Collision positioning method based on minimum discrete variance

Abstract

The invention provides a collision positioning method based on minimum discrete variance. The method comprises the following steps: simulating a fragment collision test, and acquiring collision event information generated in the simulated collision test by utilizing a plurality of sensors arranged on a to-be-tested flexible material film to obtain a collision signal; determining the arrival time of the collision signal to each sensor, calculating through a time difference positioning method to obtain at least one initial positioning point, and carrying out weighted average on the at least one initial positioning point to obtain an initial collision position; taking the collision position coordinate as a variable, and fusing the anisotropic wave velocity model of the flexible material film to establish an optimization objective function for optimizing the collision position; and taking the initial collision position as an initial value of optimization solution, optimizing the optimization objective function through an iterative optimization algorithm, and solving an optimal collision position which minimizes the optimization objective function to serve as a final collision positioning result. According to the invention, accurate positioning of the collision source of flexible structures such as polyimide is effectively realized.

Description

Technical Field

[0001] This application relates to the field of environmental simulation testing technology, and more specifically, to a collision localization method and apparatus based on minimum discrete variance. Background Technology

[0002] In recent years, with the increasing frequency of space activities, the amount of space debris has grown exponentially. Millimeter-sized debris is enough to penetrate critical components of spacecraft, while centimeter-sized debris can cause catastrophic disintegration. For manned space missions, accurate debris warnings are an absolute prerequisite for ensuring the safety of life. The space debris problem has become increasingly prominent, posing a major threat to the operation of spacecraft in orbit and extravehicular activities (EVAs). Qualitative and quantitative analysis of collision events, and the subsequent development of orbital debris models for early avoidance, are crucial for the long-term safe operation of spacecraft in orbit.

[0003] Currently, researchers both domestically and internationally have developed various debris detectors for space debris detection. The Japan Aerospace Exploration Agency (JAXA) developed the SDM debris detector, which locates collision events by detecting the continuity of resistance wires through a mesh-like resistive grid laid on a polyimide film. However, this method requires a high-precision etching process for the resistance wires, and the breakage of a single resistance wire is irreversible, making it impossible to determine secondary impacts from the same wire. NASA has deployed PVDF sensors on etched resistance wire films, using the traditional TDOA method for collision source localization. However, the etched resistance wires affect sound wave propagation, and the wave velocity changes due to temperature and material properties of the film.

[0004] Therefore, it is necessary to provide a collision localization method and apparatus based on minimum discrete variance to solve one of the aforementioned technical problems. Summary of the Invention

[0005] The purpose of this application is to provide a collision localization method, apparatus, medium, and electronic device based on minimum discrete variance, which can solve at least one of the aforementioned technical problems. The specific solution is as follows:

[0006] According to a specific embodiment of this application, this application provides a collision location method based on minimum discrete variance, comprising: simulating a fragment collision test; using multiple sensors installed on the flexible material film to be tested to collect collision event information occurring in the simulated collision test to obtain a collision signal; based on the collision signal, determining the arrival time of the collision signal at each sensor, calculating at least one preliminary location point using a time-of-arrival (TOA) positioning method, and performing a weighted average of the at least one preliminary location point to obtain an initial collision position; using the collision position coordinates as variables and fusing the anisotropic wave velocity model of the flexible material film to establish an optimization objective function for optimizing the collision position, wherein the optimization objective function is used to measure the error between the arrival time difference calculated based on the collision position coordinates and the actual measured arrival time difference; using the initial collision position as the initial value for optimization, optimizing the optimization objective function through an iterative optimization algorithm to find the optimal collision position that minimizes the optimization objective function and minimizes the discrete variance in the collision location result, as the final collision location result.

[0007] According to a specific embodiment of this application, this application also provides a collision location device based on minimum discrete variance, which executes the collision location method based on minimum discrete variance described in this application, including: a simulation test module, which simulates a fragment collision test and uses multiple sensors installed on the flexible material film to collect collision event information occurring in the simulated collision test to obtain a collision signal; a first determination module, which determines the arrival time of the collision signal to each sensor based on the collision signal, calculates at least one preliminary location point using the time difference positioning method, and performs a weighted average of the at least one preliminary location point to obtain an initial collision position; an establishment processing module, which uses the collision position coordinates as variables and integrates the anisotropic wave velocity model of the flexible material film to establish an optimization objective function for optimizing the collision position, wherein the objective function is used to measure the error between the arrival time difference calculated based on the collision position coordinates and the actual measured arrival time difference; and a second determination module, which uses the initial collision position as the initial value for optimization, optimizes the optimization objective function through an iterative optimization algorithm, and solves for the optimal collision position that minimizes the optimization objective function and minimizes the discrete variance in the collision location result, as the final collision location result.

