Electric valve operation optimization method and system based on real-time data analysis

Abstract

The invention discloses an electric valve operation optimization method and system based on real-time data analysis, and particularly relates to the technical field of valve control. The operation state of the electric valve is monitored, the nonlinear hysteresis effect is analyzed, and the influence on the response precision of the valve is evaluated; meanwhile, the microscopic fatigue state of the mechanical part of the electric valve is analyzed, and the fatigue accumulation degree of the mechanical part is evaluated. And judging whether the electric valve runs at low efficiency or not by comprehensively analyzing the nonlinear hysteresis effect and the mechanical fatigue accumulation degree. When low-efficiency operation exists, the matching degree of the opening degree of the electric valve and fluid flowing is evaluated by analyzing fluid flow data, the stress distribution gradient of the sealing face is analyzed, and the sealing performance attenuation degree of the electric valve is evaluated. The opening and flow matching degree and the sealing performance attenuation degree of the electrically operated valve are comprehensively evaluated, the operation efficiency of the electrically operated valve is evaluated, the operation stability and accuracy of the electrically operated valve can be effectively improved, and efficient operation is achieved.

Description

Technical Field

[0001] The present invention relates to the field of valve control technology, and more specifically, to a method and system for optimizing the operation of an electric valve based on real-time data analysis. Background Art

[0002] Traditional electric valve control optimization methods usually rely on fixed opening settings or adjust the working state of the valve based on simple sensor data. However, these methods often fail to accurately reflect the complex situations faced by valves in actual operation, such as nonlinear hysteresis effects, fatigue accumulation of mechanical components, and attenuation of sealing performance, which lead to problems such as inefficient operation and slow response of electric valves.

[0003] In order to solve the above problems, a method and system for optimizing the operation of an electric valve based on real-time data analysis is provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an electric valve operation optimization method and system based on real-time data analysis to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A method for optimizing the operation of an electric valve based on real-time data analysis comprises the following steps: By monitoring the operating status of the electric valve, the nonlinear hysteresis effect of the electric valve is analyzed, and the impact of the nonlinear hysteresis effect on the response accuracy of the electric valve is evaluated; Conduct micro fatigue state analysis on the mechanical parts of the electric valve to evaluate the accumulation of micro mechanical fatigue of the electric valve; According to the influence of nonlinear hysteresis effect on the response accuracy of electric valve and the accumulation degree of micro-mechanical fatigue of electric valve, it is judged whether the electric valve has inefficient operation; When the electric valve operates inefficiently: analyze the fluid flow data of the electric valve to evaluate the matching degree between the opening degree of the electric valve and the fluid flow; evaluate the degree of attenuation of the sealing performance of the electric valve by analyzing the stress distribution gradient of the sealing surface of the electric valve.

[0006] A comprehensive analysis is conducted on the matching degree between the opening degree of the electric valve and the fluid flow and the degree of attenuation of the sealing performance of the electric valve to evaluate the efficiency of the electric valve operation.

[0007] In a preferred embodiment, the nonlinear hysteresis effect of the electric valve is analyzed by monitoring the operating state of the electric valve, and the influence of the nonlinear hysteresis effect on the response accuracy of the electric valve is evaluated, specifically: Monitor the real-time operating status of electric valves and obtain key data; Calculate the lag time difference between the change in the valve control signal and the motorized valve response: The nonlinear hysteresis effect during valve operation is modeled using a data analysis algorithm to quantify the impact of the nonlinear hysteresis effect on the response accuracy of the electric valve: the response error coefficient is defined and the calculation formula is: ;in, is the response error coefficient; For the evaluation cycle; Indicates at time response error.

[0008] In a preferred embodiment, the microscopic fatigue state analysis of the mechanical parts of the electric valve is performed to evaluate the microscopic mechanical fatigue accumulation degree of the electric valve, specifically: Use a high-resolution scanning electron microscope to scan the surface of key mechanical parts of the electric valve and obtain microstructure images; Apply image processing algorithms to remove noise, enhance contrast and detect edges in acquired microstructure images; Using machine learning models to identify fatigue crack features in microstructural images; Measure crack parameter data, build fatigue life models of mechanical components, and evaluate the degree of fatigue accumulation.

[0009] In a preferred embodiment, according to the influence of nonlinear hysteresis effect on the response accuracy of the electric valve and the degree of micro-mechanical fatigue accumulation of the electric valve, it is judged whether the electric valve is inefficiently operated, specifically: When the response error coefficient is greater than the response error coefficient threshold, and the fatigue accumulation coefficient is greater than the fatigue accumulation coefficient threshold, it is determined that the electric valve is operating inefficiently; otherwise, it is determined that the electric valve is not operating inefficiently.

