On-off state detection method and system for emergency cut-off valve

Abstract

The invention discloses an on-off state detection method and system for an emergency cut-off valve, and particularly relates to the technical field of valve on-off state detection.The method comprises the steps that a multi-source sensor collects on-off state data of a valve; based on the on-off state data, constructing a time sequence correlation map of valve motion parameters and fluid parameters; constructing a space-time alignment feature matrix of the correlation coefficient based on the time sequence correlation graph; on the basis of the space-time alignment feature matrix, a three-dimensional valve opening and closing state judgment coordinate system is constructed, the valve opening and closing state of a preset qualified area of the three-dimensional coordinate system is judged, and the valve opening and closing state is fed back in real time through a double-channel feedback mechanism; according to the method, the multi-source sensor is used for collecting the valve opening and closing state data, establishing the correlation degree of the valve motion parameters and the fluid parameters and establishing the space-time alignment characteristic matrix, and the problems that in the prior art, data reliability is insufficient, dynamic data relevance is lacked, and judgment accuracy is low are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of valve switch state detection, and more particularly, to a switch state detection method and system for an emergency shut-off valve. Background Art

[0002] As a crucial safety device in industrial processes (such as petrochemicals, natural gas transmission, and hazardous chemical storage), emergency shut-off valves' core function is to quickly and reliably shut off the flow of media upon detecting a leak, overpressure, fire, or other emergency situation, preventing the escalation of the incident and safeguarding personnel, equipment, and the environment. Therefore, accurately monitoring the emergency shut-off valve's open and closed status in real time is crucial for ensuring its functional effectiveness and overall system safety.

[0003] Existing emergency shut-off valve status detection systems have generally met operational requirements, but they still have some shortcomings. First, existing methods primarily rely on single parameters, such as displacement or pressure, neglecting the comprehensive evaluation of multiple valve data sources, leading to insufficient data reliability. Second, existing methods independently analyze valve motion and fluid parameters, ignoring the dynamic correlation between the two. Finally, existing methods lack temporal and spatial alignment of sensor data, affecting the accuracy of valve status judgment. Therefore, a method is needed to address these issues: insufficient data reliability, lack of dynamic data correlation, and insufficient judgment accuracy. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and system for detecting the switch status of an emergency shut-off valve, which solves the problems raised in the above-mentioned background technology through the following scheme.

[0005] To achieve the above objectives, the present invention provides the following technical solutions: a method and system for detecting the switch status of an emergency shut-off valve, comprising: S1: A designated type of emergency shut-off valve is preset as a target valve, and multi-source sensors are installed to collect the switch status data of the target valve in real time. The switch status data includes the motion parameter data of the valve and the parameter data of the fluid in the valve pipe; S2: Based on the switch state data collected in S1, feature dimension expansion analysis is performed to obtain the displacement change rate, angle change rate, differential pressure gradient, flow velocity pulsation coefficient and sealing pressure distribution entropy value. The time series correlation map of valve motion parameters and fluid parameters is established through the cross-correlation algorithm; S3: Based on the time series correlation map of the valve motion parameters and the fluid parameters in S2, the correlation coefficients of the valve motion parameters and the fluid parameters are obtained, and the correlation coefficients are temporally and spatially aligned to obtain the temporal and spatial alignment feature matrix of the correlation coefficients; S4: Based on the spatiotemporal alignment feature matrix of the correlation coefficient in S3, a three-dimensional valve switch state judgment coordinate system is constructed. The coordinate system includes an X-axis representing the valve position achievement degree, a Y-axis representing the fluid state matching degree, and a Z-axis representing the parameter correlation degree. The valve switch state is judged by setting a qualified area for the coordinate system, and a dual-channel feedback mechanism is used to provide real-time feedback on the valve switch state.

