High-precision electric calibration method and system for wedge flowmeter

Abstract

The invention discloses a high-precision electrical calibration method and system for a wedge flowmeter. The method comprises the following steps: step 1, data acquisition and preprocessing; step 2, dynamic working condition identification; 3, updating the self-adaptive model; 4, multi-source data fusion calibration is carried out; and 5, performing closed-loop verification and compensation. According to the invention, multiple types of signals are collected in real time through the multi-dimensional sensor array and de-noised to construct a data set, so that comprehensive and accurate data collection is realized, and a good basis is provided for calibration; carrying out dynamic working condition identification by utilizing a support vector machine, triggering model updating, and endowing calibration with dynamic self-adaptive capability; model parameters are updated by adopting a recursive least square method, compensation items are established, and the characterization capability of the model is enhanced; a calibration coefficient is generated by means of a data fusion algorithm, and the calibration precision is improved; through verification and compensation of a digital twinborn model, traditional defects are effectively overcome, model transplantation errors are reduced, the measurement precision of the wedge flowmeter is improved under extreme working conditions, and production stability and product quality are guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of flow calibration, and in particular to a high-precision electrical calibration method and system for a wedge-type flowmeter. Background Art

[0002] As a key flow measurement instrument in industrial process control, the measurement accuracy of wedge-type flowmeters directly impacts production process stability and product quality. As modern industry continues to demand higher levels of measurement accuracy, traditional calibration techniques are no longer able to meet the high-precision measurement needs under complex operating conditions.

[0003] Currently, the calibration of wedge-type flowmeters relies primarily on static calibration in a laboratory setting, using a fixed mathematical model to describe the relationship between flow rate and differential pressure. This method can achieve basic measurement functions under ideal operating conditions, but in real industrial scenarios, dynamic changes in fluid parameters such as temperature, pressure, and viscosity can significantly affect measurement accuracy.

[0004] The existing technology has three major defects: first, the traditional calibration model lacks dynamic adaptive capabilities and cannot respond to changes in operating parameters in real time; second, the single sensor data source limits the model's ability to characterize complex flow fields; third, there are environmental differences between the calibration process and the actual measurement process, which leads to model transplantation errors. These problems are particularly prominent under extreme working conditions such as high temperature, high pressure, and multiphase flow. Therefore, a high-precision electrical calibration method and system for wedge flowmeters are proposed. Summary of the Invention

[0005] In view of the deficiencies in the prior art, the present invention provides a high-precision electrical calibration method and system for a wedge-type flowmeter to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a high-precision electrical calibration method for a wedge-type flowmeter, comprising the following steps: Step 1: Data collection and preprocessing: The multi-dimensional sensor array embedded in the pipeline collects fluid temperature, pressure, density and wedge vibration signals in real time, and uses the Kalman filter algorithm to denoise the raw data to construct a multi-dimensional data set containing time domain and frequency domain features. Step 2: Dynamic working condition identification: A working condition classification model is established based on a support vector machine. The processed data is input into the model for working condition identification. When significant changes in key working condition parameters are detected, the calibration model update mechanism is triggered. Step 3: Adaptive model update: The recursive least squares method is used to update the calibration model parameters online, and an improved flow metering model including temperature compensation terms, pressure correction terms, and fluid compressibility compensation terms is established. The model update cycle is dynamically adjusted according to the stability of the working conditions. Step 4: Multi-source data fusion calibration: The wedge pressure differential signal and auxiliary sensor data are fused using a data fusion algorithm to construct a weighted fusion strategy. The confidence weight of each sensor data is determined through an optimization algorithm to generate the final calibration coefficient. Step 5: Closed-loop verification and compensation; The calibration results are input into the digital twin model for simulation verification. When the simulation error exceeds the set threshold, the error compensation mechanism is activated. At the same time, the calibration process data and compensation results are stored in a distributed time series database, forming a closed-loop control system for calibration, verification, and compensation. In the multi-dimensional sensor array, thin-film platinum resistance temperature sensors reflect temperature by measuring resistance changes, piezoresistive pressure sensors measure pressure using the piezoresistive effect, vibrating string densitometers measure density based on the string vibration frequency, and triaxial accelerometers measure wedge vibration signals. The Kalman filter algorithm uses state equations and observation equations to iteratively update and remove noise from the raw data. Data acquisition and preprocessing obtain rich information through a multi-dimensional sensor array, and Kalman filtering effectively removes noise, providing high-quality data for subsequent processing. Dynamic working condition recognition can adapt to changes in a timely manner, triggering calibration model updates to ensure accuracy. Adaptive model updates are dynamically adjusted according to working conditions, and compensation items comprehensively consider multiple factors. Multi-source data fusion calibration improves data utilization and generates accurate calibration coefficients. Closed-loop verification and compensation ensure that the calibration results are reliable, forming a complete closed-loop control system and improving the overall performance and stability of the flow meter.

