Method and device for setting electromagnetic valve action characteristic parameters based on variable sliding window

Abstract

The application discloses a variable sliding window-based electromagnetic valve action characteristic parameter locating method. In the test process of a carrier rocket, the current curve in the opening and closing process of an electromagnetic valve needs to be analyzed, characteristic points are located, the electromagnetic valve action characteristic parameters are obtained according to the time and current difference between the points, and it is determined whether the electromagnetic valve switching process meets the design requirements. The application finds the characteristic points such as the starting point, the opening peak point, the opening valley point, the steady state starting point, the ending point, the closing peak point, the closing valley point and the steady state ending point in the opening and closing stages of the electromagnetic valve current curve by the sliding window method. In the finding process, the method can automatically adjust the sliding window parameters according to the rules, overcome the influence of random noise, data fluctuation and the like on the locating result of the characteristic points, quickly and accurately find the characteristic points in the electromagnetic valve current curve, and complete the automatic interpretation of the test data.

Description

Technical Field

[0001] This invention belongs to the technical field of launch vehicle measurement and control, and particularly relates to a method and device for determining the action characteristic parameters of an electromagnetic valve based on a variable sliding window. Background Technology

[0002] In liquid-fueled rocket pressurization and delivery systems, numerous and varied fluid control devices are required to perform functions such as opening and closing fluid passages, directing and reversing fluid flow, regulating flow and pressure, and protecting system safety. Solenoid valves are one such fluid control device. They can operate repeatedly to open, close, and reverse fluid passages. Commonly used in rocket piping systems, they are controlled by the control system. When the control system sends an electrical signal, the solenoid valve opens or closes, allowing high-pressure gas to pressurize or depressurize the tank. Solenoid valves are also frequently used to control pneumatic valves or other pneumatic mechanisms to achieve various programmed actions required by the launch vehicle.

[0003] In the testing process of launch vehicles, it is necessary to interpret the current change curves in the solenoid valve coils, identify characteristic points in the solenoid valve current curves, and calculate multiple action characteristic parameters, including steady-state current, start-up current, release current, end current, solenoid valve opening time, solenoid valve closing time, and peak-to-peak difference. Based on these parameters, it is determined whether the solenoid valve's response time and opening / closing characteristics meet design requirements, thereby assessing whether the launch vehicle's pressurization and delivery system and various action procedures can successfully complete their intended tasks. However, because solenoid valves experience electromagnetic interference, friction, and differences in electromagnetic characteristics during operation, the characteristics of the required characteristic points will vary among different solenoid valve current curves. Furthermore, the current curves may contain random fluctuations and sawtooth data, causing characteristic points to be confused with other noise points during the search. Therefore, finding a method that can overcome these difficulties and automatically locate characteristic points in the solenoid valve current curves and calculate the action characteristic parameters has become an important and challenging task. Summary of the Invention

[0004] The purpose of this invention is to provide a method for determining the action characteristic parameters of a solenoid valve based on a variable sliding window. This method can automatically, quickly, and accurately find characteristic points in the current curve of a solenoid valve and calculate the action characteristic parameters. It overcomes the problems of large differences in the current curve of a solenoid valve, noise, and fluctuating data, and can be used in the automatic reading and interpretation mission of launch vehicle measurement and control.

[0005] To solve the above problems, the technical solution of the present invention is as follows:

[0006] This invention first normalizes the solenoid valve current data, limiting the data range to 0 to 1 to unify the data analysis scale. On the normalized solenoid valve characteristic curve, the feature points searched include the start point, peak point, valley point, and steady-state start point of the opening phase, and the steady-state end point, valley point, peak point, and termination point of the closing phase.

[0007] For each feature point, a sliding window with independent parameters slides along the characteristic curve of the solenoid valve. Based on the data characteristics of the feature points, a target search window is sequentially determined on the solenoid valve characteristic curve for each feature point. During the determination of the target search window, the sliding window parameters are dynamically adjusted according to whether the window's position meets pre-defined detection criteria to ensure the accuracy of the target search window's position. Within the target search window, the specific location of the target feature point is determined according to the data point search rules.

