Launch control logic test method for general suspender

Abstract

The invention discloses a launch control logic test method for a general suspension object, and the method comprises the steps: constructing a simulated launch scene of the suspension object, and building a dynamic and random environment excitation model in the simulated launch scene; applying different types of environmental excitation to the simulated launching scene by using a dynamic random environmental excitation model, and simulating the launching of the suspension object under the environmental excitation condition; constructing an excitation signal vector in a hanging object launching process, driving the hanging object model to enter a launching state, and constructing an ideal state model of the hanging object in the launching process; and outputting an ideal state vector in the hanging object launching process, constructing a control logic judgment model in the hanging object launching control test process, and evaluating logic control precision in the launching control process. The environment excitation data in the target area is analyzed and processed to serve as a basis for applying environment excitation to the simulation emission scene in the test process, the simulation test result can truly reflect the target scene, and the test reliability is improved.

Description

Technical Field

[0001] The present invention relates to the field of aircraft control testing, and particularly to a method for testing the firing control logic of a general suspension. Background Art

[0002] In the fields of aerospace and high-altitude aircraft, more and more small aircraft and suspensions can be customarily released and launched at high altitudes. Using a large aircraft as a carrier, they can be opportunistically launched at high altitudes. For example, the launch of an airborne UAV. By establishing a UAV mounting platform in a large aircraft and transporting the UAV to a high altitude for launch or release, the cruising distance and action radius of the UAV can be effectively increased; another example is the projection of airborne electronic equipment, which can improve the delivery accuracy of the electronic equipment. However, the high-altitude environment is complex. When the suspension and the mounting platform fall off each other, precise control of the suspension and the mounting platform is required to avoid mutual influence. To achieve precise launch control, firing control logic testing is needed to debug the firing control logic under test conditions to ensure sufficient accuracy between the firing control logics.

[0003] Existing suspension firing control logic tests usually use dedicated test equipment and design fixed test processes for specific models of suspensions, which have the following defects: independent test programs need to be developed for different models of suspensions, resulting in high repeated development costs; it is difficult to cover complex application environments; manual comparison based on fixed thresholds is vulnerable to environmental noise interference, leading to misjudgments. Summary of the Invention

[0004] In view of the above deficiencies in the prior art, the present invention provides a method for testing the firing control logic of a general suspension, which realizes the testing of the suspension launch control logic under different application scenarios.

[0005] To achieve the above invention object, the technical solution adopted by the present invention is as follows:[[]]END]] Provide a method for testing the firing control logic of a general suspension, which includes:[[]]END]] Step S1: Construct a simulated launch scenario of the suspension and establish a dynamic random environmental excitation model in the simulated launch scenario; Step S2: Use the dynamic random environmental excitation model to apply different types of environmental excitations to the simulated launch scenario, establish a suspension model in the simulated launch scenario, and simulate the launch of the suspension under environmental excitation conditions; Step S3: Construct an excitation signal vector during the suspension launch process, apply the excitation signal vector to the suspension model, drive the suspension model into the launch state, obtain the state vector during the suspension launch process, and construct an ideal state model of the suspension during the launch process; Step S4: Output the ideal state vector during the suspension launch process through the applied excitation signal vector and the ideal state model, construct the control logic decision model during the suspension launch control test process, and evaluate the logic control accuracy during the launch control process.

[0006] Furthermore, the method for establishing the dynamic random environmental excitation model in step S1 includes: Step S11: Collect historical environmental excitation data in the target area during the suspension launch process, form an environmental excitation data set, calculate the distribution probability of the environmental excitation data over time, and obtain a distribution probability data set; Step S12: Set the distribution probability range indicating that the distribution density of the collected environmental excitation data meets the requirements, segment the environmental excitation data according to whether the distribution probability data falls within the distribution probability range, and calculate the fluctuation coefficient of the amplitude of the environmental excitation data within the environmental excitation data segment; Step S13: Use the fluctuation coefficients corresponding to the amplitudes of the environmental excitation data in all environmental excitation data segments to establish a polynomial time-varying amplitude modulation model of the environmental excitation data, and construct a dynamic differential equation of the environmental excitation based on the polynomial time-varying amplitude modulation model as the dynamic random environmental excitation model.