[0007] Preferably, the multidimensional sensor array comprises: Thin film platinum resistance temperature sensor; Piezoresistive pressure sensor; Vibrating string density meter and triaxial accelerometer; Thin-film platinum resistance temperature sensors are used to measure fluid temperature with high precision, piezoresistive pressure sensors monitor fluid pressure in real time, vibrating string densitometers measure fluid density by changes in vibration frequency, and triaxial accelerometers capture wedge vibration signals. These sensors work together to ensure comprehensive and accurate data. The Kalman filter and support vector machine algorithms are used to effectively remove noise and identify changes in operating conditions, ensuring the dynamic adaptation of the calibration model. The adaptive model update and multi-source data fusion calibration technology further improve the measurement accuracy. The closed-loop verification and compensation mechanism ensures the reliability of the calibration results. This method not only improves the measurement accuracy of the flow meter, but also enhances the adaptability and robustness of the system, providing an efficient and accurate solution for the industrial measurement field.

[0008] Preferably, the working condition classification model is trained using: The kernel function selects radial basis function; Set specific penalty factors and kernel parameters; Contains sample data of typical working conditions; First, we collected sample data covering a variety of typical operating conditions, including but not limited to data at different temperatures, pressures, fluid densities, and wedge vibration states. We selected the radial basis function as the kernel function and determined the optimal penalty factor and kernel parameters through cross-validation to ensure that the model can accurately classify under various operating conditions. At the same time, we used batch gradient descent to optimize the model parameters to improve training efficiency and classification accuracy. By selecting the radial basis function as the kernel function and combining it with the setting of specific penalty factors and kernel parameters, the model can flexibly adapt to various complex working conditions and improve classification accuracy. The training set contains a large number of typical working condition sample data, which ensures the wide applicability and robustness of the model. This method not only improves the accuracy of working condition identification, but also provides a solid foundation for subsequent adaptive model updates and multi-source data fusion calibration, thereby realizing high-precision electrical calibration of the wedge flowmeter and ensuring the stability and reliability of flow measurement.

[0009] Preferably, the improved flow metering model realizes dynamic compensation in the following manner: Temperature compensation item: adjusts model parameters according to temperature changes; Pressure correction item: real-time correction of pressure fluctuations; Fluid compressibility compensation: considers the effect of fluid density changes on measurement; A temperature sensor feedback mechanism is embedded in the model to monitor fluid temperature in real time and dynamically adjust model parameters based on a preset temperature-parameter mapping table. The pressure correction term uses a high-precision pressure sensor to capture pressure fluctuations in real time, smoothing the data using a filtering algorithm to make real-time corrections to the pressure-related parameters in the model. The fluid compressibility compensation term combines densitometer data with a density change prediction model to predict the impact of fluid compressibility and compensate for the measurement results. The temperature compensation term ensures that the model parameters can be adaptively adjusted under different temperature environments, avoiding measurement errors caused by temperature changes; the pressure correction term effectively addresses the interference of fluid pressure fluctuations on measurement and realizes real-time correction of pressure fluctuations; the fluid compressibility compensation term fully considers the impact of fluid density changes on measurement, so that the model can still maintain high accuracy under complex working conditions. These measures work together to make flow measurement results more reliable and meet the needs of industrial sites for high-precision measurement.

[0010] Preferably, the data fusion algorithm adopts: Establish data fusion strategies; Calculate the degree of data conflict; Execute data fusion rules; Generate fusion confidence assessment; First, a data fusion strategy, such as weighted averaging or Bayesian fusion, is developed based on the characteristics and importance of each sensor data. Second, the degree of data conflict is assessed by calculating the correlation or difference between different sensor data. Then, the fusion rules are adjusted according to the degree of conflict, such as directly fusing data with less conflict and performing weighted adjustments on data with more conflict. Finally, a confidence assessment of the fused data is generated to ensure the reliability and accuracy of the fusion results. By establishing a fusion strategy, the algorithm can comprehensively utilize multi-dimensional sensor data, reduce the impact of single sensor errors on the overall measurement results, calculate the degree of data conflict and execute corresponding fusion rules, effectively handle data inconsistencies between sensors, and improve the rationality of data fusion. The final generated fusion confidence assessment provides a quantitative basis for the accuracy of the measurement results, helps to timely discover and correct potential errors, thereby ensuring the stable operation and accurate measurement of the flow meter under complex working conditions.

