical property model, the calculated physical property data is obtained. The difference between the calculated physical property data and the detected physical property data is calculated, and by correcting the specific parameters of the sensor for detecting the physical properties of the liquid, the difference between the calculated physical property data and the detected physical property data is made zero, so as to obtain the adjusted parameter. Therefore, the adjusted parameter can provide a reference for the adjustment of the specific parameters in the sensor for detecting the flow characteristic data of the liquid. The specific parameters in the sensor for detecting the flow characteristic data of the liquid are adjusted through the correction method, so that the detected flow characteristic data obtains the calculated flow characteristic data through the adjustment of the parameters, and the calculated flow characteristic data can be used in the calculation of the flow value.

[0049] In power plants and steel mills, the temperature of cooling water and circulating water often changes, and the degree of temperature change is large, resulting in the inability to use high-precision sensors. Therefore, it is necessary to perform temperature compensation on the measurement method. The physical properties of liquids include the density and viscosity of liquids. The pipeline data at the installation position of the water meter are known data, including the internal dimension data of the pipeline. The flow characteristic data of liquids include data such as water flow velocity and pressure. Data preprocessing includes data cleaning, denoising, interpolation, etc., to eliminate outliers and noise in the data, improve the quality and reliability of the data. Data smoothing processing refers to smoothing the associated data to eliminate random fluctuations in the data. Smoothing processing is to process the original data through specific algorithms or technologies to reduce the fluctuations and noise in the data. There are existing technologies for smoothing processing. At the same time, the measurement method of this application can also be used when measuring the flow rate of other liquids. There is a correlation between the physical properties and flow characteristics of fluids. The density of a liquid is one of the important factors affecting the flow rate. Under the condition of a certain volumetric flow rate, the greater the density of the liquid, the greater the mass flow rate. Therefore, it is feasible to use the change of parameters in the sensor for detecting the physical properties of the liquid as a reference to provide a basis for adjusting the parameters in the sensor for detecting the flow characteristic data of the liquid subsequently.

[0050] Since the calculation of the flow rate value needs to be achieved by combining the flow characteristic data with the internal pipeline data, the flow characteristic data including the flow velocity and pressure of the liquid can be used for calculating the flow rate value. The temperature sensor detects the real-time temperature inside the pipeline to obtain real-time temperature data. The detected flow characteristic data by the sensor and the actual flow characteristic data deviate from each other due to the detection error caused by the temperature of the sensor. Therefore, it is necessary to eliminate the deviation to obtain the actual flow characteristic data of the liquid or make the obtained flow characteristic data close to the actual flow characteristic data of the liquid, and then complete the calculation of the liquid flow rate, which can reduce the overall deviation caused by temperature.

[0051] Clean, denoise, and normalize the collected associated data and performance test data to ensure the accuracy and consistency of the data. The associated data and performance test data can be sourced from accurate measurements in the laboratory, historical data records, and on-site real-time monitoring. Since the associated data and performance test data cannot fully cover all possible situations in practice, it is necessary to perform data expansion on the associated data and simulation calculations on the performance test data respectively to obtain comprehensive associated data and comprehensive test data.

[0052] Based on the real-time detection of the temperature sensor, real-time temperature data is obtained. The real-time temperature data is input into the temperature-physical property relationship to obtain calculated physical property data. The calculated physical property data and the detected physical property data are compared to obtain comparison data. By analyzing the comparison data and calculating the comparison result between the calculated physical property data and the detected physical property, the difference between the physical property detected by the sensor and the actual physical property can be preliminarily understood. Based on this difference, adjustment parameters are obtained, and the detected flow characteristic data is corrected using the adjustment parameters to obtain real-time flow characteristic data.

[0053] According to the real-time detected liquid temperature, using the previously established temperature-flow characteristic model, the theoretically physical property data at this temperature is calculated. Then, the real-time detected physical property data is compared with this theoretical curve to evaluate the flow measurement deviation caused by temperature changes. Using the temperature-parameter calibration model, the measurement parameters of the sensor, such as sensitivity and zero offset, are adjusted according to the comparison data to eliminate the influence of temperature on the sensor performance. The flow measurement deviation is applied to the real-time detected flow characteristic data to obtain flow data closer to the true value. The calculated flow characteristic data after parameter correction, such as the corrected flow velocity value, is combined with physical parameters such as the cross-sectional area of the pipeline and the fluid density, and through flow calculation formulas such as Bernoulli's equation (flow equals flow velocity multiplied by area and other calculation methods), the accurate flow value after temperature compensation is finally obtained. The flow calculation formula exists in the prior art.

