Railway vehicle impact positioning method, device, equipment and medium

Abstract

The invention discloses a railway vehicle impact positioning method, device and equipment and a medium, and relates to the technical field of computers, and the method comprises the steps: obtaining a guided wave signal outputted by each piezoelectric sensor when a to-be-detected composite material of a railway vehicle is impacted; the piezoelectric sensor is arranged on the surface of the composite material to be detected; constructing a positioning model based on the installation position of each piezoelectric sensor and the flight time of the guided wave signal, and determining an error function of the positioning model; the function is used for quantifying the deviation between the theoretical distance and the actual distance; the theoretical distance is obtained based on the assumed coordinate of the potential impact point and the mounting position of the piezoelectric sensor, and the actual distance is obtained based on the flight time and the propagation speed of the guided wave signal; and outputting the optimal coordinate estimation of the potential impact point and the optimal propagation speed estimation of the guided wave signal by minimizing the error function. According to the invention, the inherent error caused by the constant wave velocity is avoided; the engineering implementation cost is reduced; the positioning precision is obviously improved; and the real-time performance of impact positioning is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a rail vehicle impact positioning method, device, equipment and medium. BACKGROUND

[0002] In the field of rail transit, impact positioning technology is a key supporting technology for vehicle monitoring and maintenance, and its performance directly affects the reliability of the vehicle system. However, the current impact positioning method still has four technical bottlenecks to be solved in actual engineering application: first, systematic errors are caused by wave speed uncertainty, resulting in insufficient positioning accuracy; wave speed is easily affected by the coupling of multiple physical fields such as temperature and humidity, load spectrum and other environmental factors of the material, showing significant time-varying characteristics, so that the positioning model based on static wave speed has inherent errors; second, wave speed parameters depend on prior calibration, and repeated parameter calibration is required for cross-model or cross-structure migration application, resulting in high engineering implementation cost; third, there is a lack of uncertainty quantification mechanism, making it difficult to judge the reliability of the impact positioning result; fourth, the calculation complexity of the global traversal method is , which seriously restricts the real-time performance of the technology. The above technical problems need to be solved by personnel in the field. SUMMARY

[0003] Therefore, the purpose of the present application is to provide a rail vehicle impact positioning method, device, equipment and medium, which avoids the inherent error caused by the fixed wave speed; reduces the engineering implementation cost; significantly improves the positioning accuracy; ensures the real-time performance of the impact positioning, and the specific scheme is as follows:

[0004] In a first aspect, the present application discloses a rail vehicle impact positioning method, comprising:

[0005] When the to-be-tested composite material of the rail vehicle is impacted, the guided wave signals output by each piezoelectric sensor are obtained, and the flight time of the guided wave signals is determined; wherein the piezoelectric sensor is pre-arranged on the surface of the to-be-tested composite material and is used to convert the stress wave generated by the impact into the guided wave signal;

[0006] A positioning model is constructed based on the installation position of each piezoelectric sensor and the flight time of the guided wave signal, and an error function of the positioning model is determined; the error function is used to quantify the deviation between the theoretical distance and the actual distance;

[0007] Wherein, the theoretical distance is obtained based on the assumed coordinates of the potential impact point and the installation position of each piezoelectric sensor, and the actual distance is obtained based on the flight time and propagation speed of the guided wave signal;

[0008] By minimizing the error function, output the optimal coordinate estimation of the potential impact point and the optimal propagation speed estimation of the guided wave signal to realize the impact positioning of the rail vehicle.

[0009] Optionally, the determination of the time of flight of the guided wave signal comprises:

[0010] Calculating the analytical envelope of the guided wave signal to obtain the peak value of the analytical envelope;

[0011] According to the peak value, a proportional threshold is determined, and the time point at which the analytical envelope first reaches the proportional threshold is determined as the time of flight of the guided wave signal.

[0012] Optionally, the error function is defined as:

[0013] ;

[0014] is the positioning error of the i-th piezoelectric sensor, is the installation position of the i-th piezoelectric sensor, is the coordinate of the potential impact point, is the propagation speed, is the time of impact occurrence, is the time of flight of the guided wave signal detected by the i-th piezoelectric sensor, is the Euclidean norm.

[0015] Optionally, the output of the optimal coordinate estimation of the potential impact point and the optimal propagation speed estimation of the guided wave signal by minimizing the error function comprises:

[0016] Constructing a maximum likelihood function based on the error function; wherein the maximum likelihood function is:

[0017] ;

[0018] wherein, represents the parameter vector to be estimated; represents the noise standard deviation of the time of flight of the guided wave signal; N represents the number of piezoelectric sensors; represents the logarithmic likelihood function of the observation data D under the parameter ; represents the probability density of the i-th positioning error subject to the exponential term of the Gaussian distribution;

[0019] By maximizing the maximum likelihood function, the minimization solution of the error function is realized to output the optimal coordinate estimation of the potential impact point and the optimal propagation speed estimation of the guided wave signal.