[0011] Preferably, the digital twin model construction includes: Three-dimensional flow field simulation; Structural finite element analysis; Sensor layout optimization; Real-time data-driven interface; In three-dimensional flow field simulation, computational fluid dynamics (CFD) technology is used, combined with actual pipe dimensions and fluid parameters, to establish an accurate three-dimensional flow field model. Structural finite element analysis uses finite element software to perform stress and strain analysis on the wedge flowmeter structure to ensure its mechanical performance. Sensor layout optimization simulates the impact of different sensor positions on measurement accuracy to select the optimal layout scheme. The real-time data-driven interface uses an API interface to achieve real-time interaction between the model and actual measurement data, ensuring that the model can dynamically reflect actual working conditions. Through three-dimensional flow field simulation, the flow state of the fluid in the pipeline can be intuitively displayed, providing a theoretical basis for the design optimization of the flowmeter; structural finite element analysis ensures the structural stability of the flowmeter under complex working conditions and extends its service life; sensor layout optimization improves measurement accuracy and reduces errors; the real-time data-driven interface realizes the synchronous update of the model and actual working conditions, making the calibration results more accurate and reliable. This series of construction measures jointly improves the overall performance and practicality of the high-precision electrical calibration method of wedge flowmeters.

[0012] Preferably, the error compensation mechanism adopts: Input layer nodes correspond to key parameters; Hidden layer nodes perform feature extraction; The output layer nodes generate compensation values; The activation function uses a specific function: In the error compensation mechanism, the input layer nodes correspond to key parameters measured by the wedge flowmeter, such as temperature, pressure, density, and vibration signals. The hidden layer nodes perform nonlinear feature extraction on these parameters through a multi-layer neural network structure to capture the complex relationships between the data. The output layer nodes generate corresponding compensation values ​​based on the extracted features to correct the measurement error. The activation function uses specific functions such as ReLU (Rectified Linear Unit) or Sigmoid to enhance the nonlinear fitting capability of the neural network. This error compensation mechanism achieves accurate compensation for the measurement error of the wedge flowmeter through deep learning. By corresponding key parameters of the input layer nodes, it ensures that the compensation mechanism can fully consider various factors affecting the measurement. The feature extraction function of the hidden layer nodes enables the compensation mechanism to automatically learn and capture the complex relationship between data, improving the accuracy and adaptability of compensation. The output layer nodes generate compensation values, which are directly used to correct measurement errors, thereby improving the accuracy and reliability of measurement. In addition, the selection of specific activation functions enhances the nonlinear fitting ability of the neural network, enabling the compensation mechanism to better adapt to various complex working conditions and provide a strong guarantee for the high-precision measurement of the wedge flowmeter.

[0013] Preferably, it also includes an abnormal working condition processing mechanism: When cavitation is detected, a specific correction module is activated; When the fluid is in laminar flow, switch to a specific calibration mode; When two-phase flow is detected, the phase fraction compensation algorithm is activated: Cavitation detection can be achieved by analyzing the wedge vibration signal and pressure fluctuation characteristics. When the vibration frequency increases abnormally and the pressure fluctuates violently, the correction module is triggered to adjust the flow metering model parameters to eliminate the impact of cavitation. The laminar flow state switches to a specific calibration mode by identifying fluid flow rates below a critical value and activating a low-flow rate calibration curve. Two-phase flow detection utilizes a densitometer and multiphase flow analysis algorithm. When the fluid density change exceeds a preset threshold, the phase fraction compensation algorithm is activated to dynamically adjust the flow calculation based on the gas-liquid ratio. The abnormal operating condition processing mechanism significantly improves the adaptability and measurement accuracy of the wedge-type flowmeter under complex operating conditions. Through specialized processing for special operating conditions such as cavitation, laminar flow and two-phase flow, the system can automatically adjust the working mode and effectively avoid the measurement errors that are prone to occur in traditional flowmeters under these conditions. This not only ensures the stable operation of the flowmeter in various extreme environments, but also ensures the accuracy and reliability of the measurement data, providing a solid data foundation for industrial production process control and improving overall production efficiency and safety.