[0007] Furthermore, step S11 includes: Step S111: Collect historical environmental excitation data in the target area during the suspension launch process , form an environmental excitation data set , where n is the type of environmental excitation, m is the quantity of environmental excitation data, t m is the moment when the m th environmental excitation data is collected; Step S112: Calculate the distribution probability of the environmental excitation data over time according to the time interval between the collection moments of two adjacent environmental excitation data; ; where is the distribution probability of the environmental excitation data , is the moment when the m -2th environmental excitation data is collected; Step S113: Obtain the distribution probability data set corresponding to the environmental excitation data from the 2nd to the m -1th environmental excitation data .

[0008] Furthermore, step S12 includes: Step S121: Set a distribution probability range that characterizes that the distribution density of the collected environmental excitation data meets the requirements , is the maximum value of the distribution probability that meets the requirements, is the minimum value of the distribution probability that meets the requirements , traverse the distribution probability data set , and filter out the distribution probability less than or equal to the minimum value of the distribution probability as the reference distribution probability; Step S122: Using the reference distribution probability as the origin, traverse the continuous distribution probabilities on both sides in turn, and compare the continuous distribution probabilities on both sides with the maximum value until the distribution probability greater than the maximum value appears , then stop, and output the distribution probabilities farthest from the origin on both sides ; Step S123: Based on the environmental excitation data corresponding to the farthest distribution probability , extract the environmental excitation data between to form an environmental excitation data segment, and calculate the fluctuation coefficient of the environmental excitation data amplitude within the environmental excitation data segment; ; Among them, k is the number of the extracted environmental excitation data segments, t max is the moment corresponding to the amplitude of the collected environmental excitation data, is the k th fluctuation coefficient of the environmental excitation data amplitude of the environmental excitation data segment, t min is the moment corresponding to the valley value of the collected environmental excitation data.

[0009] Furthermore, step S13 includes: Step S131: According to the fluctuation coefficients corresponding to the environmental excitation data amplitudes within all the extracted environmental excitation data segments, establish a polynomial time-varying amplitude modulation model of the environmental excitation data; ; Among them, K is the number of the extracted environmental excitation data segments, is a function of the time variable, is the historical environmental excitation data amplitude, is the time-varying amplitude of the environmental excitation, t is the time variable for applying the environmental excitation; Step S132: Time-varying amplitude based on environmental excitation data Establish a dynamic differential equation of environmental excitation as a dynamic stochastic environmental excitation model; ; Among them, is the environmental excitation applied to the simulated launch scenario, is the drift coefficient, is the basic perturbation coefficient, is the fluctuation period coefficient of the perturbation coefficient, w is the unit fluctuation period of the perturbation coefficient, are the weight coefficients of the drift fluctuation and the perturbation fluctuation respectively, is the Wiener increment, which follows a normal distribution , T is the test duration.

[0010] Furthermore, step S3 includes: Step S31: Construct an excitation signal vector during the launch process of the suspended object , e is the type of excitation signal, is the e th excitation signal; Apply the excitation signal vector to the suspended object model to drive the suspended object model into the launch state; Step S32: Obtain the feedback signal in the time series during the launch process of the suspended object and construct the state vector during the launch process of the suspended object , j is the number of state signals, is the j th state signal; Step S33: Construct an ideal state model during the launch of the suspended object. The ideal state model is described by the state transition matrix ; ; Among them, g is the number of ideal states. The element in the state transition matrix represents the conditional function from state o to state q ; The conditional function is expressed as: ; Among them, is the excitation trigger function, which outputs an excitation signal using the logical combination of two excitation signals , are the numbers of two different excitation signals in the excitation signal vector respectively, is a time constraint function, is the state transition time window during the suspension launch process, is a state constraint function, and an ideal state signal during the suspension launch process is output through the state constraint function .

[0011] Furthermore, step S4 includes: Step S41: Through the applied excitation signal vector and the ideal state model, an ideal state vector during the suspension launch process is output , is the j th ideal state signal; Step S42: Construct a control logic decision model during the suspension launch control test process; ; Among them, is a control logic accuracy determination function, r is the number of the state signal, is the r th state signal, is the r th ideal state signal, is a control logic decision function, is a dynamic noise tolerance function, is the transition slope, t 0 is the state transition critical time, is the maximum allowable noise error; Step S43: Use the control logic decision model to output the control logic decision value F during the suspension launch control process. If 's duration is greater than or equal to the effective time, it is determined that the logic control accuracy during the launch control process is high; otherwise, the logic control accuracy is low.

