Fluid medium flow pressure adjusting device and adjusting performance evaluation method thereof

Abstract

The invention belongs to the technical field of fluid control, and provides a fluid medium flow pressure adjusting device and an adjusting performance evaluation method thereof.The method comprises the steps that a basic data set, including flow data, pressure data and time sequence data, of device operation is obtained, and change rate characteristics and stability characteristics are extracted from the basic data set; based on the characteristics, analyzing an operation process, dividing a steady-state working condition layer, a disturbance working condition layer and a transition working condition layer, calculating a phase difference between flow and pressure, response delay and a correlation coefficient, generating a response sensitivity value and an operation stability index, realizing quantitative evaluation of transient fluctuation and a coupling effect, establishing a comprehensive performance criterion interval, and realizing comprehensive performance evaluation. According to the method, the flow and pressure change rule can be comprehensively described, the operation state can be quantified, and a basis is provided for monitoring and optimizing the operation of the device, and performance trend evaluation logic is established based on the change rule, trend calculation and state evaluation are performed on the operation performance, and comprehensive performance evaluation data and a visual evaluation result are generated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fluid control, in particular to a fluid medium flow pressure regulating device and a regulating performance evaluation method thereof. BACKGROUND

[0002] A fluid medium flow pressure regulating device and a regulating performance evaluation method thereof is an intelligent system integrating fluid pressure and flow regulation functions and performance self-evaluation functions, which collects fluid pressure and flow data through internal sensors and uses algorithms to analyze and quantify performance indicators such as regulating response speed, steady-state error and oscillation characteristics of the device under different working conditions, thereby realizing dynamic evaluation and optimization guidance of the regulating device performance.

[0003] In the prior art, a fluid medium flow pressure regulating device and a regulating performance evaluation method thereof usually collect fluid pressure and flow variation data during device operation, and calculate response time, steady-state error, overshoot and oscillation amplitude based on these data, using these indicators as the basis for evaluating the regulating performance of the device, so as to judge the regulating speed, stability and accuracy of the device under different working conditions.

[0004] The above scheme still has some problems in actual application. Although the prior art can complete the evaluation of the regulating device, the existing fluid medium flow pressure regulating device performance evaluation method relies on static parameters collected under specific stable working conditions and performs performance determination through setting threshold values or fixed calculation models. This way makes the evaluation process extremely sensitive to dynamic response characteristics and working condition changes. When factors such as fluid characteristics, load fluctuations or control delays cause transient changes during operation, the static model cannot capture these dynamic characteristics in time, which easily leads to evaluation lag or distortion, resulting in the regulating device being judged as normal even if its dynamic response performance has decreased or its stability has deteriorated. Ultimately, the evaluation result does not match the actual running state, reducing the accuracy and prediction effectiveness of the evaluation.

[0005] Therefore, the present application provides a fluid medium flow pressure regulating device and a regulating performance evaluation method thereof. SUMMARY

[0006] The present application provides a fluid medium flow pressure regulating device and a regulating performance evaluation method thereof, which realizes quantitative evaluation of transient fluctuations of the device and provides a scientific basis for device operation state monitoring and optimization control.

[0007] To achieve the above-mentioned purpose, the present application adopts the following technical solutions: The present application provides a fluid medium flow pressure regulating performance evaluation method, which comprises: Acquire basic datasets of the fluid medium flow and pressure regulating device during operation, the basic datasets including flow data, pressure data and time series data; Feature extraction is performed on the basic dataset to obtain change rate features and stability features; Based on the change rate characteristics and the stability characteristics, the operation process of the fluid medium flow and pressure regulating device is analyzed to obtain the operating condition data sequence, which includes the steady-state operating condition data sequence, the disturbance operating condition data sequence and the transition operating condition data sequence. The flow rate data and pressure data corresponding to the time series data in each working condition layer of the basic dataset are compared to obtain the change results, and the change patterns of the flow rate data and the pressure data are obtained based on the change results. Based on the aforementioned change pattern, a performance trend evaluation logic is established. By analyzing the continuous evolution characteristics of the time series data, the operating performance of the fluid medium flow and pressure regulating device is calculated and its status is evaluated to obtain comprehensive performance evaluation data. Key feature parameters of steady-state operating condition data sequence and key feature parameters of disturbance operating condition data sequence are obtained. Based on the key feature parameters of the steady-state operating condition data sequence, the key feature parameters of the disturbance operating condition data sequence are analyzed to obtain response sensitivity values. The response sensitivity values ​​are then processed to obtain sensitivity coefficients. Based on the key feature parameters of the steady-state operating condition layer data sequence and the sensitivity coefficient, the performance criterion interval of the comprehensive evaluation data is obtained; Evaluation result data is generated based on the comprehensive evaluation data and the performance criterion range, and the evaluation result data is displayed on a screen for visualization.

[0008] In some possible implementations, the rate of change characteristics include the rate of change of flow and the rate of change of pressure, and the stability characteristics include the amplitude of flow fluctuation and the amplitude of pressure fluctuation. Based on the rate of change characteristics and the stability characteristics, the operation process of the fluid medium flow and pressure regulating device is analyzed to obtain a working condition data sequence, including: The flow rate change rate and pressure change rate are calculated using a sliding window on the time series data to obtain local change trend parameters. The instantaneous deviation value is calculated based on the local change trend parameter to assess the fluctuation range of the operating status; The fluctuation amplitude is compared with the stability characteristics to generate an operational stability index; By combining the change rate characteristics, the stability characteristics, and the operational stability index, a working condition layer data sequence is obtained.