[0014] Preferably, the distributed time series database adopts: Efficient data compression technology; Fast query response mechanism; Support multiple query methods; The distributed time series database's efficient data compression technology can compress stored data in real time by using advanced compression algorithms (such as LZ4 and Zstandard), reducing storage space usage. Fast query response mechanisms can be achieved by building indexes (such as B+ trees and hash indexes) and optimizing query paths. Support for multiple query methods can be achieved by providing SQL-like query interfaces, RESTful APIs, or custom query languages ​​to meet the query needs of different users. The fast query response mechanism enables users to quickly obtain the required data, improving the real-time performance and response speed of the system; the support for multiple query methods greatly facilitates users from different backgrounds. Both technical and non-technical personnel can retrieve data through the familiar query interface, enhancing the system's ease of use and flexibility. These features jointly improve the overall performance of the system and user experience, and provide solid technical support for data management and analysis of the flow meter calibration process.

[0015] The system for high-precision electrical calibration of a wedge-type flowmeter adopts the above-mentioned high-precision electrical calibration method of a wedge-type flowmeter, including: Smart sensing layer: integrated multi-dimensional sensor array; Edge computing layer: Deployment of high-precision electrical calibration method for wedge-type flowmeters; Cloud platform layer: performs remote model training and parameter distribution; Human-computer interaction layer: provides a visual interface for the calibration process; Thin-film platinum resistance temperature sensors are used to accurately measure fluid temperature; piezoresistive pressure sensors are used to capture pressure fluctuations; vibrating string densitometers are used to measure fluid density; and triaxial accelerometers are used to monitor wedge vibration. These sensors work together to denoise the collected data using the Kalman filter algorithm to ensure data accuracy and reliability. The system for high-precision electrical calibration of wedge-type flowmeters achieves efficient and accurate flow measurement and calibration by integrating the intelligent sensing layer, edge computing layer, cloud platform layer and human-computer interaction layer. The intelligent sensing layer uses a multi-dimensional sensor array to collect fluid parameters in real time, the edge computing layer deploys the calibration algorithm for real-time processing, the cloud platform layer is responsible for remote model training and parameter distribution, and the human-computer interaction layer provides an intuitive visual interface for operators to monitor and adjust. This system architecture not only improves calibration accuracy, but also enhances the flexibility and maintainability of the system, ensuring stable operation under different working conditions.

[0016] In summary, compared with the prior art, the present invention provides a high-precision electrical calibration method and system for a wedge-type flowmeter, which has the following beneficial effects: This invention uses a multi-dimensional sensor array to collect fluid temperature, pressure, density, and wedge vibration signals in real time during the wedge flowmeter calibration process, and uses the Kalman filter algorithm to denoise the raw data to construct a multi-dimensional data set, thereby achieving more comprehensive and accurate data collection and preprocessing. This provides a high-quality data foundation for subsequent calibration, helps improve measurement accuracy, and reduces errors caused by inaccurate data. A working condition classification model is established based on a support vector machine for dynamic working condition identification. When significant changes in key working condition parameters are detected, the calibration model update mechanism is triggered, making the calibration process dynamically adaptive and able to respond to changes in working condition parameters in real time. This effectively overcomes the lack of dynamic adaptive capabilities of traditional calibration models, can adapt to complex and changing actual industrial scenarios, and ensure measurement accuracy. The recursive least squares method is used to update the calibration model parameters online, establishing an improved flow metering model that includes temperature compensation, pressure correction, and fluid compressibility compensation. The model update cycle is dynamically adjusted based on the stability of the operating conditions, enhancing the calibration model's ability to characterize complex flow fields, avoiding the limitations of a single sensor data source, and making the calibration results more consistent with actual operating conditions. A data fusion algorithm is used to fuse the wedge pressure differential signal with the auxiliary sensor data, constructing a weighted fusion strategy and determining the confidence weights of each sensor data to generate the final calibration coefficient, further improving calibration accuracy. Finally, the calibration results are input into the digital twin model for simulation verification. When the simulation error exceeds the threshold, the error compensation mechanism is activated, and the calibration process data and compensation results are stored in a distributed time series database to form a closed-loop control system. This effectively reduces the model transplantation error caused by the environmental differences between the calibration process and the actual measurement process. In particular, under extreme working conditions such as high temperature, high pressure, and multiphase flow, it can significantly improve the measurement accuracy of the wedge flowmeter, ensure the stability of the production process and product quality, and provide more reliable flow measurement support for industrial process control. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a step-by-step diagram of the high-precision electrical calibration method for the wedge-type flowmeter of the invention.