[0012] The beneficial effects of the present invention are as follows: By analyzing and processing the environmental excitation data in the target area, and using it as the basis for simulating the environmental excitation in the test process, the simulation test results can truly reflect the target scenario, improving the reliability of the test. At the same time, by abstracting the launch control logic, only the state transition matrix and the excitation signal need to be updated to adapt to the tests of different models and types of suspensions, making the test method highly versatile. The control logic decision model has an adaptive fault tolerance function, allowing a certain amount of noise interference, avoiding deviations in the judgment of logic failures, and reducing the misjudgment rate from 12% of the traditional fixed threshold method to 1.5%. Description of the Drawings

[0013] Figure 1Flow chart of the firing control logic test method for general suspensions.

[0014] Figure 2 Schematic diagram of segmented environmental excitation data. Specific implementation mode

[0015] The following describes the specific implementation mode of the present invention to facilitate those skilled in the art of the present technology to understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation mode. For those of ordinary skill in the art of the present technology, as long as various changes are within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are within the scope of protection.

[0016] As Figure 1 shown, a firing control logic test method for general suspensions includes: Step S1: Construct a simulated launch scenario for the suspension and establish a dynamic random environmental excitation model in the simulated launch scenario; the specific method for establishing the dynamic random environmental excitation model includes: Step S11: Collect historical environmental excitation data in the target area during the launch process of the suspension to form an environmental excitation data set, and calculate the distribution probability of the environmental excitation data over time to obtain a distribution probability data set. Step S11 specifically includes: Step S111: Collect historical environmental excitation data in the target area during the launch process of the suspension to form an environmental excitation data set , where n is the type of environmental excitation, m is the quantity of environmental excitation data, t m is the moment of collecting the m th environmental excitation data; the environmental excitation data includes external excitations that affect the launch and flight of the suspension and the flight of the suspension carrier, such as wind load, environmental temperature, environmental humidity, altitude, rainfall, etc.

[0017] Step S112: Calculate the distribution probability of the environmental excitation data over time according to the time interval between the collection moments of two adjacent environmental excitation data; ; where is the distribution probability of the environmental excitation data , is the moment of collecting the m -2th environmental excitation data; Step S113: Obtain the distribution probability data set corresponding to the 2nd to the m -1th environmental excitation data 。

[0018] The distribution probability represents the distribution density of each environmental excitation data in the acquisition time series. The larger the distribution probability value, the lower the distribution density; conversely, the higher the distribution density. The greater the distribution density of the environmental excitation data, the more continuous and reliable the environmental excitation data collected in the nearby time period. There are defects such as randomness, accidental errors, or transmission delays in the fluctuations of historical environmental excitation data during the acquisition process. In order to improve the reliability of applying environmental excitation data to the simulated emission scenario in subsequent calculations, it is necessary to extract data segments with better distribution density as data calculation segments and discard data segments with poor distribution density.

[0019] Step S12: Set the distribution probability range indicating that the distribution density of the acquired environmental excitation data meets the requirements. Segment the environmental excitation data according to whether the distribution probability data falls within the distribution probability range, and calculate the fluctuation coefficient of the amplitude of the environmental excitation data within the environmental excitation data segment. Step S12 specifically includes: Step S121: Set the distribution probability range indicating that the distribution density of the acquired environmental excitation data meets the requirements , is the maximum value of the distribution probability that meets the requirements, is the minimum value of the distribution probability that meets the requirements ,traverse the distribution probability data set ,filter out the distribution probability less than or equal to the minimum value of the distribution probability as the reference distribution probability; The minimum value of the distribution probability and the maximum value can be set according to the data volume of the acquired historical environmental excitation data. If the data volume is small, the minimum value and the maximum value can be set a bit larger to ensure the data volume within the environmental excitation data segment. If the data volume is large and sufficient accuracy is required, the minimum value and the maximum value can be set a bit smaller.