[0020] Optionally, the minimizing solving of the error function by maximizing the maximum likelihood function is used to output the optimal coordinate estimation of the potential impact point and the optimal propagation speed estimation of the guided wave signal, comprising:

[0021] The parameter vector to be estimated is normalized to obtain a normalized parameter value; wherein the parameter vector to be estimated includes the coordinates of the potential impact point, the propagation speed of the guided wave signal, the time of impact occurrence, and the positioning uncertainty;

[0022] A plurality of standard unit vectors parallel to the coordinate axes are set as initial search directions;

[0023] Starting from the normalized parameter value, one-dimensional iteration is performed along each of the initial search directions based on the Powell algorithm using the golden section method, and the search direction with the greatest improvement to the maximum likelihood function is recorded at each iteration;

[0024] Based on the search direction with the greatest improvement, accelerated search is performed to determine whether the current parameter vector converges;

[0025] If the current parameter vector converges, the current parameter vector is taken as the optimal estimation, and if the current parameter vector does not converge, the search direction is updated and iteration is continued until convergence.

[0026] Optionally, the plurality of standard unit vectors parallel to the coordinate axes are respectively the x-axis coordinate dimension of the potential impact point, the y-axis coordinate dimension of the potential impact point, the propagation speed dimension of the guided wave signal, the time dimension of impact occurrence, and the positioning uncertainty dimension.

[0027] Optionally, the rail vehicle impact positioning method further comprises:

[0028] Obtaining a three-dimensional model and material parameters of the composite material to be tested, and identifying a high-impact risk area based on the three-dimensional model and the material parameters of the composite material to be tested;

[0029] Arranging piezoelectric sensors on the surface of the composite material to be tested based on the high-impact risk area.

[0030] In a second aspect, the present application discloses a rail vehicle impact positioning device, comprising:

[0031] A guided wave signal determination module is configured to obtain guided wave signals output by each piezoelectric sensor when a composite material to be tested of a rail vehicle is impacted, and determine the time of flight of the guided wave signals; wherein the piezoelectric sensors are pre-arranged on the surface of the composite material to be tested and are configured to convert stress waves generated by the impact into the guided wave signals;

[0032] a positioning model construction module, configured to construct a positioning model based on installation positions of the piezoelectric sensors and a time of flight of the guided wave signal, and determine an error function of the positioning model; the error function is used to quantify deviation of a theoretical distance from an actual distance;

[0033] wherein the theoretical distance is obtained based on assumed coordinates of a potential impact point and the installation positions of the piezoelectric sensors, and the actual distance is obtained based on the time of flight of the guided wave signal and a propagation speed;

[0034] an impact positioning module, configured to output an optimal coordinate estimation of the potential impact point and an optimal propagation speed estimation of the guided wave signal by minimizing the error function, so as to achieve impact positioning of the rail vehicle.

[0035] In a third aspect, the present application discloses an electronic device, comprising:

[0036] a memory, configured to save a computer program;

[0037] a processor, configured to execute the computer program to implement the rail vehicle impact positioning method disclosed in the foregoing.

[0038] In a fourth aspect, the present application discloses a computer readable storage medium, configured to save a computer program; wherein the computer program is executed by a processor to implement the rail vehicle impact positioning method disclosed in the foregoing.

[0039] It can be seen that the application provides a rail vehicle impact positioning method, which comprises: when a to-be-tested composite material of a rail vehicle is impacted, guided wave signals output by each piezoelectric sensor are acquired, and a time of flight of the guided wave signals is determined; wherein the piezoelectric sensor is arranged in advance on a surface of the to-be-tested composite material and is used for converting a stress wave generated by the impact into the guided wave signals; a positioning model is constructed based on installation positions of each piezoelectric sensor and the time of flight of the guided wave signals, and an error function of the positioning model is determined; the error function is used for quantifying a deviation between a theoretical distance and an actual distance; wherein the theoretical distance is obtained based on assumed coordinates of a potential impact point and the installation positions of each piezoelectric sensor, and the actual distance is obtained based on the time of flight of the guided wave signals and a propagation speed; by minimizing the error function, optimal coordinate estimation of the potential impact point and optimal propagation speed estimation of the guided wave signals are output, so as to realize impact positioning of the rail vehicle. It can be seen that the application constructs a positioning model based on the installation positions of each piezoelectric sensor and the time of flight of the guided wave signals, and quantifies a deviation between a theoretical distance and an actual distance by an error function in the positioning model, wherein the theoretical distance is calculated based on assumed coordinates of a potential impact point and the installation positions of each piezoelectric sensor, and the actual distance is derived based on the time of flight of the guided wave signals and a propagation speed, the model construction breaks the static assumption of the propagation speed in the traditional method, and avoids inherent errors caused by the fixed and unchanged propagation speed; finally, by minimizing the error function, optimal coordinate estimation of the potential impact point and optimal propagation speed estimation of the guided wave signals are output, the process does not need to calibrate the propagation speed in advance, does not need to repeatedly carry out parameter calibration work when migrating across vehicle types or structures, greatly reduces the engineering implementation cost, dynamically optimizes the propagation speed parameter, effectively solves the systematic error problem caused by the uncertainty of the propagation speed, significantly improves the positioning accuracy, the optimization process does not need to use a global traversal method, greatly reduces the calculation complexity, and ensures the real-time performance of the impact positioning. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments or prior art description. Obviously, the drawings in the following description are only embodiments of the present application, and those skilled in the art can obtain other drawings according to the provided drawings without any creative effort.