[0018] Figure 2 It is a system diagram of high-precision electrical calibration of the wedge-type flowmeter of the invention. DETAILED DESCRIPTION

[0019] The present invention provides a technical solution, a high-precision electrical calibration method for a wedge-type flowmeter, see Figure 1 , including the following steps: Step 1: Data collection and preprocessing: The multi-dimensional sensor array embedded in the pipeline collects fluid temperature, pressure, density and wedge vibration signals in real time, and uses the Kalman filter algorithm to denoise the raw data to construct a multi-dimensional data set containing time domain and frequency domain features. Step 2: Dynamic working condition identification: A working condition classification model is established based on a support vector machine. The processed data is input into the model for working condition identification. When significant changes in key working condition parameters are detected, the calibration model update mechanism is triggered. Step 3: Adaptive model update: The recursive least squares method is used to update the calibration model parameters online, and an improved flow metering model including temperature compensation terms, pressure correction terms, and fluid compressibility compensation terms is established. The model update cycle is dynamically adjusted according to the stability of the working conditions. Step 4: Multi-source data fusion calibration: The wedge pressure differential signal and auxiliary sensor data are fused using a data fusion algorithm to construct a weighted fusion strategy. The confidence weight of each sensor data is determined through an optimization algorithm to generate the final calibration coefficient. Step 5: Closed-loop verification and compensation; The calibration results are input into the digital twin model for simulation verification. When the simulation error exceeds the set threshold, the error compensation mechanism is activated. At the same time, the calibration process data and compensation results are stored in a distributed time series database, forming a closed-loop control system for calibration, verification, and compensation. In the multi-dimensional sensor array, thin-film platinum resistance temperature sensors reflect temperature by measuring resistance changes, piezoresistive pressure sensors measure pressure using the piezoresistive effect, vibrating string densitometers measure density based on the string vibration frequency, and triaxial accelerometers measure wedge vibration signals. The Kalman filter algorithm uses state equations and observation equations to iteratively update and remove noise from the raw data. Data acquisition and preprocessing obtain rich information through a multi-dimensional sensor array, and Kalman filtering effectively removes noise, providing high-quality data for subsequent processing. Dynamic working condition recognition can adapt to changes in a timely manner, triggering calibration model updates to ensure accuracy. Adaptive model updates are dynamically adjusted according to working conditions, and compensation items comprehensively consider multiple factors. Multi-source data fusion calibration improves data utilization and generates accurate calibration coefficients. Closed-loop verification and compensation ensure that the calibration results are reliable, forming a complete closed-loop control system and improving the overall performance and stability of the flow meter.

[0020] See also Figure 1 , the multi-dimensional sensor array includes: Thin film platinum resistance temperature sensor; Piezoresistive pressure sensor; Vibrating string density meter and triaxial accelerometer; Thin-film platinum resistance temperature sensors are used to measure fluid temperature with high precision, piezoresistive pressure sensors monitor fluid pressure in real time, vibrating string densitometers measure fluid density by changes in vibration frequency, and triaxial accelerometers capture wedge vibration signals. These sensors work together to ensure comprehensive and accurate data. The Kalman filter and support vector machine algorithms are used to effectively remove noise and identify changes in operating conditions, ensuring the dynamic adaptation of the calibration model. The adaptive model update and multi-source data fusion calibration technology further improve the measurement accuracy. The closed-loop verification and compensation mechanism ensures the reliability of the calibration results. This method not only improves the measurement accuracy of the flow meter, but also enhances the adaptability and robustness of the system, providing an efficient and accurate solution for the industrial measurement field.

[0021] See also Figure 1 , the working condition classification model is trained using: The kernel function selects radial basis function; Set specific penalty factors and kernel parameters; Contains sample data of typical working conditions; First, we collected sample data covering a variety of typical operating conditions, including but not limited to data at different temperatures, pressures, fluid densities, and wedge vibration states. We selected the radial basis function as the kernel function and determined the optimal penalty factor and kernel parameters through cross-validation to ensure that the model can accurately classify under various operating conditions. At the same time, we used batch gradient descent to optimize the model parameters to improve training efficiency and classification accuracy. By selecting the radial basis function as the kernel function and combining it with the setting of specific penalty factors and kernel parameters, the model can flexibly adapt to various complex working conditions and improve classification accuracy. The training set contains a large number of typical working condition sample data, which ensures the wide applicability and robustness of the model. This method not only improves the accuracy of working condition identification, but also provides a solid foundation for subsequent adaptive model updates and multi-source data fusion calibration, thereby realizing high-precision electrical calibration of the wedge flowmeter and ensuring the stability and reliability of flow measurement.