[0010] In a preferred embodiment, the fluid flow data of the electric valve is analyzed to evaluate the matching degree between the opening degree of the electric valve and the fluid flow, specifically: Collect the fluid flow data of the electric valve in real time by installing flow sensors; Record the opening degree of the electric valve, including the actual opening degree and the set opening degree of the valve; Preprocess the collected fluid flow data to remove noise and abnormal data; A mathematical model between valve opening and fluid flow data is constructed to analyze the matching degree between valve opening and fluid flow data.

[0011] In a preferred embodiment, the degree of attenuation of the sealing performance of the electric valve is evaluated by analyzing the stress distribution gradient of the sealing surface of the electric valve, specifically: Use stress sensors to monitor the sealing surface of the electric valve in real time and collect stress data on the sealing surface; Using the finite element analysis method, based on the collected stress data, the stress distribution gradient of each point on the sealing surface is calculated; Identify stress concentration areas by analyzing stress distribution gradients; The stress distribution gradient is correlated with the sealing performance of the electric valve to quantify the influence of stress distribution on the sealing effect: the performance attenuation coefficient is defined and the calculation formula is: ;in, is the performance attenuation coefficient; , is an empirical constant; is the total number of stress concentration areas; For the The area of ​​the stress concentration region; For the The average stress distribution gradient in the stress concentration area.

[0012] In a preferred embodiment, a comprehensive analysis is performed on the matching degree between the opening degree of the electric valve and the fluid flow and the attenuation degree of the sealing performance of the electric valve to evaluate the efficiency of the operation of the electric valve, specifically: The determination coefficient corresponding to the matching degree between the opening degree of the electric valve and the fluid flow and the performance attenuation coefficient corresponding to the sealing performance attenuation degree of the electric valve are normalized respectively, and the normalized determination coefficient and performance attenuation coefficient are calculated. The calculation formula is: ;in, is the operating efficiency coefficient; is the coefficient of determination; is the performance attenuation coefficient; is the adjustment parameter of the performance attenuation coefficient; The operating efficiency coefficient threshold is preset, and the operating efficiency coefficient is compared with the operating efficiency coefficient threshold: When the operating efficiency coefficient is greater than or equal to the operating efficiency coefficient threshold, it means that the operating efficiency of the electric valve is high; When the operating efficiency coefficient is less than the operating efficiency coefficient threshold, it indicates that the operating efficiency of the electric valve is low.

[0013] On the other hand, the present invention provides an electric valve operation optimization system based on real-time data analysis, including a response accuracy evaluation module, a fatigue accumulation evaluation module, an inefficient operation judgment module, a matching degree evaluation module, a performance attenuation evaluation module and a comprehensive analysis module; The response accuracy evaluation module monitors the operating status of the electric valve, analyzes the nonlinear hysteresis effect of the electric valve, and evaluates the impact of the nonlinear hysteresis effect on the response accuracy of the electric valve; The fatigue accumulation assessment module performs microscopic fatigue state analysis on the mechanical components of the electric valve and assesses the degree of microscopic mechanical fatigue accumulation of the electric valve; The inefficient operation judgment module judges whether the electric valve is inefficiently operated according to the influence of the nonlinear hysteresis effect on the response accuracy of the electric valve and the accumulation degree of microscopic mechanical fatigue of the electric valve; When the electric valve operates inefficiently: the matching degree evaluation module analyzes the fluid flow data of the electric valve to evaluate the matching degree between the opening degree of the electric valve and the fluid flow; the performance attenuation evaluation module evaluates the sealing performance attenuation degree of the electric valve by analyzing the stress distribution gradient of the sealing surface of the electric valve.

[0014] The comprehensive analysis module conducts a comprehensive analysis on the matching degree between the opening degree of the electric valve and the fluid flow and the attenuation degree of the sealing performance of the electric valve, and evaluates the efficiency of the operation of the electric valve.

[0015] Technical effects and advantages of an electric valve operation optimization method and system based on real-time data analysis of the present invention: By real-time monitoring of the operating status of the electric valve, analyzing the nonlinear hysteresis effect, and analyzing the microscopic fatigue state of the mechanical components of the electric valve, the response accuracy and fatigue accumulation of the electric valve can be accurately evaluated, and potential inefficient operation problems can be discovered in a timely manner, avoiding the limitations of manual inspection and periodic maintenance in traditional methods. Secondly, by analyzing the fluid flow data of the electric valve and the stress distribution gradient of the sealing surface of the electric valve, the operating efficiency of the electric valve can be comprehensively evaluated, providing a scientific basis for optimizing the control strategy, thereby improving the valve adjustment accuracy, and providing a more reliable and economical electric valve operation optimization solution, which has important economic and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of an electric valve operation optimization method based on real-time data analysis according to the present invention; Figure 2 It is a structural schematic diagram of an electric valve operation optimization system based on real-time data analysis of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1:

[0019] Figure 1 The present invention provides an electric valve operation optimization method based on real-time data analysis, which comprises the following steps: By monitoring the operating status of the electric valve, the nonlinear hysteresis effect of the electric valve is analyzed, and the impact of the nonlinear hysteresis effect on the response accuracy of the electric valve is evaluated; Conduct micro fatigue state analysis on the mechanical parts of the electric valve to evaluate the accumulation of micro mechanical fatigue of the electric valve; According to the influence of nonlinear hysteresis effect on the response accuracy of electric valve and the accumulation degree of micro-mechanical fatigue of electric valve, it is judged whether the electric valve has inefficient operation; When the electric valve operates inefficiently: analyze the fluid flow data of the electric valve to evaluate the matching degree between the opening degree of the electric valve and the fluid flow; evaluate the degree of attenuation of the sealing performance of the electric valve by analyzing the stress distribution gradient of the sealing surface of the electric valve.

[0020] A comprehensive analysis is conducted on the matching degree between the opening degree of the electric valve and the fluid flow and the degree of attenuation of the sealing performance of the electric valve to evaluate the efficiency of the electric valve operation.

[0021] Specifically, by monitoring the operating status of the electric valve, analyzing the nonlinear hysteresis effect of the electric valve, and evaluating the impact of the nonlinear hysteresis effect on the response accuracy of the electric valve, including: Monitor the real-time operating status of the electric valve and obtain key data: The sensors installed on the electric valve monitor the various operating data of the electric valve in real time. These sensors are used to collect signals such as current, voltage and the switch status of the electric valve. These signals reflect the working status of the electric valve and its interaction with the external environment. Specifically, the current signal reflects the load condition of the electric valve, and the voltage signal reflects the operating condition of the electric valve drive system. The switch status of the electric valve is directly related to the action and response of the valve.

[0022] High-precision sensors and real-time data acquisition devices are used to ensure the accuracy and real-time nature of data collection. The collected data will be updated at a certain frequency.

[0023] Calculate the lag time difference between the change in valve control signal and the electric valve response: Evaluate the lag effect between the electric valve control signal and the actual valve response. In the electric valve control process, the lag between the input signal and the valve response is inevitable. This lag effect can be caused by many factors, including the mechanical structure of the valve itself, the inertia of the valve drive system, the fluid mechanics characteristics, and the control response delay.

[0024] In order to quantify this hysteresis effect, the time difference between the valve control signal and the valve response is compared in real time to calculate the hysteresis time. The control signal of the electric valve is defined as , the actual response signal of the valve is ;in, Denotes the time variable, and the lag time is calculated by the following steps: Signal matching: The correlation between the control signal and the response signal is calculated using the cross-correlation analysis method to determine the preliminary position of the hysteresis; Lag calculation: The time difference between the control signal and the response signal is calculated by the peak detection algorithm to obtain the lag time , the calculation formula is: ;in, Indicates the lag time, which is the time difference between the valve control signal and the valve response; Indicates the time offset; is a mathematical function.

[0025] By calculating the correlation between the control signal and the response signal, the lag time between the control signal and the response signal is found, that is, the time delay for the electric valve to respond to the control signal.

[0026] Use data analysis algorithms to model the nonlinear hysteresis effect during valve operation and quantify the impact of nonlinear hysteresis on the response accuracy of electric valves: After obtaining the lag time, use data analysis algorithms to model the nonlinear hysteresis effect of electric valves. The nonlinear hysteresis effect of electric valves is not a linear relationship, but the result of the combined effect of multiple factors, so nonlinear modeling methods are used to accurately describe its behavior.

[0027] The modeling method adopted is the nonlinear autoregressive integral sliding mode control model. The nonlinear autoregressive integral sliding mode control model is a nonlinear model widely used in system identification and can effectively capture the complex nonlinear characteristics in the valve control process.

[0028] The form of the nonlinear autoregressive integral sliding mode control model is: ;in, Represents a nonlinear function used to describe the complex relationship between the control signal and the response signal; Represents the delay order of the valve response signal, that is, the number of past response signals included in the model; It represents the delay order of the control signal, that is, the number of past control signals included in the model.

[0029] The nonlinear autoregressive integral sliding mode control model fits the nonlinear response characteristics of the electric valve through historical data, and continuously adjusts the model parameters through the optimization algorithm to predict the ideal response of the electric valve. .

[0030] After completing the modeling of the nonlinear hysteresis effect, calculate the response error of the electric valve at different time points. It is defined as the difference between the control signal and the actual response and is calculated as: ;in, Indicates at time The response error; It represents the ideal response of the electric valve predicted by the nonlinear autoregressive integral sliding mode control model.

[0031] The response error coefficient is defined to evaluate the impact of nonlinear hysteresis on the response accuracy of the electric valve. The calculation formula is: ;in, is the response error coefficient; is the evaluation period, i.e., the length of the time period during which the response accuracy of the electric valve is evaluated; Indicates at time response error.