[0008] When finding the starting point during the initial phase, the key challenge is identifying the point where the characteristic curve begins its ascent from a stable, bottom-fluctuating region. This invention proposes the following method: In the initial phase, a sliding window starts from the first data point, and the value within that window is recorded as the baseline value. The window slides backwards; if the average value within the window exceeds the sum of the baseline value and the opening threshold, the confirmation count is incremented by 1; otherwise, it is reset to 0. After comparison, the baseline value is updated to the average value of all data points from the first data point on the curve to the end of the current window. When the confirmation count exceeds the required number of confirmation steps, the window preceding that number of steps is moved to become the target search window. Within the target search window, the first point where the difference between the data point and the preceding data point exceeds the opening threshold is identified; this point is the starting point.

[0009] When locating the activation peak during the activation phase, the key challenge is to pinpoint the local peak value during the current rise process within a characteristic curve exhibiting fluctuating data values, and to accurately mark this point. The method presented in this invention involves sliding a window from the starting point backwards. If the average value within the current window is less than the average value within the previous window, the confirmation count is incremented by 1; otherwise, the confirmation count is reset to 0. When the confirmation count exceeds the number of confirmation steps, the window preceding that number of confirmation steps is used as the target search window. Within the target search window, the point with the largest value is identified as the activation peak.

[0010] When locating the start-up valley during the start-up phase, the challenge lies in identifying the local valley value during the current rise process within the characteristic curve where data values ​​fluctuate, and accurately marking this point. The method presented in this invention involves sliding a window from the start-up peak point backwards. If the average value within the current window is greater than the average value within the previous window, the confirmation count is incremented by 1; otherwise, the confirmation count is reset to 0. When the confirmation count exceeds the number of confirmation steps, the window preceding that number of confirmation steps is used as the target search window. Within the target search window, the point with the smallest value is found; this point is the start-up valley.

[0011] When searching for the start-up peak and start-up valley, the found start-up peak and start-up valley may be located in the bottom data stable fluctuation region instead of the current rising region. This occurs because the found starting point does not meet the requirements and is not the last point in the bottom data stable fluctuation region. The solution provided by this invention is that if the values ​​of the start-up peak and start-up valley are less than the bottom gap parameter value, the sliding window size for determining the starting point is reduced by 1, and the search for the starting point, start-up peak, and start-up valley is repeated.

[0012] If the found start-up peak and start-up trough appear in the top of the data characteristic curve in a stable fluctuation region, it's because the sliding window size for the start-up peak and start-up trough is too large, missing the characteristic points of the current rising region. The solution provided by this invention is that if the difference between the average value of the window starting from the start-up peak and start-up trough and 1 is less than the top gap value, then the sliding window size for the start-up peak and start-up trough is reduced by 1, the start-up peak and start-up trough are re-identified, and the relationship between the new start-up peak / trough and the bottom gap is re-determined.

[0013] When searching for the steady-state starting point during the initial phase, the key challenge is finding the point where the characteristic curve transitions smoothly from its rising phase to a stable peak. This invention proposes a method where a sliding window moves from the initial trough point onwards. The average value within the first window is designated as comparison value 1, the average value within the second window as comparison value 2, and the difference between the two values ​​is designated as the baseline value. Sliding the window forward, if the difference between the average value of the current window and the average value of the previous window is less than the baseline value, the confirmation count is incremented by 1; otherwise, the confirmation count is reset to 0. When the confirmation count exceeds the number of confirmation steps, the average value of the current window is used as the evaluation value. All data points from the first data point in the window before the confirmation step is moved to the last data point in the current window are combined to form the target search window. The first data point in the target search window whose value is greater than the evaluation value is the steady-state starting point.

[0014] During the closing phase, the process is reversed from the opening phase. The window is slid back sequentially to find the endpoint, closing peak, closing trough, and steady-state endpoint. The search method is basically the same as that used during the opening phase.

[0015] If no target inspection window that meets the requirements is found during the search for relevant feature points, the sliding window size parameter of the corresponding feature point is decremented by 1, and the corresponding feature point is searched again.

[0016] If the window size is smaller than the corresponding number of check and confirmation steps during the adjustment of the sliding window size parameter, the adjustment will stop.