[0006] Preferably, the feature dimension expansion analysis in S2 includes extracting features from the displacement data of the valve stem and analyzing the original displacement sequence. Perform 5th order polynomial least squares fitting, the fitting formula is ,in Indicates the original displacement data of the valve stem, is the fitting coefficient of the original displacement data and the goodness of fit needs to be controlled above 95%. Taking the first-order derivative, we get the displacement change rate The calculation formula is ,right Take the second-order derivative to get the displacement acceleration The calculation formula is ; Extract the angle feature of the valve stem through the angle sequence data The Fourier transform calculation formula is: ,in Indicates the frequency of acquisition, Indicates that the angle data is collected with a change of 10 degrees, and the angle change rate is obtained , the calculation formula is ; Calculate the differential pressure gradient of the valve pipeline internal pressure data, and use the sliding window to calculate the differential pressure data. Segmentation is performed, where the sliding window size is 100 , step size is 50 Segment the differential pressure data, differential pressure gradient The calculation formula is ,in are two time points in the window, is the change value of the pressure in the pipe per unit time; for the calculation of the velocity pulsation coefficient, the db4 wavelet basis is used to decompose the velocity data, reconstruct the velocity pulsation component, and calculate the velocity pulsation coefficient The formula is ,in represents the standard deviation of the pulse component, is the average value of the flow rate data; the sealing pressure distribution entropy value is obtained by calculating the distribution entropy of the collected pressure data The specific calculation formula is ,in Indicates the The pressure value of each collection point, the collection cycle is 100 .

[0007] Preferably, a switch status detection system for an emergency shut-off valve comprises: Sensor acquisition module: collects the switch status data of the target valve in real time by installing multiple source sensors. The switch status data includes the valve displacement data, angle data, and fluid parameter data in the valve pipe; Data analysis module: Based on the switch state data, feature dimension expansion analysis is performed to obtain the displacement change rate, angle change rate, differential pressure gradient, flow velocity pulsation coefficient and sealing pressure distribution entropy value. The time series correlation map of valve motion parameters and fluid parameters is established through the cross-correlation algorithm; Data processing module: Based on the time series correlation map of valve motion parameters and fluid parameters, the correlation coefficients of valve motion parameters and fluid parameters are obtained, and the correlation coefficients are temporally and spatially aligned to obtain the temporal and spatial alignment feature matrix of the correlation coefficients; Result judgment and feedback module: Based on the spatiotemporal alignment feature matrix of the correlation coefficient, a three-dimensional valve switch state judgment coordinate system is constructed. The qualified area of ​​the three-dimensional valve switch state judgment coordinate system is preset to judge the valve switch state. A dual-channel feedback mechanism is used to provide real-time feedback on the valve switch state.

[0008] The technical effects and advantages of the present invention are as follows: 1. The present invention presets a specified type of emergency shut-off valve as the target valve and installs a multi-source sensor to collect the switch status data of the target valve in real time. Based on the data collected by the multi-source sensor, the problem of insufficient reliability of valve switch status detection data is solved; 2. The present invention solves the problem of lack of correlation of valve switch status detection data by performing feature dimension expansion analysis on the collected switch status data and constructing a time series correlation map; 3. The present invention solves the problem of time-space inconsistency in valve switch state detection by secondary calculation of the correlation degree of the time-series correlation graph and constructing a time-space alignment feature matrix based on time-space registration; 4. The present invention realizes accurate judgment and real-time feedback of valve switch status through preset qualified areas and dual-channel feedback mechanism, thereby improving the reliability and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1Schematic diagram of the method for detecting the switch status of the emergency shut-off valve of the present invention.

[0010] Figure 2 Schematic diagram of the spatiotemporal alignment matrix structure of the present invention.

[0011] Figure 3 Schematic diagram of the three-dimensional determination coordinate system structure of the present invention.

[0012] Figure 4 It is a schematic diagram of the switch status judgment structure of the emergency shut-off valve of the present invention.

[0013] Figure 5 It is a structural schematic diagram of the switch status detection system of the emergency shut-off valve of the present invention. DETAILED DESCRIPTION