[0008] According to a specific embodiment of this application, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the collision localization method based on minimum discrete variance as described in any of the preceding claims.

[0009] According to a specific embodiment of this application, this application also provides an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the collision localization method based on minimum discrete variance as described in any of the preceding claims.

[0010] Compared with the prior art, the above-described solutions of this application have at least the following beneficial effects:

[0011] This application simulates fragment collision tests and uses multiple sensors installed on the flexible material film under test to collect collision event information to obtain collision signals. The arrival time of the collision signals at each sensor is determined, and at least one preliminary location point is calculated using a time-difference positioning method. A weighted average is then performed to obtain the initial collision position. Using the collision position coordinates as variables and incorporating the anisotropic wave velocity model of the flexible material film, an optimization objective function for optimizing the collision position is established. The initial collision position is used as the initial value for optimization. An iterative optimization algorithm is used to optimize the objective function, finding the optimal collision position that minimizes the objective function and the discrete variance in the collision positioning result. This serves as the final collision positioning result, effectively achieving precise positioning of collision sources for flexible structures such as polyimide. Attached Figure Description

[0012] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0013] Figure 1 This is a schematic flowchart illustrating a collision localization method based on minimum discrete variance according to an embodiment of this application.

[0014] Figure 2 This is a schematic diagram illustrating an application example of the collision localization method based on minimum discrete variance as shown in the embodiments of this application;

[0015] Figure 3 This application provides an embodiment of a collision localization method based on minimum discrete variance, illustrating an example of a collision process.

[0016] Figure 4 This is a structural block diagram of a collision localization device based on minimum discrete variance, as shown in an embodiment of this application.

[0017] Figure 5This is a schematic diagram of the electronic device structure shown in an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0020] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0021] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0022] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0023] The purpose of this invention is to overcome the existing challenges in collision localization of flexible materials. It proposes a collision localization method based on minimum discrete variance. This method involves collecting collision signals by arranging a piezoelectric sensor array on a flexible thin film, determining the sound wave propagation velocity range on the polyimide film based on theoretical analysis, and determining the accurate arrival time of the collision signal at each sensor using a dynamic threshold method. First, the sound source is initially located using a time-difference method. Then, an objective function is established based on the time difference of signal arrival at each sensor. The result with the minimum discrete variance (minimum objective function) among the localization results, i.e., the optimal solution obtained by the particle swarm optimization algorithm, is selected as the final localization result. This method can effectively achieve precise localization of collision sources in flexible structures such as polyimide.

[0024] The following is in conjunction with the appendix Figures 1 to 3 Detailed description of optional embodiments of the method of this application.

[0025] like Figure 1 As shown, in step S101, a fragment collision test is simulated. Multiple sensors installed on the thin film of the flexible material to be tested are used to collect collision event information that occurs in the simulated collision test to obtain collision signals.

[0026] Specifically, a test system for simulating fragment collision tests is constructed. The test system includes a particle emission device, a flexible material film to be tested (e.g., a polyimide film), at least three piezoelectric sensors deployed on the flexible material film to be tested, a signal amplifier, a data acquisition card, and a computer.

[0027] exist Figure 2 In one application example, the test system includes a particle emission device, a flexible material film to be tested (e.g., a polyimide film), four piezoelectric sensors located at the four corners of the flexible material film to be tested, four signal amplifiers corresponding to the four piezoelectric sensors, a data acquisition card (e.g., an NI acquisition card), and a computer.

[0028] Specifically, the particle generating device is, for example, a collision particle generating device. This device accelerates 0.8 mm diameter steel balls to 200 m / s using an instantaneous pressure difference, impacting the flexible material film (e.g., a polyimide film) under test. A piezoelectric sensor is attached to each of the four corners of the flexible material film (e.g., the polyimide film) to form a rectangular sensing network. The four piezoelectric sensors are connected to a data acquisition card via their respective signal amplifiers. The data acquisition card is connected to a computer, which controls the data acquisition card to acquire collision signals.

[0029] For example, piezoelectric sensors and data acquisition cards both acquire acoustic signals on the flexible material film (e.g., polyimide film) under test in real time at a sampling rate of 2 MS / s. When a clear and continuous step signal appears, the test system determines that a collision event has occurred.

[0030] When a collision event is detected, collision event information from the simulated collision test is collected to obtain a collision signal.

[0031] It should be noted that the above is only an optional example and should not be construed as a limitation of the present invention.

[0032] Next, in step S102, based on the collision signal, the arrival time of the collision signal to each sensor is determined, at least one preliminary positioning point is calculated by the time difference positioning method, and the at least one preliminary positioning point is weighted and averaged to obtain the initial collision position.