[0017] After identifying all target feature points, the solenoid valve action characteristic parameters, including steady-state current, starting current, release current, ending current, peak-to-peak difference, solenoid valve opening time, and solenoid valve closing time, are calculated based on the time and current values ​​of the target feature points. This completes the automatic judgment of whether the solenoid valve opening and closing action characteristics meet the design requirements.

[0018] Based on the same concept, the present invention also provides a solenoid valve action characteristic parameter determination device based on a variable sliding window, comprising:

[0019] The data preprocessing module is used to normalize the current data during the switching process of the solenoid valve, and configure the sliding window parameters and the standard parameters for detecting the solenoid valve action characteristics.

[0020] The feature point identification module is used to automatically find the positions of target feature points on the normalized solenoid valve current curve using a sliding window, based on the feature point search rules. These target feature points include the start point, peak point, valley point, and steady-state start point of the solenoid valve opening phase, as well as the end point, peak point, valley point, and steady-state end point of the solenoid valve closing phase.

[0021] The action characteristic calculation module is used to calculate the solenoid valve action characteristic parameters, including steady-state current, starting current, release current, ending current, and peak-to-peak difference, based on the time and current value of each target characteristic point after the position of each target characteristic point is determined, so as to automatically judge whether the solenoid valve switching action characteristics meet the design requirements.

[0022] Because of the above technical solutions, this invention has the following advantages and positive effects compared with the prior art:

[0023] This invention provides a method for determining the solenoid valve's actuation characteristic parameters based on a variable sliding window. Compared to existing methods that only provide the circuit design of the current acquisition device without explicitly outlining the data analysis algorithm, this invention offers a method that can automatically, quickly, and accurately determine the solenoid valve's actuation characteristic parameters after acquiring the solenoid valve's current waveform through hardware. This invention addresses the impact of data fluctuations on trend interpretation by using a sliding window method. Combined with an understanding of the solenoid valve's actuation characteristics, it provides methods for determining the target search window and feature points. Furthermore, for situations where feature point determination errors may occur due to data fluctuations and interference, it provides error judgment conditions and methods for adjusting the sliding window to further find the correct feature points. This method can be directly applied to the automatic interpretation process of solenoid valve actuation characteristic parameters in launch vehicles. Attached Figure Description

[0024] Figure 1 This is a flowchart of a method for determining the action characteristic parameters of a solenoid valve based on a variable sliding window according to an embodiment of the present invention.

[0025] Figure 2 This is a current curve diagram of the solenoid valve when it is activated in one embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram of the result of finding the characteristic points of the solenoid valve current curve in one embodiment of the present invention;

[0027] Figure 4 This is a block diagram of a device for determining the action characteristic parameters of a solenoid valve based on a variable sliding window, according to an embodiment of the present invention. Detailed Implementation

[0028] The following detailed description, in conjunction with the accompanying drawings and specific embodiments, provides a method and apparatus for determining the actuation characteristic parameters of a solenoid valve based on a variable sliding window, as proposed in this invention. The advantages and features of this invention will become clearer from the following description and claims.

[0029] Please refer to Figure 1 This embodiment provides a method for determining the action characteristic parameters of a solenoid valve based on a variable sliding window, including:

[0030] Step S1: Normalize the solenoid valve current data, limiting the data range to 0 to 1 to unify the data analysis scale. On the normalized solenoid valve characteristic curve, the feature points to be found include the start point, peak point, valley point, and steady-state start point of the opening phase, and the end point, peak point, valley point, and steady-state end point of the closing phase.

[0031] Please refer to Figure 2These feature points are specifically: starting point 1, denoted as p1 for subsequent calculations; opening peak point 2, denoted as p2; opening valley point 3, denoted as p3; steady-state start point 4, denoted as p4; steady-state end point 5, denoted as p5; closing valley point 6, denoted as p6; closing peak point 7, denoted as p7; and ending point 8, denoted as p8. Based on these feature points, the solenoid valve action characteristic parameters to be obtained include: steady-state current I, starting current I1, release current I2, end current I3, opening peak-to-peak difference ΔI1, release peak-to-peak difference ΔI2, solenoid valve opening time t1, and solenoid valve closing time t2.

[0032] In this embodiment, when normalizing the solenoid valve current data, the current values ​​of the solenoid valve during the opening and closing phases collected by the acquisition device are extracted from the database. A maximum-minimum normalization method is then used to normalize the current data, as shown in the following formula.