[0020] Step S122: Using the reference distribution probability as the origin, traverse the continuous distribution probabilities on both sides in turn, and compare the sizes between the continuous distribution probabilities on both sides and the maximum value until the distribution probability greater than the maximum value appears and then stop, and output the distribution probabilities farthest from the origin on both sides ; Step S123: Based on the environmental excitation data corresponding to the farthest distribution probability , extract the environmental excitation data , extract the environmental excitation data The environmental excitation data between them form an environmental excitation data segment. The principle of segmenting the environmental excitation data is as Figure 2 shown, and calculate the fluctuation coefficient of the amplitude of the environmental excitation data within the environmental excitation data segment . ; Among them, k is the serial number of the extracted environmental excitation data segment, t max is the moment corresponding to the amplitude of the collected environmental excitation data , is the k th fluctuation coefficient of the amplitude of the environmental excitation data in the environmental excitation data segment, t min is the moment corresponding to the valley value of the collected environmental excitation data .

[0021] Step S13: Use the fluctuation coefficients corresponding to the amplitudes of the environmental excitation data in all environmental excitation data segments to establish a polynomial time-varying amplitude modulation model of the environmental excitation data, and construct a dynamic differential equation of the environmental excitation based on the polynomial time-varying amplitude modulation model as a dynamic random environmental excitation model. Step S13 specifically includes: Step S131: Establish a polynomial time-varying amplitude modulation model of the environmental excitation data according to the fluctuation coefficients corresponding to the amplitudes of the environmental excitation data in all the extracted environmental excitation data segments; ; Among them, K is the number of the extracted environmental excitation data segments, is a function of the time variable, is the historical amplitude of the environmental excitation data, is the time-varying amplitude of the environmental excitation, t is the time variable for applying the environmental excitation; When applying a dynamic random environmental excitation to the simulated emission scenario using the polynomial time-varying amplitude modulation model, according to the time variable function split the application time period of the environmental excitation into continuous time periods corresponding to the environmental excitation data segments k , and apply different environmental excitation amplitudes in different time periods, which can ensure that the difference between the fluctuating change of the simulated environmental excitation amplitude and the real environmental excitation amplitude is small, and improve the reliability of the simulation test.

[0022] Step S132: Establish a dynamic differential equation of the environmental excitation based on the time-varying amplitude of the environmental excitation data as a dynamic random environmental excitation model; ; Among them, is the environmental excitation applied to the simulated launch scenario, is the drift coefficient, representing the deterministic change rate of the time-varying amplitude with time, is the basic disturbance coefficient, is the fluctuation period coefficient of the disturbance coefficient, w is the unit fluctuation period of the disturbance coefficient, are the weight coefficients of the drift fluctuation and the disturbance fluctuation respectively, is the Wiener increment, which follows a normal distribution , T is the test duration.

[0023] In this embodiment, when applying dynamic and random environmental excitation to the simulated launch scenario, dynamic drift fluctuation and random disturbance are introduced based on the time-varying amplitude of the environmental excitation to achieve the random and dynamic output of the environmental excitation. The drift coefficient term represents the deterministic trend fluctuation based on the time-varying amplitude of the environmental excitation, and the disturbance coefficient term represents the random disturbance based on the time-varying amplitude of the environmental excitation.

[0024] According to the different types of environmental excitation applied, the value of the weight coefficient is also different. For example, for wind load, in a small launch area in the real launch scenario, the randomness of the wind load is large, then the weight coefficient is taken. Generally, it can be taken as . For example, for environmental temperature, which is affected by the light intensity and has periodic fluctuations within a certain range, the weight coefficient is taken. Generally, it can be taken as .

[0025] Step S2: Use the dynamic random environmental excitation model to apply different types of environmental excitation to the simulated launch scenario, and establish a suspended object model in the simulated launch scenario to simulate the launch of the suspended object under the environmental excitation conditions; Step S3: Construct the excitation signal vector during the launch process of the suspended object, and apply the excitation signal vector to the suspended object model to drive the suspended object model into the launch state, obtain the state vector during the launch process of the suspended object, and construct the ideal state model of the suspended object during the launch process. Step S3 specifically includes: Step S31: Construct the excitation signal vector during the launch process of the suspended object , e is the type of the excitation signal, is the e th excitation signal; Apply the excitation signal vector to the suspended object model to drive the suspended object model into the launch state; Step S32: Obtain the feedback signals of the suspended object during the launching process in a time series, and construct the state vector during the launching process of the suspended object , j is the number of state signals, is the j th state signal; Step S33: Construct the ideal state model of the suspended object during the launching process. The ideal state model has a state transition matrix described; ; Among them, g is the number of ideal states, and the element in the state transition matrix represents the conditional function from state o to state q ; The conditional function is expressed as: ; Among them, is the excitation trigger function, which outputs an excitation signal using the logical combination of two excitation signals , are the numbers of two different excitation signals in the excitation signal vector respectively, is the time constraint function, is the state transition time window during the launching process of the suspended object, is the state constraint function, and the ideal state signal during the launching process of the suspended object is output through the state constraint function .

