Acoustic imaging 3d array sensor layout and imaging method for vehicle parts

Abstract

This invention discloses a 3D array sensor layout and imaging method for acoustic imaging of vehicle components. The method includes acquiring and optimizing the spacing and tilt angle between sensors within the 3D array sensor based on the geometric features of the vehicle components and the corresponding acoustic wave propagation characteristics data; performing acoustic wave reflection monitoring and reception to obtain the acoustic wave propagation time and reflected reception signal inside the vehicle components; performing acoustic time-domain inversion signal processing on the vehicle components to obtain the 3D acoustic reflection signal characteristics inside the vehicle components; and performing 3D acoustic imaging reconstruction calculations to generate 3D acoustic images of the vehicle components. This invention, through acoustic time-domain inversion simulation analysis and acoustic reflection signal characteristic analysis of vehicle components, reveals the internal structural characteristics and defects of the components, making subsequent decisions more scientific and evidence-based, thereby enhancing the localization and imaging process of acoustic signals inside vehicle components.

Description

Technical Field

[0001] This invention belongs to the technical field of acoustic measurement, specifically relating to a layout and imaging method for a 3D array sensor for acoustic imaging of vehicle parts. Background Technology

[0002] In recent years, three-dimensional acoustic imaging technology has achieved higher resolution and wider sound field coverage in space by using three-dimensional array sensors. Optimizing sensor layout and employing a multi-layered three-dimensional array configuration ensures optimal spacing and angles between sensors, improving sound source capture capability and imaging accuracy. Furthermore, combining advanced signal processing techniques allows for real-time analysis of received sound signals, enhancing the signal-to-noise ratio and thus improving imaging performance. Dynamic imaging algorithms, through real-time updates and feedback, monitor the acoustic characteristics of vehicle components under different operating conditions. This algorithm automatically adjusts imaging parameters to adapt to the constantly changing sound field environment, ensuring the accuracy and reliability of imaging results.

[0003] Existing acoustic imaging technologies typically employ two-dimensional sensor arrays. However, when faced with the complex components and operating conditions of vehicles, the limitations of two-dimensional arrays become increasingly apparent. The deficiencies of two-dimensional arrays in terms of spatial resolution and imaging depth make it difficult to accurately capture the subtle features of sound sources, thereby affecting the localization of acoustic signals. Summary of the Invention

[0004] The main objective of this invention is to overcome the shortcomings and deficiencies of the prior art and provide a layout and imaging method for acoustic imaging 3D array sensors for vehicle parts.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: One aspect of the present invention provides a layout and imaging method for a 3D array sensor for acoustic imaging of vehicle components, comprising the following steps: Acquire data on the geometric features of vehicle components and the acoustic wave propagation characteristics corresponding to 3D array sensors; Based on the geometric features of vehicle components and the acoustic wave propagation characteristics data of the corresponding 3D array sensors, the spacing and tilt angle between each sensor in the 3D array sensor are optimized to generate a 3D layout optimized sensor array. By optimizing the 3D layout of each sensor in the sensor array, ultrasonic signals of different times and frequencies are emitted to vehicle components. The ultrasonic signals are then used to monitor and receive sound wave reflections, thereby obtaining the propagation time of sound waves inside the vehicle components and the 3D sound wave reflection reception signal of the vehicle components. Based on the propagation time of sound waves inside vehicle parts and the 3D sound wave reflection received signal of vehicle parts, acoustic time-domain inversion signal processing is performed on vehicle parts to obtain the 3D acoustic reflection signal characteristics inside vehicle parts. Based on the 3D acoustic reflection signal characteristics inside vehicle parts, 3D acoustic imaging reconstruction calculations are performed on the corresponding vehicle parts to generate 3D acoustic images of the vehicle parts.

[0006] As a preferred technical solution, the acquisition of the geometric features of vehicle components and the acoustic wave propagation characteristic data corresponding to the 3D array sensor specifically includes: The appearance shape of vehicle parts is identified and analyzed to obtain the appearance shape and size of the vehicle parts; Based on the appearance shape and size of the vehicle parts, geometric scanning blueprints of the vehicle parts are formulated to generate geometric scanning blueprints of the vehicle parts. Based on the geometric scanning blueprints of vehicle parts, 3D scanning modeling of vehicle parts is performed to generate a geometric three-dimensional space model of the vehicle parts. Geometric feature statistical analysis is performed on the three-dimensional spatial model of vehicle parts to obtain the geometric features of the vehicle parts. Based on the acoustic characteristics test of vehicle parts using 3D array sensors, the acoustic wave propagation characteristic data corresponding to the 3D array sensors are obtained. The acoustic wave propagation characteristic data includes the propagation speed, propagation attenuation, and propagation reflection characteristics of sound waves on vehicle parts.

[0007] As a preferred technical solution, the optimization of the spacing between the sensors within the 3D array sensor based on the geometric features of vehicle components and the acoustic wave propagation characteristic data corresponding to the 3D array sensor specifically involves: Based on the geometric features of vehicle components, the initial spacing between sensors in the 3D array sensor is set to obtain different initial sensor spacing layout conditions. Based on the propagation characteristics data of the sound wave in the vehicle parts corresponding to the 3D array sensor, the propagation speed and propagation attenuation of the sound wave are obtained under different sensor spacing conditions. The signal acquisition capability of the sensor under the corresponding initial spacing between each sensor in the 3D array sensor is evaluated based on the acoustic wave propagation efficiency of the 3D array sensor under different sensor spacing conditions. The acoustic wave propagation characteristics of the sensor under different layout spacing conditions are obtained. Based on the acoustic wave propagation characteristics of the sensors and the acoustic signal capture performance under different layout spacing conditions, the spacing between the sensors in the 3D array sensor is optimized iteratively to determine the optimal acoustic signal capture capability target corresponding to the sensor layout conditions. The particle swarm optimization algorithm is used to iteratively design the sensor spacing, gradually adjusting the spacing and layout between the sensors to achieve the optimal acoustic signal capture effect, generating a 3D array sensor spacing optimization design scheme, including the spacing between the sensors, the sensor installation position, and the signal capture configuration parameters.

[0008] As a preferred technical solution, the optimization of the tilt angle between the various sensors within the 3D array sensor based on the geometric features of vehicle components and the acoustic wave propagation characteristic data corresponding to the 3D array sensor specifically involves: Based on the geometric features of vehicle parts, the tilt angles of each sensor in the 3D array sensor are initially set to generate different initial tilt angle ranges for different sensors. Based on the propagation attenuation and reflection characteristics of sound waves corresponding to 3D array sensors on vehicle parts, sound wave parameterization modeling is performed under different initial tilt angle range conditions to generate sensor tilt angle parameterization function model. The sensor tilt angle parameterization function model is as follows: ; In the formula, R ( i ) for 3D array sensors under tilt angle conditions i The intensity of sound wave propagation and reflection under the current, R 0 represents the acoustic wave propagation and reflection intensity of the 3D array sensor in its initial state. α Let be the attenuation coefficient of sound waves propagating in vehicle components. d This refers to the distance the sound wave travels. By using the parameterized function model of sensor tilt angle, the propagation attenuation and reflection characteristics of the sound wave corresponding to the 3D array sensor on vehicle parts are statistically analyzed to obtain the sound wave reflection intensity and sound wave attenuation rate of the 3D array sensor under different tilt angle conditions. Based on the acoustic wave reflection intensity and acoustic wave attenuation rate of the 3D array sensor under different tilt angles, the signal receiving capability of each sensor in the 3D array sensor is evaluated and analyzed, and the influence factor of the sensor acoustic wave propagation characteristics on the signal receiving capability under different tilt angles is obtained. The specific formula for calculating the signal reception capability impact factor is as follows: ; In the formula,I s ( i ) for 3D array sensors under tilt angle conditions i The corresponding signal reception capability impact factor is Ω, where Ω represents the sensor signal propagation spatial region. x The spatial location point corresponding to the sensor's sound wave. x’ The receiving location point corresponding to the sensor's sound wave. S ( x , i ) represents the spatial location point x Considering the tilt angle condition i The corresponding sound wave reflection intensity S 0( x ) represents the spatial location point x Ideal sound wave intensity at that location A ( x , i ) represents the spatial location point x Considering the tilt angle condition i The corresponding reflection intensity affects the reception efficiency. β The weighting factor is determined by the influence of reflection intensity. exp It is an exponential function. e ( x , x’ () represents the spatial location point along the sound wave propagation path of the sensor. x To the receiving location x’ The sound wave attenuation rate, d This is the sound wave attenuation ratio coefficient; Based on the influence factors of signal reception capability under different tilt angle conditions according to the acoustic wave propagation characteristics of the sensor, the tilt angle of each sensor in the 3D array sensor is optimized. The tilt angle of each sensor in the 3D array sensor is iteratively adjusted using the particle swarm optimization algorithm to maximize the signal reception capability. The initial tilt angle of each sensor and the corresponding influence factor of signal reception capability are input, and the parameters are continuously adjusted by the particle swarm optimization algorithm until the final tilt angle and layout information of each sensor are found, generating a tilt angle optimization design scheme for the 3D array sensor.

