Fluid-based online calibration method and device, equipment, storage medium and computer program product

Abstract

The invention relates to the technical field of flow detection, in particular to a fluid-based online calibration method, device and equipment, a storage medium and a computer program product. The method comprises the following steps: determining a calibration fluid according to physical properties of a current fluid; fluid parameters of calibration fluid in the flowmeter to be processed are obtained, wherein the fluid parameters comprise pressure, temperature and flow velocity; according to the calibration model and the fluid parameters, the reading error of the flowmeter to be processed is determined; based on the reading error, the flowmeter to be processed is calibrated, the fluid parameters are collected in real time, the error is calculated in combination with the calibration model, rapid and efficient calibration is achieved, and time waste is reduced; calibration fluid is dynamically selected, the flowmeter is adjusted in real time, and the adaptability of the method is improved; through automatic data acquisition, error calculation and calibration adjustment, the calibration time and cost are reduced, and the calibration efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of flow detection technology, and in particular to a method, device, equipment, storage medium and computer program product based on fluid online calibration. Background Art

[0002] As an important measuring instrument in the industrial production process, flowmeter is widely used in many fields such as petroleum, petrochemical, chemical, electric power, metallurgy, sewage treatment, etc. to monitor and control the flow of material flow. In practical applications, the measurement accuracy of flowmeter has a vital impact on production efficiency, product quality and safety. However, due to factors such as equipment aging, environmental changes or differences in fluid properties, flowmeters often have measurement errors after long-term use, and need to be calibrated regularly to ensure their measurement accuracy. Traditional flowmeter calibration methods mainly include calibration methods based on the Coriolis effect and calibration methods in laboratory environments. Although these methods can achieve high calibration accuracy, they also have some obvious shortcomings in actual use: complex operation, high equipment requirements, limited scope of application and lack of real-time performance, resulting in poor calibration efficiency. Therefore, how to improve calibration efficiency has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] The main purpose of this application is to provide a fluid-based online calibration method, device, equipment, storage medium and computer program product, aiming to solve the technical problem of how to improve calibration efficiency.

[0004] To achieve the above object, the present application provides a fluid-based online calibration method, the method comprising the following steps:

[0005] Determine the calibration fluid according to the physical properties of the current fluid;

[0006] Obtaining fluid parameters of the calibration fluid in the flow meter to be processed, wherein the fluid parameters include pressure, temperature and flow rate;

[0007] Determining a reading error of the flow meter to be processed according to the calibration model and the fluid parameters;

[0008] Based on the reading error, the subject flow meter is calibrated.

[0009] In one embodiment, before the step of determining the calibration fluid according to the physical properties of the current fluid, the method further includes:

[0010] Acquire historical flow data under multiple different fluid physical properties, historical measurement data of multiple different flow meter types, and historical record data of the flow meter to be processed;

[0011] Performing data cleaning processing on the historical flow data, the historical measurement data and the historical record data to obtain historical flow measurement data, wherein the data cleaning processing includes one or more of outlier removal, data smoothing, data format matching and data labeling;

[0012] The historical flow measurement data is analyzed to establish the calibration model.

[0013] In one embodiment, the step of analyzing the historical flow measurement data and establishing the calibration model includes:

[0014] Based on a preset statistical algorithm, determining a corresponding relationship between historical fluid parameters and historical measurement errors in the historical flow measurement data;

[0015] Based on the corresponding relationship and the preset mathematical model, a preliminary model is established;

[0016] Adjusting parameters of the preliminary model based on the interaction between parameters in the historical fluid parameters;

[0017] Based on the preset test data, the preliminary model after parameter adjustment is verified, and the verified preliminary model is used as the calibration model.

[0018] In one embodiment, after the step of analyzing the historical flow measurement data and establishing the calibration model, the following step is further included:

[0019] At every preset time period, the fluid parameters and the measurement data of the calibrated flow meter to be processed are added to the model training set of the calibration model to obtain an updated training set;

[0020] Retraining the calibration model based on the updated training set;

[0021] If the model error of the retrained calibration model exceeds the preset error threshold, the model retraining process is triggered.

[0022] In one embodiment, the step of determining the reading error of the flow meter to be processed according to the calibration model and the fluid parameters comprises:

[0023] Obtaining the current measurement value of the flow meter to be processed;

[0024] Inputting the fluid parameters into the calibration model to obtain expected measurement values;

[0025] The reading error of the flow meter to be processed is determined according to the current measurement value and the expected measurement value.

[0026] In one embodiment, the step of calibrating the flow meter to be processed based on the reading error includes:

[0027] Obtaining an error direction corresponding to the reading error, and comparing the reading error with a preset calibration threshold;

[0028] If the reading error is greater than the preset calibration threshold, determining a calibration adjustment factor of the flow meter to be processed according to the reading error;

[0029] The target flow meter is calibrated based on the error direction and the calibration adjustment factor.

[0030] In addition, to achieve the above-mentioned purpose, the present application also proposes a fluid-based online calibration device, which includes:

[0031] A fluid determination module, used to determine a calibration fluid according to the physical properties of the current fluid;

[0032] A parameter acquisition module, used to acquire fluid parameters of the calibration fluid in the flow meter to be processed, wherein the fluid parameters include pressure, temperature and flow rate;

[0033] An error determination module, used for determining a reading error of the flow meter to be processed according to a calibration model and the fluid parameters;

[0034] A calibration module is used to calibrate the flow meter to be processed based on the reading error.

[0035] In addition, to achieve the above-mentioned purpose, the present application also proposes a fluid-based online calibration device, which includes: a memory, a processor, and a fluid-based online calibration program stored in the memory and executable on the processor, wherein the fluid-based online calibration program is configured to implement the steps of the fluid-based online calibration method described above.

[0036] In addition, to achieve the above objectives, the present application also proposes a storage medium, on which a fluid-based online calibration program is stored. When the fluid-based online calibration program is executed by a processor, the steps of the fluid-based online calibration method described above are implemented.

[0037] In addition, to achieve the above-mentioned purpose, the present application also proposes a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the fluid-based online calibration method as described above are implemented.