[0032] The larger the response error coefficient, the greater the deviation between the actual response and the expected control result after the electric valve receives the control signal, which means that there is an obvious nonlinear hysteresis effect in the operation of the valve, resulting in valve response lag or over-adjustment. A large response error coefficient may be caused by factors such as the mechanical structure of the valve and the inertia of the valve drive system. As the nonlinear hysteresis effect increases, it is difficult for the valve to accurately follow the changes in the control signal in actual operation, thereby affecting the overall stability of the valve. A large response error coefficient may cause serious control imbalance and reduce the response accuracy of the valve.

[0033] Specifically, the microscopic fatigue state of the mechanical components of the electric valve is analyzed to evaluate the accumulation of microscopic mechanical fatigue of the electric valve, including: Use high-resolution scanning electron microscope to scan the surface of key mechanical parts of electric valves and obtain microstructure images: In the micro fatigue analysis of electric valves, high-resolution scanning electron microscope is used to perform detailed surface scanning of key mechanical parts of electric valves (such as valve stems, valve seats, seals, etc.) to obtain their microstructure images. High-resolution scanning electron microscope forms images by scanning electron beams to illuminate the surface of objects and capturing signals generated by electron interactions. It has ultra-high resolution and can reveal the morphology and distribution of component surfaces and their micro cracks.

[0034] To ensure clear and accurate image data, the key components of the electric valve need to be cleaned to remove surface contamination or oil stains. The scanning process is usually carried out in a high vacuum environment to ensure that tiny cracks and signs of fatigue on the surface of the component can be captured.

[0035] Apply image processing algorithms to remove noise, enhance contrast and detect edges on the acquired microstructure images: The acquired microstructure images need to be processed initially to improve image quality and extract key information such as fatigue cracks. De-noising algorithms are used to remove noise signals from images, usually using spatial filtering (such as mean filtering, Gaussian filtering, etc.) or frequency domain filtering (such as Fourier transform). After denoising, the image will be clearer, which is helpful for identifying cracks and their morphology.

[0036] The contrast enhancement method is applied to improve the visibility of cracks and surface textures in the image. Common algorithms include histogram equalization, contrast stretching and other techniques. The enhanced image is further processed using edge detection algorithms (such as Sobel operator, Canny edge detection, etc.). Through edge detection, the edge information of tiny cracks in the image can be accurately identified.

[0037] Using machine learning models to identify fatigue crack features in microstructure images: After image processing, machine learning models are used to identify fatigue crack features in images. By training deep learning models (such as convolutional neural networks) and using a large number of labeled crack image data sets for learning, the model can identify the pattern and location of fatigue cracks in the image. During the training process, the network optimizes parameters through back propagation to improve the accuracy of crack identification.

[0038] Through the machine learning model, it is possible to accurately find tiny crack features in the image and extract their morphological information, including parameters such as crack length, width, depth, and crack expansion path.

[0039] Measure crack parameter data, build fatigue life model of mechanical parts, and evaluate fatigue accumulation: After identifying crack characteristics, conduct quantitative analysis of cracks. ,width and depth It is a key parameter for evaluating the degree of fatigue accumulation. Measure the geometric characteristics of the crack: , the length of the crack, usually measured along the main direction of the crack; , the width of the crack, usually measured at the crack opening; , the depth of a crack, usually measured perpendicular to the component surface.

[0040] In order to accurately evaluate the fatigue accumulation of mechanical components, the finite element analysis method is used for simulation. The geometric model of the electric valve mechanical components is discretized, the finite element mesh is established, and the corresponding mechanical loads and boundary conditions are applied to the model to perform fatigue analysis.

[0041] Combined with the crack parameter data, the fatigue accumulation coefficient is defined to evaluate the degree of fatigue accumulation. The calculation formula is: ;in, represents the fatigue accumulation coefficient; The constant of the material of the mechanical parts of the electric valve; is the fatigue limit stress; It is the fatigue constant of the material of the mechanical parts of the electric valve, reflecting the fatigue behavior of the material.

[0042] The larger the fatigue accumulation coefficient, the higher the degree of micromechanical fatigue accumulation of the electric valve during operation, which means that the key mechanical components of the valve, such as the valve stem, valve seat and seals, are subjected to a large amount of cyclic stress and strain, resulting in the gradual expansion and accumulation of fatigue cracks in the microstructure. A high fatigue accumulation coefficient indicates that the fatigue of these components is close to the design limit, increasing the risk of material fracture or functional failure. A large fatigue accumulation coefficient may be caused by frequent switching operations, high-load operating conditions, or insufficient fatigue tolerance of the material itself. The increase in the fatigue accumulation coefficient also reflects the degree of wear and aging of the valve in long-term use, indicating that timely maintenance or replacement of components is required to prevent sudden failures.