[0009] As a preferred technical solution, the method of optimizing the layout of each sensor in the 3D sensor array to emit ultrasonic signals of different time frequencies to vehicle components, and using the ultrasonic signals to monitor and receive sound wave reflections, thereby obtaining the sound wave propagation time inside the vehicle components and the 3D sound wave reflection reception signal of the vehicle components, specifically: The signal frequency propagation characteristics of each sensor in the 3D layout optimization sensor array are analyzed to obtain the acoustic wave propagation characteristic data of each sensor under different signal frequencies. Based on the acoustic wave propagation characteristic data of each sensor at different signal frequencies, the signal transmission optimization design of each corresponding sensor in the 3D layout optimization sensor array is carried out. The genetic algorithm or particle swarm optimization algorithm is used to optimize the signal transmission timing and frequency combination by inputting the corresponding acoustic wave propagation characteristic data and the preset target imaging resolution parameters of each sensor. Based on the signal transmission timing and frequency combination of each sensor, the sensor array is optimized using 3D layout to transmit ultrasonic signals of different times and frequencies to vehicle components. The sensor array is then optimized using 3D layout to monitor and receive the ultrasonic signals of different times and frequencies, thereby obtaining the propagation time of sound waves inside the vehicle components and the 3D sound wave reflection and reception signals of the vehicle components.

[0010] As a preferred technical solution, the acoustic time-domain inversion signal processing of the vehicle components based on the propagation time of sound waves inside the vehicle components and the 3D sound wave reflection received signals of the vehicle components, to obtain the 3D acoustic reflection signal characteristics inside the vehicle components, specifically includes: Based on the propagation time of sound waves inside vehicle parts and the 3D sound wave reflection received signal of vehicle parts, acoustic time-domain inversion simulation analysis is performed on vehicle parts. The finite element method or time-domain finite difference method is used to generate the time-domain inversion propagation path of the 3D sound wave signal of vehicle parts. Based on the propagation path of the 3D acoustic wave signal time domain inversion of vehicle parts, the corresponding acoustic signal in the vehicle parts is modeled by time domain signal inversion to generate a mathematical model of the 3D acoustic wave signal time domain inversion of vehicle parts. Based on the time-domain inversion mathematical model of 3D acoustic wave signals of vehicle parts, the acoustic reflection signal characteristics of the corresponding acoustic signals inside the vehicle parts are analyzed to obtain the 3D acoustic reflection signal characteristics inside the vehicle parts.

[0011] As a preferred technical solution, the acoustic time-domain inversion simulation analysis of vehicle components based on the propagation time of sound waves inside the vehicle components and the 3D sound wave reflection and reception signals of the vehicle components, to generate the time-domain inversion propagation path of the 3D sound wave signals of the vehicle components, specifically includes: The propagation time difference of sound waves inside vehicle parts from transmission to reception is obtained, and Kalman filtering is used to optimize the propagation time interference of sound waves inside vehicle parts based on the propagation time difference, so as to obtain the actual propagation time of sound waves inside vehicle parts. A method combining fixed and dynamic durations was used to divide the actual propagation time of sound waves within vehicle components into time periods, resulting in different segments of sound wave propagation time for different vehicle components. Based on the different propagation time segments of sound waves of different vehicle parts, the 3D sound wave reflection and reception signals of vehicle parts are synchronously divided. The reflection signal features in each time segment are extracted by using short-time Fourier transform or wavelet transform, and then the 3D sound wave reflection and reception sub-signals of vehicle parts under different propagation time segments are obtained. Based on the 3D acoustic wave reflection receiving sub-signals corresponding to vehicle components at different propagation time periods, the acoustic time-domain propagation law of vehicle components is analyzed. The acoustic propagation refraction and reflection law of 3D acoustic wave reflection signals inside vehicle components is calculated by using the time-domain reflection method and acoustic propagation model. Based on the acoustic propagation, refraction, and reflection laws of 3D acoustic wave reflection signals inside vehicle components, an inversion algorithm is used to perform time-domain inversion simulation calculations of the corresponding acoustic wave propagation process inside vehicle components, generating the time-domain inversion propagation path of 3D acoustic wave signals in vehicle components.

[0012] As a preferred technical solution, the step of performing 3D acoustic imaging reconstruction calculations on the corresponding vehicle parts based on the 3D acoustic reflection signal characteristics inside the vehicle parts to generate 3D acoustic images of the vehicle parts specifically involves: The 3D acoustic reflection signal features inside vehicle parts are subjected to feature standardization processing to obtain the 3D acoustic standardized signal features of vehicle parts. Signal feature corner point detection is performed on the 3D acoustic standardized signal features of vehicle parts to obtain the 3D acoustic signal feature corner point set of vehicle parts; Based on the feature corner point set of 3D acoustic signals of vehicle parts, spatial image reconstruction and transformation calculations are performed on the corresponding vehicle parts, and interpolation algorithms are applied to generate 3D acoustic reconstruction spatial models of vehicle parts. The 3D acoustic reconstruction spatial model of vehicle parts is divided into 3D meshes using the octree decomposition method or the Delaunay triangulation method to obtain the 3D acoustic reconstruction mesh sub-model of each vehicle part; acoustic reflection and feature iteration calculations are performed on the 3D acoustic reconstruction mesh sub-model of each vehicle part to obtain the 3D acoustic reflection intensity and feature value corresponding to each vehicle part mesh sub-model. Based on the 3D acoustic reflection intensity and eigenvalues ​​corresponding to the mesh sub-models of each vehicle component, the corresponding 3D acoustic reconstruction mesh sub-models of the vehicle components are subjected to 3D rendering and imaging processing to generate 3D acoustic images of the vehicle components.

[0013] As a preferred technical solution, the step of performing spatial image reconstruction and transformation calculations on the corresponding vehicle parts based on the 3D acoustic signal feature corner point set of vehicle parts to generate a 3D acoustic reconstruction spatial model of the vehicle parts is as follows: The derivative calculation method is used to calculate the local extrema of the feature corner point set of the 3D acoustic signal of vehicle parts. At the same time, the rate of change between adjacent acoustic signals is calculated, and the sliding window technique is used to smooth the acoustic signal to obtain the local extrema and the rate of change of the 3D acoustic signal of vehicle parts. Based on the local extrema and rate of change of the 3D acoustic signals of vehicle components, a filter is constructed using the local extrema and rate of change of the characteristic corner points of the 3D acoustic signals of vehicle components to perform corner smoothing optimization processing. , thereby obtaining the optimized corner point set of 3D acoustic signals for vehicle components; The spatial position relationship of corner points in the 3D acoustic signal optimization corner point set of vehicle parts is mapped by the geometric transformation method, so as to obtain the spatial position relationship of acoustic features corresponding to the corner points of the 3D acoustic signal of each vehicle part. Based on the spatial positional relationship of the acoustic features corresponding to the corner points of the 3D acoustic signals of each vehicle component, 3D acoustic reconstruction conversion calculations are performed on the corresponding vehicle components. By establishing a mesh model, each acoustic corner point is transformed into a three-dimensional acoustic reconstruction space model, generating a 3D acoustic reconstruction space model of the vehicle components.

[0014] Another aspect of the present invention provides a 3D array sensor layout and imaging system for acoustic imaging of vehicle parts, applied to the above-mentioned 3D array sensor layout and imaging method for acoustic imaging of vehicle parts, including a data acquisition module, an optimized sensor array generation module, an acoustic wave reflection monitoring and receiving module, an acoustic time-domain inversion signal processing module, and a 3D acoustic imaging reconstruction module. The data acquisition module is used to acquire the geometric features of vehicle parts and the acoustic wave propagation characteristics data corresponding to the 3D array sensor. The optimized sensor array generation module optimizes the spacing and tilt angle between each sensor in the 3D array sensor based on the geometric features of vehicle parts and the acoustic wave propagation characteristics data corresponding to the 3D array sensor, thereby generating a 3D layout optimized sensor array. The acoustic wave reflection monitoring and receiving module utilizes a 3D layout to optimize the transmission of ultrasonic signals of different frequencies to each sensor in the sensor array to the vehicle components, and uses the ultrasonic signals to perform acoustic wave reflection monitoring and receiving to obtain the propagation time of acoustic waves inside the vehicle components and the 3D acoustic wave reflection receiving signal of the vehicle components. The acoustic time-domain inversion signal processing module performs acoustic time-domain inversion signal processing on the vehicle parts based on the propagation time of sound waves inside the vehicle parts and the 3D sound wave reflection received signal of the vehicle parts, and obtains the 3D acoustic reflection signal characteristics inside the vehicle parts. The 3D acoustic imaging reconstruction module performs 3D acoustic imaging reconstruction calculations on the corresponding vehicle parts based on the characteristics of the 3D acoustic reflection signals inside the vehicle parts, and generates 3D acoustic images of the vehicle parts.

[0015] Compared with the prior art, the present invention has the following advantages and beneficial effects: (1) This invention performs appearance shape recognition analysis on vehicle parts and uses computer vision technology and machine learning algorithms to accurately identify and analyze the appearance shape and size of vehicle parts, thereby efficiently obtaining the geometric features of the parts and ensuring the accuracy and precision of subsequent steps.

[0016] (2) The present invention acquires the acoustic characteristics of vehicle parts based on 3D array sensors. By testing the propagation speed, attenuation and reflection characteristics of sound waves, it can provide important data for material performance evaluation and structural integrity inspection. It helps to identify the density, elasticity and other physical properties of materials, reveal potential defects or damage, such as cracks and voids, and provides data support for subsequent array sensor layout optimization.

[0017] (3) This invention optimizes the array layout design of the spacing and tilt angle between each sensor in the 3D array sensor based on the geometric features of vehicle parts and the acoustic wave propagation characteristics data corresponding to the 3D array sensor. This ensures the optimal configuration of the acoustic wave propagation path, thereby improving the accuracy and reliability of the detection results. It can effectively avoid interference and errors in measurement, making data acquisition more stable, improving detection speed and efficiency, making real-time monitoring and data analysis possible, providing accurate monitoring data in complex environments, and providing basic data support for the subsequent sensor 3D imaging process, thereby improving the spatial resolution of the 3D array sensor and the depth of 3D imaging.