[0041] Figure 1 A flow chart of a rail vehicle impact positioning method disclosed by the present application;

[0042] Figure 2 An effect schematic diagram of a rail vehicle impact positioning method disclosed by the present application;

[0043] Figure 3A structure diagram of an impact positioning device for a rail vehicle is disclosed in the present application.

[0044] Figure 4 A structure diagram of an electronic device is disclosed in the present application. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0046] There are still four technical bottlenecks to be solved in the actual engineering application of the existing impact positioning method: first, the systematic error caused by the uncertainty of wave velocity leads to insufficient positioning accuracy: the wave velocity is easily affected by the coupling of multiple physical fields such as temperature, humidity, load spectrum and the like of the material in the environment, and presents significant time-varying characteristics, so that the positioning model constructed based on the static wave velocity has inherent error; second, the wave velocity parameter depends on prior calibration, and repeated parameter calibration is needed when migrating across vehicle types or structures, resulting in high engineering implementation cost; third, there is a lack of uncertainty quantification mechanism, and it is difficult to judge the reliability of the impact positioning result: due to the lack of support of the probability statistical framework, the confidence of the positioning result cannot be quantitatively evaluated; fourth, the calculation complexity of the global traversal method is , and this high complexity seriously restricts the real-time performance of the technology.

[0047] Therefore, the embodiments of the present application propose an impact positioning scheme for a rail vehicle, which can avoid the inherent error caused by the fixed and unchanged wave velocity, reduce the engineering implementation cost, significantly improve the positioning accuracy, and ensure the real-time performance of the impact positioning.

[0048] The embodiments of the present application disclose an impact positioning method for a rail vehicle, referring to Figure 1 , which comprises the following steps:

[0049] Step S11: When the to-be-tested composite material of the rail vehicle is impacted, the guided wave signals output by the piezoelectric sensors are acquired, and the flight time of the guided wave signals is determined; wherein the piezoelectric sensors are pre-arranged on the surface of the to-be-tested composite material, and are used to convert the stress wave generated by the impact into the guided wave signals.

[0050] Firstly, the three-dimensional model and material parameters of the composite material to be tested are obtained, and then the structural characteristics (such as thickness distribution, etc.) and stress law of the material are analyzed to accurately identify the high-risk areas of the vehicle running process that are prone to impact. Further, based on these impact high-risk areas, piezoelectric sensors are arranged on the surface of the composite material to be tested, and the number of sensors needs to be no less than four. Through reasonable sensor spacing and distribution, a comprehensive multi-node monitoring network is constructed to ensure that the stress wave generated by the subsequent impact event can be effectively captured by the sensor. It can be understood that the piezoelectric sensors fixed on the surface of the composite material to be tested have the core function of converting the stress wave generated by the impact into a guided wave signal that can be monitored by the data acquisition system, providing data support for subsequent impact positioning analysis.

[0051] Taking the carbon fiber composite material roof of the rail vehicle as an example, firstly, the three-dimensional model (including air conditioner mounting seat and pantograph mounting seat) and material parameters (elastic modulus 230GPa, thickness 3mm) of the carbon fiber composite material roof are obtained, and it is determined through analysis that the edge of the air conditioner mounting seat, the periphery of the pantograph mounting seat, and the four corners of the roof are impact high-risk areas; then 6 piezoelectric sensors are selected and pasted on the corresponding areas of the roof: 1 on each left and right edge of the air conditioner mounting seat, 1 behind the pantograph mounting seat, 1 on each front corner of the left and right roof, and 1 in the center of the roof; finally, through the connection cable, the data acquisition system is connected to complete the multi-node monitoring network construction.