[0022] See also Figure 1 , the improved flow metering model realizes dynamic compensation in the following ways: Temperature compensation item: adjusts model parameters according to temperature changes; Pressure correction item: real-time correction of pressure fluctuations; Fluid compressibility compensation: considers the effect of fluid density changes on measurement; A temperature sensor feedback mechanism is embedded in the model to monitor fluid temperature in real time and dynamically adjust model parameters based on a preset temperature-parameter mapping table. The pressure correction term uses a high-precision pressure sensor to capture pressure fluctuations in real time, smoothing the data using a filtering algorithm to make real-time corrections to the pressure-related parameters in the model. The fluid compressibility compensation term combines densitometer data with a density change prediction model to predict the impact of fluid compressibility and compensate for the measurement results. The temperature compensation term ensures that the model parameters can be adaptively adjusted under different temperature environments, avoiding measurement errors caused by temperature changes; the pressure correction term effectively addresses the interference of fluid pressure fluctuations on measurement and realizes real-time correction of pressure fluctuations; the fluid compressibility compensation term fully considers the impact of fluid density changes on measurement, so that the model can still maintain high accuracy under complex working conditions. These measures work together to make flow measurement results more reliable and meet the needs of industrial sites for high-precision measurement.

[0023] See also Figure 1 , the data fusion algorithm adopts: Establish data fusion strategies; Calculate the degree of data conflict; Execute data fusion rules; Generate fusion confidence assessment; First, a data fusion strategy, such as weighted averaging or Bayesian fusion, is developed based on the characteristics and importance of each sensor data. Second, the degree of data conflict is assessed by calculating the correlation or difference between different sensor data. Then, the fusion rules are adjusted according to the degree of conflict, such as directly fusing data with less conflict and performing weighted adjustments on data with more conflict. Finally, a confidence assessment of the fused data is generated to ensure the reliability and accuracy of the fusion results. By establishing a fusion strategy, the algorithm can comprehensively utilize multi-dimensional sensor data, reduce the impact of single sensor errors on the overall measurement results, calculate the degree of data conflict and execute corresponding fusion rules, effectively handle data inconsistencies between sensors, and improve the rationality of data fusion. The final generated fusion confidence assessment provides a quantitative basis for the accuracy of the measurement results, helps to timely discover and correct potential errors, thereby ensuring the stable operation and accurate measurement of the flow meter under complex working conditions.

[0024] See also Figure 1 , the construction of digital twin model includes: Three-dimensional flow field simulation; Structural finite element analysis; Sensor layout optimization; Real-time data-driven interface; In three-dimensional flow field simulation, computational fluid dynamics (CFD) technology is used, combined with actual pipe dimensions and fluid parameters, to establish an accurate three-dimensional flow field model. Structural finite element analysis uses finite element software to perform stress and strain analysis on the wedge flowmeter structure to ensure its mechanical performance. Sensor layout optimization simulates the impact of different sensor positions on measurement accuracy to select the optimal layout scheme. The real-time data-driven interface uses an API interface to achieve real-time interaction between the model and actual measurement data, ensuring that the model can dynamically reflect actual working conditions. Through three-dimensional flow field simulation, the flow state of the fluid in the pipeline can be intuitively displayed, providing a theoretical basis for the design optimization of the flowmeter; structural finite element analysis ensures the structural stability of the flowmeter under complex working conditions and extends its service life; sensor layout optimization improves measurement accuracy and reduces errors; the real-time data-driven interface realizes the synchronous update of the model and actual working conditions, making the calibration results more accurate and reliable. This series of construction measures jointly improves the overall performance and practicality of the high-precision electrical calibration method of wedge flowmeters.