[0018] (4) This invention utilizes a 3D layout to optimize the sensor array so that each sensor emits ultrasonic signals of different times and frequencies to vehicle components, thereby achieving precise detection of the internal structure of vehicle components. By analyzing the propagation time of sound waves within the components and the reflected signals, key information about material density, internal defects, and their geometric characteristics can be obtained. This process enables ultrasonic testing technology to penetrate into complex structures, identify potential structural defects and performance problems, and thus effectively improve the safety and reliability of vehicles. Furthermore, this invention, combined with real-time data processing technology, can update the detection results in real time, thereby providing data support for the subsequent time-domain inversion process.

[0019] (5) This invention performs acoustic time-domain inversion simulation analysis on vehicle parts based on the propagation time of sound waves inside the vehicle parts and the 3D sound wave reflection received signal of the vehicle parts. It can accurately calculate the propagation path of sound waves in different media and geometries. By using known input and received signals, the propagation path of sound waves in the medium can be inferred. The propagation path of the generated 3D sound wave signal in time-domain inversion provides rich information, which can reveal the structural characteristics and defects inside the parts. Furthermore, by performing acoustic reflection signal feature analysis on the corresponding acoustic signal inside the vehicle parts according to the generated propagation path, this analysis not only involves the time, amplitude and frequency characteristics of the reflection signal, but also its variation law under different materials and geometries. By extracting and analyzing these features, we can gain a deeper understanding of the material uniformity, density changes and potential defects inside the parts. This process provides key data support for the subsequent 3D acoustic imaging process, enabling engineers to adjust the scheme in a timely manner during the design and manufacturing stages, thereby capturing the subtle features of the propagation of 3D acoustic signals inside the vehicle parts in real time.

[0020] (6) Based on the extracted 3D acoustic reflection signal features inside vehicle parts, the present invention performs 3D acoustic imaging reconstruction calculation on the corresponding vehicle parts. With advanced imaging algorithms, the acoustic reflection signal feature data is transformed into a visualized 3D image, showing the internal structure and defects of the vehicle parts. This 3D acoustic imaging not only provides an intuitive internal view, but also enables engineers to accurately diagnose and evaluate vehicle parts, improve the efficiency of maintenance and repair. By combining modern graphics processing technology, the generated image can clearly show the changes of different materials and structures, making subsequent decisions more scientific and based on evidence, thereby enhancing the positioning and imaging process of acoustic signals inside vehicle parts. Attached Figure Description

[0021] Figure 1 This is a flowchart of a 3D array sensor layout and imaging method for acoustic imaging of vehicle parts according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the layout and imaging system of a 3D array sensor for acoustic imaging of vehicle parts according to an embodiment of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative effort are within the scope of protection of the present application.

[0023] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0024] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0025] Example: In the embodiments of this invention, please refer to Figure 1 The diagram shown is a flowchart illustrating the steps of a 3D array sensor layout and imaging method for acoustic imaging of vehicle parts according to the present invention. In this embodiment, the 3D array sensor layout and imaging method for acoustic imaging of vehicle parts includes the following steps: Step S1: Obtain the geometric features of vehicle parts and the acoustic wave propagation characteristics data corresponding to the 3D array sensor, and optimize the array layout design of the spacing and tilt angle between each sensor in the 3D array sensor based on the geometric features of vehicle parts and the acoustic wave propagation characteristics data corresponding to the 3D array sensor to generate a 3D layout optimized sensor array. In this embodiment of the invention, a high-resolution camera is used to capture multi-angle images of vehicle components to obtain two-dimensional image data of their appearance. Then, image processing algorithms, including edge detection and contour extraction, are used to extract the appearance contour of the components. By comparing with a standard template, the appearance shape and size of the components, including length, width, height, and other dimensional parameters, are calculated to establish a preliminary geometric feature database. By combining the previously obtained appearance shape and size, a geometric scanning blueprint is formulated for the corresponding vehicle components. Using CAD software (such as SolidWorks), the identified appearance parameters are input to perform geometric modeling and generate a detailed blueprint. This blueprint contains the specific shape, size, and structural features of the components, clearly showing every detail of the components. Based on the geometric scanning blueprint, a high-precision 3D scanner is used to scan and model the components. This 3D scanner should have functions such as laser scanning and structured light imaging to capture the geometric information of the components with high precision. During the scanning process, an appropriate scanning resolution and sampling frequency are set to ensure that the generated three-dimensional spatial model can realistically reflect the subtle features of the components and extract the geometric feature parameters of the vehicle components, such as curvature and edge length, thereby obtaining the geometric features of the vehicle components. Simultaneously, during the acoustic characteristic testing of vehicle components, a 3D array sensor was first installed on the test platform, ensuring it fully covered the surface of the component. A sound wave transmitter was then used to emit sound waves of a specific frequency onto the component. The sensor array collected the sound wave propagation data in real time. This data included the sound wave propagation speed, attenuation, and reflection characteristics on the component, thus obtaining the corresponding acoustic wave propagation characteristic data for the 3D array sensor. Then, after acquiring the geometric features of the component and the acoustic wave propagation characteristic data, the layout of the 3D array sensor was optimized by considering the spacing and tilt angle between the sensors. An optimization algorithm, combined with the geometric features of the component and the acoustic wave propagation characteristics, determined the optimal spacing and tilt angle between the sensors to improve the resolution and accuracy of the acoustic imaging. This process considered the sound wave propagation path and reflection characteristics, and involved multiple iterations using simulation software (such as MATLAB) to ultimately generate an optimized 3D layout sensor array.

[0026] Step S2: Utilize the 3D layout optimization sensor array to send ultrasonic signals of different time frequencies to vehicle parts, and use the 3D layout optimization sensor array to monitor and receive the ultrasonic signals of different time frequencies to obtain the propagation time of sound waves inside the vehicle parts and the 3D sound wave reflection reception signal of the vehicle parts. In this embodiment of the invention, statistical analysis of the propagation characteristics of each sensor within the previously optimized 3D layout sensor array is performed. Multiple ultrasonic signal sources within a frequency range are selected, with frequencies gradually increasing from low (e.g., 20kHz) to high (e.g., 1MHz). Each sensor receives and processes the input signal using a high-precision signal analyzer, recording its response characteristics at different frequencies. By combining the previously analyzed acoustic wave propagation characteristic data of each sensor at different signal frequencies, the signal transmission of each corresponding sensor is optimized. Signal processing algorithms are used to analyze the response characteristics of each sensor, determining the optimal transmission frequency and timing combination. This optimization design process is then utilized. Tools such as genetic algorithms or particle swarm optimization algorithms are used to input sound wave propagation characteristic data and target imaging resolution parameters, and iterate multiple times to obtain the optimal signal transmission timing and frequency combination, such as transmitting once every 0.5 seconds at 50kHz, or once every 1 second at 100kHz, etc. Then, by combining the signal transmission timing and frequency combination obtained from the previous analysis and design, the ultrasonic signal is actually transmitted. Within a 3D layout optimized sensor array, each sensor transmits ultrasonic signals of different frequencies to the vehicle parts in sequence according to a preset timing. The transmitting equipment uses a high-frequency ultrasonic transmitter to ensure signal stability and accuracy. After the signal is transmitted, the sensor array monitors the transmitted ultrasonic signal and records the reflected echo. The reflected signal is collected by a high-sensitivity receiver, amplified and filtered, and finally analyzed in the time and frequency domains using signal analysis software to calculate the propagation time of the sound wave inside the vehicle parts and generate a corresponding 3D sound wave reflection and reception signal image. Finally, the propagation time of the sound wave inside the vehicle parts and the 3D sound wave reflection and reception signal of the vehicle parts are obtained.

[0027] Step S3: Based on the propagation time of sound waves inside the vehicle parts and the 3D sound wave reflection received signal of the vehicle parts, perform acoustic time-domain inversion signal processing on the vehicle parts to obtain the 3D acoustic reflection signal characteristics inside the vehicle parts. In this embodiment of the invention, by combining the previously monitored sound wave propagation duration and 3D sound wave reflection received signal inside the vehicle components, an acoustic time-domain inversion simulation analysis is conducted. This is achieved by using the finite element method (FEM) or the finite-difference time-domain method (FDTD) to input the acquired sound wave propagation data into a preset acoustic time-domain inversion algorithm to construct a three-dimensional sound field. First, the sound source location and sensor layout are determined, and the acoustic properties of the material, such as density and sound velocity, are set. The propagation path of the sound wave inside the components is iteratively calculated using the inversion algorithm. Then, by combining the previously simulated time-domain inversion propagation path, a time-domain signal inversion model is performed on the acoustic signal inside the vehicle components. This is achieved by applying numerical simulation tools, such as Matlab or the NumPy library in Python, to construct a time-domain inversion mathematical model. First, the propagation path is subdivided into multiple time periods, and based on the sound wave characteristics within each time period, a time-domain inversion algorithm is used. Convolution operations simulate the reflection and refraction effects of sound waves at different material interfaces. By adjusting the model parameters to fit the actual observation data, a time-domain inversion mathematical model of the corresponding sound wave signal is generated. Then, by combining the previously modeled signal time-domain inversion mathematical model, statistical analysis of the signal characteristics of the corresponding acoustic signals emitted by the 3D array sensor is performed. By extracting the acoustic signal data generated by the model, time-domain analysis techniques, such as autocorrelation analysis and Fourier transform, are applied to obtain the spectral characteristics of the signal. By comparing the reflection signal characteristics under different conditions, the peak value of sound wave reflection and its corresponding time delay are identified to analyze the reflection law of sound waves inside the component. Combining material properties and geometry, the internal defects and structural characteristics reflected by different reflection signals are analyzed, including spectral characteristics, internal defects and structural characteristics, etc. Finally, the 3D acoustic reflection signal characteristics inside the vehicle component are obtained.