[0043] Specifically, based on the impact of nonlinear hysteresis effect on the response accuracy of the electric valve and the degree of micro-mechanical fatigue accumulation of the electric valve, it is judged whether the electric valve is operating inefficiently, including: The response error coefficient threshold is preset, and the response error coefficient is compared with the response error coefficient threshold: When the response error coefficient is greater than the response error coefficient threshold, it means that the nonlinear hysteresis effect of the electric valve during operation affects the response accuracy of the electric valve, resulting in a large deviation between the actual response and the expected control result; When the response error coefficient is less than or equal to the response error coefficient threshold, it means that the nonlinear hysteresis effect of the electric valve during operation has no influence on the response accuracy of the electric valve, and the deviation between the actual response of the electric valve and the expected control result is within an acceptable range; Preset the fatigue accumulation coefficient threshold and compare the fatigue accumulation coefficient with the fatigue accumulation coefficient threshold: When the fatigue accumulation coefficient is greater than the fatigue accumulation coefficient threshold, it means that the micro-mechanical fatigue accumulation degree of the electric valve during operation is high. At this time, the mechanical properties and structural integrity of the electric valve are greatly affected, which may lead to the risk of failure of the electric valve under high load or frequent operation. When the fatigue accumulation coefficient is less than or equal to the fatigue accumulation coefficient threshold, it means that the micro-mechanical fatigue accumulation degree of the electric valve during operation is low. At this time, the fatigue damage degree of the mechanical parts of the electric valve during operation is low, and the crack extension and material degradation in the microstructure have not reached a dangerous level. The electric valve can operate under normal working conditions; When the response error coefficient is greater than the response error coefficient threshold, and the fatigue accumulation coefficient is greater than the fatigue accumulation coefficient threshold, it is determined that the electric valve is operating inefficiently; otherwise, it is determined that the electric valve is not operating inefficiently.

[0044] Specifically, the fluid flow data of the electric valve is analyzed to evaluate the matching degree between the opening degree of the electric valve and the fluid flow, including: Collect the fluid flow data of the electric valve in real time by installing flow sensors: Install high-precision flow sensors at key positions of the electric valve. High-precision flow sensors can monitor and collect the flow data of the fluid passing through the valve in real time, including parameters such as flow velocity, flow fluctuation and fluid pressure.

[0045] The selection of flow sensors is based on the working environment and fluid characteristics of the electric valve. For example, for high-pressure and high-temperature fluid environments, high-temperature and high-pressure flow sensors should be selected to ensure the accuracy and reliability of data collection. The data collection frequency is set to 100 times per second to ensure that the dynamic changes of fluid flow can be captured to meet the needs of high-precision evaluation.

[0046] Record the opening degree of the electric valve, including the actual opening degree and the set opening degree of the valve: The opening degree of the electric valve is divided into the actual opening degree and the set opening degree. The actual opening degree refers to the current physical opening degree of the valve, usually expressed in degrees or percentages; the set opening degree is the target opening degree set according to the demand instruction.

[0047] Preprocess the collected fluid flow data to remove noise and abnormal data: The preprocessing steps include: The wavelet transform method is used to denoise the fluid flow data. Wavelet transform can separate the noise components in the signal, retain useful flow information, and improve the signal-to-noise ratio of the data; Apply anomaly detection algorithms based on statistical analysis, such as the Z-score method, to identify and remove outliers in fluid flow data; The exponentially weighted moving average method is used to smooth the processed traffic data to reduce short-term fluctuations in the data.

[0048] Construct a mathematical model between valve opening and fluid flow data, and analyze the matching degree between valve opening and fluid flow data: Fluid flow data and opening data have different sampling frequencies or time delays. The two sets of data are synchronized through interpolation or resampling methods to ensure that the flow value at each time point corresponds to an opening value.

[0049] According to the relationship between valve opening and flow rate, the support vector regression model is selected for modeling, and its mathematical expression is: ;in, is the predicted flow value; It is the opening data of the electric valve; For the The weight parameters of the support vectors; is the kernel function; is the bias term of the support vector regression model; is the total number of support vectors; Represents the opening data of the electric valve corresponding to the support vector.

[0050] Use historical data to train the support vector regression model. As input, the predicted flow value As output, optimize the model parameters by minimizing the prediction error and During the training process, the cross-validation method is used to evaluate the generalization ability of the model and ensure the prediction accuracy of the model on unknown data.

[0051] The trained support vector regression model is used to predict the opening data of the electric valve in operation and obtain the predicted flow rate. . Compare the predicted flow with the actual flow Compare and calculate the coefficient of determination, the calculation formula is: ;in, is the coefficient of determination; For the The actual flow value of samples; For the The predicted flow value of samples; is the average value of the actual flow value; is the total number of times flow data is collected, which is calculated based on the collection time and frequency.