[0009] In some possible implementations, the change results include phase difference data, response delay data, and correlation coefficients. The step of analyzing the change results to obtain the variation patterns of the flow rate data and the pressure data includes: The phase difference is obtained by performing Fourier transform or cross-correlation analysis on the flow rate data and the pressure data, and represents the lag angle of the flow rate response to the pressure. The response delay is obtained through cross-correlation function analysis and represents the lag time of pressure to flow rate change; The correlation coefficient is calculated using a sliding time window and reflects the degree of coupling between flow rate and pressure and their dynamic changes over time. Calculate the phase difference, response delay, and correlation coefficient between the flow rate data and the pressure data, and obtain the variation pattern based on the joint analysis of the phase difference, response delay, and correlation coefficient.

[0010] In some possible implementations, the performance criterion interval for obtaining the comprehensive evaluation data based on the key feature parameters of the steady-state operating condition layer data sequence and the key feature parameters of the disturbance operating condition layer data sequence includes: Obtain the key feature parameters of the steady-state operating condition layer data sequence, including the average value, standard deviation and stability of each key parameter; The key feature parameters of the disturbance condition layer data sequence are compared with the key feature parameters of the steady-state condition layer data sequence to obtain the response sensitivity value, and the sensitivity coefficient is generated through processing. Based on the average value of the key characteristic parameters of the steady-state operating condition layer data sequence, the initial allowable fluctuation range of each parameter is calculated according to the standard deviation, and the fluctuation range is adjusted in combination with the sensitivity coefficient to reflect the sensitivity of different parameters to the overall performance. The adjusted upper and lower limits of each key parameter are combined to generate a performance criterion interval for comprehensive evaluation data, which is used to generate subsequent evaluation results.

[0011] In some possible implementations, the establishment of performance trend evaluation logic based on the aforementioned change pattern includes: Based on the time evolution trend of phase difference, response delay and correlation coefficient in the aforementioned change law, a multi-dimensional performance index set is constructed, which reflects the dynamic matching relationship between flow response and pressure feedback. Based on the variation characteristics of the multidimensional performance index set in continuous time series data, a performance state mapping relationship is established, which describes the trend of the operating performance of the fluid medium flow and pressure regulating device evolving from a stable state to a deteriorated or abnormal state. By fitting and modeling the performance state mapping relationship, a performance trend evaluation logic model is formed, and a performance trend evaluation logic is established based on the performance trend evaluation logic model.

[0012] In some possible implementations, the trend calculation and status assessment of the operating performance of the fluid medium flow rate and pressure regulating device to obtain comprehensive performance evaluation data includes: The performance trend evaluation logic is used to perform trend calculation and status evaluation on the operating performance of the fluid medium flow and pressure regulating device in the time series data, and the trend calculation results and status evaluation results are obtained. Based on the performance criterion range of the comprehensive evaluation data, the trend calculation results and state evaluation results of each key performance parameter are verified and quantitatively analyzed to generate comprehensive performance evaluation data.

[0013] In some possible implementations, generating evaluation result data based on the comprehensive evaluation data and the performance criterion interval, and displaying the visualized or numerical evaluation result data on a display screen, includes: The comprehensive performance evaluation data is compared and analyzed with the performance criterion interval to obtain the comparison and analysis results; Based on the comparison and analysis results, the deviation, trend and anomaly level of each key performance parameter are calculated, and comprehensive evaluation result data is generated. The evaluation result data is processed to generate visualized display data, and the evaluation result data is displayed on the display screen.

[0014] A fluid medium flow rate and pressure regulating device, specifically: Data acquisition module: used to acquire basic datasets during the operation of the fluid medium flow and pressure regulating device, the basic datasets including flow data, pressure data and time series data; Feature extraction module: used to extract features from the basic dataset to obtain change rate features and stability features; Operating condition generation module: used to analyze the operation process of the fluid medium flow and pressure regulating device based on the change rate characteristics and the stability characteristics, and obtain the operating condition data sequence, which includes the steady-state operating condition data sequence, the disturbance operating condition data sequence and the transition operating condition data sequence. Time series analysis module: used to compare the flow data and pressure data corresponding to the time series data in each working condition layer, obtain the change results, and analyze the change pattern of the flow data and pressure data based on the change results. The change results include phase difference data, response delay data and correlation coefficient. Data processing module: used to acquire key feature parameters of steady-state operating condition data sequence and disturbed operating condition data sequence, compare key feature parameters of disturbed operating condition data sequence based on key feature parameters of steady-state operating condition data sequence, calculate response sensitivity value and generate sensitivity coefficient, and obtain performance criterion interval of comprehensive evaluation data based on key feature parameters of steady-state operating condition data sequence and sensitivity coefficient. Performance trend evaluation module: used to establish performance trend evaluation logic based on the change pattern, and to perform trend calculation and status evaluation on the operating performance of the fluid medium flow and pressure regulating device by analyzing the continuous evolution characteristics of time series data, so as to obtain comprehensive performance evaluation data; Results display module: Used to generate evaluation result data based on the comprehensive evaluation data and the performance criterion range, and to display the visualized or numerical evaluation result data on the display screen.