[0052] In this embodiment, when the composite material to be tested of the rail vehicle is impacted, the piezoelectric sensor arranged in advance on the surface of the material will respond and convert the stress wave caused by the impact into a guided wave signal in the form of an electric signal. At this time, the data acquisition system will synchronously receive and obtain the guided wave signal output by each piezoelectric sensor. In order to accurately extract the characteristics related to impact positioning in the guided wave signal, signal processing is needed for the obtained guided wave signal: the analytical envelope of the guided wave signal is calculated through Hilbert transform, then the peak value of the signal is extracted from the calculated analytical envelope, and 0.1 times of the peak value is taken as the proportional threshold; finally, the time point when the analytical envelope of the guided wave signal first reaches the proportional threshold is determined as the time of flight (TOF) of the guided wave signal, which can be expressed as wherein N represents the number of piezoelectric sensors, and it should be noted that the time of flight of the guided wave signal refers to the time interval from the occurrence of the impact to the detection of the guided wave signal by the piezoelectric sensor.

[0053] Step S12: constructing a positioning model based on the installation positions of the piezoelectric sensors and the time of flight of the guided wave signal, and determining an error function of the positioning model; the error function is used to quantify the deviation between a theoretical distance and an actual distance; the theoretical distance is obtained based on the assumed coordinates of the potential impact point and the installation positions of the piezoelectric sensors, and the actual distance is obtained based on the time of flight of the guided wave signal and the propagation speed.

[0054] In this embodiment, a positioning model is constructed based on the installation positions of the piezoelectric sensors and the time of flight of the guided wave signal, and an error function of the positioning model is determined, the error function being used to quantify the deviation between a theoretical distance and an actual distance; the theoretical distance is obtained based on the assumed coordinates of the potential impact point and the installation positions of the piezoelectric sensors, and the actual distance is obtained based on the time of flight of the guided wave signal and the propagation speed.

[0055] Specifically, in the positioning model, the error function is defined as:

[0056] ;

[0057] is the positioning error of the i-th piezoelectric sensor, is the installation position of the i-th piezoelectric sensor, is the coordinate of the potential impact point, is the propagation speed, is the time of impact occurrence, is the time of flight of the guided wave signal detected by the i-th piezoelectric sensor, is the Euclidean norm.

[0058] It should be noted that the propagation speed of the guided wave signal is simply referred to as wave speed, which is the distance passed by the guided wave signal in the composite material to be tested per unit time when propagating; the assumed position of the potential impact point refers to the impact occurrence position coordinate to be optimized in the positioning model; the installation position of the piezoelectric sensor refers to the position coordinate of the sensor fixed on the surface of the composite material to be tested for receiving the guided wave signal.

[0059] Step S13: outputting the optimal coordinate estimation of the potential impact point and the optimal propagation speed estimation of the guided wave signal by minimizing the error function, so as to realize impact positioning of the rail vehicle.

[0060] In this embodiment, a maximum likelihood function is constructed based on the error function; the maximum likelihood function is:

[0061] ;

[0062] wherein, represents the parameter vector to be estimated; a noise standard deviation representing a time of flight of the guided wave signal; N represents a number of the piezoelectric sensors; a log-likelihood function of observation data D under parameters a probability density of a Gaussian distribution.

[0063] The minimization solution of the error function is achieved by maximizing the maximum likelihood function, to output the optimal coordinate estimation of the potential impact point and the optimal propagation speed estimation of the guided wave signal.

[0064] Specifically, the minimization solution of the error function is achieved by maximizing the maximum likelihood function, to output the optimal coordinate estimation of the potential impact point and the optimal propagation speed estimation of the guided wave signal, comprising: performing normalization preprocessing on the parameter vector to be estimated, to obtain normalized parameter values , a priori of the parameter vector to be estimated, a normalized parameter to be estimated; the parameter vector to be estimated includes coordinates of the potential impact point, a propagation speed of the guided wave signal, a time of impact occurrence, and a positioning uncertainty; a plurality of standard unit vectors parallel to the coordinate axes are set as initial search directions; starting from the normalized parameter values, one-dimensional iteration is performed along each initial search direction based on the Powell algorithm using the golden section method, and the search direction with the maximum improvement of the maximum likelihood function is recorded at each iteration; accelerated search is performed based on the search direction with the maximum improvement, and it is judged whether the current parameter vector converges; if the current parameter vector converges, the current parameter vector is taken as the optimal estimation, and if the current parameter vector does not converge, the search direction is updated and iteration is continued until convergence. Wherein, the plurality of standard unit vectors parallel to the coordinate axes are set as initial search directions, comprising: setting 5 standard unit vectors parallel to the coordinate axes as initial search directions ; the 5 standard unit vectors parallel to the coordinate axes are respectively an x-axis coordinate dimension of the potential impact point , a y-axis coordinate dimension of the potential impact point , a propagation speed dimension of the guided wave signal , a time dimension of impact occurrence , and a positioning uncertainty dimension , and , a displacement initial threshold value, and a termination threshold value of the displacement amount is set as