[0028] Step S4: Based on the 3D acoustic reflection signal characteristics inside the vehicle parts, perform 3D acoustic imaging reconstruction calculations on the corresponding vehicle parts to generate 3D acoustic images of the vehicle parts.

[0029] In this embodiment of the invention, the 3D acoustic reflection signal features inside vehicle components obtained from previous statistical analysis are normalized to eliminate interference caused by environmental noise or differences in sensor sensitivity. This process includes removing DC components and applying Z-score normalization to ensure that the mean of all signal features is zero and the variance is one. The normalized acoustic signal features are then processed using specific algorithms (such as Harris corner detection or the Shi-Tomasi method). These algorithms can identify points with significant changes in the signal features, which typically represent abrupt changes in sound wave reflection intensity. During processing, a certain threshold is set to filter out noise signals below this threshold, ensuring that only valid corner points are retained, forming a 3D acoustic signal feature corner point set. Simultaneously, the corresponding vehicle components are spatially reconstructed by combining the previously detected acoustic signal feature corner point set, and the 2D signal is mapped into 3D space using a computational geometric transformation model (such as perspective projection). Using the corner points in a 3D coordinate system, a spatial model of the vehicle components is established. Interpolation algorithms (such as Kriging interpolation or cubic spline interpolation) are applied to generate smooth surfaces between the corner points, forming a complete 3D acoustic reconstruction spatial model. The previously reconstructed acoustic reconstruction spatial model is then divided into multiple sub-meshes using octree decomposition or Delaunay triangulation techniques. Each sub-mesh represents a local area of ​​a vehicle component, facilitating detailed acoustic analysis. This yields 3D acoustic reconstruction mesh sub-models for each vehicle component. For each mesh sub-model, acoustic reflection and feature iteration calculations are performed using acoustic simulation software (such as COMSOL or ANSYS) to obtain the corresponding 3D acoustic reflection intensity and feature values. This process accurately simulates the propagation characteristics of sound waves in each sub-model by setting appropriate boundary conditions and acoustic parameters (such as material density and sound velocity), thereby generating detailed acoustic reflection intensity and feature values. Then, based on the acoustic reflection intensity and characteristic values ​​of each mesh sub-model, a 3D rendering technique is applied to generate a visualization image. Advanced graphics processing software (such as OpenGL or Unity) is used to assign different colors and lighting effects to each mesh according to the acoustic characteristic values ​​to enhance the visual effect. In this rendering process, factors such as the position of the light source and material properties are considered to simulate the sound wave reflection in the real scene, so that designers can better understand the impact of acoustic characteristics on vehicle performance. Finally, 3D acoustic imaging of vehicle parts is generated.

[0030] As an embodiment of the present invention, step S1 includes the following steps: Step S11: Perform appearance shape recognition analysis on vehicle parts to obtain the appearance shape and size of vehicle parts; In this embodiment of the invention, a high-resolution camera is used to capture images of vehicle parts from multiple angles to obtain two-dimensional image data of their appearance. Then, image processing algorithms, including edge detection and contour extraction, are used to extract the appearance contour of the parts. By comparing with a standard template, the appearance shape and size of the parts, including dimensional parameters such as length, width, and height, are calculated to establish a preliminary geometric feature database. This process can be implemented using Python combined with the OpenCV library to ensure that the appearance data of the parts is accurate and error-free, and finally the appearance shape and size of the vehicle parts are obtained.

[0031] Step S12: Based on the appearance shape and size of the vehicle parts, a geometric scan blueprint is generated for the vehicle parts to design and produce the geometric scan blueprint. In this embodiment of the invention, a geometric scanning blueprint for the corresponding vehicle parts is formulated by combining the previously obtained appearance shape and size of the vehicle parts. By using CAD software (such as SolidWorks) to input the identified appearance parameters, geometric modeling is performed to generate a detailed blueprint. This blueprint contains the specific shape, size and structural features of the parts, which can clearly show every detail of the parts. At the same time, the blueprint is exported as a standard 3D model file format, such as STL or IGES, and finally the geometric scanning blueprint of the vehicle parts is designed and generated.

[0032] Step S13: Based on the geometric scanning blueprint of the vehicle parts, perform high-precision 3D scanning modeling of the vehicle parts to generate a high-precision three-dimensional spatial model of the vehicle parts; perform geometric feature statistical analysis on the high-precision three-dimensional spatial model of the vehicle parts to obtain the geometric features of the vehicle parts. In this embodiment of the invention, based on the geometric scanning blueprint of vehicle parts, a high-precision 3D scanner is used to scan and model the parts. The 3D scanner should have functions such as laser scanning and structured light imaging to capture the geometric information of the parts with high precision. During the scanning process, an appropriate scanning resolution and sampling frequency are set to ensure that the generated three-dimensional spatial model can truly reflect the subtle features of the parts. After the scanning is completed, data processing is performed using appropriate software to remove noise and repair the model, thereby generating a high-precision three-dimensional spatial model of the vehicle parts. This model is then subjected to geometric feature statistical analysis to extract the geometric feature parameters of the vehicle parts, such as curvature and edge length, to establish a complete geometric feature database, and finally obtain the geometric features of the vehicle parts.

[0033] Step S14: Conduct acoustic characteristic test and analysis on vehicle parts based on the 3D array sensor to obtain acoustic wave propagation characteristic data corresponding to the 3D array sensor. The acoustic wave propagation characteristic data includes the propagation speed, propagation attenuation and propagation reflection characteristics of the acoustic wave on the vehicle parts. In this embodiment of the invention, when conducting acoustic characteristic experiments on vehicle components, a 3D array sensor is first installed on the test platform, ensuring that it can fully cover the surface of the component. A sound wave of a certain frequency is emitted to the component by a sound wave transmitter. The sensor array collects the propagation data of the sound wave in real time. The sound wave propagation characteristic data includes the propagation speed, propagation attenuation, and reflection characteristics of the sound wave on the component. This data is analyzed using a signal processing algorithm to obtain an accurate sound wave propagation characteristic curve, and the values ​​of each parameter are recorded for subsequent use. Finally, the sound wave propagation characteristic data corresponding to the 3D array sensor is obtained, including the propagation speed, propagation attenuation, and propagation reflection characteristics of the sound wave on the vehicle component.

[0034] Step S15: Based on the geometric features of vehicle parts and the acoustic wave propagation characteristics data corresponding to the 3D array sensor, optimize the array layout design of the spacing and tilt angle between each sensor in the 3D array sensor to generate a 3D layout optimized sensor array.

[0035] In this embodiment of the invention, after acquiring the geometric features and sound wave propagation characteristics of the components, the layout design of the 3D array sensor is optimized by considering the spacing and tilt angle between the sensors. The optimization algorithm is used to determine the optimal spacing and tilt angle between the sensors by combining the geometric features of the components and the sound wave propagation characteristics, so as to improve the resolution and accuracy of the sound wave imaging. In this process, the propagation path and reflection characteristics of the sound waves are considered, and multiple iterations are performed by simulation software (such as MATLAB) to finally optimize and generate a 3D layout optimized sensor array.

[0036] Furthermore, step S15 includes the following steps: Step S151: Based on the geometric features of vehicle parts and the acoustic wave propagation characteristics data corresponding to the 3D array sensor, the propagation speed and propagation attenuation of the acoustic waves on the vehicle parts are used to optimize the spacing between the sensors in the 3D array sensor to generate a 3D array sensor spacing optimization design scheme. Step S152: Based on the geometric features of vehicle parts and the acoustic wave propagation characteristics data of the 3D array sensor, the propagation attenuation and propagation reflection characteristics of the acoustic waves on the vehicle parts are used to optimize the tilt angle between each sensor in the 3D array sensor to generate a tilt angle optimization design scheme for the 3D array sensor. Step S153: Based on the 3D array sensor spacing optimization design scheme and the 3D array sensor tilt angle optimization design scheme, perform comprehensive layout optimization design of the 3D array sensor to generate a 3D optimized sensor array layout diagram; Step S154: Based on the 3D optimized sensor array layout diagram, perform array layout adjustment processing on the comprehensive layout of the 3D array sensor to generate a 3D layout optimized sensor array.

[0037] As an embodiment of the present invention, step S15 includes the following steps: Step S151: Based on the geometric features of vehicle parts and the acoustic wave propagation characteristics data corresponding to the 3D array sensor, the propagation speed and propagation attenuation of the acoustic waves on the vehicle parts are used to optimize the spacing between the sensors in the 3D array sensor to generate a 3D array sensor spacing optimization design scheme. In this embodiment of the invention, by combining the previously analyzed geometric features of vehicle parts with the acoustic wave propagation characteristic data corresponding to the 3D array sensor, the propagation speed and attenuation of acoustic waves on the vehicle parts are used to optimize the spacing between the sensors in the 3D array sensor. This is done by using the acoustic wave propagation characteristic data to establish a model of the propagation speed of acoustic waves in different materials, taking into account the influence of environmental factors such as temperature and pressure on acoustic wave propagation, and combining the acoustic wave propagation speed with the propagation attenuation characteristics. Optimization algorithms, such as genetic algorithms or particle swarm optimization, are then used to adjust the spacing between the sensors in the 3D array sensor. By repeatedly simulating the propagation characteristics of acoustic waves between sensors, the optimal spacing configuration scheme is generated to ensure that the acoustic waves can effectively cover the entire surface of the parts. Finally, an optimized spacing design scheme for the 3D array sensor is designed and generated.