[0014] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0015] See also Figures 1-4 As shown, an embodiment of the present invention provides a method for detecting the switch state of an emergency shut-off valve. The method collects target valve switch state data, performs feature dimension expansion analysis on the collected data to construct a time series correlation map of valve motion parameters and fluid parameters, constructs a spatiotemporal alignment feature matrix of correlation coefficients based on the spatiotemporal alignment feature matrix, and constructs a three-dimensional valve switch state judgment coordinate system based on the spatiotemporal alignment feature matrix, thereby realizing switch state detection of the emergency shut-off valve. The embodiment of the present invention discloses a method for detecting the switch state of an emergency shut-off valve, comprising the following steps: S1: A designated type of emergency shut-off valve is preset as a target valve, and multi-source sensors are installed to collect the switch status data of the target valve in real time. The switch status data includes the motion parameter data of the valve and the parameter data of the fluid in the valve pipe; S2: Based on the switch state data collected in S1, feature dimension expansion analysis is performed to obtain the displacement change rate, angle change rate, differential pressure gradient, flow velocity pulsation coefficient and sealing pressure distribution entropy value. The time series correlation map of valve motion parameters and fluid parameters is established through the cross-correlation algorithm; S3: Based on the time series correlation map of the valve motion parameters and the fluid parameters in S2, the correlation coefficients of the valve motion parameters and the fluid parameters are obtained, and the correlation coefficients are temporally and spatially aligned to obtain the temporal and spatial alignment feature matrix of the correlation coefficients; S4: Based on the spatiotemporal alignment feature matrix of the correlation coefficient in S3, a three-dimensional valve switch state judgment coordinate system is constructed. The coordinate system includes an X-axis representing the valve position achievement degree, a Y-axis representing the fluid state matching degree, and a Z-axis representing the parameter correlation degree. The valve switch state is judged by setting a qualified area for the coordinate system, and a dual-channel feedback mechanism is used to provide real-time feedback on the valve switch state.

[0016] In S1, a designated type of emergency shut-off valve is preset as a target valve, and multi-source sensors are installed to collect the switch data of the target valve in real time. The switch status data includes valve displacement data, angle data, and fluid parameter data in the valve pipe. It should be further explained that the displacement data uses a laser displacement sensor to collect the valve stem displacement change rate when the valve is open and closed, and is specifically installed directly above the valve stem axis; the angle data uses a high-precision angle encoder to collect the valve stem angle change rate when the valve is open and closed, and is specifically installed at every 90-degree interval around the valve stem; the fluid parameter data includes the fluid pressure data and fluid flow rate data, and the differential pressure gradient change value and the sealing pressure distribution entropy value in the pipe of the target valve in the open and closed states are collected through the differential pressure transmitter, which is specifically installed at the upstream inlet of the valve pipeline; the flow velocity pulsation coefficient in the pipe of the target valve in the open and closed states is collected through the electromagnetic flowmeter, which is specifically installed at the downstream outlet of the valve pipeline.

[0017] In S2, the acquired valve switch state data is subjected to feature dimension expansion analysis to provide calculation data for the subsequent establishment of a time series correlation map between valve motion parameters and fluid parameters; Extract the features of the valve stem displacement data and analyze the original displacement sequence Perform 5th order polynomial least squares fitting, the fitting formula is ,in Indicates the original displacement data of the valve stem, is the fitting coefficient of the original displacement data and the goodness of fit needs to be controlled above 95%. Taking the first-order derivative, we get the displacement change rate The calculation formula is ,right Take the second-order derivative to get the displacement acceleration The calculation formula is ; Extract the angle feature of the valve stem through the angle sequence data The Fourier transform calculation formula is: ,in Indicates the frequency of acquisition, Indicates that the angle data is collected at a frequency of 10 degrees to obtain the angle change rate , the calculation formula is ; Calculate the differential pressure gradient of the valve pipeline internal pressure data, and use the sliding window to calculate the differential pressure data. Segmentation is performed, where the sliding window size is 100 , step size is 50 Segment the differential pressure data, differential pressure gradient The calculation formula is ,in are two time points in the window, is the change in pressure in the pipe per unit time; Calculation of flow velocity pulsation coefficient: Use db4 wavelet basis to decompose flow velocity data, reconstruct flow velocity pulsation component, and calculate flow velocity pulsation coefficient. The formula is ,in represents the standard deviation of the pulse component, is the average value of the flow velocity data; The sealing pressure distribution entropy value is obtained by calculating the distribution entropy of the collected pressure data. The specific calculation formula is ,in Indicates the The pressure value of each collection point, the collection cycle is 100 ; Based on the numerical value of feature dimension expansion analysis, a time series correlation map of valve motion parameters and fluid parameters is constructed, and the displacement change rate of the valve opening and closing process is selected. As a reference signal for calculating the cross-correlation algorithm , select the flow velocity pulsation coefficient during the valve opening and closing process As the target signal for calculating the cross-correlation algorithm , by using the cross-correlation algorithm, the correlation between the valve displacement change rate and the flow velocity pulsation coefficient within the time interval is calculated ; It should be further explained that the reference signal and the target signal are first pre-processed, the effective displacement change data and flow rate data of the valve switch movement period are intercepted and the displacement change rate is removed. and flow velocity pulsation coefficient The mean of is used as the calculation data of the cross-correlation function. The specific function calculation formula is as follows in Indicates time delay, Indicates the displacement change data and flow rate pulsation coefficient value corresponding to the effective displacement change data and flow rate data during the valve switching movement period. To judge the relevance, When the displacement change rate parameter and the flow velocity pulsation coefficient parameter are significantly correlated, the correlation value is saved to construct a time series correlation map; the correlation judgment threshold of 0.60 is obtained by selecting the same type of emergency shut-off valve and the valve diameter is controlled below DN500, collecting 300 sets of synchronous displacement change rate and flow velocity pulsation data and calculating the mutual correlation coefficient between the two. When the correlation data is 0.60, 98% of the valve switch state detection parameters are effectively correlated. If the same type of emergency shut-off valve is selected and the valve diameter is controlled above DN500, the cross-correlation function between the displacement change rate parameter and the flow velocity pulsation coefficient parameter needs to be recalculated; The specific construction includes building a time series correlation map by calculating the correlation of the switch data of the target valve, using the cross-correlation algorithm to calculate the correlation between the displacement data and angle data of the valve stem and the fluid parameter data in the pipe, and constructing a directed graph of the obtained parameter correlation values, in which the graph nodes represent the values ​​corresponding to each parameter, and the valve motion nodes are connected to the valve. and Arrange on the left side of the graph, and put the fluid parameter data and Arrange on the right side of the graph, set the graph edge attributes to make association judgments on the results of the correlation calculation. When there is a significant correlation between two parameters, it is indicated by a red edge line, and when there is no significant correlation, it is indicated by a gray edge line.