[0033]

[0034] Where, x i x represents the original data point current value. max x is the maximum value among all data points. min The minimum value among all data points is obtained. This yields a solenoid valve action characteristic curve with a value limited to the range of 0 to 1, and a data length of n. Feature points for the opening and closing phases are then located on this curve.

[0035] Step S2: For each feature point, set the sliding window parameters, including the sliding window size α, the number of sliding steps β, and the number of check and confirmation steps ω. For the entire search process, set the solenoid valve action feature check standard parameters, including the opening threshold δ. o , closing threshold δ c Bottom gap σ b With top gap σ t In practical implementation, the parameter can be set to δ. o =0.01,δ c =-0.01,σ b =0.05,σ t =0.1.

[0036] In other words, each feature point has its own corresponding sliding window size parameter. When searching for a feature point, the values ​​of all data points within that window are calculated. Each feature point also has its own corresponding window sliding step parameter. The number of steps the window slides on the curve each time during feature point search is determined by this parameter. Each feature point also has its own corresponding check and confirmation step parameter. When the sliding window has slid the number of steps specified by this parameter, and the feature parameters calculated by the sliding window each time satisfy the feature point search conditions, the target search window is determined according to the rules. Within the target search window, the feature point position is confirmed according to the corresponding feature point search algorithm. The threshold parameter is used when searching for the starting point of a feature point, and the threshold parameter is used when searching for the ending point of a feature point. The bottom gap and top gap parameters are used to dynamically adjust the sliding window size for each feature point.

[0037] In each step, the current window is denoted as ζk. ， This means from point x k Initially, a sliding window of length α is used. The parameter ζ... k:l This means from point x k Point x l A sliding window. Window ζ k The average of all data points within the range is denoted as ρ. k ,Right now

[0038]

[0039] Window ζ kl The average of all data points within the range is denoted as ρ. kl ,Right now

[0040]

[0041] Step S3: In the opening phase, feature points are searched from front to back. Specifically, this includes:

[0042] Step S3.1: In the startup phase, find the starting point, point p1. Initialize the confirmation count μ. p1 =0, parameter α p1 =20,β p1 =1,ω p1 =5. The sliding window starts from the first data point x1, and the average value within the first window is recorded as the baseline value.

[0043]

[0044] Slide the window from front to back. When ρ k >Ω p1 +δ o At that time, μ p1 =μ p1 +1, otherwise, μ p1=0. After sliding the window, update the baseline value Ω. p1 This is the average value of all data points from the first data point on the curve to the end point of the current window.

[0045]

[0046] When μ p1 >ω p1 At that time, open the window As the target search window, let's call it Z. p1 In the target search window, find the first data point whose difference from the previous data point is greater than the opening threshold.

[0047] x p1 =min{x i ∈Z p1 |x i -x i-1 >δ o}

[0048] This point is the starting point.

[0049] Step S3.2: Locate the start-up peak point, p2, during the start-up phase. Initialize the confirmation count μ. p2 =0, parameter α p2 =20,β p2 =1,ω p2 =5. The sliding window starts from point x. p1 Begin by sliding the window from front to back. When... At that time, μ p2 =μ p2 +1, otherwise, μ p2 =0. When μ p2 >ω p2 At that time, open the window As the target search window, let's call it Z. p2 In the target search window, find the point with the largest value, i.e.

[0050] x p2 =max(Z) p2 )

[0051] This point is the starting peak point.

[0052] Step S3.3: Locate the start-up valley point, p3, during the start-up phase. Initialize the confirmation count μ. p3 =0, parameter α p3 =20,β p3 =1,ω p3 =5. The sliding window starts from point x. p2 Begin by sliding the window from front to back. When... At that time, μ p3 =μp3 +1, otherwise, μ p3 =0. When μ p3 >ω p3 At that time, open the window As the target search window, let's call it Z. p3 In the target search window, find the point with the smallest value, i.e.

[0053] x p3 =min(Z) p3 )

[0054] This point is the valley opening point.