[0038] Step S152: Based on the geometric features of vehicle parts and the acoustic wave propagation characteristics data of the 3D array sensor, the propagation attenuation and propagation reflection characteristics of the acoustic waves on the vehicle parts are used to optimize the tilt angle between each sensor in the 3D array sensor to generate a tilt angle optimization design scheme for the 3D array sensor. In this embodiment of the invention, the tilt angles of the corresponding sensors are optimized by combining the previously analyzed geometric features of vehicle components with the sound wave propagation attenuation and reflection characteristics of the 3D array sensors. This involves experimentally measuring the behavior of sound waves on different geometric shapes and material contact surfaces, and setting multiple sensors to emit sound waves at specific angles, recording the intensity and attenuation of the reflected signals. Based on this data, a mathematical model is established to describe the influence of the tilt angle on the sound wave propagation effect. Finite element analysis software, such as ANSYS or COMSOL Multiphysics, is used to simulate the sound field distribution under different tilt angles. Optimization techniques are employed to calculate the optimal configuration of the sensors under different tilt angles, ensuring that maximum reflection and minimum attenuation can be achieved on the surface of the target component. Finally, an optimized tilt angle design scheme for the 3D array sensors is designed and generated.

[0039] Step S153: Based on the 3D array sensor spacing optimization design scheme and the 3D array sensor tilt angle optimization design scheme, perform comprehensive layout optimization design of the 3D array sensor to generate a 3D optimized sensor array layout diagram; In this embodiment of the invention, by combining the previously designed spacing optimization design scheme and tilt angle optimization design scheme, the 3D array sensor is comprehensively optimized in layout. By using computer-aided design (CAD) software and combining optimization algorithms, the optimal spacing and tilt angle of each sensor are reasonably configured in three-dimensional space. Through multiple iterative simulations, the sound field distribution effect under different layouts is evaluated, and the sensor layout with the most uniform sound field coverage and the strongest signal reception is selected. Finally, a 3D optimized sensor array layout diagram is designed and generated.

[0040] Step S154: Based on the 3D optimized sensor array layout diagram, perform array layout adjustment processing on the comprehensive layout of the 3D array sensor to generate a 3D layout optimized sensor array.

[0041] In this embodiment of the invention, the actual array layout of the sensors is adjusted by combining the previously designed 3D optimized sensor array layout diagram. By using a robotic arm or precision positioning device, each sensor is accurately installed on the vehicle parts according to the layout diagram. During the installation process, it is necessary to ensure that the spacing and tilt angle of each sensor conform to the optimization scheme to avoid human error. At the same time, after each sensor is installed, the system's acoustic wave test is performed to verify whether its function meets the design requirements. Any deviations are fine-tuned to ensure that the final 3D layout optimized sensor array can effectively perform acoustic imaging, thus generating the 3D layout optimized sensor array.

[0042] Furthermore, step S151 includes the following steps: Based on the geometric features of vehicle components, the spacing between the sensors in the 3D array sensor is initially designed to obtain different initial sensor spacing layout conditions. In this embodiment of the invention, when initially designing the spacing of the 3D array sensors, the geometric features of the vehicle components are first comprehensively analyzed. Detailed CAD models of the components are established using 3D modeling software (such as SolidWorks). Based on the models, the initial spacing of each sensor is calculated. Considering the effectiveness of sound wave propagation and the sensitivity of signal reception, preliminary layout conditions for different sensor spacings are set according to the shape and characteristics of the components, such as symmetry and thickness. For example, the sensor spacing is set to 20mm to 50mm in planar areas and 10mm to 30mm in complex curved surface areas. Based on these conditions, a preliminary layout document is generated, ultimately obtaining the preliminary spacing layout conditions for different sensors.

[0043] Preferably, the propagation efficiency of the sound waves on the vehicle parts is evaluated and analyzed based on the propagation speed and propagation attenuation of the sound waves in the sound wave propagation characteristic data corresponding to the 3D array sensor, so as to obtain the sound wave propagation efficiency of the 3D array sensor under different sensor spacing conditions. In this embodiment of the invention, in the sound wave propagation efficiency evaluation and analysis, characteristic data related to sound wave propagation are first collected, including the propagation speed and attenuation characteristics of sound waves in different materials (such as metals and plastics). Then, an acoustic simulation software (such as COMSOL Multiphysics) is used to establish a sound wave propagation model. Different initial sensor spacing layout conditions are input, and multiple simulations are performed to calculate the propagation efficiency of sound waves inside the components. Attention is paid to reflection intensity and propagation loss. By analyzing the sound wave propagation efficiency under different layout conditions, specific numerical results are obtained, and finally, the sound wave propagation efficiency of the 3D array sensor corresponding to different sensor spacing conditions is obtained.

[0044] Preferably, the signal acquisition performance of the corresponding preliminary spacing between each sensor in the 3D array sensor is evaluated and analyzed based on the acoustic wave propagation efficiency of the 3D array sensor under different sensor spacing conditions, so as to obtain the acoustic wave signal acquisition performance results of the sensor acoustic wave propagation characteristics under different layout spacing conditions. In this embodiment of the invention, the signal capture performance of the corresponding preliminary spacing between the sensors in the 3D array sensor is evaluated and analyzed by combining the sound wave propagation efficiency obtained under different sensor spacing conditions from previous evaluation and analysis. Using the previously obtained propagation efficiency, a signal capture model is constructed, and the reception of sound wave signals is analyzed using acoustic signal processing tools. Through experimental setup, sound wave transmission and reception experiments are conducted in a real environment, and the signal strength and quality under different spacing conditions are recorded. This experiment, combined with signal analysis software (such as LabVIEW), evaluates the signal capture capability of the sensors under various layout conditions, thereby obtaining the sound wave signal capture performance results under different layout spacing conditions. Finally, the sound wave propagation characteristics of the sensors under different layout spacing conditions are obtained.

[0045] Preferably, the spacing between the sensors in the 3D array sensor is iteratively optimized based on the acoustic wave propagation characteristics of the sensor and the acoustic wave signal capture performance under different layout spacing conditions, so as to generate a 3D array sensor spacing optimization design scheme.

[0046] In this embodiment of the invention, the spacing between the sensors in the 3D array sensor is optimized by combining the acoustic signal capture performance results obtained from previous evaluation and analysis under different layout spacing conditions. Based on the analysis results of acoustic signal capture performance, key factors affecting signal quality are identified, an optimization model is established, and a multi-objective optimization algorithm is used to evaluate the acoustic propagation effect and signal capture capability of different configurations by adjusting the spacing between the sensors. Through iterative design, a new layout scheme is generated to ensure maximum signal quality and optimization of overall system performance, and the optimized 3D array sensor spacing design scheme is output, ultimately generating the optimized 3D array sensor spacing design scheme.

[0047] Furthermore, step S152 includes the following steps: Based on the geometric features of vehicle components, the tilt angles between the individual sensors in the 3D array sensor are initially designed to generate different preliminary tilt angle ranges for the sensors. In this embodiment of the invention, when initially designing the tilt angles between the various sensors within the 3D array sensor, the geometric features of the vehicle components are first analyzed, including the shape, size, and material properties of the components. Using computer-aided design (CAD) software, the three-dimensional model of the vehicle components is imported and virtual simulation is performed to identify the optimal installation position and tilt angle of the sensors. Based on the curvature and surface features of the components, the initial tilt angle range for each sensor is determined. For example, for sensors in planar areas, the tilt angle can be set to 0° to 15°, while in curved areas, the tilt angle can be set to 15° to 30°. This step outputs a document containing the initial tilt angle ranges of each sensor, ultimately generating the initial tilt angle range conditions for different sensors.

[0048] Preferably, based on the acoustic wave propagation characteristic data corresponding to the 3D array sensor, the propagation attenuation and propagation reflection characteristics of the acoustic waves on the vehicle parts are used to perform acoustic wave parameterization modeling for different sensor initial tilt angle range conditions, so as to generate sensor tilt angle parameterization function model. In this embodiment of the invention, when performing sound wave parameterization modeling based on sound wave propagation characteristic data, sound wave propagation attenuation and reflection characteristic data at different tilt angles are first collected. These data come from laboratory experiments. By transmitting and receiving sound waves of different frequencies on various materials, the reflection intensity and attenuation rate of the sound waves are recorded. The collected data are then analyzed using mathematical modeling software (such as MATLAB) to establish a sound wave parameterization function model. This model describes the propagation characteristics of sound waves on vehicle parts at different tilt angles, including parameters such as propagation reflection intensity, propagation distance, and attenuation coefficient. This model provides an important mathematical foundation for subsequent simulation and optimization, and finally generates a sensor tilt angle parameterization function model.

[0049] The sensor tilt angle parameterization function model is as follows: ; In the formula, R ( i ) for 3D array sensors under tilt angle conditions i The intensity of sound wave propagation and reflection under the current, R 0 represents the acoustic wave propagation and reflection intensity of the 3D array sensor in its initial state. α Let be the attenuation coefficient of sound waves propagating in vehicle components. d This refers to the distance the sound wave travels. Preferably, a simulation environment framework for the acoustic wave propagation characteristics at tilt angle is constructed by a parameterized function model of the sensor tilt angle. Based on the simulation environment framework for the acoustic wave propagation characteristics at tilt angle, the acoustic wave propagation attenuation and propagation reflection characteristics in the acoustic wave propagation characteristic data of the 3D array sensor on the vehicle parts are statistically analyzed to obtain the acoustic wave reflection intensity and acoustic wave attenuation rate of the 3D array sensor under different tilt angle conditions. In this embodiment of the invention, a simulation environment framework for the acoustic wave propagation characteristics at tilt angles is constructed by using a previously modeled parametric function model of tilt angles. Based on the constructed simulation environment framework, a 3D acoustic wave propagation model is established using finite element analysis (FEA) software, such as ANSYS. The initial tilt angles of each sensor are input, and the acoustic wave propagation simulation is run in the simulation environment. The reflection and attenuation characteristics of acoustic waves inside vehicle components are statistically analyzed. Based on the simulation results, the acoustic wave reflection intensity and attenuation rate under different tilt angle conditions are collected and the statistical data are displayed in the form of charts. Finally, the acoustic wave reflection intensity and acoustic wave attenuation rate of the 3D array sensor under different tilt angle conditions are obtained.