[0054] The calculated physical property data and the detected physical property data are compared to obtain comparison data. The detected physical property data is changed by adjusting the internal operating parameters of the sensor, so that the specific value of the detected physical property data is the same as the specific value of the calculated physical property data, and then adjustment parameters are obtained. The internal parameters of the sensor corresponding to the detected flow characteristic data are corrected using the adjustment parameters, so that the originally calculated parameters of the detected flow characteristic data are changed, and calculated flow characteristic data is obtained.

[0055] The real-time temperature data is input into the mathematical model between the temperature and the physical property of the liquid to obtain calculated physical property data. The calculated physical property data and the detected physical property data are compared to obtain comparison data. The detected physical property data has a detection error due to temperature changes in the sensor itself. Therefore, when calculating the flow value, the detection error of the sensor itself needs to be reduced.

[0056] The physical modeling method includes the principles of fluid mechanics and the laws of thermodynamics. The statistical analysis techniques include regression analysis, neural networks, support vector machines, etc. There is prior art for the physical modeling method. The comprehensive associated data is utilized to establish a mathematical model between the liquid temperature and the physical properties of the liquid using the physical modeling method. Through this mathematical model, the association between the liquid temperature and the physical properties of the liquid can be obtained.

[0057] The comprehensive associated data is processed and analyzed to extract the first type of characteristic data between the temperature and the physical properties. The comprehensive test data is processed and analyzed to extract the second type of characteristic data between the temperature and the parameter calibration. The first type of characteristic data and the second type of characteristic data can provide effective support for the subsequent model establishment. The method for obtaining the associated data includes detecting the selected liquid at different temperatures to obtain the physical property data of the liquid at different temperatures. The associated data is expanded through data expansion to supplement the blank data and obtain the comprehensive associated data. Using the selected modeling method and the first type of characteristic data, a temperature - parameter calibration model is established. The application of data expansion and simulation calculation enables the method to obtain and utilize data more comprehensively, improving the reliability and integrity of the data. Through data expansion and simulation calculation, the comprehensive associated data and the comprehensive test data are obtained. The existence of the comprehensive associated data and the comprehensive test data can assist in the establishment of the subsequent temperature - physical property model and the temperature - parameter calibration model. At the same time, the comprehensive associated data and the comprehensive test data provide a theoretical basis for subsequent data reference and calculation, ensuring the reliability of the subsequent flow value calculation.

[0058] The method for obtaining the performance test data includes performing performance tests on the sensor at different temperatures to obtain the performance test data of the sensor parameters such as sensitivity and zero - point offset changing with temperature. The performance test data is processed and analyzed. Based on the existing data between the temperature and the performance test data, simulation calculations are performed on this data to obtain the comprehensive test data. Feature extraction is performed on the comprehensive test data, and using the statistical analysis techniques and the second type of characteristic data, a temperature - parameter calibration model is established. The statistical analysis technique is an important means of analyzing data and revealing data relationships. The statistical analysis techniques include correlation analysis. Correlation analysis measures the degree and direction of the association between variables by calculating correlation coefficients such as the Pearson correlation coefficient and the Spearman rank correlation coefficient, and is frequently used in exploratory analysis, which helps to discover potential relationships between variables. There is prior art for the statistical analysis technique.

[0059] The method for obtaining the adjusted parameter is based on the comparison data. The comparison data calculates the difference between the calculated physical property data and the detected physical property data. Since the detected physical property data is obtained by multiplying the point - signal data collected by the sensor by the parameter, by adjusting the specific parameter, the difference between the two is made zero. The adjusted parameter is the adjusted parameter.

[0060] Through the setting of the correction method, adjust the parameters and calculate the sensor calibration parameters. Correct the detected flow characteristic data through the correction method. There is a difference between the calculated physical characteristic data and the detected physical characteristic data. Taking the change of the parameters in the sensor for detecting the physical characteristics of the detected liquid as a reference, it provides a basis for the adjustment of the parameters in the sensor for the subsequent detected flow characteristic data of the liquid. There are positive and negative situations for the first difference in the correction method. Determine the magnitude relationship between the adjustment parameters and the calculated sensor calibration parameters through positive and negative judgment. Based on the positive and negative relationship, it can be judged whether the adjusted flow characteristic parameters are larger or smaller than the original flow characteristic parameters. Adjust the flow characteristic parameters by adjusting the reference value, including adjusting the flow characteristic parameters to be equal to the flow characteristic parameters multiplied by (1 + K * the first difference) or other calculation methods, where K is a coefficient, and K is between 0.5 and 1. The specific magnitude of K can be adjusted by comparing the subsequent actual flow value with the flow value calculated through the calculated flow characteristic data, so as to realize the adjustment of the specific parameters in the sensor for the detected flow characteristic data of the liquid. Through the design of the temperature - physical characteristic model and the temperature - parameter calibration model, obtain the corresponding calculated physical characteristic data from the temperature - physical characteristic model through the real - time temperature data. Input the real - time temperature data into the temperature - parameter calibration model to obtain the calculated calibration parameter data. Compare the calculated physical characteristic data with the detected physical characteristic data to obtain the comparison data. Substitute the comparison data into the correction method to further realize the calculation of the flow value. This method can effectively reduce the influence brought by temperature changes to the sensor and the water itself, and reduce the difference between the calculated flow value and the actual flow value.