[0055] Step S3.4: Determine if the sliding window size of the starting point needs to be adjusted. If no opening peak or valley point is found, or if the values ​​of these two points are less than the bottom gap parameter value, i.e., x... p2 <σ b ∨x p3 <σ b , then α p1 =α p1 -1, and repeat steps S3.1, S3.2, and S3.3. If none of the above conditions are met, jump to step S3.5. Or, when α p1 ≤ω p1 If necessary, proceed to step S3.5.

[0056] Step S3.5: Determine if the window size at the opening peak and opening trough needs adjustment. If the difference between the average value of the windows starting from the opening peak and opening trough and 1 is less than the top gap value, then... Then α p2 =α p2 -1,α p3 =α p3 -1, and repeat steps S3.2, S3.3, and S3.4. If none of the above conditions are met, jump to step S3.6. Or, when α p1 ≤ω p1 ∨α p2 ≤ω p2 ∨α p3 ≤ω p3 If so, skip to step S3.6.

[0057] Step S3.6: Locate the steady-state start point, p4, during the start-up phase. Initialize the confirmation count μ. p4 =0, parameter α p4 =20,β p4 =20,ω p4 =5. Determine the baseline value.

[0058]

[0059] From point x p3 Begin by sliding the window from front to back. When... At that time, μ p4 =μ p4 +1, otherwise, μ p4 =0. When μ p4 >ω p4 At that time, open the window As the target search window, let's call it Z. p4 In the target search window, the first value is greater than ρ. k Data points, i.e.

[0060] x p4 =min{x∈Z p4 |x>ρ k}

[0061] This point is the starting point of steady state.

[0062] Step S4: In the closing phase, find feature points from back to front. Specifically, this includes:

[0063] Step S4.1: Locate the endpoint, p8, during the shutdown phase. Initialize the acknowledgment count μ. p8 =0, parameter α p8 =20,β p8 =1,ω p8 =5. The sliding window starts from the last data point x. n To begin, the average value in the first window is recorded as the baseline.

[0064]

[0065] Slide the window from back to front. When ρ k >Ω p8 +δ o At that time, μ p8 =μ p8 +1, otherwise, μ p8 =0. After sliding the window, update the baseline value Ω. p8 This is the average value from the first data point in the current window to the last data point on the curve.

[0066]

[0067] When μ p8 >ω p8 At that time, the window ζ k +ω p8 *β p8 As the target search window, let's call it Z. p8 In the target search window, find the last data point whose difference from the previous data points is less than the closing threshold.

[0068] x p8 =max{x i ∈Z p8 |x i -x i-1 <δ c}

[0069] This point is the endpoint.

[0070] Step S4.2: Locate the shutdown peak point, p7, during the shutdown phase. Initialize the confirmation count μ. p7 =0, parameter α p7 =20,β p7 =1,ω p7 =5. The sliding window starts from point x. p8 Begin by sliding the window from back to front. When At that time, μ p7 =μ p7 +1, otherwise, μ p7 =0. When μ p7 >ω p7 At that time, open the window As the target search window, let's call it Z. p7 In the target search window, find the point with the largest value, i.e.

[0071] x p7 =max(Z) p7 )

[0072] This point is the closing peak point.

[0073] Step S4.3: Locate the closing valley point, p6, during the closing phase. Initialize the confirmation count μ. p6 =0, parameter α p6 =20,β p6 =1,ω p6 =5. The sliding window starts from point x. p7 Begin by sliding the window from back to front. When At that time, μ p6 =μ p6 +1, otherwise, μ p6 =0. When μ p6 >ω p6 At that time, open the window As the target search window, let's call it Z. p6 In the target search window, find the point with the smallest value, i.e.

[0074] x p6 =min(Z) p6 )

[0075] This point is the closing valley point.

[0076] Step S4.4: Determine if the sliding window size of the endpoint needs to be adjusted. If no closing peak or valley point is found, or if the values ​​of these two points are less than the bottom gap parameter value, i.e., x... p6 <σ b ∨x p7 <σ b , then α p8 =α p8 -1, and repeat steps S4.1, S4.2, and S4.3. If none of the above conditions are met, skip to step S4.5. Or, when α p8 ≤ω p8 If so, skip to step S4.5.

