Intelligent water meter flow measurement method with temperature compensation

Abstract

The invention discloses an intelligent water meter flow measurement method with a temperature compensation function, and relates to the technical field of water meter measurement. The measurement method comprises the following steps: collecting data: collecting associated data between the temperature of liquid and the physical property of the liquid, and collecting performance test data of a sensor at different temperatures; and data expansion: performing data expansion on the associated data to obtain comprehensive associated data. The corresponding calculation physical characteristic data are obtained from the temperature-physical characteristic model through the real-time temperature data, the real-time temperature data are input into the temperature-parameter calibration model, the calculation calibration parameter data are obtained, the calculation physical characteristic data and the detection physical characteristic data are compared, the comparison data are obtained, and the calibration accuracy is improved. Comparative data are substituted into the correction method to calculate the flow value, the method can effectively reduce the influence of temperature change on the sensor and water, and the difference value between the flow value obtained through calculation and the actual flow value is reduced.

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: 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; Extended data: Expand the associated data to obtain comprehensive associated data, and simulate the performance test data to obtain comprehensive test data; Establish a temperature-physical property model; establish a mathematical model between temperature and liquid physical properties using the No. 1 modeling method based on comprehensive correlation data; Establishing temperature-parameter calibration model: Based on comprehensive test data, a mathematical model between temperature and sensor calibration parameters is established using the second modeling method; Real-time detection: Real-time detection of 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, and the detected liquid characteristic data includes detected flow characteristic data and detected physical characteristic data; Comparison: Real-time temperature data is input into the temperature-physical property model to obtain calculated physical property data, and the calculated physical property data is compared with the detected physical property data to obtain comparative data; Correction: Based on the comparison data, the adjustment parameters are obtained, and the real-time temperature data is input into the temperature-parameter calibration model to obtain the calculated sensor calibration parameters. The adjustment parameters and the calculated sensor calibration parameters are used to correct the detected flow characteristic data through the correction method to obtain the calculated flow characteristic data; Calculation: Calculate the flow characteristic data to obtain the flow value by combining the flow calculation formula.

[0007] Furthermore, the data expansion includes: establishing a first correlation between the temperature of the liquid and the physical properties of the liquid based on the associated data, and using data interpolation to fill in the missing data points in the associated data, supplementing the data in the associated data, and obtaining comprehensive associated data.

[0008] Furthermore, the No. 1 modeling method includes: extracting features from the comprehensive change data to obtain No. 1 feature data between the temperature and flow characteristics in the data, and using physical modeling methods, statistical analysis techniques and No. 1 feature data to establish a mathematical model between the temperature and flow characteristics.

[0009] Furthermore, the second modeling method includes: Feature extraction is performed on the comprehensive test data to extract the second feature data between temperature and sensor parameters, and a mathematical model between temperature and flow characteristics is established using statistical analysis technology and the second feature data.

[0010] Furthermore, the method for simulating and calculating performance test data includes: establishing a simulation model of data, analyzing the secondary correlation between temperature and performance test data in the performance test data through mathematical mining methods, simulating the performance test data through the simulation model, obtaining simulated performance test data, and combining the simulated performance test data with the performance test data to obtain comprehensive test data.

[0011] Furthermore, the data interpolation method includes: obtaining the correlation between the temperature of the liquid and the physical properties of the liquid based on a number one correlation, inputting a specific temperature to obtain specific data of the corresponding liquid physical properties based on the correlation, and supplementing the associated data based on the specific temperature and the specific data of the corresponding liquid physical properties to achieve data interpolation.

[0012] Furthermore, the method for obtaining the adjustment parameters includes: comparing data including calculating the difference between physical characteristic data and detecting physical characteristic data, the detected physical characteristic data is regarded as the electrical signal data collected by the sensor multiplied by the sensor parameters, and by adjusting the specific data of the sensor parameters, the difference between the two is controlled to become zero, and the specific data of the adjusted sensor parameters is the adjustment parameter.

[0013] Furthermore, the correction method includes: the detected flow characteristic data is regarded as obtained by combining the detected electrical signal and the flow characteristic parameters, based on the No. 1 difference between the adjustment parameter and the calculated sensor calibration parameter, the No. 1 difference is divided by the calculated sensor calibration parameter to obtain the adjustment reference value, the flow characteristic parameters are adjusted by the adjustment reference value to obtain the adjusted flow characteristic parameters, and the detected electrical signal and the adjusted flow characteristic parameters are combined to obtain the calculated flow characteristic data.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The flow measurement method of the intelligent water meter with temperature compensation is designed through a temperature-physical characteristic model and a temperature-parameter calibration model. The corresponding calculated physical characteristic data are obtained from the temperature-physical characteristic model through real-time temperature data. The real-time temperature data is input into the temperature-parameter calibration model to obtain calculated calibration parameter data. The calculated physical characteristic data and the detected physical characteristic data are compared to obtain comparison data. The comparison data is substituted into a correction method to realize the calculation of the flow value. The method can effectively reduce the impact of temperature changes on the sensor and the water itself, and reduce the difference between the calculated flow value and the actual flow value.