[0025] See also Figure 1 , the error compensation mechanism adopts: Input layer nodes correspond to key parameters; Hidden layer nodes perform feature extraction; The output layer nodes generate compensation values; The activation function uses a specific function: In the error compensation mechanism, the input layer nodes correspond to key parameters measured by the wedge flowmeter, such as temperature, pressure, density, and vibration signals. The hidden layer nodes perform nonlinear feature extraction on these parameters through a multi-layer neural network structure to capture the complex relationships between the data. The output layer nodes generate corresponding compensation values ​​based on the extracted features to correct the measurement error. The activation function uses specific functions such as ReLU (Rectified Linear Unit) or Sigmoid to enhance the nonlinear fitting capability of the neural network. This error compensation mechanism achieves accurate compensation for the measurement error of the wedge flowmeter through deep learning. By corresponding key parameters of the input layer nodes, it ensures that the compensation mechanism can fully consider various factors affecting the measurement. The feature extraction function of the hidden layer nodes enables the compensation mechanism to automatically learn and capture the complex relationship between data, improving the accuracy and adaptability of compensation. The output layer nodes generate compensation values, which are directly used to correct measurement errors, thereby improving the accuracy and reliability of measurement. In addition, the selection of specific activation functions enhances the nonlinear fitting ability of the neural network, enabling the compensation mechanism to better adapt to various complex working conditions and provide a strong guarantee for the high-precision measurement of the wedge flowmeter.

[0026] See also Figure 1, also includes abnormal working condition handling mechanism: When cavitation is detected, a specific correction module is activated; When the fluid is in laminar flow, switch to a specific calibration mode; When two-phase flow is detected, the phase fraction compensation algorithm is activated: Cavitation detection can be achieved by analyzing the wedge vibration signal and pressure fluctuation characteristics. When the vibration frequency increases abnormally and the pressure fluctuates violently, the correction module is triggered to adjust the flow metering model parameters to eliminate the impact of cavitation. The laminar flow state switches to a specific calibration mode by identifying fluid flow rates below a critical value and activating a low-flow rate calibration curve. Two-phase flow detection utilizes a densitometer and multiphase flow analysis algorithm. When the fluid density change exceeds a preset threshold, the phase fraction compensation algorithm is activated to dynamically adjust the flow calculation based on the gas-liquid ratio. The abnormal operating condition processing mechanism significantly improves the adaptability and measurement accuracy of the wedge-type flowmeter under complex operating conditions. Through specialized processing for special operating conditions such as cavitation, laminar flow and two-phase flow, the system can automatically adjust the working mode and effectively avoid the measurement errors that are prone to occur in traditional flowmeters under these conditions. This not only ensures the stable operation of the flowmeter in various extreme environments, but also ensures the accuracy and reliability of the measurement data, providing a solid data foundation for industrial production process control and improving overall production efficiency and safety.

[0027] See also Figure 1 , the distributed time series database uses: Efficient data compression technology; Fast query response mechanism; Support multiple query methods; The distributed time series database's efficient data compression technology can compress stored data in real time by using advanced compression algorithms (such as LZ4 and Zstandard), reducing storage space usage. Fast query response mechanisms can be achieved by building indexes (such as B+ trees and hash indexes) and optimizing query paths. Support for multiple query methods can be achieved by providing SQL-like query interfaces, RESTful APIs, or custom query languages ​​to meet the query needs of different users. The fast query response mechanism enables users to quickly obtain the required data, improving the real-time performance and response speed of the system; the support for multiple query methods greatly facilitates users from different backgrounds. Both technical and non-technical personnel can retrieve data through the familiar query interface, enhancing the system's ease of use and flexibility. These features jointly improve the overall performance of the system and user experience, and provide solid technical support for data management and analysis of the flow meter calibration process.

[0028] The wedge flowmeter high-precision electrical calibration system uses the above-mentioned wedge flowmeter high-precision electrical calibration method. Figure 2 ,include: Smart sensing layer: integrated multi-dimensional sensor array; Edge computing layer: Deployment of high-precision electrical calibration method for wedge-type flowmeters; Cloud platform layer: performs remote model training and parameter distribution; Human-computer interaction layer: provides a visual interface for the calibration process; Thin-film platinum resistance temperature sensors are used to accurately measure fluid temperature; piezoresistive pressure sensors are used to capture pressure fluctuations; vibrating string densitometers are used to measure fluid density; and triaxial accelerometers are used to monitor wedge vibration. These sensors work together to denoise the collected data using the Kalman filter algorithm to ensure data accuracy and reliability. The system for high-precision electrical calibration of wedge-type flowmeters achieves efficient and accurate flow measurement and calibration by integrating the intelligent sensing layer, edge computing layer, cloud platform layer and human-computer interaction layer. The intelligent sensing layer uses a multi-dimensional sensor array to collect fluid parameters in real time, the edge computing layer deploys the calibration algorithm for real-time processing, the cloud platform layer is responsible for remote model training and parameter distribution, and the human-computer interaction layer provides an intuitive visual interface for operators to monitor and adjust. This system architecture not only improves calibration accuracy, but also enhances the flexibility and maintainability of the system, ensuring stable operation under different working conditions.