[0038] The present application determines the calibration fluid according to the physical properties of the current fluid; obtains the fluid parameters of the calibration fluid in the flow meter to be processed, the fluid parameters including pressure, temperature and flow rate; determines the reading error of the flow meter to be processed according to the calibration model and fluid parameters; and calibrates the flow meter to be processed based on the reading error. The present application realizes fast and efficient calibration by collecting fluid parameters in real time and calculating errors in combination with the calibration model, thereby reducing a large amount of time wasted in traditional calibration methods; dynamically selects the calibration fluid and adjusts the flow meter in real time, thereby improving the adaptability of the method and avoiding calibration errors caused by fluid differences in traditional methods; through automated data collection, error calculation and calibration adjustment, it reduces dependence on professionals and complex equipment, thereby reducing calibration time and cost and improving calibration efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of the first embodiment of the fluid online calibration method of the present application;

[0040] Figure 2 This is a schematic diagram of a sub-process in the second embodiment of the fluid online calibration method of the present application;

[0041] Figure 3 This is a schematic diagram of a sub-process in the third embodiment of the fluid online calibration method of the present application;

[0042] Figure 4 This is a schematic diagram of the module structure of a fluid online calibration device according to an embodiment of the present application;

[0043] Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the fluid online calibration method in the embodiment of the present application.

[0044] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0045] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0046] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0047] It should be noted that flowmeters, as important measuring instruments in the industrial production process, are widely used in many fields such as petroleum and petrochemicals, chemicals, electricity, metallurgy, sewage treatment, etc., to monitor and control the flow of material flow. In practical applications, the measurement accuracy of flowmeters has a vital impact on production efficiency, product quality and safety. However, due to factors such as equipment aging, environmental changes or differences in fluid properties, flowmeters often have measurement errors after long-term use, and need to be calibrated regularly to ensure their measurement accuracy. Traditional flowmeter calibration methods mainly include calibration methods based on the Coriolis effect and calibration methods in laboratory environments. Although these methods can achieve high calibration accuracy, they also have some obvious shortcomings in actual use: complex operation, high equipment requirements, limited scope of application, and lack of real-time performance, resulting in poor calibration efficiency. Therefore, how to improve calibration efficiency has become a technical problem that needs to be solved urgently.

[0048] The main solution of this application is: determine the calibration fluid based on the physical properties of the current fluid; obtain the fluid parameters of the calibration fluid in the flow meter to be processed, the fluid parameters include pressure, temperature and flow rate; determine the reading error of the flow meter to be processed based on the calibration model and fluid parameters; calibrate the flow meter to be processed based on the reading error.

[0049] The present application realizes fast and efficient calibration by real-time collection of fluid parameters and calculation of errors in combination with a calibration model, thereby reducing a large amount of time wasted in traditional calibration methods; dynamically selects calibration fluids and adjusts flow meters in real time, thereby improving the adaptability of the method and avoiding calibration errors caused by fluid differences in traditional methods; through automated data collection, error calculation and calibration adjustment, it reduces dependence on professionals and complex equipment, thereby reducing calibration time and cost and improving calibration efficiency.

[0050] It should be noted that the execution subject of the method of this embodiment can be a computing service device with data processing, network communication and program running functions, or it can be the above-mentioned fluid-based online calibration device with the same or similar functions. This embodiment and the following embodiments will be described by taking the fluid-based online calibration device as an example.

[0051] Based on this, the first embodiment of the fluid online calibration method of this application is proposed, please refer to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the fluid online calibration method of the present application.

[0052] In this embodiment, the fluid-based online calibration method includes the following steps:

[0053] S1: Determine the calibration fluid according to the physical properties of the current fluid;

[0054] It should be noted that fluid refers to a substance with continuous deformation ability, including liquids, gases and multiphase flow. The physical properties of the fluid may change under different conditions (such as pressure, temperature). The physical properties of the fluid include but are not limited to the following parameters: pressure, temperature, flow rate, density, viscosity. The calibration fluid refers to the substance used to simulate or replace the actual working fluid during the calibration process. Its physical properties should be as similar as possible to the actual use conditions of the fluid to be measured to ensure the accuracy of the calibration.

[0055] Specifically, the real-time parameters of the fluid under actual operating conditions are obtained through pressure sensors, temperature sensors, flow meters and other equipment. The measured physical parameters are statistically analyzed and evaluated, with a focus on analyzing the fluid's pressure range, temperature fluctuations, flow rate distribution, and correlation with density and viscosity. The key characteristics of the current fluid can be obtained through analysis.

[0056] Furthermore, based on the measured fluid parameters, define the physical properties that the calibration fluid needs to simulate, such as temperature, pressure, and flow rate range. Select a fluid (such as water, oil, air, etc.) that is closest to the current fluid parameters from the calibration fluid library, or make appropriate corrections to the properties of the calibration fluid by adjusting the experimental conditions (such as pressure, temperature). Test the selected calibration fluid in the experimental environment to ensure that its physical properties under the calibration conditions can meet the simulation requirements of the current fluid.

[0057] By dynamically selecting the calibration fluid according to the physical properties of the current fluid, it can ensure that the calibration conditions are highly similar to the actual use environment, reduce the measurement errors caused by differences in fluid properties, and significantly improve the accuracy and applicability of the calibration. There is no need to use a single standard fluid or rely on high-precision sensors for complex calibration operations. Instead, by selecting a suitable calibration fluid, the calibration is completed in an environment closer to the actual use conditions, thereby simplifying the process and reducing equipment and labor costs. Compared with the traditional method of directly using standard fluids for calibration, this method makes the calibration steps more efficient by dynamically adjusting the selection of calibration fluids, avoiding repeated adjustments or attempts, and shortening the calibration time. Since the calibration fluid can be flexibly selected, this method is applicable to various types of flow meters and fluids (such as liquids, gases, multiphase flows, etc.), which significantly improves the applicability of the method in different industrial scenarios. By performing a detailed analysis of the physical properties of the current fluid and selecting a calibration fluid that matches its characteristics, this step not only ensures the accuracy and efficiency of the calibration process, but also reduces costs and operational complexity, laying a solid foundation for subsequent calibration processes.