[0052] The larger the determination coefficient, the higher the degree of match between the valve opening and the fluid flow data. When the value of the determination coefficient is close to 1, it indicates that the opening degree of the electric valve can effectively control the fluid flow, and the valve adjustment action can accurately respond to the fluid flow demand.

[0053] Specifically, by analyzing the stress distribution gradient of the sealing surface of the electric valve, the degree of attenuation of the sealing performance of the electric valve is evaluated, including: Use stress sensors to monitor the sealing surface of the electric valve in real time and collect stress data on the sealing surface: Install high-precision stress sensors on the sealing surface of the electric valve to achieve real-time monitoring of the stress state of the sealing surface. These stress sensors include strain gauges and piezoelectric sensors, which can accurately measure the stress changes of the sealing surface during operation. Strain gauges measure stress by detecting the slight deformation of the sealing surface material under stress, and piezoelectric sensors obtain stress information by sensing the charge changes caused by stress changes.

[0054] The data collected by the sensor include the stress values ​​at each point, including the longitudinal stress , transverse stress and normal stress ,These stress components together reflect the stress distribution of the sealing surface under different working conditions.

[0055] Using the finite element analysis method, based on the collected stress data, the stress distribution gradient of each point on the sealing surface is calculated: After obtaining the sealing surface stress data, the finite element analysis method is used to calculate the stress distribution gradient of each point on the sealing surface, including: According to the geometric structure and material properties of the electric valve, a finite element model of the sealing surface is constructed. The finite element model contains the geometric details of all key components and accurately reflects the mechanical parameters of the material such as elastic modulus and Poisson's ratio; When meshing the finite element model, high-density meshes are usually used to improve calculation accuracy. The level of meshing directly affects the calculation accuracy of the stress distribution gradient; According to the actual working conditions, the boundary conditions of the finite element model are set and external loads are applied; the boundary conditions include fixed support, symmetry conditions, etc., and the loads include internal pressure, operating torque, etc.; The finite element model is solved using finite element analysis software to obtain the stress distribution data of each point on the sealing surface; the calculation formula for the stress distribution gradient is: ;in, represents the stress distribution gradient; is the longitudinal stress, which represents the stress component of the sealing surface along the length direction of the sealing surface; is the transverse stress, which represents the stress component along the width direction of the sealing surface; is the normal stress, which represents the stress component perpendicular to the sealing surface.

[0056] The stress distribution gradient calculated by finite element analysis can directly reflect the stress changes at different locations on the sealing surface. A higher stress distribution gradient indicates a dramatic stress change, while a lower stress distribution gradient indicates a gentle stress change, and the sealing surface is in a relatively stable stress state.

[0057] By analyzing the stress distribution gradient, stress concentration areas are identified: stress concentration areas refer to areas where the stress distribution gradient is significantly higher than the surrounding areas, usually located at the edge of the sealing surface. By setting the stress gradient threshold, areas where the stress distribution gradient is greater than the stress gradient threshold are screened out.

[0058] For the identified stress concentration area, extract its geometric characteristics and stress distribution characteristics, such as area , average stress distribution gradient Etc., for sealing performance correlation analysis.

[0059] The stress distribution gradient is correlated with the sealing performance of the electric valve to quantify the influence of stress distribution on the sealing effect: the performance attenuation coefficient is defined, and its calculation formula is: ;in, is the performance attenuation coefficient; , is an empirical constant obtained by data fitting; is the total number of stress concentration areas; For the The area of ​​the stress concentration region; For the The average stress distribution gradient in the stress concentration area.

[0060] The larger the performance attenuation coefficient, the more serious the sealing performance attenuation of the electric valve is, and the worse the sealing effect is. As the electric valve runs for a long time, the stress on the sealing surface gradually accumulates, and the stress concentration area increases. As the performance attenuation coefficient increases, the sealing performance of the electric valve will not meet the demand, which may cause equipment failure or safety risks. Appropriate maintenance and replacement measures must be taken to ensure the stability and safety of the equipment.

[0061] Specifically, a comprehensive analysis is conducted on the matching degree between the opening degree of the electric valve and the fluid flow and the attenuation degree of the sealing performance of the electric valve to evaluate the efficiency of the electric valve operation, including: The determination coefficient corresponding to the matching degree between the opening degree of the electric valve and the fluid flow and the performance attenuation coefficient corresponding to the sealing performance attenuation degree of the electric valve are normalized respectively, and the normalized determination coefficient and performance attenuation coefficient are calculated. The calculation formula is: ;in, is the operating efficiency coefficient; is the coefficient of determination; is the performance attenuation coefficient; is the adjustment parameter of the performance attenuation coefficient.