[0050] Preferably, the signal receiving capability of each sensor in the 3D array sensor is evaluated and analyzed based on the acoustic wave reflection intensity and acoustic wave attenuation rate corresponding to the 3D array sensor under different tilt angles, so as to obtain the signal receiving capability influence factor of the sensor acoustic wave propagation characteristics under different tilt angles. In this embodiment of the invention, a signal receiving model is constructed by combining the acoustic wave reflection intensity and attenuation rate data obtained from previous statistical analysis. The signal receiving capability under different tilt angles is simulated and analyzed to evaluate the signal receiving capability influence factor of each sensor under a specific tilt angle. By using statistical analysis software, multivariate regression analysis is performed on the intensity and quality of the acoustic wave received signal to determine the degree of influence of each tilt angle on the sensor's signal receiving capability. Finally, the signal receiving capability influence factor corresponding to the sensor's acoustic wave propagation characteristics under different tilt angle conditions is obtained.

[0051] The specific formula for calculating the signal reception capability impact factor is as follows: ; In the formula, I s ( i ) for 3D array sensors under tilt angle conditions i The corresponding signal reception capability impact factor is Ω, where Ω represents the sensor signal propagation spatial region. x The spatial location point corresponding to the sensor's sound wave. x’ The receiving location point corresponding to the sensor's sound wave. S ( x , i ) represents the spatial location point x Considering the tilt angle condition i The corresponding sound wave reflection intensity S 0( x ) represents the spatial location point x Ideal sound wave intensity at that location A ( x , i ) represents the spatial location point x Considering the tilt angle condition i The corresponding reflection intensity affects the reception efficiency. β The weighting factor is determined by the influence of reflection intensity. exp It is an exponential function. e ( x , x’ () represents the spatial location point along the sound wave propagation path of the sensor. x To the receiving location x’ The sound wave attenuation rate, d This is the sound wave attenuation ratio coefficient.

[0052] Preferably, based on the influence factor of the signal receiving capability under different tilt angle conditions corresponding to the sensor's acoustic wave propagation characteristics, the tilt angle between each sensor in the 3D array sensor is optimized to generate a 3D array sensor tilt angle optimization design scheme, specifically as follows: The tilt angle of each sensor in the 3D array sensor is iteratively adjusted using the particle swarm optimization algorithm to maximize signal reception capability. The initial tilt angle of each sensor and the corresponding signal reception capability influencing factor are input, and the parameters are continuously adjusted by the particle swarm optimization algorithm until the final tilt angle and layout information of each sensor are found, thus generating a tilt angle optimization design scheme for the 3D array sensor.

[0053] In this embodiment of the invention, the tilt angles between the sensors in the 3D array sensor are optimized by combining the signal reception capability influencing factors obtained from previous evaluation and analysis. At this stage, optimization algorithms (such as particle swarm optimization or genetic algorithms) are used to iteratively adjust the tilt angles of different sensors to maximize signal reception capability. The initial tilt angles of each sensor and the corresponding influencing factors are input, and the parameters are continuously adjusted through the optimization model until the optimal combination of tilt angles is found, thereby generating an optimized design scheme for the sensor tilt angles, which includes the final tilt angles and layout information of each sensor, providing detailed guidance for actual installation. Finally, an optimized design scheme for the tilt angles of the 3D array sensor is generated.

[0054] As an embodiment of the present invention, step S2 includes the following steps: Step S21: Analyze the signal frequency propagation characteristics of each sensor in the 3D layout optimization sensor array to obtain the acoustic wave propagation characteristic data of each sensor under different signal frequencies. In this embodiment of the invention, statistical analysis of the propagation characteristics of each sensor in the previously optimized 3D layout sensor array is performed. By selecting multiple ultrasonic signal sources within a frequency range, the frequencies of these signal sources can be gradually increased from low frequency (e.g., 20kHz) to high frequency (e.g., 1MHz). Each sensor receives and processes the input signal through a high-precision signal analyzer, recording the response characteristics at different frequencies. Using spectrum analysis technology, the sound wave propagation speed, attenuation characteristics, and reflectivity data of each sensor at various frequencies can be obtained. These data are organized in tabular form, and finally, the sound wave propagation characteristic data of each sensor at different signal frequencies are obtained.

[0055] Step S22: Based on the acoustic wave propagation characteristic data of each sensor at different signal frequencies, perform signal transmission optimization design for each corresponding sensor in the 3D layout optimization sensor array to generate the signal transmission timing and frequency combination for each sensor. In this embodiment of the invention, the signal transmission of each sensor is optimized by combining the previously analyzed acoustic wave propagation characteristic data of each sensor at different signal frequencies. The response characteristics of each sensor are analyzed by using signal processing algorithms to determine the optimal combination of transmission frequency and timing. Optimization design tools, such as genetic algorithms or particle swarm optimization algorithms, are used to input acoustic wave propagation characteristic data and target imaging resolution parameters, and multiple iterations are performed to obtain the optimal combination of signal transmission timing and frequency. These combinations not only consider effective signal transmission but also ensure reduced mutual interference and improved imaging quality. For example, transmission times of 0.5 seconds and 50kHz, transmission times of 1 second and 100kHz, etc., are used to design and generate the signal transmission timing and frequency combination corresponding to each sensor.

[0056] Step S23: Based on the signal transmission timing and frequency combination of each sensor, the sensor array is optimized using 3D layout to transmit ultrasonic signals of different times and frequencies to the vehicle parts. The sensor array is optimized using 3D layout to monitor and receive the ultrasonic signals of different times and frequencies to obtain the propagation time of the sound waves inside the vehicle parts and the 3D sound wave reflection reception signal of the vehicle parts.

[0057] In this embodiment of the invention, ultrasonic signals are actually transmitted by combining the signal transmission timing and frequency combinations corresponding to each sensor obtained from previous analysis and design. Within a 3D layout optimized sensor array, each sensor sequentially transmits ultrasonic signals of different frequencies to the vehicle components according to a preset timing sequence. The transmitting device uses a high-frequency ultrasonic transmitter to ensure signal stability and accuracy. After signal transmission, the transmitted ultrasonic signals are monitored by the sensor array, and the reflected echoes are recorded. The reflected signals are collected by a high-sensitivity receiver, amplified, and filtered. Finally, signal analysis software is used to perform time-domain and frequency-domain analysis on the reflected signals to calculate the propagation time of the sound waves inside the vehicle components and generate corresponding 3D sound wave reflection and reception signal images. Ultimately, the propagation time of the sound waves inside the vehicle components and the 3D sound wave reflection and reception signals of the vehicle components are obtained.

[0058] As an embodiment of the present invention, step S3 includes the following steps: Step S31: Based on the propagation time of sound waves inside the vehicle parts and the 3D sound wave reflection and reception signal of the vehicle parts, perform acoustic time-domain inversion simulation analysis on the vehicle parts to generate the time-domain inversion propagation path of the 3D sound wave signal of the vehicle parts. In this embodiment of the invention, by combining the previously monitored sound wave propagation duration inside vehicle components and the 3D sound wave reflection received signal, an acoustic time-domain inversion simulation analysis is carried out. The acquired sound wave propagation data is input into a preset acoustic time-domain inversion algorithm using the finite element method (FEM) or the finite-difference time-domain method (FDTD) to construct a three-dimensional sound field. First, the sound source location and sensor layout are determined, and the acoustic properties of the material, such as density and sound velocity, are set. The propagation path of the sound wave inside the component is iteratively calculated through the inversion algorithm to generate the 3D sound wave signal time-domain inversion propagation path of the vehicle component. This process involves a large number of numerical calculations to ensure that the inversion model can accurately reproduce the actual sound wave propagation behavior inside the component. Finally, the 3D sound wave signal time-domain inversion propagation path of the vehicle component is simulated and generated.

[0059] Step S32: Based on the propagation path of the 3D acoustic wave signal time domain inversion of the vehicle parts, perform time domain signal inversion modeling of the corresponding acoustic signal in the vehicle parts to generate a mathematical model of the 3D acoustic wave signal time domain inversion of the vehicle parts. In this embodiment of the invention, the acoustic signals within the vehicle components are inverted and modeled in the time domain by combining the propagation path of the 3D acoustic wave signals generated by previous simulations. This is achieved by using numerical simulation tools, such as Matlab or the NumPy library in Python, to construct a time-domain inversion mathematical model. First, the propagation path is subdivided into multiple time periods. Based on the acoustic wave characteristics within each time period, convolution operations are used to simulate the reflection and refraction effects of acoustic waves at different material interfaces. By adjusting the model parameters, the model is fitted to the actual observation data, thereby generating the corresponding time-domain inversion mathematical model of the acoustic wave signal. Finally, a time-domain inversion mathematical model of the 3D acoustic wave signals of the vehicle components is generated.

[0060] Step S33: Based on the time-domain inversion mathematical model of the 3D acoustic wave signal of the vehicle parts, perform acoustic reflection signal feature analysis on the corresponding acoustic signal inside the vehicle parts to obtain the 3D acoustic reflection signal features inside the vehicle parts.