[0077] Step S4.5: Determine if the window sizes for the closing peak and closing trough need adjustment. If the difference between the average value of the windows ending at the closing peak and closing trough and 1 is less than the top gap value, then... Then α p6 =α p6 -1,α p7 =α p7 -1, and repeat steps S4.2, S4.3, and S4.4. If none of the above conditions are met, jump to step S4.6. Or, when α p6 <ω p6 ∨α p7 <ω p7 ∨α p8 <ω p8 If so, skip to step S4.6.

[0078] Step S4.6: Locate the steady-state termination point, p5, during the shutdown phase. Initialize the acknowledgment count μ. p5 =0, parameter α p5 =20,β p5 =20,ω p5 =5. Determine the baseline value.

[0079]

[0080] From point x p6 Begin by sliding the window from back to front. When At that time, μ p5 =μ p5 +1, otherwise, μ p5 =0. When μ p5 >ω p5 At that time, open the window As the target search window, let's call it Z. p5 In the target search window, the last value is greater than ρ. k Data points, i.e.

[0081] x p5=max{x∈Z p5 |x>ρ k}

[0082] This point is the steady-state termination point.

[0083] Step S5: After identifying all feature points, mark each feature point on the solenoid valve current curve, such as... Figure 3 As shown. Based on the location of the feature points, the current value and time at each feature point can be obtained. Based on the current value and time, the action parameters for each solenoid valve switching action can be calculated.

[0084] Steady-state current: I = I p4 ;

[0085] Starting current: I1 = I p2 ;

[0086] Release current: I2 = I p6 ;

[0087] End current: I3 = I p8 ;

[0088] Start-up peak-to-peak difference: ΔI1=I p2 -I p3 ;

[0089] Release peak-to-peak difference: ΔI² = I p7 -I p6 ;

[0090] Solenoid valve opening time: t1 = t p3 -t p1 ;

[0091] Solenoid valve closing time: t2 = t p7 -t p6 .

[0092] The solenoid valve's action characteristic parameters are stored in a database for use by the solenoid valve automatic interpretation module.

[0093] Based on the same concept, this embodiment also provides a solenoid valve action characteristic parameter determination device based on a variable sliding window, please refer to... Figure 4 The device includes:

[0094] The data preprocessing module is used to normalize the current data during the switching process of the solenoid valve, and configure the sliding window parameters and the standard parameters for detecting the solenoid valve action characteristics.

[0095] The feature point identification module is used to automatically find the positions of target feature points on the normalized solenoid valve current curve using a sliding window, based on the feature point search rules. These target feature points include the start point, peak point, valley point, and steady-state start point of the solenoid valve opening phase, and the steady-state end point, valley point, peak point, and end point of the solenoid valve closing phase.

[0096] The action characteristic calculation module is used to calculate the solenoid valve action characteristic parameters, including steady-state current, starting current, release current, ending current, and peak-to-peak difference, based on the time and current value of each target characteristic point after the position of each target characteristic point is determined, so as to automatically judge whether the solenoid valve switching action characteristics meet the design requirements.

[0097] This device is used to implement the steps in the above-mentioned method for determining the action characteristic parameters of a solenoid valve based on a variable sliding window. Its specific implementation is similar and will not be repeated here.

[0098] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments. Even if various changes are made to the present invention, if these changes fall within the scope of the claims of the present invention and their equivalents, they shall still fall within the protection scope of the present invention.