[0062] The operating efficiency coefficient threshold is preset, and the operating efficiency coefficient is compared with the operating efficiency coefficient threshold: When the operating efficiency coefficient is greater than or equal to the operating efficiency coefficient threshold, it indicates that the operating efficiency of the electric valve is high; the electric valve can continue to maintain normal operation without adjustment or replacement; When the operating efficiency coefficient is less than the operating efficiency coefficient threshold, it means that the operating efficiency of the electric valve is low; a lower operating efficiency coefficient means that the electric valve may not be able to accurately respond to changes in fluid flow, and the control effect does not meet expectations, which may lead to pressure fluctuations, unstable flow, energy waste or abnormal operation. The electric valve needs to be inspected, debugged or repaired to restore its normal working efficiency; possible measures include adjusting the valve opening, repairing or replacing sealing components.

[0063] Embodiment 2:

[0064] The difference between Example 2 of the present invention and Example 1 is that this example introduces an electric valve operation optimization system based on real-time data analysis.

[0065] Figure 2 A structural schematic diagram of an electric valve operation optimization system based on real-time data analysis of the present invention is given, and an electric valve operation optimization system based on real-time data analysis includes a response accuracy evaluation module, a fatigue accumulation evaluation module, an inefficient operation judgment module, a matching degree evaluation module, a performance attenuation evaluation module and a comprehensive analysis module; The response accuracy evaluation module monitors the operating status of the electric valve, analyzes the nonlinear hysteresis effect of the electric valve, and evaluates the impact of the nonlinear hysteresis effect on the response accuracy of the electric valve; The fatigue accumulation assessment module performs microscopic fatigue state analysis on the mechanical components of the electric valve and assesses the degree of microscopic mechanical fatigue accumulation of the electric valve; The inefficient operation judgment module judges whether the electric valve is inefficiently operated according to the influence of the nonlinear hysteresis effect on the response accuracy of the electric valve and the accumulation degree of microscopic mechanical fatigue of the electric valve; When the electric valve operates inefficiently: the matching degree evaluation module analyzes the fluid flow data of the electric valve to evaluate the matching degree between the opening degree of the electric valve and the fluid flow; the performance attenuation evaluation module evaluates the sealing performance attenuation degree of the electric valve by analyzing the stress distribution gradient of the sealing surface of the electric valve.

[0066] The comprehensive analysis module conducts a comprehensive analysis on the matching degree between the opening degree of the electric valve and the fluid flow and the attenuation degree of the sealing performance of the electric valve, and evaluates the efficiency of the operation of the electric valve.

[0067] The above formulas are dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions. The parts not described in the present invention are applicable to the prior art.

Claims

1. A method for optimizing the operation of an electric valve based on real-time data analysis, characterized in that: The steps include: By monitoring the operating status of the electric valve, the nonlinear hysteresis effect of the electric valve is analyzed, and the impact of the nonlinear hysteresis effect on the response accuracy of the electric valve is evaluated; Conduct micro fatigue state analysis on the mechanical parts of the electric valve to evaluate the accumulation of micro mechanical fatigue of the electric valve; According to the influence of nonlinear hysteresis effect on the response accuracy of electric valve and the accumulation degree of micro-mechanical fatigue of electric valve, it is judged whether the electric valve has inefficient operation; When the electric valve operates inefficiently: Analyze the fluid flow data of the electric valve to evaluate the matching degree between the opening degree of the electric valve and the fluid flow; By analyzing the stress distribution gradient of the electric valve's sealing surface, the degree of attenuation of the electric valve's sealing performance is evaluated; A comprehensive analysis is conducted on the matching degree between the opening degree of the electric valve and the fluid flow and the degree of attenuation of the sealing performance of the electric valve to evaluate the efficiency of the electric valve operation.

2. The method for optimizing the operation of an electric valve based on real-time data analysis according to claim 1, characterized in that: By monitoring the operating status of the electric valve, analyzing the nonlinear hysteresis effect of the electric valve, and evaluating the impact of the nonlinear hysteresis effect on the response accuracy of the electric valve, specifically: Monitor the real-time operating status of electric valves and obtain key data; Calculate the lag time difference between the change in the valve control signal and the motorized valve response: The nonlinear hysteresis effect during valve operation is modeled using a data analysis algorithm to quantify the impact of the nonlinear hysteresis effect on the response accuracy of the electric valve: the response error coefficient is defined and the calculation formula is: ;in, is the response error coefficient; For the evaluation cycle; Indicates at time response error.

3. The method for optimizing the operation of an electric valve based on real-time data analysis according to claim 2, characterized in that: The micro fatigue state of the mechanical parts of the electric valve is analyzed to evaluate the accumulation of micro mechanical fatigue of the electric valve, specifically: Use a high-resolution scanning electron microscope to scan the surface of key mechanical parts of the electric valve and obtain microstructure images; Apply image processing algorithms to remove noise, enhance contrast and detect edges on acquired microstructure images; Using machine learning models to identify fatigue crack features in microstructural images; Measure crack parameter data, build fatigue life models of mechanical components, and evaluate the degree of fatigue accumulation.