[0015] At the same time, the application of data expansion and simulation calculation enables this method to acquire and utilize data more comprehensively, improve the reliability and integrity of data, and obtain comprehensive correlation data and comprehensive test data through data expansion and simulation calculation. The existence of comprehensive correlation data and comprehensive test data can provide assistance for the subsequent establishment of temperature-physical property model and temperature-parameter calibration model. 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.

[0016] By setting the correction method, adjusting the parameters and calculating the sensor calibration parameters, the detected flow characteristic data is corrected through the correction method. There is a difference between the calculated physical characteristic data and the detected physical characteristic data. By taking the change of parameters in the sensor for detecting the physical characteristics of the liquid as a reference, a basis is provided for the subsequent adjustment of the parameters in the sensor for detecting the flow characteristic data of the liquid. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a schematic diagram of the measurement method of the present invention; Figure 2 is a flow chart of the measurement method of the present invention; Figure 3 Schematic diagram of the correction method of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] like Figure 1 — Figure 3 As shown, the present invention provides a technical solution: a flow measurement method of an intelligent water meter with temperature compensation, the measurement method comprising: 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; Extended data: Expand the associated data to obtain comprehensive associated data, and simulate the performance test data to obtain comprehensive test data; Establish a temperature-physical property model; establish a mathematical model between temperature and liquid physical properties using the No. 1 modeling method based on comprehensive correlation data; Establishing temperature-parameter calibration model: Based on comprehensive test data, a mathematical model between temperature and sensor calibration parameters is established using the second modeling method; Real-time detection: Real-time detection of liquid data inside the pipeline to obtain real-time detection results. 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. Comparison: Real-time temperature data is input into the temperature-physical property model to obtain calculated physical property data, and the calculated physical property data is compared with the detected physical property data to obtain comparative data; Correction: Based on the comparison data, the adjustment parameters are obtained, and the real-time temperature data is input into the temperature-parameter calibration model to obtain the calculated sensor calibration parameters. The adjustment parameters and the calculated sensor calibration parameters are used to correct the detected flow characteristic data through the correction method to obtain the calculated flow characteristic data; Calculation: Calculate the flow characteristic data to obtain the flow value by combining the flow calculation formula.

[0020] Data expansion includes: establishing a number one association between the temperature of the liquid and the physical properties of the liquid based on the associated data, using data interpolation to fill in the missing data points in the associated data, supplementing the data in the associated data, and obtaining comprehensive associated data.

[0021] The No. 1 modeling method includes: extracting features from the comprehensive change data, obtaining the No. 1 characteristic data between the temperature and flow characteristics in the data, and establishing a mathematical model between the temperature and flow characteristics using physical modeling methods, statistical analysis techniques and the No. 1 characteristic data.

[0022] Modeling method number two includes: Feature extraction is performed on the comprehensive test data to extract the second feature data between temperature and sensor parameters, and a mathematical model between temperature and flow characteristics is established using statistical analysis technology and the second feature data.

[0023] The method for simulating and calculating performance test data includes: establishing a simulation model of data, analyzing the secondary correlation between temperature and performance test data in the performance test data by mathematical mining methods, simulating the performance test data by the simulation model, obtaining simulated performance test data, and combining the simulated performance test data with the performance test data to obtain comprehensive test data.

[0024] 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 number one correlation, inputting a specific temperature to obtain specific data of the corresponding liquid physical properties based on the correlation, and supplementing the associated data based on the specific temperature and the specific data of the corresponding liquid physical properties to achieve data interpolation.

[0025] The method for obtaining the adjustment parameters includes: comparing data including calculating the difference between the physical characteristic data and the detected physical characteristic data, the detected physical characteristic data is regarded as the electrical signal data collected by the sensor multiplied by the sensor parameters, and by adjusting the specific data of the sensor parameters, the difference between the two is controlled to become zero, and the specific data of the sensor parameters after adjustment is the adjustment parameter.