[0065] It should be noted that the positioning uncertainty represents the reliability of the positioning result, and the smaller the positioning uncertainty, the higher the quality of the guided wave signal, the better the convergence of the algorithm, and the stronger the credibility of the impact positioning result. ​​

[0066] The detailed analysis is as follows: the Powell algorithm is used to maximize the maximum likelihood function, and then the minimization of the error function is realized, and finally the optimal coordinate estimation of the potential impact point and the optimal propagation speed estimation of the guided wave signal are output. As a direct search method, the Powell algorithm does not need to calculate the derivative of the maximum likelihood function, but only needs to compare the maximum likelihood function value to perform iterative search, which can effectively reduce the complexity of algorithm implementation. (1) When the algorithm is executed, first, the parameter vector to be estimated is normalized and preprocessed to obtain the normalized parameter value. The parameter vector includes the assumed coordinates of the potential impact point, the initial propagation speed of the guided wave signal, the impact occurrence time and the positioning uncertainty. (2) Then, five standard unit vectors parallel to the coordinate axes are set as the initial search directions, and the five standard unit vectors correspond to the x-axis coordinate dimension of the potential impact point, the y-axis coordinate dimension of the potential impact point, the propagation speed dimension of the guided wave signal, the time dimension of the impact occurrence and the positioning uncertainty dimension. (3) From the normalized parameter value, along each initial search direction One-dimensional iteration is performed by using the golden section method: , , , the intermediate optimization point in the direction that maximizes the maximum likelihood function value is obtained , and the search direction that improves the maximum likelihood function is recorded. (4) After each iteration, the new conjugate direction is constructed by the starting point and the ending point of the current round, and if the preset direction update threshold condition is met, the new direction is used to replace the direction corresponding to the dimension in the initial search direction group, forming the search direction group of the next iteration, so as to realize the dynamic optimization of the search direction and improve the convergence speed. (5) Then, accelerated search is performed based on the search direction that improves the most: , , and one-dimensional line search is performed: , . Determine whether the current parameter vector converges: that is, whether is satisfied. If the current parameter vector converges, the current parameter vector is taken as the optimal estimation; if the current parameter vector does not converge, the search direction is updated, that is, ; the iteration process is repeated along the new search direction until convergence is achieved, or the iteration number reaches the set upper limit, and the algorithm stops searching. At this time, the corresponding parameter vector is the optimal estimation result.

[0067] In this embodiment, the optimization process can quickly approach the optimal solution through the conjugate direction acceleration characteristics of the Powell algorithm, greatly reducing the computational complexity compared to the global traversal method and ensuring the real-time performance of the impact positioning. At the same time, since the algorithm simultaneously optimizes the potential impact point coordinates and the propagation speed, the propagation speed does not need to be calibrated in advance, and it can dynamically adapt to the change of the propagation speed of the composite material to be tested of the rail vehicle under different working conditions, effectively solving the systematic error problem caused by the static wave speed assumption in the traditional method, and improving the accuracy of impact positioning. In addition, when migrating across vehicle types or structures, there is no need to repeat the wave speed calibration work for different scenarios, which significantly reduces the engineering implementation cost and ensures the real-time performance of impact positioning.

[0068] Taking the curved stiffened skirt panel to be tested as an example, first, the three-dimensional model and material parameters of the skirt panel are obtained, and key information such as the curvature of the curved surface, the distribution of the ribs, and the elastic modulus and thickness of the material is determined; then, combined with the stress law that the skirt panel is easily impacted by stones and airflow-entrained foreign objects when the rail vehicle is running, it is analyzed that the edge area with large curvature change at the connection of the skirt panel ribs is a high-risk area of impact; then, 8 piezoelectric sensors are arranged in these areas and on the surface of the skirt panel, for example, 1 sensor is arranged at each of the upper left corner, the upper right corner, the lower left corner, and the lower right corner of the skirt panel, and 1 sensor is arranged at the middle of the left rib, the middle of the right rib, the midpoint of the upper edge rib, and the midpoint of the lower edge rib, respectively, to construct a multi-node monitoring network; when the skirt panel is impacted, the sensors will convert the stress wave into guided wave signals and transmit them to the data acquisition system, and subsequently, by analyzing the time of flight and other information of the signals, the impact position can be located. Referring to Figure 2 , Figure 2 The two subgraphs in the figure are "Impact Position 1 Prediction" and "Impact Position 2 Prediction", respectively. The white squares represent the arrangement positions of the 8 piezoelectric sensors, the star-shaped markers are the impact point positions predicted by the algorithm, and the gray gradient directly reflects the distribution of the positioning probability (the darker the color, the higher the probability of impact occurrence). From Figure 2 It can be seen that the deviation between the star-shaped markers and the actual impact points is extremely small, verifying the impact positioning accuracy of the present application on the curved stiffened skirt panel.