[0058] S2: Obtaining fluid parameters of the calibration fluid in the flow meter to be processed, wherein the fluid parameters include pressure, temperature and flow rate;

[0059] It should be noted that the flowmeter to be processed refers to the flowmeter equipment that needs to be calibrated, which is used to measure the flow of fluid, including mass flowmeter, volume flowmeter, etc. The equipment may have measurement errors due to long-term use, environmental changes, etc., and needs to be calibrated. Fluid parameters refer to physical quantities that describe the state and characteristics of the fluid, including the following three core parameters: Pressure: The force generated per unit area of ​​the fluid, usually measured by a pressure sensor; Temperature: The thermal state of the fluid, which affects its density, viscosity and other properties; Flow rate: The speed at which the fluid moves in a pipeline or measuring device, which is an important variable for determining the flow rate.

[0060] Specifically, introduce the selected calibration fluid into the measuring pipe of the flow meter to be processed, and ensure that the calibration fluid flows through the flow meter and is in a stable state. Install pressure, temperature and flow rate sensors on the flow meter measuring pipe, or use the built-in sensors of the flow meter itself for data collection. Ensure that the sensor equipment has been calibrated and has sufficient accuracy.

[0061] Furthermore, a pressure sensor is used to measure the pressure of the calibration fluid in the flow meter to be processed in real time, and the pressure value in the stable state is recorded. A temperature sensor is used to measure the temperature of the calibration fluid, and the ambient temperature change is monitored. A flow rate sensor (such as a turbine flow meter) is used to measure the instantaneous flow rate of the calibration fluid. A preliminary check is performed on the real-time collected data to eliminate possible abnormal values ​​(such as invalid data caused by instantaneous fluctuations). Combined with the time series data of the sensor, the average value within a stable period of time is taken to ensure the accuracy and representativeness of the collected parameters.

[0062] By acquiring the parameters such as pressure, temperature and flow rate of the calibration fluid in real time, the state of the calibration fluid can be dynamically reflected to avoid calibration errors caused by parameter lag or incomplete sampling. The pressure, temperature and flow rate of the calibration fluid directly affect the measured value of the flowmeter. By acquiring these core parameters, the data input into the calibration model can be ensured to be accurate and the reliability of subsequent error calculation can be improved. Multi-parameter collaborative analysis (such as the dependence of pressure and flow rate on temperature changes) helps to comprehensively judge the state of the fluid under calibration conditions. By directly acquiring the calibration fluid parameters under the running state of the flowmeter, the cumbersome process of disassembling the flowmeter and transferring it to the laboratory in the traditional method is avoided, which greatly improves the calibration efficiency. Since the parameter acquisition method is independent of the fluid properties, the core parameters can be obtained by the same method regardless of whether the calibration fluid is liquid, gas or multiphase flow, thereby expanding the applicable scenarios of the calibration method. By acquiring the pressure, temperature and flow rate parameters of the calibration fluid in real time, this step not only provides accurate basic data for subsequent error calculation, but also significantly improves the real-time, efficiency and adaptability of the calibration process, providing a universal solution for the calibration needs of flowmeters in different industrial scenarios.

[0063] S3: Determine the reading error of the flow meter to be processed according to the calibration model and the fluid parameters.

[0064] It should be noted that the calibration model refers to a mathematical or statistical model used to describe the relationship between the flow meter measurement value and the actual flow rate. This model is usually derived from the data analysis of historical flow measurement data, fluid parameters and flow meter types, and can reflect the error law of the flow meter under different fluid conditions. The reading error refers to the deviation between the actual measurement value of the flow meter and the expected (or true) flow value, which is used to measure the accuracy of the flow meter measurement. It is usually expressed in absolute values ​​(such as L / min) or relative percentages (such as %).

[0065] Specifically, the fluid parameters (such as pressure, temperature and flow rate) obtained from the flow meter to be processed are used as input data for the calibration model to ensure that the input parameters cover the applicable range of the calibration model. The fluid parameters are standardized to ensure that the unit and accuracy of the input data are consistent with the calibration model. The current flow value is obtained from the real-time measurement record of the flow meter to be processed as the benchmark data for calibration error calculation. If the flow meter supports historical data records, it can be combined with time series data for further analysis.

[0066] Furthermore, according to the calibration model, the expected measurement value of the flow meter to be processed under the current conditions is calculated using the input fluid parameters. This predicted value is the output that the device should have under ideal conditions. The actual measurement value is compared with the expected measurement value, and the difference between the two is analyzed. The difference includes positive deviation (the actual measurement value is higher than the expected value) and negative deviation (the actual measurement value is lower than the expected value). The error can be expressed as a specific value or a certain proportional size, which is used to measure the degree of deviation of the flow meter measurement result. The calculated error is organized into a clear output result, including the error direction (too high or too low) and the error magnitude, and recorded as reference data for the subsequent calibration steps.

[0067] By combining the calibration model with real-time fluid parameters, the error evaluation can comprehensively consider the dynamic changes of the fluid state, so that the results are more in line with the characteristics of the actual operating environment. Compared with the traditional static error table or fixed value calculation, this method provides a dynamic error evaluation based on actual working conditions to ensure the accuracy of the error results. The calibration model embeds complex error calculation rules, and the error results can be quickly obtained by inputting fluid parameters and current measurement values, simplifying the cumbersome traditional error calculation process. This process can be completed in a short time, which is particularly suitable for the rapid calibration needs of industrial sites. The dynamic input of fluid parameters ensures that the calibration method can adapt to a variety of fluids and flowmeter types, significantly expanding the application scope of calibration. Through the precise calculation of the error, a clear basis can be provided for the calibration adjustment of the flowmeter, avoiding multiple repeated adjustments caused by inaccurate error estimation. At the same time, the error data can also be used to further optimize the calibration model and improve the accuracy and efficiency of future calibration. This step realizes a fast and accurate error determination process through the combination of the calibration model and fluid parameters, which not only improves the calibration efficiency, but also ensures the measurement accuracy of the flowmeter under different working conditions, providing a reliable data basis for subsequent calibration.