[0033] Specifically, the computer-controlled data acquisition card stores the continuous step signals appearing in the four channels corresponding to the four piezoelectric sensors, denoted as... Where i represents the i-th piezoelectric sensor.

[0034] Furthermore, the signals from the four channels are filtered, denoted as follows: Where i represents the i-th piezoelectric sensor.

[0035] The dynamic threshold method is used to obtain the arrival time of the filtered collision signal at each piezoelectric sensor, determine the threshold reference for each channel, and express it using the following expression:

[0036]

[0037] Among them, T b (i) represents the threshold benchmark for determining the arrival time of the collision signal at the i-th piezoelectric sensor; It is the nth sampling point in the collision signal filtered by the i-th piezoelectric sensor, where n is a counting variable; N represents the length of the noise floor signal involved in the calculation, and K represents the empirical coefficient obtained from the experiment, expressed by the following expression:

[0038]

[0039] Where K represents the empirical coefficient obtained from the experiment; A represents the amplification factor of the signal amplifier.

[0040] When determining the arrival time, the signal is traversed point by point starting from the signal header. To eliminate interference from other glitches, when the amplitude of a certain point in the signal and the average amplitude of the 50 consecutive points starting from that point are both greater than a threshold, and the amplitude of that point is greater than the amplitudes of the two adjacent points, then the corresponding time of that point is considered the time when the collision signal arrives at the piezoelectric sensor, satisfying the following relationship:

[0041]

[0042] Among them, Tb (i) represents the threshold reference for determining the time when the filtered collision signal arrives at the i-th piezoelectric sensor; is the collision signal after filtering by the i-th piezoelectric sensor, and f indicates that the collision signal is filtered; Let represent a sampling point in the signal filtered by the i-th piezoelectric sensor, n be a variable characterizing the n-th sampling point, and j represent the j-th sampling point.

[0043] Through the above calculations, the arrival times t1, t2, ..., t of m (m = 3, 4, 5...) channels are obtained. m Take the average value of the arrival times of the m channel signals, and express it using the following expression.

[0044] t avg = (t1+t2+…+t) m ) / m

[0045] Among them, t avg tm represents the average arrival time of the signals from the m channels corresponding to the m piezoelectric sensors; t1 represents the arrival time of the signal from the channel corresponding to the first piezoelectric sensor; t2 represents the arrival time of the signal from the channel corresponding to the second piezoelectric sensor; tm represents the average ... m This indicates the arrival time of the signal from the channel corresponding to the m-th piezoelectric sensor.

[0046] Specifically, using the obtained average value t avg Centered on the data, a specified length (e.g., 2ms) is extracted to the left and right, resulting in a total data length of 4ms. This provides a more accurate signal arrival time. The time threshold calculation is then repeatedly performed on the signals within the extracted data lengths to obtain the optimized signal arrival times t for the m channels corresponding to the m piezoelectric sensors. ’ 1,t ’ 2,…,t ’ m .

[0047] Furthermore, by relating the time difference between any two piezoelectric sensors and the sound source (the collision point of the flexible material film under test in this example) to the position of the piezoelectric sensors, a hyperbola can be determined. When the number of piezoelectric sensors is m, then... There are two hyperbolas, and the intersection of any two hyperbolas is the observation point for the collision localization result.

[0048] Therefore, for example, four piezoelectric sensors can generate six hyperbolas, and any two hyperbolas intersect at 15 points. Based on theoretical and experimental analysis, the longitudinal (parallel to the film stretching direction) and transverse (perpendicular to the film stretching direction) acoustic wave velocities on the flexible material film under test are determined, using V0 as the metric. z and V hThe initial collision position of the flexible material film under test is obtained by weighting and optimizing the coordinate position information (including X and Y coordinate values) of 15 observation points based on the distribution of observation points, using the average wave velocity in the horizontal and vertical directions. For example, (x... p 0, y p0 )express.

[0049]

[0050] Where, x p0 The x-coordinate of the initial collision location point; y p0 X represents the ordinate of the initial collision location point; i represents the i-th observation point, which is the i-th intersection point among the 15 intersection points of any two hyperbolas; X i Y represents the horizontal coordinate of the i-th intersection point out of 15 intersection points of any two hyperbolas; i k represents the ordinate of the i-th intersection point out of 15 intersection points of any two hyperbolas; xi This represents the horizontal weighting coefficient, or first weighting coefficient, calculated based on the discrete characteristics of the 15 intersection points of any two hyperbolas as observation points. It is used to reduce the influence of observation points with large errors; k yi This represents the longitudinal weighting coefficient, or second weighting coefficient, calculated based on the discrete characteristics of the 15 intersection points of any two hyperbolas as observation points. It is used to reduce the influence of observation points with large errors. The first and second weighting coefficients are expressed by the following expressions:

[0051]

[0052] Where, k xi This represents the horizontal weighting coefficient, or first weighting coefficient, calculated based on the discrete characteristics of the 15 intersection points of any two hyperbolas as observation points. It is used to reduce the influence of observation points with large errors; k yi This represents the longitudinal weighting coefficient, or second weighting coefficient, calculated based on the discrete characteristics of the 15 intersection points of any two hyperbolas as observation points. It is used to reduce the influence of observation points with large errors; u represents a counting variable; X u Y represents the x-coordinate of the u-th observation point; u X represents the ordinate of the u-th observation point; avg Y represents the arithmetic mean of the lateral coordinates of the 15 intersection points (using four sensors as an example) of any two hyperbolas; avg It represents the arithmetic mean of the longitudinal coordinates of the 15 intersection points of any two hyperbolas as observation points.

[0053] It should be noted that a hyperbola can be determined by the arrival time difference between any two piezoelectric sensors, and six hyperbolas can be obtained from four piezoelectric sensors. The intersection points of these hyperbolas (theoretically a maximum of 15 intersection points) are used as observation points for the collision location. Further calculations are then performed on all observation points (X...). i ,Y i The initial collision position of the collision point of the flexible material film under test is obtained by weighting the average value of the two methods. The above is only an optional example and should not be construed as a limitation of the present invention.

[0054] Next, in step S103, the collision position coordinates are used as variables, and the anisotropic wave velocity model of the flexible material film is fused to establish an optimization objective function for the flexible material film under test. The objective function is used to measure the error between the arrival time difference calculated based on the collision position coordinates and the actual measured arrival time difference.

[0055] Specifically, the transverse wave velocity V h and longitudinal wave velocity V z As the extreme value of the wave velocity on the flexible material film under test, the wave velocity at any point on the flexible material film under test can be expressed as:

[0056] V 任意 =V h -(V h -V z )*sinθ

[0057] Among them, V 任意 V represents the wave velocity at any point on the flexible material film under test; θ is the angle between the line connecting the sound source location (i.e., the collision point of the flexible material film under test) and the piezoelectric sensor location and the X-axis (lateral direction); z V represents the longitudinal wave velocity on the flexible material film under test; h This represents the transverse wave velocity on the flexible material film under test. Assume the sound source location, i.e., the collision point of the flexible material film under test, is (x0, y0), and the position coordinates of the piezoelectric sensor are (x...y0...). s ,y s Then we have:

[0058]

[0059] This allows us to obtain the time from the sound source, i.e., the collision point, to each sensor:

[0060]

[0061] Where T1 represents the assumed time from the collision point to the first sensor; T2 represents the assumed time from the collision point to the second sensor; T mV represents the time from the assumed collision point to the m-th sensor; m represents the number of sensors, and the value of m is greater than or equal to 3; V1 represents the wave speed of the sound wave from the assumed collision point to the first sensor; V2 represents the wave speed of the sound wave from the assumed collision point to the second sensor; V m Let x represent the wave velocity of the sound wave from the assumed collision point to the m-th sensor; x0 and y0 represent the abscissa and ordinate values ​​of the assumed position of the assumed collision point of the flexible material film under test, respectively; x s1 y s1 The x and y coordinates represent the position of the first sensor, respectively; s2 y s2 The x and y coordinates represent the position of the second sensor, respectively; sm y sm These represent the x-coordinate and y-coordinate values ​​of the position of the m-th sensor, respectively.

[0062] Furthermore, the time difference between any two sensors is calculated using the following expression. For example, the time difference between other sensors relative to the first sensor is:

[0063]

[0064] Among them, t 1,2 t represents the time difference between the assumed time from the collision point to the first sensor and the assumed time from the collision point to the second sensor; 1,m This represents the time difference between the assumed collision point to the first sensor and the assumed collision point to the m-th sensor.

[0065] Therefore, the time difference between the arrival times of the signals from other sensors and the first sensor, determined by the dynamic threshold method, is:

[0066]

[0067] Among them, t ’ 1 represents the signal arrival time of the channel corresponding to the optimized first piezoelectric sensor; t ’ 2 represents the signal arrival time of the channel corresponding to the optimized second piezoelectric sensor; t ’ m Δt represents the arrival time of the signal corresponding to the channel of the optimized m-th piezoelectric sensor. 1,2 Δt represents the time difference between the arrival time of the signal from the channel corresponding to the optimized first piezoelectric sensor and the arrival time of the signal from the channel corresponding to the optimized second piezoelectric sensor. 1,m This represents the time difference between the arrival time of the signal from the channel corresponding to the optimized first piezoelectric sensor and the arrival time of the signal from the channel corresponding to the optimized m-th piezoelectric sensor.