[0015] The data processing module specifically includes: Key parameter acquisition unit: used to acquire key feature parameters of steady-state operating condition layer data sequence, including the average value, standard deviation and stability of flow data and pressure data, and to acquire key feature parameters of disturbed operating condition layer data sequence; Sensitivity calculation unit: used to compare the key feature parameters of the disturbance condition layer data sequence based on the key feature parameters of the steady-state condition layer data sequence, calculate the response sensitivity value, and generate a sensitivity coefficient based on the response sensitivity value; Performance criterion generation unit: used to determine the adjusted allowable fluctuation range of each key parameter based on the key feature parameters of the steady-state operating condition layer data sequence and the sensitivity coefficient, and to combine the adjusted allowable fluctuation range to form the performance criterion interval of the comprehensive evaluation data.

[0016] The beneficial effects of this invention are as follows: 1. The fluid medium flow and pressure regulation performance evaluation method of the present invention, by real-time acquisition of flow data, pressure data and time series data, and extraction of change rate characteristics and stability characteristics, realizes dynamic response analysis of fluid medium flow and pressure regulation device under different operating conditions. By dividing the steady-state operating condition layer, disturbance operating condition layer and transition operating condition layer, and calculating the phase difference, response delay and correlation coefficient of flow and pressure, it can comprehensively describe the flow and pressure change law, capture transient disturbances and coupling effects, generate operating stability index, realize quantitative evaluation of transient fluctuations of device, and provide scientific basis for device operating status monitoring and optimized control; 2. The fluid medium flow and pressure regulation performance evaluation method of the present invention constructs a performance trend evaluation logic based on phase difference, response delay and correlation coefficient, and generates a comprehensive performance criterion interval by combining key characteristic parameters of steady state and disturbance conditions. This enables continuous trend calculation and state evaluation of the device's operating performance. The system can quantify the dynamic matching relationship between flow response and pressure feedback, output comprehensive performance evaluation results across operating conditions, including deviation, change trend and anomaly level, and visualize the results to allow operators to intuitively grasp the device's performance status. This enables dynamic monitoring, trend analysis and early warning of potential performance degradation, providing a basis for decision-making in operation management and safety control. Attached Figure Description

[0017] The invention will now be further described with reference to the accompanying drawings.

[0018] Figure 1 This is a flowchart of a fluid medium flow and pressure regulation performance evaluation method according to the present invention; Figure 2 This is a flowchart of a fluid medium flow and pressure regulating device according to the present invention; Figure 3 This is an internal flowchart of the data processing module of the present invention; Detailed Implementation

[0019] The terms "first," "second," and "third," etc., used in this application specification, claims, and drawings are used to distinguish different objects, not to limit a specific order.

[0020] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0021] Research has revealed that while existing technologies can evaluate regulating devices, current performance evaluation methods for fluid medium flow and pressure regulating devices rely on static parameters collected under specific stable operating conditions and use set thresholds or fixed calculation models for performance judgment. This approach makes the evaluation process extremely sensitive to dynamic response characteristics and changes in operating conditions. When transient changes occur due to factors such as fluid characteristics, load fluctuations, or control delays during operation, the static model cannot capture these dynamic characteristics in a timely manner, easily leading to evaluation lag or distortion. This results in regulating devices being judged as normal even when their dynamic response performance has decreased or their stability has deteriorated, ultimately causing the evaluation results to be inconsistent with the actual operating state, reducing the accuracy and predictive effectiveness of the evaluation. Example