[0068] S4: based on the reading error, calibrating the flow meter to be processed;

[0069] Specifically, based on the reading error determined in the previous step, analyze the magnitude and direction of the error (positive or negative deviation). Determine whether the error is within the allowable calibration range. For small errors, they can be corrected by software; for large errors, hardware adjustments may be required. If the error magnitude is small (such as within a specific percentage range), software calibration is preferred to correct the error by adjusting the measurement algorithm or output formula. If the error magnitude is large, or there is a significant problem with the internal hardware of the flow meter, a hardware calibration method is required, such as recalibrating the sensor or adjusting the mechanical structure.

[0070] Furthermore, a calibration factor is calculated based on the error amplitude, and the calibration factor is used to correct the flow meter's measurement formula or output value to make it close to the true value. The calibration factor is input into the flow meter's control system to update the measurement algorithm. The flow meter output value is tested in real time to ensure that the error is within the allowable range. The sensitivity or zero offset of the flow meter's internal sensor is recalibrated to make the measurement more accurate. The mechanical part of the flow meter (such as the impeller or pipe) is checked to repair or replace any structural problems that may cause measurement deviations. After completing the hardware adjustment, remeasure the fluid parameters and flow value of the calibration fluid to verify whether the error has been reduced to an acceptable range.

[0071] Optionally, save key data from the calibration process, including initial error values, adjustment methods, calibration factors, and error data after calibration. Record the calibration time, calibration personnel, and equipment used for later reference and tracking. Generate a complete report based on the calibration results, including a comparison of the measured values ​​before and after error correction, a description of the adjustment method, and the status of the equipment after calibration.

[0072] Through dynamic calibration based on reading error, the measured value of the flow meter is closer to the actual flow, which significantly improves the measurement accuracy and reduces the impact of errors on the production process. Software calibration quickly corrects errors by adjusting the measurement algorithm in real time, without disassembling the equipment, simplifying the calibration operation; hardware calibration solves the problem of large errors by adjusting sensors and mechanical components in a targeted manner, thereby reducing the time waste of repeated calibration. The combined calibration of hardware and software not only restores the measurement accuracy of the flow meter, but also extends the service life of the equipment and improves the stability of equipment operation. The calibration process is suitable for measurement scenarios of liquids, gases and multiphase fluids, and is also compatible with the calibration requirements of different types of flow meters, expanding the application scope of the calibration method. The systematic recording of calibration results provides basic data support for the subsequent maintenance and model optimization of the flow meter, and promotes the digitalization and intelligence of equipment management. Through the calibration operation based on reading error, this step can quickly and accurately correct the measurement results of the flow meter to be processed, while taking into account the software and hardware calibration methods, improving the calibration efficiency, accuracy and adaptability of the equipment, and providing important guarantees for the reliable operation of industrial field flow meters.

[0073] This embodiment determines the calibration fluid according to the physical properties of the current fluid; obtains the fluid parameters of the calibration fluid in the flow meter to be processed, and the fluid parameters include pressure, temperature and flow rate; determines the reading error of the flow meter to be processed according to the calibration model and the fluid parameters; and calibrates the flow meter to be processed based on the reading error. This embodiment achieves fast and efficient calibration by collecting fluid parameters in real time and calculating errors in combination with the calibration model, thereby reducing a large amount of time wasted in traditional calibration methods; dynamically selects the calibration fluid and adjusts the flow meter in real time, thereby improving the adaptability of the method and avoiding calibration errors caused by fluid differences in traditional methods; through automated data collection, error calculation and calibration adjustment, it reduces dependence on professionals and complex equipment, thereby reducing calibration time and cost and improving the efficiency of flow meter calibration.

[0074] Based on the above first embodiment, a second embodiment of the present application based on a fluid online calibration method is proposed. Figure 2 , Figure 2 This is a schematic diagram of a sub-process in the second embodiment of the fluid online calibration method of the present application.

[0075] like Figure 2 As shown, in this embodiment, before step S1, the following steps are also included:

[0076] S1a: Acquire historical flow data under multiple different fluid physical properties, historical measurement data of multiple different flow meter types, and historical record data of the flow meter to be processed;

[0077] S1b: performing data cleaning processing on the historical flow data, the historical measurement data and the historical record data to obtain historical flow measurement data, wherein the data cleaning processing includes one or more of outlier removal, data smoothing, data format matching and data labeling;

[0078] S1c: Analyze the historical flow measurement data and establish the calibration model.

[0079] It should be noted that historical flow data refers to flow data measured under different fluid physical properties (such as pressure, temperature, and flow rate). These data are derived from actual operation, experimental records, etc., and reflect the flow characteristics of the fluid under various working conditions. Historical measurement data includes data measured by different types of flow meters (such as mass flow meters, volume flow meters, etc.) under various operating conditions, which are used to analyze the measurement performance and error characteristics of different flow meters. The historical record data of the flow meter to be processed refers to the historical operation and calibration data of the flow meter that currently needs to be calibrated, including its measurement values, error records, operating environment information, etc. Data cleaning processing refers to the process of organizing and cleaning the original data, including eliminating invalid data, smoothing data trends, formatting unification, and data labeling, etc., to ensure the accuracy of subsequent analysis and modeling. Mathematical or statistical models built based on historical data are used to describe the relationship between fluid parameters and flow meter errors, which can predict errors and be used for calibration.

[0080] Specifically, collect flow data from various fluids (liquids, gases, multiphase flows) under different physical conditions (such as temperature, pressure, flow rate), as well as measurement data from various types of flow meters. Focus on obtaining historical record data of the flow meter to be processed. Preliminary sorting of the data is carried out, classifying it into fluid property data, flow meter performance data, and historical record data of the flow meter to be processed, to ensure that the data source is clear and convenient for subsequent processing.

[0081] Furthermore, erroneous data caused by equipment failure, operating errors or special environments are identified and removed, such as records of abnormal fluctuations in instantaneous flow values. Random noise in the measured data is eliminated through a smoothing algorithm to extract flow trend characteristics. The units and formats of data from different sources are standardized to ensure data compatibility during analysis. Data is classified and labeled according to fluid type, flow meter model and measurement conditions to provide structured information for model establishment.