[0068] When the collision point (x0, y0) is assumed to be the actual collision location, the difference between the arrival time difference of other sensors relative to the first sensor and the arrival time difference of other sensors relative to the first sensor calculated by the dynamic threshold method is minimized. (x0, y0) is then replaced with the variable (x, y) to be solved. Thus, the optimization objective function for the flexible material film under test can be established, expressed by the following expression:

[0069]

[0070] Where F(x,y) represents the optimization objective function (sometimes simply called the "objective function") for solving the optimal solution assuming the collision point coordinates, x represents the x-coordinate value of the collision point, y represents the y-coordinate value of the collision point; m represents the number of sensors, and m is a positive integer starting from 3. T 1,n Δt represents the time difference between the assumed collision point's time to the first sensor and the assumed collision point's time to the nth sensor. 1,n This represents the time difference between the arrival time of the signal from the channel corresponding to the first piezoelectric sensor obtained by the dynamic threshold method and the arrival time of the signal from the channel corresponding to the nth piezoelectric sensor.

[0071] The above-mentioned objective function is used to measure the error between the time difference of arrival calculated based on the collision location coordinates and the actual measured time difference of arrival. When the collision point (x0, y0) is assumed to be the actual collision location, t... 1,n With Δt 1,n The goal is to minimize the squared difference. With m sensors, there are (m-1) squared differences. The sum of these (m-1) squared differences is the objective function F(x,y). Solve for a position and calculate t. 1,n (n=2,3,…,m) such that the sum of squares of the differences in arrival time differences between the (m-1) sensors relative to the first sensor obtained by the dynamic threshold method is minimized, that is, the relative discrete variance is minimized.

[0072] It should be noted that the above is only an optional example and should not be construed as a limitation of the present invention.

[0073] Next, in step S104, the initial collision position is used as the initial value for optimization. The optimization objective function is optimized by an iterative optimization algorithm to find the optimal collision position that minimizes the optimization objective function and minimizes the discrete variance in the collision localization result, which is then used as the final collision localization result.

[0074] The particle swarm optimization algorithm is used to find the optimal solution corresponding to the minimum value of the objective function. This involves iteratively optimizing the objective function, initializing the particle positions to the previously solved collision points (xi, xi). p0 ,y p0The particle velocity is 0. Specifically, the particle velocity in the particle swarm algorithm is updated using the following expression:

[0075]

[0076] in, This indicates the assumed iteration velocity of the i-th particle in the (T+1)-th iteration of the adaptive PSO method used. This represents the iteration velocity of the i-th particle in the T-th iteration of the adaptive PSO method, where i represents the i-th particle in the adaptive PSO method. The adaptive PSO method assumes the position of the i-th particle in the T-th iteration; ω(T) represents the inertia weight, used to control the particle to maintain its original orientation; c1(T): represents the first learning factor, used to control the influence of individual experience; c2(T): represents the second learning factor, used to control the influence of group experience; r1 represents the first random number used to increase the randomness of the search, r1 belongs to [0,1]; r2 represents the second random number used to increase the randomness of the search, r2 belongs to [0,1]; pbest i This represents the assumed optimal position of the i-th particle in the adaptive PSO method; gbest represents the optimal position of the population.

[0077] Optionally, the inertial weight ω(T) in the above expression for updating particle velocity is calculated using the following expression:

[0078]

[0079] Where ω(T) represents the inertia weight in the T-th iteration; ω max ω represents the maximum value of the inertia weight during the iteration process. min I represents the minimum value of the inertia weight during the iteration process; max This represents the maximum number of iterations of the algorithm. For two-dimensional nonlinear optimization problems, the number of particles is between 20 and 40. To increase the sufficiency of the search, a particle number of 40 is chosen, based on the empirical formula I. max =50D~200D, where D is the dimension, the maximum number of iterations is 100~400, ω max ∈[0.9,1.2], ω min ∈[0.2,0.4]. The inertia weight is determined by comprehensively optimizing the complexity, computational accuracy, and computation time of the objective function.

[0080] Optionally, ω max =0.9, ω min =0.4, I max =200.

[0081] It should be noted that in this application, ω(T) is the inertia weight, which controls the particle to maintain its original direction. The inertia weight is large in the early stage, which is beneficial to global search and avoids getting trapped in local optima. The inertia weight is small in the later stage, which is beneficial to fine convergence. Thus, a more accurate iterative process can be obtained, and a more accurate optimal collision position can be obtained.