Claims

1. A method for determining the action characteristic parameters of a solenoid valve based on a variable sliding window, characterized in that, include: The current data during the switching process of the solenoid valve is normalized, and the sliding window parameters and standard parameters for detecting the solenoid valve action characteristics are configured. According to the feature point search rules, on the normalized solenoid valve current curve, the sliding window is used to automatically find the positions of target feature points, including the start point, peak point, valley point, and steady-state start point of the solenoid valve opening phase, and the steady-state end point, valley point, peak point, and end point of the solenoid valve closing phase. After locating the positions of each target feature point, the electromagnetic valve action characteristic parameters, including steady-state current, starting current, release current, ending current, peak-to-peak difference, solenoid valve opening time, and solenoid valve closing time, are calculated based on the time and current values ​​of the target feature points. This completes the automatic judgment of whether the solenoid valve switching action characteristics meet the design requirements. Specifically, based on the relevant configuration parameters and the starting point determination algorithm, starting from the first data point, the starting point of the opening phase is found in the solenoid valve current curve; Starting from the starting point, the opening peak point is found in the solenoid valve current curve according to the opening peak point determination algorithm. Starting from the peak point of activation, the valley point of activation during the activation phase is found in the current curve of the solenoid valve according to the valley point of activation algorithm. Starting from the opening valley point, the steady-state start point is found in the solenoid valve current curve according to the steady-state start point determination algorithm. Each time a target feature point is searched, an initial confirmation count parameter is set up and compared with the check confirmation step count parameter. When searching for the starting point of the opening phase in the solenoid valve current curve, during the solenoid valve opening phase, the sliding window starts from the first data point, and the average value within the first window is recorded as the baseline value. Moving the window forward, if the average value within the window is greater than the sum of the baseline value and the opening threshold, the confirmation count is incremented by 1; otherwise, the confirmation count is reset to 0. After comparison, the baseline value is updated to the average value of all data points from the first data point of the current curve to the end of the current window. When the confirmation count is greater than the number of check confirmation steps, the window before the number of check confirmation steps is moved as the target search window. In the target search window, the first point where the difference between the current data point and the previous data point is greater than the opening threshold is found, which is the starting point. Let moving along the positive time axis on the current curve be considered moving backward; When searching for the start-up peak in the solenoid valve current curve, the sliding window starts from the starting point and slides backward. If the average value in the current window is less than the average value in the previous window, the confirmation count is incremented by 1; otherwise, the confirmation count is reset to 0. When the confirmation count is greater than the number of check confirmation steps, the window before the number of check confirmation steps is moved as the target search window. In the target search window, the point with the largest value is found, which is the start-up peak. When searching for the opening valley point in the solenoid valve current curve, the sliding window starts from the opening peak point and slides backward. If the average value in the current window is greater than the average value in the previous window, the confirmation count is incremented by 1; otherwise, the confirmation count is reset to 0. When the confirmation count is greater than the number of check confirmation steps, the window before the number of check confirmation steps is moved as the target search window. In the target search window, the point with the smallest value is found, which is the opening valley point. When searching for the steady-state start point of the opening phase in the solenoid valve current curve, the sliding window starts from the opening valley point. The average value in the first window is recorded as the first comparison value, the average value in the second window is recorded as the second comparison value, and the difference between the two comparison values ​​is recorded as the reference value. Sliding the window forward, if the difference between the average value of the current window and the average value of the previous window is less than the reference value, the confirmation count is incremented by 1; otherwise, the confirmation count is reset to 0. When the confirmation count is greater than the number of check confirmation steps, the average value of the current window is used as the evaluation value. All points from the first data point of the window before the number of check confirmation steps to the last data point of the current window are combined as the target search window. In the target search window, the first point whose value is greater than the evaluation value is the steady-state start point.

2. The method for determining the action characteristic parameters of a solenoid valve based on a variable sliding window as described in claim 1, characterized in that, Configure a sliding window for each target feature point; For different target feature points, a sliding window is used on the solenoid valve current curve. The parameters of the sliding window are dynamically adjusted according to the target feature point detection standard to determine the target search window for each feature point. In the target search window, the location of the target feature point is determined according to the feature point search rules.

3. The method for determining the action characteristic parameters of a solenoid valve based on a variable sliding window as described in claim 1, characterized in that, If the corresponding feature point is not found according to the algorithm for determining the opening peak and opening valley, or if the position of the opening peak and opening valley does not meet the requirements of the bottom clearance and top clearance parameters in the standard parameters for detecting the action characteristics of the solenoid valve, then it is necessary to adjust the size of the sliding window of the starting point, opening peak, and opening valley, and re-find the position of the starting point, opening peak, and opening valley.

4. The method for determining the action characteristic parameters of a solenoid valve based on a variable sliding window as described in claim 1, characterized in that, Based on the relevant configuration parameters and the endpoint determination algorithm, starting from the last data point, the endpoint of the closing phase is found in the solenoid valve current curve. Starting from the endpoint and working backwards, the closing peak point is located in the solenoid valve current curve according to the closing peak point determination algorithm. Starting from the closing peak point and working backwards, the closing valley point is found in the solenoid valve current curve according to the closing valley point determination algorithm; Starting from the closing valley point and working backwards, the steady-state termination point is located in the solenoid valve current curve according to the steady-state termination point search algorithm.