[0022] To solve the above problems, such as Figure 1 As shown, this application provides a method for evaluating the performance of fluid medium flow rate and pressure regulation, the method comprising: Step 1: Obtain the basic dataset of the fluid medium flow and pressure regulating device during operation. The basic dataset includes flow data, pressure data, and time series data. In step one: Fluid medium flow and pressure regulating device: refers to a mechanical or electrical control device used to control the flow and pressure of fluid medium in a pipeline or system, including valves, pumps, actuators, and sensor modules. This device achieves precise control of the fluid medium by adjusting the flow control valve or pressure regulating valve; Basic dataset: refers to the set of all raw data recorded in real time by the data acquisition module during the operation of the device, including but not limited to instantaneous flow values ​​collected by flow sensors, instantaneous pressure values ​​collected by pressure sensors, and their corresponding timestamp information. The basic dataset provides a complete data foundation for subsequent feature extraction and operating condition analysis; Flow data: refers to the basic data that centrally represents the change in volume or mass of fluid medium passing through a pipeline per unit time. Flow data is usually recorded in liters per second (L / s) or cubic meters per hour (m³ / h). Pressure data: refers to the basic dataset that centrally represents the instantaneous pressure values ​​of the fluid medium in a device or pipeline, usually expressed in Pascals (Pa) or megapascals (MPa). Pressure data reflects the dynamic state of the fluid in the system and is an important indicator for evaluating the performance of the device; Time series data refers to flow rate and pressure data arranged chronologically within a base dataset, used to analyze the dynamic changes of fluid media during continuous operation. Time series data can be acquired through sensor sampling frequency, such as sampling once per second to form a continuous sequence. Rate of change characteristics: refers to the instantaneous rate of change obtained by differentiating or differentiating time series data of flow and pressure data. It is used to describe the response speed of the device to external disturbances or operating commands. Rate of change characteristics may include flow rate of change (the rate of change of flow over time) and pressure rate of change (the rate of change of pressure over time). Stability characteristics refer to indicators obtained through statistical analysis of the fluctuation range of flow and pressure data within a certain time window, used to measure the smoothness of equipment operation. Stability characteristics may include flow fluctuation range, pressure fluctuation range, standard deviation or variance, etc. For example, in order to acquire the basic dataset, the data acquisition module collects continuous flow and pressure data at a fixed sampling frequency through flow sensors and pressure sensors installed on the device, and records the corresponding timestamps, thereby forming a complete time series data basic dataset. When extracting features from the basic dataset, the rate of change feature can be obtained by numerical difference or first derivative within a sliding time window. For example, the flow rate change is obtained by dividing the difference between the flow rate value at each time point and the flow rate value at the previous time point by the sampling time interval, while the pressure change rate is obtained by numerical difference of pressure. Stability characteristics can be obtained by calculating the standard deviation or maximum fluctuation of flow and pressure data within each sliding time window, which reflects the smooth operation of the device during that time period. To ensure the accuracy of feature extraction, the raw data can be denoised first, such as by using moving average filtering or wavelet denoising, to eliminate the influence of sensor measurement errors or short-term outliers. It should be noted that by acquiring the basic dataset and extracting the rate of change and stability characteristics, a scientific basis can be provided for subsequent operating condition layer division, enabling the system to distinguish between steady-state operation, disturbance response, and transition stages. This allows for quantitative evaluation and trend analysis of the operating performance of the fluid medium flow and pressure regulating device. At the same time, this step ensures the integrity and continuity of the characteristic parameters, avoids interference from single-point abnormal data on subsequent analysis results, and provides a reliable data foundation and quantitative indicators for device performance evaluation.

[0023] Step 2: Based on the change rate characteristics and the stability characteristics, analyze the operation process of the fluid medium flow and pressure regulating device to obtain the operating condition data sequence, which includes the steady-state operating condition data sequence, the disturbance operating condition data sequence, and the transition operating condition data sequence. In step two, the operating condition layer data sequence refers to the time series data set that divides the device operation process into different operating states. Each operating condition layer contains corresponding flow data, pressure data and time series data information, which are used to distinguish between steady state, disturbance and transient states. Steady-state operating condition data sequence: used to describe the time period during which the flow and pressure changes are small and the system is generally stable during the operation of the device, and can be used as a benchmark state for the performance of the device; Disturbance condition layer data sequence: used to describe the time period during which the flow or pressure of the device fluctuates significantly or is affected by external disturbances during operation, and can be used to analyze the dynamic response characteristics of the device; Transitional operating condition layer data sequence: used to describe the transition stage of the device from steady state to disturbance state or from disturbance state back to steady state, reflecting the dynamic change process of the device response; For example, the rate of change of flow and pressure at each time point can be compared with preset steady-state thresholds and disturbance thresholds. Time points below the steady-state threshold are classified into the steady-state operating condition layer, time points above the disturbance threshold are classified into the disturbance operating condition layer, and time points in between are classified into the transition operating condition layer. To avoid the impact of single-point anomalies on the operating condition classification, the changing trend of continuous time points can be combined, for example, by calculating the average rate of change or the median rate of change within a sliding window for smoothing before classification. Within the data sequence of each operating condition layer, the corresponding flow data, pressure data, and time series data information are integrated to form a complete dataset, providing a reliable basis for subsequent analysis. At the same time, the average value, standard deviation, and maximum fluctuation amplitude of flow and pressure for each operating condition layer can be further calculated, providing core parameters for sensitivity analysis and performance criterion generation. It should be noted that by dividing the operating condition data sequence based on the characteristics of change rate and stability, steady state, disturbance and transient state can be accurately distinguished, avoiding the influence of single-point anomalies or instantaneous fluctuations on the analysis results. This step provides a reliable hierarchical data foundation for the analysis of flow and pressure change patterns, sensitivity calculation and performance trend evaluation, and improves the accuracy of device performance evaluation and anomaly detection capability.