[0082] The first learning factor c1(T) and the second learning factor c2(T) are represented by the following expressions:

[0083]

[0084]

[0085] Where c1(T) represents the first learning factor in the T-th iteration; c2(T) represents the second learning factor in the T-th iteration; This represents the maximum value of the first learning factor during the iteration process; This represents the minimum value of the first learning factor during the iteration process; This represents the maximum value of the second learning factor during the iteration process; This represents the minimum value of the second learning factor during the iteration process. The values ​​range from 1.2 to 2.5.

[0086] Optionally, It is 2.5. It is 1.2. It is 2.5. The value is 1.2. The first learning factor c1(t) decreases as the number of iterations increases, while the second learning factor c2(t) increases as the number of iterations increases. This makes individual learning strong in the early stage, which helps to explore more directions, and group learning strong in the later stage, which makes the solution stable and convergent. Thus, a more accurate iterative process can be obtained, and a more accurate optimal collision position can be obtained.

[0087] Furthermore, the collision point position is updated using the following expression:

[0088]

[0089] in, This indicates the assumed position of the i-th particle in the (T+1)-th iteration of the adaptive PSO method used. This indicates the assumed position of the i-th particle in the T-th iteration of the adaptive PSO method used. This represents the iteration rate of the assumed i-th particle in the adaptive PSO method used in the (T+1)-th iteration, where i represents the assumed i-th particle in the adaptive PSO method used.

[0090] Optionally, the adaptive PSO method is used to solve for the optimal solution and optimal collision position of the above-mentioned objective function, which specifically includes the following steps.

[0091] Step S201: Initialize the particle swarm parameters, including the parameters in the velocity and position update formulas, and use the preliminary positioning results as the initial solutions for the individual optimal position and the swarm optimal position.

[0092] Specifically, the particle swarm parameters include: maximum number of iterations, number of particles, inertia weight, particle velocity, particle position, search range, individual learning factor, swarm learning factor, individual optimal position, and swarm optimal position.

[0093] Step S202: Calculate the value of the objective function.

[0094] Step S203: Update the individual optimal position and the group optimal position based on the updated inertia weight, the first learning factor, and the second learning factor.

[0095] Step S204: Repeatedly check whether the current solution has converged or exceeded the maximum number of iterations until the current solution meets the convergence condition, then stop iterating and obtain the optimal collision position (specifically the optimal solution of particle collision position).

[0096] For example, the maximum number of iterations is 200.

[0097] The convergence conditions include either keeping the optimal solution unchanged or exceeding the maximum number of iterations.

[0098] If the optimal solution remains unchanged or the maximum number of iterations is exceeded, the iteration stops and the optimal collision position is obtained.

[0099] If the optimal solution does not remain unchanged or the maximum number of iterations is exceeded, steps S202 and S203 are repeated until the current solution meets the convergence condition, at which point the iteration stops and the optimal collision position is obtained.

[0100] The optimal collision position (specifically, the optimal solution for particle collision position) obtained using the above adaptive PSO algorithm is used as the final collision localization result.

[0101] It should be noted that the above is only an optional example and should not be construed as a limitation of the present invention.

[0102] Compared with existing technologies, this application simulates fragment collision tests and uses multiple sensors installed on the flexible material film under test to collect collision event information during the simulated collision test to obtain collision signals. The arrival time of the collision signals to each sensor is determined, and at least one preliminary positioning point is calculated using the time difference positioning method. A weighted average is then performed to obtain the initial collision position. Using the collision position coordinates as variables and incorporating the anisotropic wave velocity model of the flexible material film, an optimization objective function for optimizing the collision position is established. The initial collision position is used as the initial value for optimization. An iterative optimization algorithm is used to optimize the objective function to find the optimal collision position that minimizes the objective function and the discrete variance in the collision positioning result. This is the final collision positioning result, effectively achieving precise positioning of the collision source of flexible structures such as polyimide.

[0103] The following is in conjunction with the appendix Figure 4 Detailed description of optional embodiments of the device in this application.

[0104] This application provides a collision positioning device based on minimum discrete variance, which executes the collision positioning method based on minimum discrete variance described in this application. The collision positioning device 400 based on minimum discrete variance includes a simulation test module 410, a first determination module 420, a setup processing module 430, and a second determination module 440.