[0029] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0030] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. High-precision electrical calibration method for wedge-type flowmeter, characterized by: The steps include: Step 1: Data collection and preprocessing: The multi-dimensional sensor array embedded in the pipeline collects fluid temperature, pressure, density and wedge vibration signals in real time, and uses the Kalman filter algorithm to denoise the raw data to construct a multi-dimensional data set containing time domain and frequency domain features. Step 2: Dynamic working condition identification: A working condition classification model is established based on a support vector machine. The processed data is input into the model for working condition identification. When significant changes in key working condition parameters are detected, the calibration model update mechanism is triggered. Step 3: Adaptive model update: The recursive least squares method is used to update the calibration model parameters online, and an improved flow metering model including temperature compensation terms, pressure correction terms, and fluid compressibility compensation terms is established. The model update cycle is dynamically adjusted according to the stability of the working conditions. Step 4: Multi-source data fusion calibration: The wedge pressure differential signal and auxiliary sensor data are fused using a data fusion algorithm to construct a weighted fusion strategy. The confidence weight of each sensor data is determined through an optimization algorithm to generate the final calibration coefficient. Step 5: Closed-loop verification and compensation; The calibration results are input into the digital twin model for simulation verification. When the simulation error exceeds the set threshold, the error compensation mechanism is activated. At the same time, the calibration process data and compensation results are stored in a distributed time series database to form a closed-loop control system for calibration, verification and compensation.

2. The high-precision electrical calibration method for a wedge-type flowmeter according to claim 1, characterized in that: The multi-dimensional sensor array comprises: Thin film platinum resistance temperature sensor; Piezoresistive pressure sensor; Vibrating string density meter and triaxial accelerometer.

3. The high-precision electrical calibration method for a wedge-type flowmeter according to claim 1, characterized in that: The working condition classification model is trained using: The kernel function selects radial basis function; Set specific penalty factors and kernel parameters; Contains sample data for typical operating conditions.

4. The high-precision electrical calibration method for a wedge-type flowmeter according to claim 1, characterized in that: The improved flow metering model achieves dynamic compensation in the following ways: Temperature compensation item: adjusts model parameters according to temperature changes; Pressure correction item: real-time correction of pressure fluctuations; Fluid compressibility compensation: considers the effect of fluid density changes on measurement.

5. The high-precision electrical calibration method for a wedge-type flowmeter according to claim 1, characterized in that: The data fusion algorithm adopts: Establish data fusion strategies; Calculate the degree of data conflict; Execute data fusion rules; Generate fusion confidence estimates.

6. The high-precision electrical calibration method for a wedge-type flowmeter according to claim 1, characterized in that: The digital twin model construction includes: Three-dimensional flow field simulation; Structural finite element analysis; Sensor layout optimization; Real-time data driven interface.

7. The high-precision electrical calibration method for a wedge-type flowmeter according to claim 1, characterized in that: The error compensation mechanism adopts: Input layer nodes correspond to key parameters; Hidden layer nodes perform feature extraction; The output layer nodes generate compensation values; The activation function selects a specific function.

8. The high-precision electrical calibration method for a wedge-type flowmeter according to claim 1, characterized in that: It also includes abnormal operating condition handling mechanism: When cavitation is detected, a specific correction module is activated; When the fluid is in laminar flow, switch to a specific calibration mode; When two-phase flow is detected, the phase fraction compensation algorithm is activated.

9. The high-precision electrical calibration method for a wedge-type flowmeter according to claim 1, characterized in that: The distributed time series database adopts: Efficient data compression technology; Fast query response mechanism; Supports multiple query methods.

10. A system for high-precision electrical calibration of a wedge-type flowmeter, comprising the method for high-precision electrical calibration of a wedge-type flowmeter according to any one of claims 1 to 9, characterized in that: include: Smart sensing layer: integrated multi-dimensional sensor array; Edge computing layer: Deployment of high-precision electrical calibration method for wedge-type flowmeters; Cloud platform layer: performs remote model training and parameter distribution; Human-computer interaction layer: provides a visual interface for the calibration process.

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