[0082] Going further, the historical data after cleaning is used to analyze the relationship between fluid parameters (such as temperature, pressure, flow rate) and flow meter measurement errors to extract characteristic patterns. Statistical regression analysis or machine learning methods (such as neural networks and random forests) are used to establish an error prediction model, with fluid parameters as input and measurement errors as output. The model parameters are adjusted to consider the interaction between fluid parameters, the accuracy and stability of the model are verified using test data, and the optimal model is selected as the calibration model.

[0083] By analyzing historical data of various fluids and flowmeter types, the calibration model can accurately reflect the measurement errors under different working conditions and significantly improve the prediction accuracy. Integrating data of various fluids and flowmeters makes the model applicable to various equipment types and fluid scenarios, expanding the scope of application of the calibration method. The data cleaning process eliminates abnormal data and noise, improves the reliability of the analysis data, and provides a solid data foundation for the calibration model. Through advance analysis and training, the calibration model reduces the need for on-site experiments and multiple adjustments, greatly shortening the calibration time. By accumulating new data, the calibration model can be continuously updated and optimized to adapt to changes in equipment and environment, and achieve long-term and stable calibration effects. This method establishes an accurate and reliable calibration model through cleaning and analysis of historical data. The model can adapt to a variety of fluids and flowmeter types, improves the efficiency, accuracy and applicability of calibration, and provides an efficient solution for flowmeter calibration under complex working conditions.

[0084] Based on the above first embodiment, in this embodiment, step S1c includes:

[0085] S1c1: Determine the correspondence between the historical fluid parameters and the historical measurement errors in the historical flow measurement data based on a preset statistical algorithm;

[0086] S1c2: establishing a preliminary model based on the corresponding relationship and the preset mathematical model;

[0087] S1c3: adjusting parameters of the preliminary model based on the interaction between parameters in the historical fluid parameters;

[0088] S1c4: Based on the preset test data, the preliminary model after parameter adjustment is verified, and the verified preliminary model is used as the calibration model.

[0089] It should be noted that the preset statistical algorithm refers to a statistical method selected in advance for analyzing the relationship between data, such as regression analysis, correlation analysis, etc., which aims to explore the regularity between historical fluid parameters and flow meter measurement errors. The preset mathematical model is a mathematical framework for constructing error prediction, which can be a linear model (such as multivariate linear regression) or a nonlinear model (such as polynomial regression, neural network). The preliminary model refers to the initial error prediction model constructed based on the corresponding relationship and the preset mathematical model, which is used to describe the basic relationship between fluid parameters and measurement errors. Interaction refers to the mutual influence between historical fluid parameters, such as the joint influence of the synergistic effect of pressure and temperature on flow errors. The preset test data is a part of historical data independent of the training data, which is used to verify the accuracy and generalization ability of the model.

[0090] Specifically, input the historical flow measurement data after cleaning, including fluid parameters (such as pressure, temperature, flow rate) and the corresponding measurement errors. Use a preset statistical algorithm (such as correlation analysis) to calculate the strength of the relationship between each fluid parameter and the measurement error. Determine which fluid parameters have a significant impact on the error (such as high correlation or parameters that pass the significance test), and eliminate variables with less impact to reduce model complexity. According to the relationship between fluid parameters and errors, select a suitable mathematical model (such as linear regression, polynomial regression or neural network) to build a basic framework for error prediction. Use fluid parameters as input variables and measurement errors as output variables, and use historical data to train a preliminary model so that it can predict error values.

[0091] Furthermore, evaluate the impact of interactions between fluid parameters on errors, such as the combined effect of pressure and temperature. Introduce new variables (such as product terms or nonlinear combination terms of parameters) based on the interactions to optimize the model structure. Adjust the parameter configuration of the preliminary model based on the interaction results to ensure that the model can better fit the data and capture complex error variation patterns. Use independent preset test data (not involved in model training) to verify the performance of the model after parameter adjustment. Compare the difference between the model prediction error and the actual error to evaluate the prediction accuracy and generalization ability of the model. If the model passes the verification and the prediction performance meets the preset standard, it will be determined as the final calibration model; if it fails the verification, return to the adjustment stage to continue optimization.

[0092] By extracting the correspondence between fluid parameters and errors through statistical algorithms, the model can clearly identify which parameters have the greatest impact on the error, thereby ensuring the pertinence and effectiveness of the calibration model. The analysis of parameter interactions and model adjustment increase the complexity of the model, enabling it to capture the impact of the combined effect of multiple parameters on the error and improve the accuracy of error prediction. Using preset test data to verify the model can effectively test the generalization ability of the model and avoid the problem that the model performs well on training data but makes prediction errors on new data. After the calibration model is verified, it can be directly used for error prediction of different fluids and flow meters, reducing the repeated testing steps in the calibration process and improving the calibration efficiency. With the increase of historical data, the model can be continuously optimized and adjusted to adapt to changes in the environment and equipment and maintain long-term stable calibration capabilities. By successively determining the correspondence between fluid parameters and errors, building a preliminary model, optimizing parameter interactions and verifying the final model, the process establishes an accurate and efficient calibration model. The model can not only effectively predict the measurement error of the flow meter, but also improve the calibration efficiency and applicability, providing a scientific basis for the calibration of flow meters under complex working conditions.

[0093] This embodiment determines the calibration fluid according to the physical properties of the current fluid; obtains the fluid parameters of the calibration fluid in the flow meter to be processed, and the fluid parameters include pressure, temperature and flow rate; determines the reading error of the flow meter to be processed according to the calibration model and the fluid parameters; and calibrates the flow meter to be processed based on the reading error. This embodiment achieves fast and efficient calibration by collecting fluid parameters in real time and calculating errors in combination with the calibration model, thereby reducing a large amount of time wasted in traditional calibration methods; dynamically selects the calibration fluid and adjusts the flow meter in real time, thereby improving the adaptability of the method and avoiding calibration errors caused by fluid differences in traditional methods; through automated data collection, error calculation and calibration adjustment, it reduces dependence on professionals and complex equipment, thereby reducing calibration time and cost and improving the efficiency of flow meter calibration.