[0105] Specifically, the simulation test module 410 is used to simulate fragment collision tests. Multiple sensors installed on the flexible material film under test collect collision event information from the simulated collision test to obtain collision signals. The first determination module 420, based on the collision signals, determines the arrival time of the collision signals at each sensor, calculates at least one preliminary location point using the time-of-arrival (TOA) method, and performs a weighted average of the at least one preliminary location point to obtain the initial collision position. The establishment processing module 430 uses the collision position coordinates as variables and integrates the anisotropic wave velocity model of the flexible material film to establish an optimization objective function for optimizing the collision position. The objective function measures the error between the arrival time difference calculated based on the collision position coordinates and the actual measured arrival time difference. The second determination module 440 uses the initial collision position as the initial value for optimization, and optimizes the objective function using an iterative optimization algorithm to find the optimal collision position that minimizes the objective function and the discrete variance in the collision positioning result, which is the final collision positioning result.

[0106] According to an optional implementation, the step of using the collision position coordinates as variables and incorporating the anisotropic wave velocity model of the flexible material film to establish an optimization objective function for optimizing the collision position includes:

[0107] The objective function for optimizing the flexible thin film under test is established and expressed by the following expression:

[0108]

[0109] Where F(x,y) represents the objective function for finding the optimal solution for the collision point coordinates, x represents the x-coordinate of the collision point, and y represents the y-coordinate of the collision point; m represents the number of sensors, and m is a positive integer starting from 3; t 1,n Δt represents the time difference between the time from the assumed collision point to the first sensor and the time to the nth sensor. 1,n This represents the time difference between the arrival time of the signal from the channel corresponding to the first piezoelectric sensor obtained by the dynamic threshold method and the arrival time of the signal from the channel corresponding to the nth piezoelectric sensor.

[0110] According to an optional implementation, a hyperbola can be determined by relating the time difference between any two piezoelectric sensors and the collision point of the flexible material film under test to the position of the piezoelectric sensors. When the number of piezoelectric sensors is m, then... There are two hyperbolas, and the intersection of any two hyperbolas is the observation point for the collision localization result.

[0111] The initial collision position of the collision point of the flexible material film under test is obtained by weighted optimization based on the coordinate position information of all observation points.

[0112] According to an optional implementation, when the number of piezoelectric sensors is four, i.e., m=4, the four piezoelectric sensors are arranged at the four corners of the flexible material film to be tested to form a rectangular sensing network.

[0113] According to an optional implementation, the particle swarm optimization algorithm is used to find the optimal solution corresponding to the minimum value of the objective function, specifically updating the particle velocity and collision point position.

[0114] According to the optional implementation, the particle velocity in the iterative optimization algorithm is updated using the following expression:

[0115]

[0116] in, This indicates the assumed iteration velocity of the i-th particle in the (T+1)-th iteration of the adaptive PSO method used. This represents the iteration velocity of the i-th particle in the T-th iteration of the adaptive PSO method, where i represents the i-th particle in the adaptive PSO method. The adaptive PSO method assumes the position of the i-th particle in the T-th iteration; ω(T) represents the inertia weight, used to control the particle to maintain its original orientation; c1(T) represents the first learning factor, used to control the influence of individual experience; c2(T) represents the second learning factor, used to control the influence of group experience; r1 represents the first random number used to increase the randomness of the search, r1 belongs to [0,1]; r2 represents the second random number used to increase the randomness of the search, r2 belongs to [0,1]; pbest i This represents the assumed optimal position of the i-th particle in the adaptive PSO method; gbest represents the optimal position of the population.

[0117] According to an optional implementation, the inertia weight is calculated based on the maximum and minimum values ​​of the inertia weight during the iteration process.

[0118] According to an optional implementation, the first learning factor decreases as the number of iterations increases, while the second learning factor increases as the number of iterations increases.

[0119] like Figure 5 As shown, this embodiment provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the method steps described in the above embodiment.

[0120] This application provides a non-volatile computer storage medium storing computer-executable instructions that can perform the steps described in the above embodiments.

[0121] The following is for reference. Figure 5 The diagram illustrates a structural schematic of an electronic device suitable for implementing the embodiments of this application. The terminal devices in the embodiments of this application may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0122] like Figure 5As shown, the electronic device may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage device 408 into a random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the electronic device. The processing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0123] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0124] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 409, or installed from a storage device 408, or installed from a ROM 402. When the computer program is executed by the processing device 401, it performs the functions defined in the methods of the embodiments of this application.

[0125] It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0126] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0127] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0128] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0129] The units described in the embodiments of this application can be implemented in software or hardware. The names of the units are not, in some cases, limiting the scope of the unit itself.