[0069] It can be seen that the application provides a rail vehicle impact positioning method, which comprises: when a to-be-tested composite material of a rail vehicle is impacted, guided wave signals output by each piezoelectric sensor are acquired, and a flight time of the guided wave signals is determined; wherein the piezoelectric sensor is arranged in advance on a surface of the to-be-tested composite material and is used for converting stress waves generated by the impact into the guided wave signals; a positioning model is constructed based on installation positions of each piezoelectric sensor and the flight time of the guided wave signals, and an error function of the positioning model is determined; the error function is used for quantifying deviation of a theoretical distance from an actual distance; wherein the theoretical distance is obtained based on assumed coordinates of a potential impact point and the installation positions of each piezoelectric sensor, and the actual distance is obtained based on the flight time of the guided wave signals and a propagation speed; optimal coordinate estimation of the potential impact point and optimal propagation speed estimation of the guided wave signals are output by minimizing the error function, so as to realize impact positioning of the rail vehicle. It can be seen that the application constructs a positioning model based on installation positions of each piezoelectric sensor and the flight time of the guided wave signals, and quantifies deviation of a theoretical distance from an actual distance by an error function in the positioning model, wherein the theoretical distance is calculated from assumed coordinates of a potential impact point and the installation positions of each piezoelectric sensor, and the actual distance is derived from the flight time of the guided wave signals and a propagation speed. This model construction mode breaks the static assumption of the propagation speed in the traditional method, avoids inherent errors caused by the fixed and unchanged propagation speed, finally outputs optimal coordinate estimation of the potential impact point and optimal propagation speed estimation of the guided wave signals by minimizing the error function. This process does not need to calibrate the propagation speed in advance, does not need to repeatedly carry out parameter calibration work when migrating across vehicle types or structures, greatly reduces the engineering implementation cost, effectively solves the systematic error problem caused by the uncertainty of the propagation speed by dynamically optimizing the propagation speed parameter, significantly improves the positioning accuracy, and greatly reduces the calculation complexity without using a global traversal method, thereby ensuring the real-time performance of the impact positioning.

[0070] Correspondingly, the application also discloses a rail vehicle impact positioning device, which is shown in Figure 3 The device comprises:

[0071] A guided wave signal determination module 11 is configured to acquire guided wave signals output by each piezoelectric sensor when a to-be-tested composite material of a rail vehicle is impacted, and determine a flight time of the guided wave signals; wherein the piezoelectric sensor is arranged in advance on a surface of the to-be-tested composite material and is used for converting stress waves generated by the impact into the guided wave signals.

[0072] A positioning model construction module 12 is configured to construct a positioning model based on installation positions of each piezoelectric sensor and the flight time of the guided wave signals, and determine an error function of the positioning model; the error function is used for quantifying deviation of a theoretical distance from an actual distance.

[0073] wherein the theoretical distance is obtained based on assumed coordinates of the potential impact point and installation positions of the piezoelectric sensors, and the actual distance is obtained based on the time of flight and the propagation speed of the guided wave signal;

[0074] an impact positioning module 13, configured to output optimal coordinate estimation of the potential impact point and optimal propagation speed estimation of the guided wave signal by minimizing the error function, so as to realize impact positioning of the rail vehicle.

[0075] wherein more specific working processes of the above modules can refer to the corresponding contents disclosed in the foregoing embodiments, which will not be repeated here.

[0076] It can be seen that the present application provides a rail vehicle impact positioning method, which comprises: when a to-be-tested composite material of a rail vehicle is impacted, a guided wave signal output by each piezoelectric sensor is acquired, and the time of flight of the guided wave signal is determined; wherein the piezoelectric sensor is pre-arranged on the surface of the to-be-tested composite material and is used to convert a stress wave generated by the impact into the guided wave signal; a positioning model is constructed based on the installation positions of the piezoelectric sensors and the time of flight of the guided wave signal, and an error function of the positioning model is determined; the error function is used to quantify the deviation between a theoretical distance and an actual distance; wherein the theoretical distance is obtained based on assumed coordinates of the potential impact point and the installation positions of the piezoelectric sensors, and the actual distance is obtained based on the time of flight and the propagation speed of the guided wave signal; optimal coordinate estimation of the potential impact point and optimal propagation speed estimation of the guided wave signal are output by minimizing the error function, so as to realize impact positioning of the rail vehicle. It can be seen that the present application constructs a positioning model based on the installation positions of the piezoelectric sensors and the time of flight of the guided wave signal, and quantifies the deviation between the theoretical distance and the actual distance by the error function in the positioning model, wherein the theoretical distance is calculated from the assumed coordinates of the potential impact point and the installation positions of the piezoelectric sensors, and the actual distance is derived from the time of flight and the propagation speed of the guided wave signal. This model construction breaks the static assumption of the propagation speed in the traditional method, avoids the inherent error caused by the fixed and unchanged propagation speed, and finally outputs the optimal coordinate estimation of the potential impact point and the optimal propagation speed estimation of the guided wave signal by minimizing the error function. This process does not need to calibrate the propagation speed in advance, does not need to repeat the parameter calibration work when migrating across vehicle types or structures, greatly reduces the engineering implementation cost, effectively solves the systematic error problem caused by the uncertainty of the propagation speed by dynamically optimizing the propagation speed parameter, significantly improves the positioning accuracy, and greatly reduces the calculation complexity by not using the global traversal method, thereby ensuring the real-time performance of the impact positioning.