[0094] Based on the above second embodiment, a third embodiment of the present application based on a fluid online calibration method is proposed. Figure 3 , Figure 3 This is a schematic diagram of a sub-process in the third embodiment of the fluid online calibration method of the present application.

[0095] In this embodiment, after step S1c, the method further includes:

[0096] S1ca: adding the fluid parameters and the measurement data of the calibrated flow meter to be processed to the model training set of the calibration model at every preset time period to obtain an updated training set;

[0097] S1cb: retraining the calibration model based on the updated training set;

[0098] S1cc: If the model error of the retrained calibration model exceeds the preset error threshold, the model retraining process is triggered.

[0099] It should be noted that the preset time period refers to a pre-set fixed time interval used to periodically update the data input of the calibration model, such as daily, weekly or monthly. The specific time is determined by the application requirements and the flow meter usage environment. The model training set is a data set used to build and optimize the calibration model. It contains historical flow measurement data and its corresponding fluid parameters and measurement errors. The model training set ensures the dynamic adaptability of the model through continuous updates. The model error refers to the gap between the error predicted by the calibration model and the actual measurement error, which is usually used to evaluate the prediction accuracy of the model. If the model error exceeds the preset error threshold, it indicates that the model may need to be further optimized or rebuilt. The model retraining process refers to the retraining process triggered when the performance of the existing calibration model deteriorates. Retraining updates the model parameters to restore prediction accuracy by introducing new data or optimization algorithms.

[0100] Specifically, after each preset time period, the latest fluid parameters (such as real-time pressure, temperature, flow rate) and calibrated flow meter measurement data are collected. Ensure that the newly collected data covers a variety of operating conditions and fluid types to improve the applicability of the model. Add the newly collected fluid parameters and measurement data to the existing model training set to form an updated training set. When integrating, clean the new data (eliminate outliers, smooth noise) and unify the format to ensure data quality.

[0101] Furthermore, the calibration model is retrained using the updated training set to adapt to the new environment or changes in flowmeter performance. The model parameters are adjusted to capture the latest relationship between fluid parameters and errors. After the retraining is completed, the prediction accuracy of the model is evaluated using independent test data. The model error (the difference between the actual error and the predicted error) is recorded to monitor model performance. The retrained calibration model is compared with the historical model error. If the current model error exceeds the preset threshold, it means that the model performance has degraded and cannot meet the calibration requirements. If the threshold condition is triggered, the model training process is restarted, including optimizing the training set (eliminating outdated data, introducing more up-to-date data) and adjusting the training algorithm (such as a more complex machine learning model). After the retraining is completed, the model is verified again until the error meets the preset standard.

[0102] By regularly updating the model training set and introducing the latest fluid parameters and measurement data, the calibration model can dynamically adapt to changes in the flowmeter's operating environment and performance, and maintain a high prediction accuracy. The retraining mechanism can be triggered in time when the model performance deteriorates, ensuring that the calibration model can always meet the error prediction requirements and avoid measurement inaccuracies caused by model aging. Periodically adding new data can not only enrich the model's training set, but also avoid the problem of excessive accumulation of outdated data leading to increased model complexity and training costs, thereby optimizing data usage efficiency. The dynamic update and retraining mechanism can reduce the need for manual intervention and reduce the cost of frequent reconstruction of the calibration model due to environmental changes or flowmeter aging. The update mechanism enables the calibration model to have stronger environmental adaptability, and can cope with complex changes in fluid properties, flowmeter performance and operating conditions, expanding the scope of application of the model. Through the mechanism of periodically updating data, retraining the model, and triggering the retraining process, this method can maintain the prediction performance and adaptability of the calibration model, ensuring that the measurement accuracy of the flowmeter remains stable in long-term use. At the same time, this process reduces the frequency of manual calibration, optimizes the use of data resources, and is suitable for flowmeter calibration needs in complex industrial environments and dynamic conditions.

[0103] Based on the above second embodiment, in this embodiment, step S3 includes:

[0104] S31: Acquire the current measurement value of the flow meter to be processed;

[0105] S32: inputting the fluid parameters into the calibration model to obtain expected measurement values;

[0106] S33: Determine the reading error of the flow meter to be processed according to the current measurement value and the expected measurement value.

[0107] It should be noted that the current measurement value refers to the flow value actually measured by the flow meter to be processed during calibration, reflecting the measurement result of the fluid flow under the current operating state of the flow meter. The expected measurement value is the ideal measurement value of the flow meter under the current fluid parameter conditions calculated based on the calibration model, which is used to compare with the actual measurement value to determine the error. The reading error is the deviation between the current measurement value and the expected measurement value, indicating the measurement accuracy problem of the flow meter and is the basis for calibration adjustment.

[0108] Specifically, the current measurement value is obtained through the real-time output interface of the flow meter to be processed. This is usually the instantaneous flow or cumulative flow value automatically recorded by the device under the current fluid conditions. Ensure that the obtained measurement value is true and valid, and eliminate abnormal data points caused by short-term fluctuations or sensor abnormalities. Use the current measurement value as the benchmark data for this calibration and record it for subsequent error calculations. Input the fluid parameters (such as pressure, temperature, and flow rate) obtained in real time into the calibration model. The calibration model outputs the expected measurement value under the current conditions based on the input fluid parameters combined with its preset mathematical relationship or statistical law. This value represents the result that the flow meter should output under ideal conditions.

[0109] Furthermore, compare the current measurement value of the flow meter with the expected measurement value output by the calibration model and analyze the difference between the two. Record the size and direction of the reading error. A positive error indicates that the measurement value is too high, and a negative error indicates that the measurement value is too low. Use the error result as the basis for calibration adjustment, and record the error data for subsequent model optimization or system analysis.