[0026] Step S4: Through the applied excitation signal vector and the ideal state model, output the ideal state vector during the launching process of the suspended object, construct the control logic decision model during the control test process of the suspended object launching, and evaluate the logical control accuracy during the launching control process. Step S4 specifically includes: Step S41: Through the applied excitation signal vector and the ideal state model, output the ideal state vector during the launching process of the suspended object, is the j th ideal state signal; Step S42: Construct the control logic decision model during the control test process of the suspended object launching; ; Among them, is the control logic accuracy determination function, r is the number of the state signal, is the r ​A status signal, is the r th ideal status signal, is the control logic decision function, is the dynamic noise tolerance function, is the transition slope, used to control the change rate of the dynamic noise tolerance, t 0 is the critical time for state transition, is the maximum allowable noise error, which can be set to 5% in this embodiment; Step S43: Use the control logic decision model to output the control logic decision value during the launch control process of the suspended object F , if 's duration is greater than or equal to the effective time, it is determined that the logic control accuracy during the launch control process is high, otherwise, the logic control accuracy is low.

[0027] The present invention analyzes and processes the environmental excitation data in the target area, which is used as the basis for applying environmental excitation during the simulation launch scenario in the test process. The simulation test results can truly reflect the target scenario and improve the reliability of the test. At the same time, by abstracting the launch control logic, only the state transition matrix and the excitation signal need to be updated to adapt to the tests of different models and types of suspended objects, making the test method highly versatile. The control logic decision model has an adaptive fault tolerance function, allowing a certain amount of noise interference, avoiding deviation in the judgment of logic faults, and reducing the misjudgment rate from 12% of the traditional fixed threshold method to 1.5%.

Claims

1. A method for testing the firing control logic of a general suspension, characterized in that, Including: Step S1: Construct a simulated launch scenario for the suspended object, and establish a dynamic random environmental excitation model in the simulated launch scenario; Step S2: Apply different types of environmental excitations to the simulated launch scenario using the dynamic random environmental excitation model, establish a suspended object model in the simulated launch scenario, and simulate the launch of the suspended object under environmental excitation conditions; Step S3: Construct an excitation signal vector during the launch process of the suspended object, apply the excitation signal vector to the suspended object model, drive the suspended object model into the launch state, obtain the state vector during the launch process of the suspended object, and construct an ideal state model during the launch process of the suspended object; Step S4: Through the applied excitation signal vector and the ideal state model, output the ideal state vector during the launch process of the suspended object, construct a control logic decision model during the launch control test process of the suspended object, and evaluate the logic control accuracy during the launch control process.

2. The method for testing the firing control logic of the general suspension according to claim 1, characterized in that, The method for establishing the dynamic random environmental excitation model in step S1 includes: Step S11: Collect historical environmental excitation data in the target area during the launch process of the suspended object to form an environmental excitation data set, calculate the distribution probability of the environmental excitation data in time, and obtain a distribution probability data set; Step S12: Set a distribution probability range indicating that the distribution density of the collected environmental excitation data meets the requirements, segment the environmental excitation data according to whether the distribution probability data falls within the distribution probability range, and calculate the fluctuation coefficient of the environmental excitation data amplitude within the environmental excitation data segment; Step S13: Use the fluctuation coefficients corresponding to the environmental excitation data amplitudes in all environmental excitation data segments to establish a polynomial time-varying amplitude modulation model of the environmental excitation data, and construct a dynamic differential equation of the environmental excitation based on the polynomial time-varying amplitude modulation model as the dynamic random environmental excitation model.

3. The method for testing the launch control logic of the general suspension according to claim 2, wherein, Step S11 includes: Step S111: Collect historical environmental excitation data in the target area during the launch of the suspended object , and form an environmental excitation data set , where n is the type of environmental excitation, m is the quantity of environmental excitation data, t m is the moment when the m th environmental excitation data is collected; Step S112: Calculate the distribution probability of the environmental excitation data in time according to the time interval between the acquisition times of two adjacent environmental excitation data; ； Among them, is the distribution probability of environmental excitation data , is the moment when the m -2nd environmental excitation data is collected; Step S113: Obtain the distribution probability data set corresponding to the 2nd environmental excitation data to the m -1st environmental excitation data .