Claims

1. A method for collision location based on minimum discrete variance, characterized in that, The method comprises the following steps: A simulated debris impact test is performed, and a plurality of sensors are arranged on a flexible material film to be tested to collect impact event information occurring in the simulated impact test to obtain impact signals; Based on the impact signals, arrival times of the impact signals at the sensors are determined, at least one preliminary positioning point is calculated by a time difference positioning method, and a weighted average of the at least one preliminary positioning point is obtained to obtain an initial impact position; An optimization objective function for optimizing the impact position is established by taking the impact position coordinates as variables and fusing an anisotropic wave speed model of the flexible material film, and the optimization objective function is used to measure an error between a calculated time difference based on the impact position coordinates and an actually measured time difference; The initial impact position is taken as an initial value of optimization solving, and an iterative optimization algorithm is used to optimize the optimization objective function to solve an optimal impact position that minimizes the optimization objective function and has the smallest dispersion variance in the impact positioning result, and the optimal impact position is taken as a final impact positioning result.

2. The minimum discrete variance based collision location method of claim 1, wherein, The step of taking the impact position coordinates as variables and fusing the anisotropic wave speed model of the flexible material film to establish the optimization objective function for optimizing the impact position comprises the following steps: The optimization objective function of the flexible material film to be tested is established, and the following expression is used to represent the optimization objective function: Wherein, F(x, y) represents an optimization objective function for solving the optimal solution of the collision point coordinates, x represents the horizontal coordinate value of the collision point, and y represents the vertical coordinate value of the collision point; m represents the number of sensors, m is a positive integer starting from 3; t 1,n represents the time difference between the time from the assumed collision point to the first sensor and the time from the assumed collision point to the nth sensor; Δt 1,n represents the time difference between the signal arrival time of the channel corresponding to the first piezoelectric sensor obtained by the dynamic threshold method and the signal arrival time of the channel corresponding to the nth piezoelectric sensor.

3. The minimum discrete variance based collision location method of claim 1 or 2, wherein, The step of obtaining the initial impact position of the impact point of the flexible material film to be tested by performing weighted optimization according to the obtained coordinate position information of all observation points comprises the following steps: By the relationship between the time difference of the collision point of any two piezoelectric sensors to the flexible material film to be measured and the position of the piezoelectric sensor, a hyperbola can be determined, when the number of piezoelectric sensors is m, then there are hyperbolas, and the intersection point of any two hyperbolas is an observation point of the collision positioning result; When the number of piezoelectric sensors is four, that is, m=4, the four piezoelectric sensors are arranged at four corner portions of the flexible material film to be tested to form a rectangular sensor network.

4. The minimum discrete variance based collision location method of claim 3, wherein, The step of solving the optimal solution corresponding to the minimum value of the optimization objective function by using a particle swarm algorithm comprises the following steps of updating a particle speed and an impact point position. The step of updating the particle speed in the iterative optimization algorithm by using the following expression comprises the following steps:

5. The minimum discrete variance based collision location method of claim 1, wherein, The step of calculating the inertia weight based on the maximum value and the minimum value of the inertia weight in the iteration process comprises the following steps: The first learning factor decreases with an increase in the number of iterations, and the second learning factor increases with the increase in the number of iterations.

6. The minimum discrete variance based collision location method of claim 5, wherein, The step of obtaining the time when the filtered impact signal arrives at each piezoelectric sensor by using a dynamic threshold method and determining a threshold reference of each channel comprises the following steps: The step of obtaining the time when the filtered impact signal arrives at each piezoelectric sensor by using a dynamic threshold method and determining a threshold reference of each channel comprises the following steps: wherein, denotes the (T+1)th iteration velocity of the i-th particle of the assumed swarm in the adaptive PSO method employed; denotes the Tth iteration velocity of the i-th particle of the assumed swarm in the adaptive PSO method employed, i denotes the i-th particle of the assumed swarm in the adaptive PSO method employed; denotes the Tth iteration position of the i-th particle of the assumed swarm in the adaptive PSO method employed; ω(T) denotes an inertia weight for controlling the particle to keep the original direction; c1(T) denotes a first learning factor for controlling the influence of individual experience; c2(T) denotes a second learning factor for controlling the influence of group experience; r1 denotes a first random number for increasing the randomness of the search, r1 belongs to [0, 1]; r2 denotes a second random number for increasing the randomness of the search, r2 belongs to [0, 1]; pbest i denotes the individual optimal position of the i-th particle of the assumed swarm in the adaptive PSO method employed; gbest denotes the group optimal position.

7. The minimum discrete variance based collision location method of claim 6, wherein, The step of obtaining the time when the filtered impact signal arrives at each piezoelectric sensor by using a dynamic threshold method and determining a threshold reference of each channel comprises the following steps: ​ 8. The minimum discrete variance based collision location method of claim 6, wherein, ​ ​ 9. The minimum discrete variance based collision location method of claim 1, wherein, ​ ​ 10. The minimum discrete variance based collision location method of claim 1 or 9, wherein, ​ ​

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