[0018] In S3, the construction of the spatiotemporal alignment feature matrix of the correlation coefficient includes calculating the linear correlation coefficients of the motion parameters and the fluid parameters respectively based on the construction method of the correlation map, calculating the nonlinear correlation coefficients of the sealing pressure distribution entropy value and the fluid parameters using grey correlation analysis, and mapping the correlation coefficients to a unified spatiotemporal grid through spatiotemporal registration to form a spatiotemporal alignment matrix of the correlation coefficients.

[0019] It should be further explained that the parameter correlation is preliminarily calculated based on the correlation map, and the quadratic linear correlation and nonlinear correlation calculations are performed between the various parameters, which improves the accuracy of the parameter correlation. Based on the calculation of the quadratic correlation, spatiotemporal registration processing is performed to construct a spatiotemporal alignment feature matrix. The quadratic linear correlation calculation uses the Pearson correlation coefficient to calculate the linear correlation between the valve motion parameters and the fluid parameters in the valve tube. Here, the displacement change rate and the differential pressure gradient in the valve tube are taken as examples to calculate the linear correlation between the two. The specific calculation formula is: , Where V is the displacement change rate, G is the differential pressure gradient value, i valve switch state is the sampling point index, represents the average value of the flow velocity data, It represents the average value of the differential pressure gradient. The result range of R is within the range of plus or minus 1. When R is greater than 0.7, it indicates a strong correlation, and when R is less than 0.3, it indicates a weak correlation. The nonlinear correlation is mainly used to process the fluid data in the valve tube. Here, the nonlinear correlation between the sealing pressure distribution entropy value and the fluid parameters is used. The flow velocity pulsation coefficient is selected with reference to the fluid parameters. , the specific calculation formula is: , Where H is the entropy value of the sealing pressure distribution, is the flow velocity pulsation coefficient value, the valve switch status i is the sampling point index, represents the average value of the sealing pressure distribution entropy, It represents the average value of the velocity pulsation coefficient. The result range of R is between 0 and 1. The closer the R value is to 1, the stronger the correlation is. The correlation results of the secondary calculation are subjected to spatiotemporal registration processing, including time registration. By establishing a unified time axis, all parameter sampling points are mapped to a time axis with a precision of 1ms, and the parameter data timestamps are interpolated so that the parameters after interpolation have corresponding parameter values ​​at the same time point, and the time error needs to be controlled within ms; spatial registration, taking the valve center as the origin, defining the three-dimensional spatial coordinates (x, y, z) and calibrating the sensor position, recording the installation position coordinates of each sensor in the spatial coordinate system, and mapping the valve switch status data parameter values ​​collected by sensors at different positions to a unified spatial grid to achieve spatial registration; constructing a spatiotemporal alignment feature matrix through spatiotemporal registration, the matrix is ​​defined by time dimension data, space dimension data, and valve switch parameter dimension, wherein the time dimension is divided into 0.1s intervals, taking the latest 10s data for a total of 100 time nodes, the space dimension is divided according to the sensor installation position, taking a total of 8 space nodes for key monitoring points, and the parameter dimension contains 4 types of correlation parameters to form 4 feature channels, and the correlation coefficients after spatiotemporal registration are arranged according to the above dimensions to form a 100×8×4 three-dimensional spatiotemporal alignment matrix.