4. The method for testing the firing control logic of the general suspension according to claim 3, characterized in that, Step S12 includes: Step S121: Set a distribution probability range that characterizes that the distribution density of the collected environmental excitation data meets the requirements , is the maximum value of the distribution probability that meets the requirements, is the minimum value of the distribution probability that meets the requirements , traverse the distribution probability data set , and filter out the distribution probability less than or equal to the minimum value of the distribution probability as the reference distribution probability; Step S122: Using the reference distribution probability as the origin, traverse the continuous distribution probabilities on both sides in sequence, and compare the continuous distribution probabilities on both sides with the maximum value until the distribution probability greater than the maximum value appears and then stop, and output the distribution probabilities on both sides that are farthest from the origin ; Step S123: Based on the farthest distribution probability The corresponding environmental excitation data , extract the environmental excitation data The environmental excitation data between them forms an environmental excitation data segment, and calculate the fluctuation coefficient of the environmental excitation data amplitude within the environmental excitation data segment ; ； Among them, k is the serial number of the extracted environmental excitation data segment, t max is the amplitude of the collected environmental excitation data, corresponding to the moment, is the k fluctuation coefficient of the environmental excitation data amplitude of the t min is the moment corresponding to the valley value of the collected environmental excitation data, corresponding to the moment.

5. The method for testing the firing control logic of the general suspension according to claim 4, wherein Step S13 includes: Step S131: Establish a polynomial time-varying amplitude modulation model of the environmental excitation data according to the fluctuation coefficients corresponding to the environmental excitation data amplitudes in all extracted environmental excitation data segments; ； Among them, K is the number of extracted environmental excitation data segments, is a time-variable function, is the amplitude of historical environmental excitation data, is the time-varying amplitude of environmental excitation, t is the time variable for applying environmental excitation; Step S132: Time-varying amplitude based on environmental excitation data Establish a dynamic differential equation of environmental excitation as a dynamic stochastic environmental excitation model; ； Among them, is the environmental excitation applied to the simulated emission scenario, is the drift coefficient, is the basic perturbation coefficient, is the fluctuation period coefficient of the perturbation coefficient, w is the unit fluctuation period of the perturbation coefficient, are the weight coefficients of the drift fluctuation and the perturbation fluctuation respectively, is the Wiener increment, which follows a normal distribution , T is the test duration.

6. The test method for the launch control logic of the general suspension according to claim 5, wherein, Step S3 includes: Step S31: Construct an excitation signal vector during the launch process of the suspended object , e is the type of excitation signal, is the e th excitation signal; Apply the excitation signal vector to the suspended object model to drive the suspended object model into the launch state; Step S32: Obtain the feedback signals of the suspension object during the launch process in the time series, and construct the state vector during the launch process of the suspension object , j is the number of state signals, is the j th state signal; Step S33: Construct an ideal state model of the suspended object during the launch process, and the ideal state model has a state transition matrix Description; ； Among them, g is the number of ideal states, and the elements in the state transition matrix represent the conditional function from state to state o transferring to state q ; The conditional function is expressed as: ； Among them, is an excitation trigger function that outputs an excitation signal using the logical combination of two excitation signals . and are two different excitation signal numbers in the excitation signal vector respectively. is a time constraint function, is the state transition time window during the suspension launch process, and is a state constraint function that outputs the ideal state signal during the suspension launch process through the state constraint function.

7. The method for testing the launch control logic of the general suspension according to claim 6, wherein, Step S4 includes: Step S41: Output the ideal state vector during the suspension launch process through the applied excitation signal vector and the ideal state model, , where j is the j th ideal state signal; Step S42: Construct a control logic decision model during the launch control test process of the suspended object; ； Among them, is the control logic precision determination function, r is the number of the status signal, is the r th status signal, is the r th ideal status signal, is the control logic determination function, is the dynamic noise tolerance function, is the transition slope, t 0 is the status transition critical time, is the maximum allowable noise error; Step S43: Use the control logic decision model to output the control logic decision value in the suspension object launch control process F , if the duration is greater than or equal to the effective time, it is determined that the logic control accuracy in the launch control process is high; otherwise, the logic control accuracy is low.

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