4. The method for optimizing the operation of an electric valve based on real-time data analysis according to claim 3 is characterized in that: According to the influence of nonlinear hysteresis effect on the response accuracy of electric valve and the accumulation degree of micro-mechanical fatigue of electric valve, it is judged whether the electric valve has inefficient operation, specifically: When the response error coefficient is greater than the response error coefficient threshold, and the fatigue accumulation coefficient is greater than the fatigue accumulation coefficient threshold, it is determined that the electric valve is operating inefficiently; otherwise, it is determined that the electric valve is not operating inefficiently.

5. The method for optimizing the operation of an electric valve based on real-time data analysis according to claim 4, characterized in that: Analyze the fluid flow data of the electric valve to evaluate the matching degree between the opening degree of the electric valve and the fluid flow, specifically: Collect the fluid flow data of the electric valve in real time by installing flow sensors; Record the opening degree of the electric valve, including the actual opening degree and the set opening degree of the valve; Preprocess the collected fluid flow data to remove noise and abnormal data; A mathematical model between valve opening and fluid flow data is constructed to analyze the matching degree between valve opening and fluid flow data.

6. The method for optimizing the operation of an electric valve based on real-time data analysis according to claim 5, characterized in that: By analyzing the stress distribution gradient of the sealing surface of the electric valve, the degree of attenuation of the sealing performance of the electric valve is evaluated, specifically: Use stress sensors to monitor the sealing surface of the electric valve in real time and collect stress data on the sealing surface; Using the finite element analysis method, based on the collected stress data, the stress distribution gradient of each point on the sealing surface is calculated; Identify stress concentration areas by analyzing stress distribution gradients; The stress distribution gradient is correlated with the sealing performance of the electric valve to quantify the influence of stress distribution on the sealing effect: the performance attenuation coefficient is defined and the calculation formula is: ;in, is the performance attenuation coefficient; , is an empirical constant; is the total number of stress concentration areas; For the The area of ​​the stress concentration region; For the The average stress distribution gradient in the stress concentration area.

7. The method for optimizing the operation of an electric valve based on real-time data analysis according to claim 6, characterized in that: Comprehensively analyze the matching degree between the opening degree of the electric valve and the fluid flow and the attenuation degree of the sealing performance of the electric valve to evaluate the efficiency of the electric valve operation, specifically: The determination coefficient corresponding to the matching degree between the opening degree of the electric valve and the fluid flow and the performance attenuation coefficient corresponding to the sealing performance attenuation degree of the electric valve are normalized respectively, and the normalized determination coefficient and performance attenuation coefficient are calculated. The calculation formula is: ;in, is the operating efficiency coefficient; is the coefficient of determination; is the performance attenuation coefficient; is the adjustment parameter of the performance attenuation coefficient; The operating efficiency coefficient threshold is preset, and the operating efficiency coefficient is compared with the operating efficiency coefficient threshold: When the operating efficiency coefficient is greater than or equal to the operating efficiency coefficient threshold, it means that the operating efficiency of the electric valve is high; When the operating efficiency coefficient is less than the operating efficiency coefficient threshold, it indicates that the operating efficiency of the electric valve is low.

8. An electric valve operation optimization system based on real-time data analysis, used to implement an electric valve operation optimization method based on real-time data analysis as described in any one of claims 1 to 7, characterized in that: It includes response accuracy assessment module, fatigue accumulation assessment module, inefficient operation judgment module, matching degree assessment module, performance attenuation assessment module and comprehensive analysis module; The response accuracy evaluation module monitors the operating status of the electric valve, analyzes the nonlinear hysteresis effect of the electric valve, and evaluates the impact of the nonlinear hysteresis effect on the response accuracy of the electric valve; The fatigue accumulation assessment module performs microscopic fatigue state analysis on the mechanical components of the electric valve and assesses the degree of microscopic mechanical fatigue accumulation of the electric valve; The inefficient operation judgment module judges whether the electric valve is inefficiently operated according to the influence of the nonlinear hysteresis effect on the response accuracy of the electric valve and the accumulation degree of microscopic mechanical fatigue of the electric valve; When the electric valve operates inefficiently: the matching degree evaluation module analyzes the fluid flow data of the electric valve to evaluate the matching degree between the opening degree of the electric valve and the fluid flow; The performance attenuation evaluation module evaluates the degree of sealing performance attenuation of the electric valve by analyzing the stress distribution gradient of the sealing surface of the electric valve; The comprehensive analysis module conducts a comprehensive analysis on the matching degree between the opening degree of the electric valve and the fluid flow and the attenuation degree of the sealing performance of the electric valve, and evaluates the efficiency of the operation of the electric valve.

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