[0024] Step 3: Compare the flow rate data and pressure data corresponding to the time series data in each working condition layer of the basic dataset to obtain the change results, and analyze the change results to obtain the change patterns of the flow rate data and the pressure data. In step three, the change results refer to the phase difference data, response delay data, and correlation coefficients obtained by quantitatively analyzing the flow and pressure data within the data sequences of each operating condition layer. These data are used to describe the dynamic response characteristics of flow to pressure and the coupling relationship between the two. Phase difference data: refers to the lag angle of flow response relative to pressure, which can be obtained through Fourier transform or cross-correlation analysis, and represents the time lag characteristics of flow response to pressure changes; Response delay data: refers to the time lag between pressure change and flow rate change, which can be calculated by cross-correlation function and is used to measure the system's response speed under disturbance; Correlation coefficient: refers to the degree of linear or nonlinear coupling between flow rate data and pressure data within a sliding time window, used to reflect the dynamic relationship between the two over time. For example, for the flow rate data and pressure data in the data sequence of each operating condition layer, a Fourier transform is first performed within the sliding time window to calculate the phase difference between flow rate and pressure in order to quantify the lag angle. At the same time, the cross-correlation function is used to analyze and calculate the response delay time of pressure to flow rate change in order to reflect the dynamic response speed of the device under different operating conditions. Within the sliding time window, the correlation coefficient between flow rate and pressure can also be calculated to reflect the coupling strength between the two and their variation over time. Subsequently, the phase difference, response delay, and correlation coefficient are jointly analyzed to form a data set of flow rate and pressure variation patterns for each operating condition layer, providing a basis for subsequent sensitivity analysis and trend assessment. To ensure the continuity and reliability of the variation patterns, the analysis results can be smoothed to remove short-term abnormal fluctuations. It should be noted that by comparing flow and pressure data at each operating level and extracting the changes, the dynamic response characteristics of the device under different operating conditions can be quantified, accurately reflecting the response differences in steady state, disturbance, and transient phases. This step provides a reliable data foundation for subsequent sensitivity analysis and performance trend evaluation, helping to improve the accuracy of system performance monitoring and anomaly detection.

[0025] Step 4: Obtain the key feature parameters of the steady-state operating condition data sequence and the key feature parameters of the disturbance operating condition data sequence. Based on the key feature parameters of the steady-state operating condition data sequence, analyze the key feature parameters of the disturbance operating condition data sequence to obtain the response sensitivity value. Process the response sensitivity value to obtain the sensitivity coefficient. In step four, key characteristic parameters refer to the core statistical indicators extracted from the data sequences of each operating condition layer, including the average value, standard deviation, and fluctuation range of flow and pressure data, used to describe the performance of the device under different operating conditions. Key characteristic parameters of the steady-state operating condition layer reflect the system's baseline performance, while key characteristic parameters of the disturbed operating condition layer reflect the system's response characteristics after being disturbed. Response sensitivity value: This refers to a quantitative indicator that measures the sensitivity of a device to changes in steady-state conditions under disturbance conditions. It can be calculated by the difference or ratio between key characteristic parameters of the disturbance condition layer and key characteristic parameters of the steady-state condition layer. For example, the flow response sensitivity can be obtained by calculating the ratio of the flow fluctuation amplitude during the disturbance to the steady-state average flow; similarly, the pressure response sensitivity can be calculated. Sensitivity coefficient: refers to the quantitative coefficient obtained by normalizing or nonlinearly mapping the response sensitivity value, which can be used for comprehensive performance evaluation and is used to represent the sensitivity of different parameters to the overall performance. For example, the key characteristic parameters of the steady-state operating condition layer are compared with those of the key characteristic parameters of the disturbance operating condition layer one by one, and the change range or relative deviation of each parameter is calculated to form a preliminary response sensitivity value. The preliminary response sensitivity value is then normalized so that each parameter is mapped to a uniform dimension range to obtain the sensitivity coefficient. The sensitivity coefficient can be weighted or nonlinearly adjusted by combining historical data or preset thresholds to reflect the differences in sensitivity of different flow or pressure parameters to the overall performance. Finally, the generated sensitivity coefficient is used to construct the performance criterion interval for subsequent comprehensive evaluation data, providing a quantitative basis for performance trend analysis. It should be noted that by calculating the response sensitivity value and generating the sensitivity coefficient, the device's response capability to changes in flow and pressure under disturbance conditions can be quantified, and high-sensitivity and low-sensitivity parameters can be identified. This step provides core indicators for establishing subsequent comprehensive performance criteria, improves the accuracy of performance evaluation and anomaly detection capabilities, and provides data support for the optimized control of fluid medium flow and pressure regulating devices.

[0026] Step 5: Obtain the performance criterion interval of the comprehensive evaluation data based on the key feature parameters of the steady-state operating condition layer data sequence and the sensitivity coefficient; In step five, the performance criterion interval refers to the allowable fluctuation range of each key parameter determined based on the key characteristic parameters and sensitivity coefficients of the steady-state operating condition layer. This range is used to quantify whether the device's operating performance is in a normal or abnormal state. The performance criterion interval can be used to evaluate the stability of flow and pressure, as well as the device's response characteristics. Comprehensive evaluation data: refers to a data set that includes performance criterion ranges for each key parameter and related statistical indicators, which can be used for comprehensive performance analysis and trend evaluation; For example, key characteristic parameters of the steady-state operating condition layer are obtained, including the average value, standard deviation, and fluctuation amplitude of flow and pressure. Based on the average value, the initial allowable fluctuation range of each key parameter is calculated according to the standard deviation. The sensitivity coefficient generated in step four is applied to the initial allowable fluctuation range to adjust the fluctuation amplitude of different parameters. The upper and lower limits of each key parameter after adjustment are combined to form a performance criterion interval of comprehensive evaluation data, which is used to determine whether each key parameter of the device is within the allowable range during subsequent operation. At the same time, the performance criterion interval can be compared and analyzed with real-time or historical data to achieve early identification of abnormal fluctuations in flow and pressure and performance trend assessment. It should be noted that by constructing performance criterion intervals based on key characteristic parameters and sensitivity coefficients of the steady-state operating condition layer, the normal operating range and allowable fluctuation range of the device under different operating conditions can be quantified, enabling quantitative monitoring of the device's operating performance. This step provides the core data foundation for subsequent trend calculations, state assessments, and visualization, improving the accuracy of system performance evaluation and anomaly detection capabilities, and helping to optimize device control strategies and enhance operational reliability.