[0026] The correction method includes: the detected flow characteristic data is regarded as a combination of the detected electrical signal and the flow characteristic parameters, based on the No. 1 difference between the adjustment parameter and the calculated sensor calibration parameter, the No. 1 difference is divided by the calculated sensor calibration parameter to obtain the adjustment reference value, the flow characteristic parameters are adjusted by the adjustment reference value to obtain the adjusted flow characteristic parameters, and the detected electrical signal and the adjusted flow characteristic parameters are combined to obtain the calculated flow characteristic data.

[0027] Temperature changes will cause changes 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 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, and the difference between the calculated physical property data and the detected physical property data is calculated. By correcting the specific parameters of the sensor for detecting the physical properties of the liquid, it is worthwhile to make the difference between the calculated physical property data and the detected physical property data zero, thereby obtaining the adjustment parameters. Therefore, the adjustment parameters can provide a reference for adjusting 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 can obtain the calculated flow characteristic data through the adjustment of the parameters, and the calculated flow characteristic data can be used for the calculation of the flow value.

[0028] The water temperature of cooling water and circulating water in power plants and steel mills changes frequently and the degree of change is large, which makes high-precision sensors unusable. Therefore, it is necessary to perform temperature compensation on the measurement method. The physical properties of the liquid include the density and viscosity of the liquid. The pipeline data at the water meter installation location is known data. The pipeline data includes the size data inside the pipeline. The flow characteristic data of the liquid includes water flow rate and pressure data. Data preprocessing includes data cleaning, denoising, interpolation, etc. to eliminate outliers and noise in the data and improve the quality and reliability of the data. Data smoothing refers to smoothing the associated data to eliminate random fluctuations in the data. Smoothing is to process the original data through specific algorithms or technologies to reduce fluctuations and noise in the data. There is existing technology for smoothing. At the same time, the measurement method of this application can also be used when measuring the flow of other liquids. There is a correlation between the physical properties of the fluid and the flow characteristics. The density of the liquid is one of the important factors affecting the flow. When the volume flow rate is constant, the greater the density of the liquid, the greater the mass flow rate. Therefore, it is feasible to use the changes in parameters in the sensor that detects the physical properties of the liquid as a reference to provide a basis for adjusting the parameters in the sensor that subsequently detects the flow characteristic data of the liquid.

[0029] Since the calculation of flow value needs to be realized by combining flow characteristic data with internal data of pipeline, flow characteristic data includes flow velocity pressure of liquid which can be used to calculate flow value. Temperature sensor detects the temperature inside real-time pipeline to obtain real-time temperature data. Detected flow characteristic data detected by sensor and actual flow characteristic data deviate from each other due to detection error caused by temperature of sensor. Therefore, it is necessary to eliminate the deviation to obtain actual flow characteristic data of liquid or make the obtained flow characteristic data close to the actual flow characteristic data of liquid. Then, the calculation of liquid flow is completed to reduce the overall deviation caused by temperature.

[0030] The collected correlation data and performance test data are cleaned, denoised and normalized to ensure the accuracy and consistency of the data. The correlation data and performance test data can be derived from precise laboratory measurements, historical data records and real-time on-site monitoring. Since the correlation data and performance test data cannot fully cover the possible situations in reality, it is necessary to expand the correlation data and simulate the performance test data to obtain comprehensive correlation data and comprehensive test data.

[0031] Based on the real-time detection of the temperature sensor, real-time temperature data is obtained, and the real-time temperature data is input into the temperature-physical characteristics to obtain calculated physical characteristic data, and the calculated physical characteristic data is compared with the detected physical characteristic data to obtain comparative data, and by analyzing the comparative data and comparing the calculated physical characteristic data with the detected physical characteristic data, the difference between the physical characteristics detected by the sensor and the actual physical characteristics can be preliminarily understood, and based on the difference, adjustment parameters are obtained, and the detected flow characteristic data is corrected by adjusting the parameters to obtain real-time flow characteristic data.

[0032] According to the real-time detected liquid temperature, the previously established temperature-flow characteristic model is used to calculate the theoretical physical characteristic data at this temperature. Then, the real-time detected physical characteristic data is compared with the theoretical curve to evaluate the flow measurement deviation caused by temperature changes. The temperature-parameter calibration model is used to adjust the measurement parameters of the sensor, such as sensitivity, zero offset, etc., 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 pipe cross-sectional area and fluid density, and through flow calculation formulas such as the Bernoulli equation, flow equals flow velocity multiplied by area and other calculation methods, an accurate flow value after temperature compensation is finally obtained. The flow calculation formula exists in the prior art.