[0077] Further, the present application also provides an electronic device. Figure 4is a structural diagram of an electronic device 20 according to an exemplary embodiment, and the content in the figure should not be considered as any limitation on the use range of the present application.

[0078] Figure 4 A structural diagram of an electronic device 20 is provided in the present embodiment. The electronic device 20 can specifically include at least one processor 21, at least one memory 22, a display screen 23, an input / output interface 24, a communication interface 25, a power supply 26, and a communication bus 27. The memory 22 is configured to store a computer program, which is loaded and executed by the processor 21 to implement the related steps in the rail vehicle impact positioning method disclosed in any of the preceding embodiments. In addition, the electronic device 20 in the present embodiment can be specifically an electronic computer.

[0079] In the present embodiment, the power supply 26 is configured to provide working voltage for each hardware device on the electronic device 20; the communication interface 25 is capable of creating a data transmission channel between the electronic device 20 and external devices, and the communication protocol followed by the communication interface 25 can be any communication protocol applicable to the technical solution of the present application, which is not limited specifically herein; the input / output interface 24 is configured to obtain external input data or output data to the outside, and the specific interface type can be selected according to the specific application needs, which is not limited specifically herein.

[0080] In addition, the memory 22 as a carrier for resource storage can be a read-only memory, a random access memory, a magnetic disk, or an optical disk, etc., and the resources stored thereon can include a computer program 221, and the storage mode can be temporary storage or permanent storage. In addition to the computer program capable of completing the rail vehicle impact positioning method executed by the electronic device 20 disclosed in any of the preceding embodiments, the computer program 221 can further include a computer program capable of completing other specific work.

[0081] Further, the present embodiment further discloses a computer readable storage medium for storing a computer program; wherein the computer program is executed by a processor to implement the rail vehicle impact positioning method disclosed above.

[0082] The specific steps of the method can refer to the corresponding content disclosed in the preceding embodiments, which will not be repeated here.

[0083] Each embodiment in the present application is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts of each embodiment can be referred to each other. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part.

[0084] Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality, without referring to a specific sequence of operations for implementing the functions. The order of various illustrative blocks, modules, circuits, and steps may be re-arranged or otherwise implemented without departing from the spirit of the application, which is defined by the appended claims.

[0085] The steps of a method or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in random access memory (RAM), flash memory, read-only memory (ROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0086] Finally, it should be noted that the terms "first", "second", and the like, herein do not denote any order, quantity, combination, or importance, but rather are used to distinguish one element from another, and are more especially used for the purpose of distinction from other elements in the specification. Also, the terms "comprise", "include" or "contain" or any other variant thereof are intended to encompass non-exclusive inclusions, such that processes, methods, articles, or apparatuses that comprise, include, or contain a list of elements are not limited to those elements, but can include other elements not expressly listed or inherent to such processes, methods, articles, or apparatuses. Without further limitation, an element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0087] The above provides a rail vehicle impact positioning method, device, equipment, and storage medium, and the principle and implementation manner of the present application are described by using specific examples. The above example is only used to help understand the method and core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manner and application range can be changed, and the above description should not be understood as limiting the present application.

Claims

1. A method for impact positioning of rail vehicles, characterized in that, include: When the composite material under test of the rail vehicle is subjected to impact, the guided wave signals output by each piezoelectric sensor are acquired, and the flight time of the guided wave signals is determined; wherein, the piezoelectric sensors are pre-arranged on the surface of the composite material under test and are used to convert the stress wave generated by the impact into the guided wave signal; A positioning model is constructed based on the installation position of each piezoelectric sensor and the flight time of the guided wave signal, and an error function of the positioning model is determined; the error function is used to quantify the deviation between the theoretical distance and the actual distance. The theoretical distance is obtained based on the assumed coordinates of the potential impact point and the installation position of each piezoelectric sensor, while the actual distance is obtained based on the flight time and propagation speed of the guided wave signal. By minimizing the error function, the optimal coordinate estimate of the potential impact point and the optimal propagation velocity estimate of the guided wave signal are output to achieve impact positioning of the rail vehicle.