[0027] Step Six: Based on the aforementioned change pattern, establish a performance trend evaluation logic. By analyzing the continuous evolution characteristics of the time series data, perform trend calculations and status evaluations on the operating performance of the fluid medium flow and pressure regulating device to obtain comprehensive performance evaluation data. Performance trend assessment logic: refers to the mathematical or logical model used to describe the evolution of the operating performance of the device over time. By analyzing the changes in flow and pressure and the response characteristics of each key parameter, it determines the trend of the device performance evolving from a stable state to a deteriorating or abnormal state. Trend calculation: refers to the continuous evolution analysis of time series data based on performance trend evaluation logic, quantifying the changing trends of key parameters of the device, including increases, decreases, or expansion of fluctuation ranges; Status assessment: refers to classifying and judging the operating status of the device based on the performance criterion range, and assessing whether the device is currently operating normally, has minor abnormalities, or has significant abnormalities; Comprehensive performance evaluation data: refers to the set of results obtained through trend calculation and status assessment, including the trend values, status levels and change patterns of each key parameter, which can be used for subsequent evaluation result generation and visualization. For example, based on the time evolution trends of phase difference, response delay, and correlation coefficient obtained in step three, a multidimensional performance index set is constructed to reflect the matching relationship between flow response and pressure feedback; the multidimensional performance index set is mapped to the performance state space to form a performance state mapping relationship; the performance state mapping relationship is fitted and modeled to establish a performance trend evaluation logic model; the performance trend evaluation logic model is used to analyze time series data to obtain the trend values ​​of each key parameter; the state of each key parameter is classified in combination with the performance criterion interval to generate comprehensive performance evaluation data; the trend calculation results are smoothed and outliers are removed to ensure the continuity and accuracy of the comprehensive performance evaluation data; It should be noted that by establishing a performance trend evaluation logic and performing trend calculations and status assessments, the dynamic changes in performance during device operation can be quantified, and potential anomalies and deterioration trends can be identified. This step provides a scientific basis for device performance monitoring, predictive maintenance, and control strategy optimization, thereby improving the operational reliability and safety of fluid medium flow and pressure regulating devices.

[0028] Step 7: Generate evaluation result data based on the comprehensive evaluation data and the performance criterion range, and display the visualized evaluation result data on the display screen; In step seven, the evaluation result data refers to the data set of key parameters, including their status, deviation, trend, and anomaly level, obtained by comparing, analyzing, and comprehensively calculating the comprehensive performance evaluation data with the performance criterion interval. This data is used to intuitively reflect the operating performance of the device. Visualization: This refers to presenting the evaluation results data intuitively on the display screen in the form of charts, curves, dashboards or color indicators, so that operators can quickly identify the operating status of the device and abnormal situations. For example, the comprehensive performance evaluation data is compared with the performance criterion interval to calculate the deviation, trend and anomaly level of each key parameter. Based on the calculation results, evaluation result data is generated, including steady-state deviation, disturbance response deviation and transient condition anomaly index. Then, the evaluation result data is visualized, for example, by using a line graph to show the trend of key parameters over time, using colors to indicate different anomaly levels, or displaying the current status level through a dashboard. It should be noted that by generating and visualizing the evaluation results data, the operating status of the device and the performance changes of each key parameter can be intuitively reflected, which facilitates operators to monitor the system operation in real time and quickly detect abnormalities or potential risks. This step realizes a complete closed loop from data acquisition and analysis to intuitive presentation, thereby improving the monitoring efficiency and operational safety of the fluid medium flow and pressure regulation device. Example

[0029] like Figure 2 and Figure 3 As shown in the comparative embodiment one, another embodiment of the present invention is: a fluid medium flow rate and pressure regulating device, specifically: Data acquisition module: used to acquire basic datasets during the operation of the fluid medium flow and pressure regulating device, the basic datasets including flow data, pressure data and time series data; Feature extraction module: used to extract features from the basic dataset to obtain change rate features and stability features; Operating condition generation module: used to analyze the operation process of the fluid medium flow and pressure regulating device based on the change rate characteristics and the stability characteristics, and obtain the operating condition data sequence, which includes the steady-state operating condition data sequence, the disturbance operating condition data sequence and the transition operating condition data sequence. Time series analysis module: used to compare the flow data and pressure data corresponding to the time series data in each working condition layer, obtain the change results, and analyze the change pattern of the flow data and pressure data based on the change results. The change results include phase difference data, response delay data and correlation coefficient. Data processing module: used to acquire key feature parameters of steady-state operating condition data sequence and disturbed operating condition data sequence, compare key feature parameters of disturbed operating condition data sequence based on key feature parameters of steady-state operating condition data sequence, calculate response sensitivity value and generate sensitivity coefficient, and obtain performance criterion interval of comprehensive evaluation data based on key feature parameters of steady-state operating condition data sequence and sensitivity coefficient. Performance trend evaluation module: used to establish performance trend evaluation logic based on the change pattern, and to perform trend calculation and status evaluation on the operating performance of the fluid medium flow and pressure regulating device by analyzing the continuous evolution characteristics of time series data, so as to obtain comprehensive performance evaluation data; Results display module: Used to generate evaluation result data based on the comprehensive evaluation data and the performance criterion range, and to display the visualized or numerical evaluation result data on the display screen.