[0033] The calculated physical characteristic data is compared with the detected physical characteristic data to obtain comparison data. The detected physical characteristic data is changed by adjusting the operating parameters inside the sensor, so that the specific values ​​of the detected physical characteristic data are consistent with the specific values ​​of the calculated physical characteristic data, thereby obtaining adjustment parameters. The internal parameters of the sensor corresponding to the detected flow characteristic data are corrected by adjusting the parameters, thereby changing the parameters of the detected flow characteristic data originally obtained by calculation, and obtaining calculated flow characteristic data.

[0034] The real-time temperature data is input into the mathematical model between temperature and the physical properties of the liquid to obtain calculated physical property data. The calculated physical property data is compared with the detected physical property data to obtain comparative data. The detected physical property data may cause detection errors in the sensor itself due to temperature changes. Therefore, when calculating the flow value, the detection error of the sensor itself needs to be reduced.

[0035] Physical modeling methods include the principles of fluid mechanics and the laws of thermodynamics. Statistical analysis techniques include regression analysis, neural networks, support vector machines, etc. There are existing technologies for physical modeling methods. Comprehensively correlated data use physical modeling methods to establish a mathematical model between liquid temperature and the physical properties of the liquid. Through this mathematical model, the correlation between liquid temperature and the physical properties of the liquid can be obtained.

[0036] The comprehensive correlation data is processed and analyzed to extract the No. 1 characteristic data between temperature and physical characteristics. The comprehensive test data is processed and analyzed to extract the No. 2 characteristic data between temperature and parameter calibration. The No. 1 characteristic data and the No. 2 characteristic data can provide effective support for the establishment of subsequent models. The method for obtaining the correlation data includes testing the selected liquid at different temperatures to obtain the physical property data of the liquid at different temperatures. The correlation data is expanded by data expansion to supplement the blank data to obtain comprehensive correlation data. The selected modeling method and the No. 1 characteristic data are used to establish a temperature-parameter calibration model. The application of data expansion and simulation calculation enables the method to obtain and utilize data more comprehensively and improve 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 can help the establishment of subsequent temperature-physical property models and temperature-parameter calibration models. At the same time, the 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.

[0037] The method for obtaining performance test data includes performing performance tests on sensors at different temperatures, obtaining performance test data of sensor parameters such as sensitivity and zero offset that change with temperature, processing and analyzing the performance test data, simulating and calculating the data based on the existing data between temperature and performance test data, obtaining comprehensive test data, extracting features from the comprehensive test data, and establishing a temperature-parameter calibration model using statistical analysis techniques and feature data No. 2. Statistical analysis techniques are important means of analyzing data and revealing data relationships. Statistical analysis techniques include correlation analysis, which measures the degree and direction of association between variables by calculating correlation coefficients such as the Pearson correlation coefficient and the Spearman rank correlation coefficient. It is frequently used in exploratory analysis and helps to discover potential relationships between variables. There are existing technologies for statistical analysis techniques.

[0038] The method for obtaining the adjustment parameters is based on comparison data. The comparison data includes the difference between the calculated physical characteristic data and the detected physical characteristic data. Since the detected physical characteristic data is obtained based on the point signal data collected by the sensor multiplied by the parameters, the specific parameters are adjusted so that the difference between the two becomes zero. The adjusted parameters are the adjustment parameters.

[0039] By setting the correction method, adjusting parameters and calculating sensor calibration parameters, the detected flow characteristic data is corrected by the correction method. There is a difference between the calculated physical characteristic data and the detected physical characteristic data. By taking the change of parameters in the sensor for detecting the physical characteristics of the liquid as a reference, a basis is provided for adjusting the parameters in the sensor for subsequent detection of the flow characteristic data of the liquid. There are positive and negative situations in the correction method. The size relationship between the adjustment parameters and the calculated sensor calibration parameters is determined by the positive and negative. The size of the adjusted flow characteristic parameters relative to the original flow characteristic parameters can be determined based on the positive and negative relationship. The flow characteristic parameters are adjusted by adjusting the reference value, including adjusting the flow characteristic parameters equal to the flow characteristic parameters multiplied by (1+K No. 1 difference) or other calculation methods, where K is a coefficient, and K is between 0.5 and 1, the specific size of K can be compared with the subsequent actual flow value and the flow value obtained by calculating the flow characteristic data, so as to adjust K, thereby adjusting the specific parameters of the sensor for detecting the flow characteristic data of the liquid, and designing the temperature-physical characteristic model and the temperature-parameter calibration model. The corresponding calculated physical characteristic data is obtained from the temperature-physical characteristic model through real-time temperature data, and the real-time temperature data is input into the temperature-parameter calibration model to obtain the calculated calibration parameter data. The calculated physical characteristic data is compared with the detected physical characteristic data to obtain the comparison data, and the comparison data is substituted into the correction method to realize the calculation of the flow value. This method can effectively reduce the impact of temperature changes on the sensor and the water itself, and reduce the difference between the calculated flow value and the actual flow value.