[0020] In S4, the three-dimensional valve switch state judgment coordinate system parameter definition includes: the X-axis represents the valve position achievement, the Y-axis represents the fluid state matching degree, and the Z-axis represents the parameter correlation degree. The position achievement degree includes the displacement change rate and the angle change rate. The fluid state matching degree includes the differential pressure gradient and the flow velocity pulsation coefficient. The valve switch state data parameter correlation degree is the correlation coefficient in the time-space alignment feature matrix.

[0021] It should be further explained that, from the 100×8×4 three-dimensional spatiotemporal alignment matrix output from S3, data strongly correlated with the valve switch status is extracted, and the qualified area for normal operation of the valve switch is divided by calculating the parameters of each axis of the three-dimensional coordinate system, where the X-axis represents the valve position achievement calculation. The actual displacement change rate value V collected by the laser displacement sensor and the theoretical valve switch full stroke displacement L are collected, and the measured angle change rate of the angular displacement encoder is collected. The theoretical valve fully closed angle A; calculate the judgment basis of the X axis according to the formula, the specific formula is , the Y axis represents the fluid state matching degree. The specific calculation formula is: ,in They represent the standard differential pressure and standard flow rate under the valve switching state respectively. The Z axis represents the parameter correlation degree. The average value of the correlation coefficient of all valve parameters is calculated for the preprocessed space-time matrix.

[0022] The qualified area is preset to judge the valve switch status, and a dual-channel feedback mechanism is used to provide real-time feedback on the valve switch status, including: A qualified area is constructed based on the historical data of valve switch status. The qualified area of ​​valve switch status is determined based on statistics of multi-working condition experimental data and failure risk analysis. The qualified area is a three-dimensional cube area with an X-axis greater than 0.95, a Y-axis greater than 0.90, and a Z-axis greater than 0.75; the dual-channel feedback mechanism includes a physical channel and a communication channel. The physical channel receives the three-dimensional valve switch status judgment coordinate value and status judgment result in real time, and the communication channel transmits the valve switch status data to the gateway and feeds back to the user in JSON format.

[0023] It should be further explained that the method for constructing the qualified area for valve switching status includes selecting the same type of emergency shut-off valve under standard operating conditions (design pressure 1.6MPa, medium water or natural gas), simulating 1000 normal switching processes, and collecting original parameters such as the displacement change rate, angle change rate, and differential pressure gradient for each switch. At the same time, 500 typical failure scenarios (such as valve jamming, internal leakage, and sensor offset) are simulated, and the parameter values ​​at the time of failure are recorded. The distribution intervals of the X-axis, Y-axis, and Z-axis are calculated for normal samples to determine the lower limit of the normal threshold. When (X, Y, Z) completely falls within the three-dimensional cube divided by the qualified area, the emergency shut-off valve is judged to be operating normally. When X is less than 0.95, it indicates that the emergency shut-off valve position does not meet the standard. The reasons include valve obstruction and the valve not being opened or closed. When Y is less than 0.90, it indicates that the fluid in the valve pipeline is abnormal. The reasons include obstruction or internal leakage. When Z is less than 0.75, it indicates that the parameter correlation is chaotic. The reasons include failure of the acquisition sensor and the failure of the correlation between motion parameters and fluid parameters. Dual-channel feedback mechanism: the physical channel uses an OLED display to display the three-dimensional valve switch status judgment coordinate value, and determines the switch status of the emergency shut-off valve based on the status indicator light on the OLED display. When the indicator light is solid green, it indicates that the emergency shut-off valve switch status is normal; flashing red indicates that the X-axis does not meet the standard, because the emergency shut-off valve switch position is not in place; flashing yellow indicates that the Y-axis does not meet the standard, because the fluid parameters in the emergency shut-off valve pipeline are abnormal; solid red indicates that the Z-axis does not meet the standard, because the emergency shut-off valve switch status detection sensor is damaged or the parameter correlation coordination fails; the communication channel uploads a data packet every 30 seconds when the emergency shut-off valve switch status is normal, and the data packet contains the three-dimensional valve switch status judgment coordinate value and the corresponding timestamp. When the emergency shut-off valve switch status is abnormal, the data packet is uploaded immediately to achieve millisecond-level response, and the abnormal status is fed back to the user in JSON format.