[0110] By obtaining the current measurement value of the flow meter in real time and comparing it with the expected value of the calibration model, the measurement deviation of the flow meter can be accurately determined and the measurement accuracy of the equipment can be fully understood. After the fluid parameters are input into the calibration model, the expected measurement value can be calculated immediately, and the error analysis can be completed quickly, which is suitable for the rapid calibration needs of industrial sites. By calculating the expected measurement value through the calibration model, there is no need for the process of repeated experimental verification of the measurement value in the traditional calibration method, which greatly simplifies the calibration operation and improves efficiency. With the real-time input of fluid parameters, the calibration model can adapt to the diverse working conditions of different fluids and flow meter types, with a wide range of error evaluation and strong applicability. The archiving of error data not only provides a basis for calibration adjustment, but also can be used for the optimization of subsequent calibration models, making the calibration process more accurate and efficient. By obtaining the current measurement value in real time and inputting the calibration model to calculate the expected value, the reading error of the flow meter can be quickly determined, which not only simplifies the error evaluation process, but also significantly improves the calibration efficiency and accuracy. At the same time, dynamic adaptability and data support functions provide strong technical guarantees for flow meter calibration under complex working conditions.

[0111] Based on the above second embodiment, in this embodiment, step S4 includes:

[0112] S41: Obtaining an error direction corresponding to the reading error, and comparing the reading error with a preset calibration threshold;

[0113] S42: if the reading error is greater than the preset calibration threshold, determining a calibration adjustment factor of the flow meter to be processed according to the reading error;

[0114] S43: Calibrate the flow meter to be processed based on the error direction and the calibration adjustment factor.

[0115] It should be noted that the error direction refers to the positive or negative attribute of the reading error. A positive error indicates that the flow meter measurement value is too high, and a negative error indicates that the flow meter measurement value is too low. The preset calibration threshold is the error range limit preset by the calibration system. When the reading error exceeds this threshold, the calibration operation is triggered. The calibration adjustment factor is a parameter used to correct the flow meter measurement value. It is calculated based on the error size and direction and applied to the measurement logic or hardware adjustment of the flow meter.

[0116] Specifically, the direction of the error is determined based on the positive and negative attributes of the reading error. Positive error: the flow meter measurement value is too high; negative error: the flow meter measurement value is too low. Compare the absolute value of the reading error with the preset calibration threshold. If the error is within the threshold range, no calibration is required, and the flow meter is marked as "normal measurement". If the error exceeds the threshold, proceed to the next calibration operation. The adjustment factor is calculated based on the error size and direction and is used to correct the flow meter measurement value. The calculation of the adjustment factor needs to take into account the flow meter type, current fluid parameters, etc. to ensure that the correction effect is suitable for the specific equipment. The adjustment factor is stored together with the error value for calibration records and subsequent performance verification.

[0117] Furthermore, an adjustment factor is introduced into the control logic of the flow meter to correct the flow meter's measured value in real time. For example, the adjustment factor is directly added to the measurement result. For larger errors or hardware problems, the calibration adjustment factor is applied through physical adjustments (such as sensor calibration, mechanical zero correction). After the calibration is completed, the flow meter performance is retested to ensure that the corrected measurement value is within the allowable error range with the expected value. The error, adjustment factor, corrected measurement value and error direction are archived to provide a reference for subsequent maintenance and model optimization.

[0118] Through the clear analysis of the error direction and the accurate calculation of the adjustment factor, the calibration operation can efficiently correct the specific deviation and significantly improve the measurement accuracy of the flowmeter. The judgment of the error direction enables the calibration operation to flexibly adapt to different types of deviations (positive or negative), realize targeted calibration, and avoid over-adjustment or under-correction. By comparing with the calibration threshold, only the flowmeter with excessive error is calibrated, which reduces unnecessary adjustment operations and improves the overall calibration efficiency. The calculation of the calibration adjustment factor takes into account the error direction, size and fluid characteristics, and can adapt to different flowmeter types and operating conditions, expanding the scope of application of the method. The archiving of calibration data provides a detailed historical basis for subsequent equipment maintenance and calibration model optimization, ensuring that the flowmeter maintains high accuracy in long-term use. Through the analysis of the reading error direction, dynamic comparison of the calibration threshold, accurate calculation of the calibration adjustment factor and targeted calibration operations, this method can quickly and accurately correct the measurement results of the flowmeter, ensure its reliability and stability in complex working conditions, and improve the efficiency and adaptability of the calibration operation, providing an efficient solution for industrial sites.

[0119] This embodiment determines the calibration fluid according to the physical properties of the current fluid; obtains the fluid parameters of the calibration fluid in the flow meter to be processed, and the fluid parameters include pressure, temperature and flow rate; determines the reading error of the flow meter to be processed according to the calibration model and the fluid parameters; and calibrates the flow meter to be processed based on the reading error. This embodiment achieves fast and efficient calibration by collecting fluid parameters in real time and calculating errors in combination with the calibration model, thereby reducing a large amount of time wasted in traditional calibration methods; dynamically selects the calibration fluid and adjusts the flow meter in real time, thereby improving the adaptability of the method and avoiding calibration errors caused by fluid differences in traditional methods; through automated data collection, error calculation and calibration adjustment, it reduces dependence on professionals and complex equipment, thereby reducing calibration time and cost and improving calibration efficiency.

[0120] The present application also provides a fluid-based online calibration device, please refer to Figure 4 , Figure 4 This is a schematic diagram of the module structure of a fluid online calibration device according to an embodiment of the present application. The fluid online calibration device includes:

[0121] The fluid determination module 401 is used to determine the calibration fluid according to the physical properties of the current fluid;

[0122] A parameter acquisition module 402 is used to acquire fluid parameters of the calibration fluid in the flow meter to be processed, wherein the fluid parameters include pressure, temperature and flow rate;

[0123] An error determination module 403 is used to determine the reading error of the flow meter to be processed according to the calibration model and the fluid parameters;

[0124] The calibration module 404 is used to calibrate the flow meter to be processed based on the reading error.

[0125] The fluid-based online calibration device provided in the embodiment of the present application adopts the fluid-based online calibration method in the above embodiment, which can solve the technical problem of how to improve the calibration efficiency. Compared with the prior art, the beneficial effects of the fluid-based online calibration device provided in the embodiment of the present application are the same as the beneficial effects of the fluid-based online calibration method provided in the above embodiment, and the other technical features in the fluid-based online calibration device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0126] The present application provides a fluid-based online calibration device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the fluid-based online calibration method in the above-mentioned embodiment.