[0030] The data processing module specifically includes: Key parameter acquisition unit: used to acquire key feature parameters of steady-state operating condition layer data sequence, including the average value, standard deviation and stability of flow data and pressure data, and to acquire key feature parameters of disturbed operating condition layer data sequence; Sensitivity calculation unit: used to compare the key feature parameters of the disturbance condition layer data sequence based on the key feature parameters of the steady-state condition layer data sequence, calculate the response sensitivity value, and generate a sensitivity coefficient based on the response sensitivity value; Performance criterion generation unit: used to determine the adjusted allowable fluctuation range of each key parameter based on the key feature parameters of the steady-state operating condition layer data sequence and the sensitivity coefficient, and to combine the adjusted allowable fluctuation range to form the performance criterion interval of the comprehensive evaluation data.

[0031] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the performance of fluid medium flow rate and pressure regulation, characterized in that, The method includes: Acquire basic datasets of the fluid medium flow and pressure regulating device during operation, the basic datasets including flow data, pressure data and time series data; Feature extraction is performed on the basic dataset to obtain change rate features and stability features; Based on the change rate characteristics and the stability characteristics, the operation process of the fluid medium flow and pressure regulating device is analyzed to obtain the operating condition data sequence, which includes the steady-state operating condition data sequence, the disturbance operating condition data sequence and the transition operating condition data sequence. The flow rate data and pressure data corresponding to the time series data in each working condition layer of the basic dataset are compared to obtain the change results, and the change patterns of the flow rate data and the pressure data are obtained based on the change results. Key feature parameters of steady-state operating condition data sequence and key feature parameters of disturbance operating condition data sequence are obtained. Based on the key feature parameters of the steady-state operating condition data sequence, the key feature parameters of the disturbance operating condition data sequence are analyzed to obtain response sensitivity values. The response sensitivity values ​​are then processed to obtain sensitivity coefficients. Based on the key feature parameters of the steady-state operating condition layer data sequence and the sensitivity coefficient, the performance criterion interval of the comprehensive evaluation data is obtained; Based on the aforementioned change pattern, a performance trend evaluation logic is established. By analyzing the continuous evolution characteristics of the time series data, the operating performance of the fluid medium flow and pressure regulating device is calculated and its status is evaluated to obtain comprehensive performance evaluation data. Evaluation result data is generated based on the comprehensive evaluation data and the performance criterion range, and the evaluation result data is displayed on a screen for visualization.

2. The method for evaluating the performance of fluid medium flow rate and pressure regulation according to claim 1, characterized in that, The rate of change characteristics include the rate of change of flow and the rate of change of pressure; the stability characteristics include the amplitude of flow fluctuation and the amplitude of pressure fluctuation; based on the rate of change characteristics and the stability characteristics, the operation process of the fluid medium flow and pressure regulating device is analyzed to obtain a working condition data sequence, including: The flow rate change rate and pressure change rate are calculated using a sliding window on the time series data to obtain local change trend parameters. The instantaneous deviation value is calculated based on the local change trend parameter to assess the fluctuation range of the operating status; The fluctuation amplitude is compared with the stability characteristics to generate an operational stability index; By combining the change rate characteristics, the stability characteristics, and the operational stability index, a working condition layer data sequence is obtained.

3. The method for evaluating the performance of fluid medium flow rate and pressure regulation according to claim 1, characterized in that, The change results include phase difference data, response delay data, and correlation coefficients. The analysis based on these change results to obtain the variation patterns of the flow rate data and the pressure data includes: The phase difference is obtained by performing Fourier transform or cross-correlation analysis on the flow rate data and the pressure data, and represents the lag angle of the flow rate response to the pressure. The response delay is obtained through cross-correlation function analysis and represents the lag time of pressure to flow rate change; The correlation coefficient is calculated using a sliding time window and reflects the degree of coupling between flow rate and pressure and their dynamic changes over time. Calculate the phase difference, response delay, and correlation coefficient between the flow rate data and the pressure data, and obtain the variation pattern based on the joint analysis of the phase difference, response delay, and correlation coefficient.