[0024] See also Figure 5 As shown, an embodiment of the present invention provides a switch status detection system for an emergency shut-off valve, and the specific modules include the following: Sensor acquisition module: collects the switch status data of the target valve in real time by installing multiple source sensors. The switch status data includes the valve displacement data, angle data, and fluid parameter data in the valve pipe; Data analysis module: Based on the switch state data, feature dimension expansion analysis is performed to obtain the displacement change rate, angle change rate, differential pressure gradient, flow velocity pulsation coefficient and sealing pressure distribution entropy value. The time series correlation map of valve motion parameters and fluid parameters is established through the cross-correlation algorithm; Data processing module: Based on the time series correlation map of valve motion parameters and fluid parameters, the correlation coefficients of valve motion parameters and fluid parameters are obtained, and the correlation coefficients are temporally and spatially aligned to obtain the temporal and spatial alignment feature matrix of the correlation coefficients; Result judgment and feedback module: Based on the spatiotemporal alignment feature matrix of the correlation coefficient, a three-dimensional valve switch state judgment coordinate system is constructed. The qualified area of ​​the three-dimensional valve switch state judgment coordinate system is preset to judge the valve switch state. A dual-channel feedback mechanism is used to provide real-time feedback on the valve switch state.

[0025] Secondly: The drawings of the embodiments disclosed in the present invention only involve structures related to the embodiments disclosed in the present invention. Other structures may refer to conventional designs. The same embodiment and different embodiments of the present invention may be combined with each other without conflict. Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for detecting the switch status of an emergency shut-off valve, characterized in that: include: S1: A designated type of emergency shut-off valve is preset as a target valve, and multi-source sensors are installed to collect the switch status data of the target valve in real time. The switch status data includes the motion parameter data of the valve and the parameter data of the fluid in the valve pipe; S2: Based on the switch state data collected in S1, feature dimension expansion analysis is performed to obtain valve motion parameters including displacement change rate and angle change rate, and flow parameters including differential pressure gradient value, flow velocity pulsation coefficient and sealing pressure distribution entropy value. A time series correlation map of valve motion parameters and fluid parameters is established through a cross-correlation algorithm; S3: Based on the time series correlation map of the valve motion parameters and the fluid parameters in S2, the correlation coefficients of the valve motion parameters and the fluid parameters are obtained, and the correlation coefficients are temporally and spatially aligned to obtain the temporal and spatial alignment feature matrix of the correlation coefficients; S4: Based on the spatiotemporal alignment feature matrix of the correlation coefficient in S3, a three-dimensional valve switch state judgment coordinate system is constructed. The coordinate system includes an X-axis representing the valve position achievement degree, a Y-axis representing the fluid state matching degree, and a Z-axis representing the parameter correlation degree. The valve switch state is judged by setting a qualified area for the coordinate system, and a dual-channel feedback mechanism is used to provide real-time feedback on the valve switch state.

2. A method for detecting the switch status of an emergency shut-off valve according to claim 1, characterized in that: The motion parameter data of the valve in S1 include: The displacement data is used to record the rate of change of valve stem displacement collected when the valve is open and closed, and the angle data is used to record the rate of change of valve stem angle collected when the valve is open and closed.

3. A method for detecting the switch status of an emergency shut-off valve according to claim 1, characterized in that: The fluid parameter data in S1 includes: Fluid pressure data and fluid flow rate data, the fluid pressure data records the differential pressure gradient change value and the sealing pressure distribution entropy value in the pipe when the target valve is in the open state and the closed state, and the fluid flow rate data records the flow rate pulsation coefficient in the pipe when the target valve is in the open state and the closed state.