[0061] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended embodiments and their equivalents.

Claims

1. An intelligent water meter flow measurement method with temperature compensation, characterized in that, The measurement method includes: Collecting data: Collecting the correlation data between the temperature of the liquid and the physical properties of the liquid, and collecting the performance test data of the sensor at different temperatures; Expanding data: Expanding the correlation data to obtain comprehensive correlation data, and performing simulation calculations on the performance test data to obtain comprehensive test data; Establishing a temperature - physical property model: Establishing a mathematical model between the temperature and the physical properties of the liquid based on the comprehensive correlation data using the first modeling method; Establishing a temperature - parameter calibration model: Establishing a mathematical model between the temperature and the calibration parameters of the sensor based on the comprehensive test data using the second modeling method; Real - time detection: Real - time detecting the liquid data inside the pipeline to obtain real - time detection results. The real - time detection results include real - time temperature data and detected liquid property data. The detected liquid property data includes detected flow property data and detected physical property data; Comparison: Inputting the real - time temperature data into the temperature - physical property model to obtain calculated physical property data, and comparing the calculated physical property data with the detected physical property data to obtain comparison data; Correction: Obtaining adjustment parameters based on the comparison data, inputting the real - time temperature data into the temperature - parameter calibration model to obtain calculated sensor calibration parameters, and correcting the detected flow property data by using the adjustment parameters and the calculated sensor calibration parameters through the correction method to obtain calculated flow property data; Calculation: Calculating the flow value by combining the calculated flow property data with the flow calculation formula; The data expansion includes: Establishing a first correlation between the temperature of the liquid and the physical properties of the liquid based on the correlation data. For the missing data points in the correlation data, data interpolation is used to fill them, supplementing the data in the correlation data to obtain comprehensive correlation data; The first modeling method includes: Extracting features from the comprehensive change data to obtain the first feature data between the temperature and the flow property in the data, and establishing a mathematical model between the temperature and the flow property by using the physical modeling method, statistical analysis techniques and the first feature data; The second modeling method includes: Extracting features from the comprehensive test data to extract the second feature data between the temperature and the sensor parameters, and establishing a mathematical model between the temperature and the flow property by using statistical analysis techniques and the second feature data; The correction method includes: Considering the detected flow property data as obtained by combining the detected electrical signal and the flow property parameters. Based on the first difference between the adjustment parameter and the calculated sensor calibration parameter, dividing the first difference by the calculated sensor calibration parameter to obtain an adjustment reference value, adjusting the flow property parameters by the adjustment reference value to obtain adjusted flow property parameters, and combining the detected electrical signal and the adjusted flow property parameters to calculate the calculated flow property data.

2. A flow measurement method for an intelligent water meter with temperature compensation according to claim 1, characterized in that: The method for performing simulation calculations on the performance test data includes: Establishing a simulation model of the data, analyzing the second correlation between the temperature and the performance test data in the performance test data through mathematical mining methods, simulating the performance test data through the simulation model to obtain simulated performance test data, and combining the simulated performance test data and the performance test data to obtain comprehensive test data.

3. A flow measurement method for an intelligent water meter with temperature compensation according to claim 1, characterized in that: The method of data interpolation includes: obtaining the correlation between the temperature of the liquid and the physical properties of the liquid based on a first association, inputting a specific temperature to obtain specific data of the corresponding physical properties of the liquid based on the correlation, and supplementing the associated data based on the specific temperature and the specific data of the corresponding physical properties of the liquid to achieve data interpolation.

4. A flow measurement method for an intelligent water meter with temperature compensation according to claim 1, characterized in that: The method for obtaining the adjusted parameters includes: the comparison data includes calculating the difference between the physical property data and the detected physical property data. The detected physical property data is obtained by multiplying the electrical signal data collected by the sensor by the sensor parameters. By adjusting the specific data of the sensor parameters, the difference between the two is controlled to be zero, and the specific data of the adjusted sensor parameters is the adjusted parameter.

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