[0061] In this embodiment of the invention, the corresponding acoustic signals emitted by the 3D array sensor are statistically analyzed by combining the time-domain inversion mathematical model of the 3D acoustic wave signal of the vehicle parts generated by the previous modeling. By extracting the acoustic signal data generated by the model, time-domain analysis techniques, such as autocorrelation analysis and Fourier transform, are applied to obtain the spectral characteristics of the signal. By comparing the reflection signal characteristics under different conditions, the peak value of the acoustic wave reflection and its corresponding time delay are identified to analyze the reflection law of the acoustic wave inside the parts. Combined with material properties and geometry, the internal defects and structural characteristics reflected by different reflection signals are analyzed, including spectral characteristics, internal defects and structural characteristics, etc., and finally the 3D acoustic reflection signal characteristics inside the vehicle parts are obtained.

[0062] Furthermore, step S31 includes the following steps: The difference in propagation time of sound waves inside vehicle components from transmission to reception is obtained, and the propagation time interference is optimized based on the difference in propagation time to obtain the actual propagation time of sound waves inside vehicle components. In this embodiment of the invention, by using multiple array sensors to synchronously emit sound waves and record their propagation time inside vehicle components, and calculating the time difference between sound wave emission and reception, the propagation characteristics of sound waves on different paths can be determined. To eliminate interference, digital signal processing techniques, such as Kalman filtering, are applied to optimize the propagation time data in real time. This process compares the original data with the expected value based on sound wave propagation theory, thereby adjusting the measurement data to eliminate the influence of environmental factors on the sound wave propagation time, thus obtaining the actual propagation time of sound waves inside vehicle components.

[0063] Preferably, the actual propagation time of sound waves within vehicle components is divided into time periods to obtain different segments of sound wave propagation time for different vehicle components; In this embodiment of the invention, the actual propagation time of the acquired sound waves is divided into time periods to adopt a method that combines fixed and dynamic durations. Specifically, the operation includes first setting a basic time interval, such as 100 milliseconds, and grouping the propagation time data according to this interval. For areas with large variations in propagation time, a dynamic division method is used to automatically identify and subdivide the data into multiple segments based on sound wave feature extraction algorithms (such as signal segmentation based on wavelet transform). This division method can ensure that the segmentation of sound wave duration reflects the actual acoustic characteristics under different propagation conditions, forming multiple sound wave propagation time segments for vehicle components, and finally obtaining different sound wave propagation time segments for vehicle components.

[0064] Preferably, the 3D sound wave reflection and reception signals of vehicle parts are segmented based on different sound wave propagation durations to obtain the corresponding 3D sound wave reflection and reception sub-signals of vehicle parts under different propagation periods. In this embodiment of the invention, the 3D sound wave reflection and reception signal of the vehicle parts is sliced ​​according to the previously divided sound wave propagation time segments. Each sound wave segment corresponds to a time window. The reflection signal features in each time period are extracted by using short-time Fourier transform (STFT) or wavelet transform. By analyzing the signal characteristics in different time periods, the reflection signal can be effectively divided into multiple sub-signals. These sub-signals represent the sound wave reflection characteristics of the vehicle parts in different propagation time periods. Finally, the 3D sound wave reflection and reception sub-signals of the vehicle parts in different propagation time periods are obtained.

[0065] Preferably, the acoustic time-domain propagation law of the vehicle components is analyzed based on the 3D acoustic wave reflection receiving sub-signals corresponding to the vehicle components at different propagation time periods, so as to obtain the acoustic propagation, refraction and reflection law of the 3D acoustic wave reflection signal inside the vehicle components. In this embodiment of the invention, the acoustic time-domain propagation law of the corresponding vehicle components is analyzed by combining the previously divided 3D acoustic wave reflection receiving sub-signals corresponding to different propagation periods. By employing time-domain analysis techniques, such as time-domain reflection (TDR) and acoustic propagation models, the propagation, refraction, and reflection laws of sound waves inside the vehicle components are calculated. Furthermore, by constructing mathematical models, the behavior of sound waves at different medium interfaces is simulated, and the influence of component shape, material properties, and geometric structure on sound waves is analyzed. This analysis will help to understand the propagation characteristics of sound waves inside vehicle components, and ultimately obtain the acoustic propagation, refraction, and reflection laws of 3D acoustic wave reflection signals inside vehicle components.

[0066] Preferably, based on the acoustic propagation, refraction, and reflection laws of the 3D acoustic wave reflection signal inside the vehicle component, a time-domain inversion simulation calculation is performed on the corresponding acoustic wave propagation process inside the vehicle component to generate the time-domain inversion propagation path of the 3D acoustic wave signal of the vehicle component.

[0067] In this embodiment of the invention, the acoustic propagation, refraction, and reflection laws of the 3D acoustic wave reflection signal inside the vehicle component, obtained from previous analysis, are used to simulate the corresponding acoustic wave propagation process within the vehicle component in the time domain. An inversion algorithm, such as the least squares method or a genetic algorithm, is employed to gradually adjust the model parameters to approximate the actual acoustic wave propagation path, based on the refraction and reflection laws of the acoustic wave inside the vehicle component. By simulating signal changes under different propagation conditions, the time domain inversion propagation path of the 3D acoustic wave signal of the vehicle component is generated. This path will provide an important reference for the optimization of vehicle acoustic characteristics and subsequent design, enabling designers to more effectively predict the behavior of acoustic waves in vehicle components. Finally, the time domain inversion propagation path of the 3D acoustic wave signal of the vehicle component is simulated and generated.

[0068] Furthermore, step S4 includes the following steps: Step S41: Perform feature normalization processing on the 3D acoustic reflection signal features inside the vehicle parts to obtain the 3D acoustic normalized signal features of the vehicle parts; In this embodiment of the invention, the 3D acoustic reflection signal features inside vehicle parts obtained by previous statistical analysis are normalized to eliminate interference caused by environmental noise or differences in sensor sensitivity. This process includes removing DC components and applying Z-score normalization to make the mean of all signal features zero and the variance one, thus obtaining the 3D acoustic normalized signal features of vehicle parts.

[0069] Step S42: Detect the corner points of the 3D acoustic standardized signal features of the vehicle parts to obtain the 3D acoustic signal feature corner point set of the vehicle parts; In this embodiment of the invention, during the detection of signal feature corner points, specific algorithms (such as Harris corner detection or Shi-Tomasi method) are used to process the standardized 3D acoustic signal features of vehicle parts. These algorithms can identify points with significant changes in the signal features. These points usually represent abrupt changes in the intensity of sound wave reflection. During the processing, a certain threshold is set to filter out noise signals below that value to ensure that only valid corner points are retained. After processing, a set of 3D acoustic signal feature corner points is formed, and finally, the set of 3D acoustic signal feature corner points of vehicle parts is obtained.

[0070] Step S43: Based on the feature corner point set of the 3D acoustic signal of the vehicle parts, perform spatial image reconstruction transformation calculation on the corresponding vehicle parts to generate a 3D acoustic reconstruction spatial model of the vehicle parts. In this embodiment of the invention, the corresponding vehicle parts are reconstructed and transformed in space by combining the previously detected 3D acoustic signal feature corner point set of vehicle parts. The 2D signal is mapped into 3D space through computational geometric transformation model (such as perspective projection), and a spatial model of the vehicle parts is established using the corner point positions in the three-dimensional coordinate system. Interpolation algorithms (such as Kriging interpolation or cubic spline interpolation) are applied to generate smooth surfaces between the corner points to form a complete 3D acoustic reconstruction spatial model. This model effectively reflects the distribution characteristics of acoustic signals in three-dimensional space, and finally reconstructs and generates a 3D acoustic reconstruction spatial model of vehicle parts.

[0071] Step S44: Divide the 3D acoustic reconstruction spatial model of vehicle parts into 3D meshes to obtain 3D acoustic reconstruction mesh sub-models of each vehicle part; perform acoustic reflection and feature iteration calculations on each 3D acoustic reconstruction mesh sub-model of each vehicle part to obtain the 3D acoustic reflection intensity and feature value corresponding to each vehicle part mesh sub-model. In this embodiment of the invention, the previously reconstructed 3D acoustic reconstruction spatial model of vehicle components is divided into 3D meshes using octree decomposition or Delaunay triangulation techniques. Each sub-mesh represents a local region of the vehicle component, facilitating detailed acoustic analysis and resulting in 3D acoustic reconstruction mesh sub-models for each vehicle component. Simultaneously, for each mesh sub-model, acoustic reflection and feature iteration calculations are performed using acoustic simulation software (such as COMSOL or ANSYS) to obtain the corresponding 3D acoustic reflection intensity and eigenvalues. This process, by setting appropriate boundary conditions and acoustic parameters (such as material density and sound velocity), accurately simulates the propagation characteristics of sound waves in each sub-model, thereby generating detailed acoustic reflection characteristic parameters and ultimately obtaining the 3D acoustic reflection intensity and eigenvalues ​​corresponding to each vehicle component mesh sub-model.

[0072] Step S45: Based on the 3D acoustic reflection intensity and eigenvalues ​​of each vehicle component mesh sub-model, perform 3D rendering and imaging processing on the corresponding vehicle component 3D acoustic reconstruction mesh sub-model to generate 3D acoustic imaging of the vehicle component.

[0073] In this embodiment of the invention, during the 3D rendering and imaging process, a visual image is generated by applying 3D rendering technology based on the acoustic reflection intensity and characteristic values ​​of each mesh sub-model. Advanced graphics processing software (such as OpenGL or Unity) is used to assign different colors and lighting effects to each mesh according to the acoustic characteristic values ​​to enhance the visual effect. In this rendering process, factors such as the position of the light source and material properties are considered to simulate the sound wave reflection in the real scene, so that designers can better understand the impact of acoustic characteristics on vehicle performance. Finally, 3D acoustic imaging of vehicle parts is generated.