4. The method for evaluating the performance of fluid medium flow rate and pressure regulation according to claim 1, characterized in that, The performance criterion interval for obtaining comprehensive evaluation data based on key feature parameters of steady-state operating condition layer data sequences and key feature parameters of disturbed operating condition layer data sequences includes: Obtain the key feature parameters of the steady-state operating condition layer data sequence, including the average value, standard deviation and stability of each key parameter; The key feature parameters of the disturbance condition layer data sequence are compared with the key feature parameters of the steady-state condition layer data sequence to obtain the response sensitivity value, and the sensitivity coefficient is generated through processing. Based on the average value of the key characteristic parameters of the steady-state operating condition layer data sequence, the initial allowable fluctuation range of each parameter is calculated according to the standard deviation, and the fluctuation range is adjusted in combination with the sensitivity coefficient to reflect the sensitivity of different parameters to the overall performance. The adjusted upper and lower limits of each key parameter are combined to generate a performance criterion interval for comprehensive evaluation data, which is used to generate subsequent evaluation results.

5. The method for evaluating the performance of fluid medium flow rate and pressure regulation according to claim 4, characterized in that, The performance trend evaluation logic established based on the aforementioned change pattern includes: Based on the time evolution trend of phase difference, response delay and correlation coefficient in the aforementioned change law, a multi-dimensional performance index set is constructed, which reflects the dynamic matching relationship between flow response and pressure feedback. Based on the variation characteristics of the multidimensional performance index set in continuous time series data, a performance state mapping relationship is established, which describes the trend of the operating performance of the fluid medium flow and pressure regulating device evolving from a stable state to a deteriorated or abnormal state. By fitting and modeling the performance state mapping relationship, a performance trend evaluation logic model is formed, and a performance trend evaluation logic is established based on the performance trend evaluation logic model.

6. The method for evaluating the performance of fluid medium flow rate and pressure regulation according to claim 5, characterized in that, The process involves trend calculation and status evaluation of the operating performance of the fluid medium flow and pressure regulating device to obtain comprehensive performance evaluation data, including: The performance trend evaluation logic is used to perform trend calculation and status evaluation on the operating performance of the fluid medium flow and pressure regulating device in the time series data, and the trend calculation results and status evaluation results are obtained. Based on the performance criterion range of the comprehensive evaluation data, the trend calculation results and state evaluation results of each key performance parameter are verified and quantitatively analyzed to generate comprehensive performance evaluation data.

7. The method for evaluating the flow and pressure regulation performance of a fluid medium according to claim 1, characterized in that, The step of generating evaluation result data based on the comprehensive evaluation data and the performance criterion range, and displaying the visualized or numerical evaluation result data on a display screen, includes: The comprehensive performance evaluation data is compared and analyzed with the performance criterion interval to obtain the comparison and analysis results; Based on the comparison and analysis results, the deviation, trend and anomaly level of each key performance parameter are calculated, and comprehensive evaluation result data is generated. The evaluation result data is processed to generate visualized display data, and the evaluation result data is displayed on the display screen.

8. A fluid medium flow rate and pressure regulating device, characterized in that, The apparatus, used in any of the methods described in claims 1-7, comprises: Data acquisition module: used to acquire basic datasets during the operation of the fluid medium flow and pressure regulating device, the basic datasets including flow data, pressure data and time series data; Feature extraction module: used to extract features from the basic dataset to obtain change rate features and stability features; Operating condition generation module: used to analyze the operation process of the fluid medium flow and pressure regulating device based on the change rate characteristics and the stability characteristics, and obtain the operating condition data sequence, which includes the steady-state operating condition data sequence, the disturbance operating condition data sequence and the transition operating condition data sequence. Time series analysis module: used to compare the flow data and pressure data corresponding to the time series data in each working condition layer, obtain the change results, and analyze the change pattern of the flow data and pressure data based on the change results. The change results include phase difference data, response delay data and correlation coefficient. Data processing module: used to acquire key feature parameters of steady-state operating condition data sequence and disturbed operating condition data sequence, compare key feature parameters of disturbed operating condition data sequence based on key feature parameters of steady-state operating condition data sequence, calculate response sensitivity value and generate sensitivity coefficient, and obtain performance criterion interval of comprehensive evaluation data based on key feature parameters of steady-state operating condition data sequence and sensitivity coefficient. Performance trend evaluation module: used to establish performance trend evaluation logic based on the change pattern, and to perform trend calculation and status evaluation on the operating performance of the fluid medium flow and pressure regulating device by analyzing the continuous evolution characteristics of time series data, so as to obtain comprehensive performance evaluation data; Results display module: Used to generate evaluation result data based on the comprehensive evaluation data and the performance criterion range, and to display the visualized or numerical evaluation result data on the display screen.

9. A fluid medium flow rate and pressure regulating device according to claim 8, characterized in that, The data processing module includes: Key parameter acquisition unit: used to acquire key feature parameters of steady-state operating condition layer data sequence, including the average value, standard deviation and stability of flow data and pressure data, and to acquire key feature parameters of disturbed operating condition layer data sequence; Sensitivity calculation unit: used to compare the key feature parameters of the disturbance condition layer data sequence based on the key feature parameters of the steady-state condition layer data sequence, calculate the response sensitivity value, and generate a sensitivity coefficient based on the response sensitivity value; Performance criterion generation unit: used to determine the adjusted allowable fluctuation range of each key parameter based on the key feature parameters of the steady-state operating condition layer data sequence and the sensitivity coefficient, and to combine the adjusted allowable fluctuation range to form the performance criterion interval of the comprehensive evaluation data.

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