2. The method for impact positioning of rail vehicles according to claim 1, characterized in that, Determining the flight time of the guided wave signal includes: Calculate the analytical envelope of the guided wave signal to obtain the peak value of the analytical envelope; The proportional threshold is determined based on the peak value, and the time point when the parsed envelope first reaches the proportional threshold is determined as the flight time of the guided wave signal.

3. The method for impact positioning of rail vehicles according to claim 1, characterized in that, The error function is defined as follows: ； Let be the positioning error of the i-th piezoelectric sensor. Let i be the installation position of the i-th piezoelectric sensor. The coordinates of the potential impact point are given. For the speed of transmission, The time when the impact occurred, Let be the flight time of the guided wave signal detected by the i-th piezoelectric sensor. It is the Euclidean norm.

4. The method for impact positioning of rail vehicles according to claim 3, characterized in that, The step of minimizing the error function to output the optimal coordinate estimate of the potential impact point and the optimal propagation velocity estimate of the guided wave signal includes: A maximum likelihood function is constructed based on the error function; wherein the maximum likelihood function is: ； in, Represents the vector of parameters to be estimated; The noise standard deviation represents the flight time of the guided wave signal; N represents the number of piezoelectric sensors. Indicates in the parameter Below, the log-likelihood function of the observed data D; Indicates the i-th positioning error The exponential term of the probability density function that follows a Gaussian distribution; By maximizing the maximum likelihood function, the error function is minimized, thereby outputting the optimal coordinate estimate of the potential impact point and the optimal propagation velocity estimate of the guided wave signal.

5. The method for impact positioning of rail vehicles according to claim 4, characterized in that, The step of minimizing the error function by maximizing the maximum likelihood function to output the optimal coordinate estimate of the potential impact point and the optimal propagation velocity estimate of the guided wave signal includes: The parameter vector to be estimated is preprocessed by normalization to obtain normalized parameter values; wherein, the parameter vector to be estimated includes the coordinates of the potential impact point, the propagation speed of the guided wave signal, the time of impact, and the positioning uncertainty. Several standard unit vectors parallel to the coordinate axes are set as the initial search directions; Starting from the normalized parameter values, the golden section method is used sequentially along each initial search direction based on the Powell algorithm for one-dimensional iteration, and the search direction that maximizes the improvement of the maximum likelihood function is recorded at each iteration. Accelerate the search based on the search direction with the greatest improvement, and determine whether the current parameter vector has converged. If the current parameter vector converges, then the current parameter vector is taken as the optimal estimate. If the current parameter vector does not converge, then the search direction is updated and the iteration continues until convergence.

6. The method for impact positioning of rail vehicles according to claim 5, characterized in that, The plurality of standard unit vectors parallel to the coordinate axes are respectively the x-axis coordinate dimension of the potential impact point, the y-axis coordinate dimension of the potential impact point, the propagation speed dimension of the guided wave signal, the time dimension of the impact occurrence, and the positioning uncertainty dimension.

7. The method for impact positioning of rail vehicles according to any one of claims 1 to 6, characterized in that, Also includes: Obtain the three-dimensional model and material parameters of the composite material to be tested, and identify high-risk areas for impact based on the three-dimensional model and material parameters of the composite material to be tested; Piezoelectric sensors are placed on the surface of the composite material to be tested in high-risk areas where impact is frequent.

8. A rail vehicle impact positioning device, characterized in that, include: A guided wave signal determination module is used to acquire guided wave signals output by each piezoelectric sensor and determine the flight time of the guided wave signals when the composite material under test of the rail vehicle is subjected to impact; wherein, the piezoelectric sensors are pre-arranged on the surface of the composite material under test and are used to convert the stress wave generated by the impact into the guided wave signal; A positioning model construction module is used to construct a positioning model based on the installation position of each piezoelectric sensor and the flight time of the guided wave signal, and to determine the error function of the positioning model; the error function is used to quantify the deviation between the theoretical distance and the actual distance. The theoretical distance is obtained based on the assumed coordinates of the potential impact point and the installation position of each piezoelectric sensor, while the actual distance is obtained based on the flight time and propagation speed of the guided wave signal. The impact positioning module is used to output the optimal coordinate estimate of the potential impact point and the optimal propagation velocity estimate of the guided wave signal by minimizing the error function, so as to realize the impact positioning of the rail vehicle.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the rail vehicle impact positioning method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store a computer program; wherein, when the computer program is executed by a processor, it implements the rail vehicle impact positioning method as described in any one of claims 1 to 7.

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