5. The method for determining the action characteristic parameters of a solenoid valve based on a variable sliding window as described in claim 4, characterized in that, Each time a target feature point is searched, an initial confirmation count parameter is set up and compared with the check confirmation step count parameter. When searching for the endpoint of the closing phase in the solenoid valve current curve, during the solenoid valve closing phase, the sliding window starts from the last data point, and the average value within the first window is recorded as the baseline value. The window is then slid forward; if the average value within the window is greater than the sum of the baseline value and the closing threshold, the confirmation count is incremented by 1; otherwise, the confirmation count is reset to 0. After comparison, the baseline value is updated to the average value from the first data point in the current window to the last data point on the curve. When the confirmation count exceeds the number of check confirmation steps, the window before the check confirmation steps is moved as the target search window. In the target search window, the point where the difference between the last current data point and the previous data point is less than the closing threshold is found; this is the endpoint. Let sliding along the negative time axis on the current curve be considered sliding forward. When searching for the shutdown peak in the solenoid valve current curve, the sliding window starts from the end point and slides forward. If the average value in the current window is less than the average value in the previous window, the confirmation count is incremented by 1; otherwise, the confirmation count is reset to 0. When the confirmation count is greater than the number of check confirmation steps, the window before the number of check confirmation steps is moved as the target search window. In the target search window, the point with the largest value is found, which is the shutdown peak. When searching for the closure valley in the solenoid valve current curve, the sliding window starts from the closure peak and slides backward. If the average value in the current window is greater than the average value in the previous window, the confirmation count is incremented by 1; otherwise, the confirmation count is reset to 0. When the confirmation count is greater than the number of confirmation steps, the window before the number of confirmation steps is moved as the target search window. In the target search window, the point with the smallest value is found, which is the closure valley. When searching for the steady-state end point of the closing phase in the solenoid valve current curve, the sliding window starts with the closing valley point as the end of the window. The average value in the first window is recorded as the first comparison value, the average value in the second window is recorded as the second comparison value, and the difference between the two comparison values ​​is recorded as the reference value. Sliding the window forward, if the difference between the average value of the current window and the average value of the previous window is less than the reference value, the confirmation count is incremented by 1; otherwise, the confirmation count is reset to 0. When the confirmation count is greater than the number of check confirmation steps, the average value of the current window is used as the evaluation value. All points from the first data point of the current window to the last data point of the window before the number of check confirmation steps are combined to form the target search window. In the target search window, the last data point with a value greater than the evaluation value is the steady-state end point.

6. The method for determining the action characteristic parameters of a solenoid valve based on a variable sliding window as described in claim 4, characterized in that, If the corresponding feature point is not found according to the algorithm for determining the closing peak and closing valley, or if the position of the closing peak and closing valley does not meet the requirements of the bottom clearance and top clearance parameters in the standard parameters for detecting the action characteristics of the solenoid valve, then it is necessary to adjust the size of the sliding window of the endpoint, closing peak, and closing valley, and re-find the position of the endpoint, closing peak, and closing valley.

7. A device for determining the action characteristic parameters of a solenoid valve based on a variable sliding window, realizing the method for determining the action characteristic parameters of a solenoid valve based on a variable sliding window as described in claim 1, characterized in that, include: The data preprocessing module is used to normalize the current data during the switching process of the solenoid valve, and configure the sliding window parameters and the standard parameters for detecting the solenoid valve action characteristics. The feature point identification module is used to automatically find the positions of target feature points on the normalized solenoid valve current curve using a sliding window, based on the feature point search rules. These target feature points include the start point, peak point, valley point, and steady-state start point of the solenoid valve opening phase, and the steady-state end point, valley point, peak point, and end point of the solenoid valve closing phase. The action characteristic calculation module is used to calculate the solenoid valve action characteristic parameters, including steady-state current, starting current, release current, ending current, peak-to-peak difference, solenoid valve opening time, and solenoid valve closing time, based on the time and current value of each target characteristic point after the position of each target characteristic point is determined. This allows for the automatic judgment of whether the solenoid valve switching action characteristics meet the design requirements.

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