[0040] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is limited by the attached embodiments and their equivalents.

Claims

1. A flow measurement method for an intelligent water meter with temperature compensation, characterized in that: The measuring method comprises: 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; Extended data: Expand the associated data to obtain comprehensive associated data, and simulate the performance test data to obtain comprehensive test data; Establish a temperature-physical property model; establish a mathematical model between temperature and liquid physical properties using the No. 1 modeling method based on comprehensive correlation data; Establishing temperature-parameter calibration model: Based on comprehensive test data, a mathematical model between temperature and sensor calibration parameters is established using the second modeling method; Real-time detection: Real-time detection of 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, and the detected liquid characteristic data includes detected flow characteristic data and detected physical characteristic data; Comparison: Real-time temperature data is input into the temperature-physical property model to obtain calculated physical property data, and the calculated physical property data is compared with the detected physical property data to obtain comparative data; Correction: Based on the comparison data, the adjustment parameters are obtained, and the real-time temperature data is input into the temperature-parameter calibration model to obtain the calculated sensor calibration parameters. The adjustment parameters and the calculated sensor calibration parameters are used to correct the detected flow characteristic data through the correction method to obtain the calculated flow characteristic data; Calculation: Calculate the flow characteristic data to obtain the flow value by combining the flow calculation formula.

2. The method for measuring flow rate of an intelligent water meter with temperature compensation according to claim 1, characterized in that: The data expansion includes: establishing a first correlation between the temperature of the liquid and the physical properties of the liquid based on the associated data, filling in missing data points in the associated data by data interpolation, supplementing the data in the associated data, and obtaining comprehensive associated data.

3. The method for measuring flow rate of an intelligent water meter with temperature compensation according to claim 1, characterized in that: The No. 1 modeling method includes: extracting features from the comprehensive change data to obtain No. 1 feature data between the temperature and flow characteristics in the data, and using physical modeling methods, statistical analysis techniques and No. 1 feature data to establish a mathematical model between the temperature and flow characteristics.

4. The method for measuring flow rate of an intelligent water meter with temperature compensation according to claim 1, characterized in that: The second modeling method comprises: Feature extraction is performed on the comprehensive test data to extract the second characteristic data between temperature and sensor parameters, and a mathematical model between temperature and flow characteristics is established using statistical analysis technology and the second characteristic data.

5. The method for measuring flow rate of an intelligent water meter with temperature compensation according to claim 1, characterized in that: The method for simulating and calculating performance test data includes: establishing a simulation model of data, analyzing the secondary correlation between temperature and performance test data in the performance test data by mathematical mining methods, simulating the performance test data by the simulation model, obtaining simulated performance test data, and combining the simulated performance test data with the performance test data to obtain comprehensive test data.

6. The method for measuring flow rate of an intelligent water meter with temperature compensation according to claim 2, characterized in that: The data interpolation method includes: obtaining the correlation between the temperature of the liquid and the physical properties of the liquid based on a number one correlation, inputting a specific temperature to obtain specific data of the corresponding liquid physical properties based on the correlation, and supplementing the associated data based on the specific temperature and the specific data of the corresponding liquid physical properties to achieve data interpolation.

7. The method for measuring flow rate of an intelligent water meter with temperature compensation according to claim 1, characterized in that: The method for obtaining the adjustment parameters includes: comparing data including calculating the difference between the physical characteristic data and the detected physical characteristic data, the detected physical characteristic data is regarded as the electrical signal data collected by the sensor multiplied by the sensor parameters, and by adjusting the specific data of the sensor parameters, the difference between the two is controlled to become zero, and the specific data of the sensor parameters after adjustment is the adjustment parameter.

8. The method for measuring flow rate of an intelligent water meter with temperature compensation according to claim 1, characterized in that: The correction method includes: the detected flow characteristic data is regarded as a combination of the detected electrical signal and the flow characteristic parameter, based on the No. 1 difference between the adjustment parameter and the calculated sensor calibration parameter, the No. 1 difference is divided by the calculated sensor calibration parameter to obtain the adjustment reference value, the flow characteristic parameter is adjusted by the adjustment reference value to obtain the adjusted flow characteristic parameter, and the detected electrical signal and the adjusted flow characteristic parameter are combined to obtain the calculated flow characteristic data.

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