4. A method for detecting the switch status of an emergency shut-off valve according to claim 1, characterized in that: The feature dimension expansion analysis in S2 includes: Displacement feature extraction, the original displacement sequence Perform the fifth-order polynomial least squares fitting, and Taking the first-order derivative, we get the displacement change rate ,right Taking the second-order derivative yields the displacement acceleration; Angle feature extraction, angle sequence data Perform Fourier transform to obtain the angle change rate ; Differential pressure gradient calculation, using sliding window to calculate differential pressure data Segmentation to obtain differential pressure gradient ; Calculation of flow velocity pulsation coefficient, using wavelet basis to decompose flow velocity data and calculate flow velocity pulsation coefficient ; Sealing pressure distribution entropy value, the distribution entropy of the collected pressure data is calculated to obtain the sealing pressure distribution entropy value .

5. A method for detecting the switch status of an emergency shut-off valve according to claim 1, characterized in that: The cross-correlation algorithm in S2 establishes a time series correlation graph including: Select the displacement change rate of the valve opening and closing process As a reference signal for calculating the cross-correlation algorithm , select the flow velocity pulsation coefficient during the valve opening and closing process As the target signal for calculating the cross-correlation algorithm , by using the cross-correlation algorithm, the correlation between the valve displacement change rate and the flow velocity pulsation coefficient within the time interval is calculated ,based on A directed graph structure is used to construct the association graph.

6. A method for detecting the switch status of an emergency shut-off valve according to claim 1, characterized in that: The spatiotemporal alignment feature matrix for constructing the correlation coefficient in S3 includes: Based on the construction method of the correlation map in S2, the linear correlation coefficients of the motion parameters and the fluid parameters are calculated respectively. The nonlinear correlation coefficients of the sealing pressure distribution entropy and the fluid parameters are calculated using grey correlation analysis. The correlation coefficients are mapped to a unified space-time grid through space-time registration to form a space-time alignment matrix of the correlation coefficients.

7. A method for detecting the switch status of an emergency shut-off valve according to claim 1, characterized in that: The three-dimensional valve switch state judgment coordinate system parameter definition in S4 includes: The X-axis represents the valve position achievement, the Y-axis represents the fluid state matching, and the Z-axis represents the parameter correlation degree. The position achievement degree includes the displacement change rate and the angle change rate. The fluid state matching degree includes the differential pressure gradient and the flow velocity pulsation coefficient. The parameter correlation degree is the correlation coefficient in the spatiotemporal alignment feature matrix.

8. A method for detecting the switch status of an emergency shut-off valve according to claim 1, characterized in that: The qualified area and dual-channel feedback mechanism set in S4 include: A qualified area is constructed based on the historical data of valve switch status. The qualified area is a three-dimensional cube area with an X-axis greater than 0.95, a Y-axis greater than 0.90, and a Z-axis greater than 0.

75. The dual-channel feedback mechanism includes a physical channel and a communication channel. The physical channel receives the three-dimensional valve switch status judgment coordinate value and status judgment result in real time, and the communication channel transmits the valve switch status data to the gateway and feeds back to the user in JSON format.

9. A switch status detection system for an emergency shut-off valve, characterized in that: Includes the following modules: Sensor acquisition module: collects the switch status data of the target valve in real time by installing multiple source sensors. The switch status data includes the valve displacement data, angle data, and fluid parameter data in the valve pipe; Data analysis module: Based on the switch state data, feature dimension expansion analysis is performed to obtain the displacement change rate, angle change rate, differential pressure gradient, flow velocity pulsation coefficient and sealing pressure distribution entropy value. The time series correlation map of valve motion parameters and fluid parameters is established through the cross-correlation algorithm; Data processing module: Based on the time series correlation map of valve motion parameters and fluid parameters, the correlation coefficients of valve motion parameters and fluid parameters are obtained, and the correlation coefficients are temporally and spatially aligned to obtain the temporal and spatial alignment feature matrix of the correlation coefficients; Result judgment and feedback module: Based on the spatiotemporal alignment feature matrix of the correlation coefficient, a three-dimensional valve switch state judgment coordinate system is constructed. The qualified area of ​​the three-dimensional valve switch state judgment coordinate system is preset to judge the valve switch state. A dual-channel feedback mechanism is used to provide real-time feedback on the valve switch state.

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