[0127] Reference below Figure 5 , Figure 5 The schematic diagram of the device structure of the hardware operating environment involved in the fluid online calibration method in the embodiment of the present application shows a schematic diagram of the structure of the fluid online calibration device suitable for implementing the embodiment of the present application. The fluid online calibration device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The fluid-based online calibration device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0128] like Figure 5 As shown, the fluid-based online calibration device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 to the random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the fluid-based online calibration device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the fluid-based online calibration device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a fluid-based online calibration device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0129] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0130] The fluid-based online calibration device provided by the present application adopts the fluid-based online calibration method in the above embodiment, which can solve the technical problem of how to improve the calibration efficiency. Compared with the prior art, the beneficial effects of the fluid-based online calibration device provided by the present application are the same as the beneficial effects of the fluid-based online calibration method provided by the above embodiment, and the other technical features in the fluid-based online calibration device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0131] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0132] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0133] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the fluid-based online calibration method in the above-mentioned embodiment.

[0134] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0135] The computer-readable storage medium may be included in the fluid-based online calibration device; or may exist independently without being assembled into the fluid-based online calibration device.

[0136] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the fluid-based online calibration device, the fluid-based online calibration device: determines the calibration fluid according to the physical properties of the current fluid; obtains the fluid parameters of the calibration fluid in the flow meter to be processed, the fluid parameters including pressure, temperature and flow rate; determines the reading error of the flow meter to be processed according to the calibration model and the fluid parameters; calibrates the flow meter to be processed based on the reading error. The computer program code for performing the operations of the present application can be written in one or more programming languages ​​or a combination thereof, the programming languages ​​including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or it can be connected to an external computer (for example, through the Internet using an Internet service provider).

[0137] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0138] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0139] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned fluid-based online calibration method, and can solve the technical problem of how to improve the calibration efficiency. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the fluid-based online calibration method provided in the above-mentioned embodiment, and will not be repeated here.

[0140] An embodiment of the present application provides a computer program product, including a computer program, which implements the steps of the above-mentioned fluid-based online calibration method when executed by a processor.

[0141] The computer program product provided in this application can solve the technical problem of how to improve calibration efficiency. Compared with the prior art, the beneficial effects of the computer program product provided in the embodiment of this application are the same as the beneficial effects of the fluid online calibration method provided in the above embodiment, which will not be repeated here.

[0142] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent processing scope of the present application.

Claims

1. A fluid-based online calibration method, characterized in that: The method comprises: Determine the calibration fluid according to the physical properties of the current fluid; Obtaining fluid parameters of the calibration fluid in the flow meter to be processed, wherein the fluid parameters include pressure, temperature and flow rate; Determining a reading error of the flow meter to be processed according to the calibration model and the fluid parameters; Based on the reading error, the subject flow meter is calibrated.

2. The method according to claim 1, characterized in that Before the step of determining the calibration fluid according to the physical properties of the current fluid, the method further includes: Acquire historical flow data under multiple different fluid physical properties, historical measurement data of multiple different flow meter types, and historical record data of the flow meter to be processed; Performing data cleaning processing on the historical flow data, the historical measurement data and the historical record data to obtain historical flow measurement data, wherein the data cleaning processing includes one or more of outlier removal, data smoothing, data format matching and data labeling; The historical flow measurement data is analyzed to establish the calibration model.

3. The method according to claim 2, characterized in that The step of analyzing the historical flow measurement data and establishing the calibration model comprises: Based on a preset statistical algorithm, determining a corresponding relationship between historical fluid parameters and historical measurement errors in the historical flow measurement data; Based on the corresponding relationship and the preset mathematical model, a preliminary model is established; Adjusting parameters of the preliminary model based on the interaction between parameters in the historical fluid parameters; Based on the preset test data, the preliminary model after parameter adjustment is verified, and the verified preliminary model is used as the calibration model.

4. The method according to claim 2, characterized in that After the step of analyzing the historical flow measurement data and establishing the calibration model, the method further includes: At every preset time period, the fluid parameters and the measurement data of the calibrated flow meter to be processed are added to the model training set of the calibration model to obtain an updated training set; Retraining the calibration model based on the updated training set; If the model error of the retrained calibration model exceeds the preset error threshold, the model retraining process is triggered.

5. The method according to claim 1, characterized in that The step of determining the reading error of the flow meter to be processed according to the calibration model and the fluid parameters comprises: Obtaining the current measurement value of the flow meter to be processed; Inputting the fluid parameters into the calibration model to obtain expected measurement values; The reading error of the flow meter to be processed is determined according to the current measurement value and the expected measurement value.

6. The method according to claim 1, characterized in that The step of calibrating the flow meter to be processed based on the reading error comprises: Obtaining an error direction corresponding to the reading error, and comparing the reading error with a preset calibration threshold; If the reading error is greater than the preset calibration threshold, determining a calibration adjustment factor of the flow meter to be processed according to the reading error; The target flow meter is calibrated based on the error direction and the calibration adjustment factor.

7. A fluid-based online calibration device, characterized in that: The device comprises: A fluid determination module, used to determine a calibration fluid according to the physical properties of the current fluid; A parameter acquisition module, used to acquire fluid parameters of the calibration fluid in the flow meter to be processed, wherein the fluid parameters include pressure, temperature and flow rate; An error determination module, used for determining a reading error of the flow meter to be processed according to a calibration model and the fluid parameters; A calibration module is used to calibrate the flow meter to be processed based on the reading error.

8. A computer device, characterized in that: The device comprises: a memory, a processor, and a fluid-based online calibration program stored in the memory and executable on the processor, wherein the fluid-based online calibration program is configured to implement the steps of the fluid-based online calibration method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium stores a fluid-based online calibration program, and when the fluid-based online calibration program is executed by the processor, the steps of the fluid-based online calibration method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the fluid-based online calibration method according to any one of claims 1 to 6 are implemented.

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