[0074] Furthermore, step S43 includes the following steps: Step S431: Perform statistical analysis on the local extrema and rate of change of the feature corner point set of the 3D acoustic signal of the vehicle parts to obtain the local extrema and rate of change of the 3D acoustic signal of the vehicle parts. In this embodiment of the invention, statistical analysis of local extrema and rate of change of the feature corner point set of the 3D acoustic signal of vehicle parts obtained by previous detection is performed. Specifically, the derivative calculation method is used to identify local maxima and minima in the corner points of the acoustic signal and record the coordinate information of these extrema. At the same time, the rate of change between adjacent acoustic signals is calculated, and the acoustic signal is smoothed by the sliding window technique to reduce noise interference, forming a set of local extrema and rate of change of the 3D acoustic signal of vehicle parts. Finally, the local extrema and rate of change of the 3D acoustic signal of vehicle parts are obtained.

[0075] Step S432: Based on the local extrema and the rate of change of the 3D acoustic signal of the vehicle parts, perform corner smoothing optimization on the feature corner point set of the 3D acoustic signal of the vehicle parts to obtain the optimized corner point set of the 3D acoustic signal of the vehicle parts. In this embodiment of the invention, the acoustic signal feature corner point set is smoothed and optimized by combining the local extrema and the rate of change of the 3D acoustic signal of the vehicle parts obtained from previous statistical analysis. In specific operation, filters (such as Gaussian filtering or median filtering) are constructed using the signal local extrema and the rate of change to process the acoustic signal feature corner points, that is... To eliminate the influence of high-frequency noise, during this process, it is ensured that the smoothing of extreme points does not affect their position and recognition accuracy. After optimization, the set of acoustic signal feature corner points obtained by reconstruction is called the optimized corner point set. This set contains effective signal features after smoothing, which facilitates subsequent mapping and analysis of spatial position relationships, and finally obtains the optimized corner point set of 3D acoustic signals of vehicle parts.

[0076] Step S433: Optimize the corner point set of the 3D acoustic signals of vehicle parts and perform corner point spatial position relationship mapping to obtain the acoustic feature spatial position relationship corresponding to the corner points of the 3D acoustic signals of each vehicle part. In this embodiment of the invention, by optimizing the corner point set for the 3D acoustic signals of vehicle components, a three-dimensional spatial coordinate system is used to establish the spatial positional relationship of acoustic features. By calibrating the position of each corner point in the 3D coordinate system, geometric transformation methods (such as affine transformation or rigid body transformation) are used to map the spatial position of each corner point. Specifically, spatial relationship mapping technology in computer graphics is applied to calculate the relative positional relationship between each acoustic signal corner point, and finally the spatial positional relationship of acoustic features corresponding to the 3D acoustic signal corner points of each vehicle component is obtained.

[0077] Step S434: Based on the spatial positional relationship of the acoustic features corresponding to the corner points of the 3D acoustic signals of each vehicle component, perform 3D acoustic reconstruction transformation calculation on the corresponding vehicle components to generate a 3D acoustic reconstruction spatial model of the vehicle components.

[0078] In this embodiment of the invention, 3D acoustic reconstruction calculations are performed on the corresponding vehicle parts by combining the spatial positional relationships of the acoustic features corresponding to the corner points of the 3D acoustic signals generated by previous mapping. The data of the optimized corner point set is imported using 3D modeling software (such as Blender or SolidWorks). By establishing a mesh model, each acoustic corner point is transformed into a three-dimensional acoustic reconstruction space model. During this process, the material properties and acoustic parameters of the model are set according to the spatial position of the acoustic features to ensure that the generated model can truly reflect the acoustic characteristics of the vehicle parts. Acoustic simulation is performed using simulation software to verify the accuracy and effectiveness of the generated 3D acoustic reconstruction model, and finally, a 3D acoustic reconstruction space model of the vehicle parts is generated.

[0079] like Figure 2 As shown, in another embodiment of this application, a 3D array sensor layout and imaging system for acoustic imaging of vehicle parts is provided. The system includes a data acquisition module, an optimized sensor array generation module, an acoustic wave reflection monitoring and receiving module, an acoustic time-domain inversion signal processing module, and a 3D acoustic imaging reconstruction module. The data acquisition module is used to acquire the geometric features of vehicle parts and the acoustic wave propagation characteristics data corresponding to the 3D array sensor. The optimized sensor array generation module optimizes the spacing and tilt angle between each sensor in the 3D array sensor based on the geometric features of vehicle parts and the acoustic wave propagation characteristics data corresponding to the 3D array sensor, thereby generating a 3D layout optimized sensor array. The acoustic wave reflection monitoring and receiving module utilizes a 3D layout to optimize the transmission of ultrasonic signals of different frequencies to each sensor in the sensor array to the vehicle components, and uses the ultrasonic signals to perform acoustic wave reflection monitoring and receiving to obtain the propagation time of acoustic waves inside the vehicle components and the 3D acoustic wave reflection receiving signal of the vehicle components. The acoustic time-domain inversion signal processing module performs acoustic time-domain inversion signal processing on the vehicle parts based on the propagation time of sound waves inside the vehicle parts and the 3D sound wave reflection received signal of the vehicle parts, and obtains the 3D acoustic reflection signal characteristics inside the vehicle parts. The 3D acoustic imaging reconstruction module performs 3D acoustic imaging reconstruction calculations on the corresponding vehicle parts based on the characteristics of the 3D acoustic reflection signals inside the vehicle parts, and generates 3D acoustic images of the vehicle parts.

[0080] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure can be divided into different functional modules to complete all or part of the functions described above. This system is a method for the layout and imaging of acoustic imaging 3D array sensors for vehicle parts applied to the above embodiments.

[0081] In another embodiment of this application, a storage medium is also provided, storing a program that, when executed by a processor, implements a method for the layout and imaging of a 3D array sensor for acoustic imaging of vehicle components, specifically: Acquire data on the geometric features of vehicle components and the acoustic wave propagation characteristics corresponding to 3D array sensors; Based on the geometric features of vehicle components and the acoustic wave propagation characteristics data of the corresponding 3D array sensors, the spacing and tilt angle between each sensor in the 3D array sensor are optimized to generate a 3D layout optimized sensor array. By optimizing the 3D layout of each sensor in the sensor array, ultrasonic signals of different times and frequencies are emitted to vehicle components. The ultrasonic signals are then used to monitor and receive sound wave reflections, thereby obtaining the propagation time of sound waves inside the vehicle components and the 3D sound wave reflection reception signal of the vehicle components. Based on the propagation time of sound waves inside vehicle parts and the 3D sound wave reflection received signal of vehicle parts, acoustic time-domain inversion signal processing is performed on vehicle parts to obtain the 3D acoustic reflection signal characteristics inside vehicle parts. Based on the 3D acoustic reflection signal characteristics inside vehicle parts, 3D acoustic imaging reconstruction calculations are performed on the corresponding vehicle parts to generate 3D acoustic images of the vehicle parts.

[0082] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0083] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A 3D array sensor layout and imaging method for acoustic imaging of vehicle parts, characterized in that, Includes the following steps: Acquire data on the geometric features of vehicle components and the acoustic wave propagation characteristics corresponding to 3D array sensors; Based on the geometric features of vehicle components and the acoustic wave propagation characteristics data corresponding to the 3D array sensor, the spacing and tilt angle between the sensors within the 3D array sensor are optimized to generate a 3D layout-optimized sensor array, specifically: Based on the geometric features of vehicle parts, the tilt angles of each sensor in the 3D array sensor are initially set to generate different initial tilt angle ranges for different sensors. Based on the propagation attenuation and reflection characteristics of sound waves corresponding to 3D array sensors on vehicle parts, sound wave parameterization modeling is performed under different initial tilt angle range conditions to generate sensor tilt angle parameterization function model. The sensor tilt angle parameterization function model is as follows: ； In the formula, R(θ) is the sound wave propagation and reflection intensity of the 3D array sensor under the tilt angle condition θ, R0 is the sound wave propagation and reflection intensity of the 3D array sensor in the initial state, α is the attenuation coefficient of the sound wave propagation in the vehicle parts, and d is the sound wave propagation distance. By optimizing the 3D layout of each sensor in the sensor array, ultrasonic signals of different times and frequencies are emitted to vehicle components. The ultrasonic signals are then used to monitor and receive sound wave reflections, thereby obtaining the propagation time of sound waves inside the vehicle components and the 3D sound wave reflection reception signal of the vehicle components. Based on the propagation time of sound waves inside vehicle parts and the 3D sound wave reflection received signal of vehicle parts, acoustic time-domain inversion signal processing is performed on vehicle parts to obtain the 3D acoustic reflection signal characteristics inside vehicle parts. Based on the 3D acoustic reflection signal characteristics inside vehicle parts, 3D acoustic imaging reconstruction calculations are performed on the corresponding vehicle parts to generate 3D acoustic images of the vehicle parts.

2. The 3D array sensor layout and imaging method for acoustic imaging of vehicle parts according to claim 1, characterized in that, The acquisition of geometric features of vehicle components and acoustic wave propagation characteristic data corresponding to the 3D array sensor specifically includes: The appearance shape of vehicle parts is identified and analyzed to obtain the appearance shape and size of the vehicle parts; Based on the appearance shape and size of the vehicle parts, geometric scanning blueprints of the vehicle parts are formulated to generate geometric scanning blueprints of the vehicle parts. Based on the geometric scanning blueprints of vehicle parts, 3D scanning modeling of vehicle parts is performed to generate a geometric three-dimensional space model of the vehicle parts. Geometric feature statistical analysis is performed on the three-dimensional spatial model of vehicle parts to obtain the geometric features of the vehicle parts. Based on the acoustic characteristics test of vehicle parts using 3D array sensors, the acoustic wave propagation characteristic data corresponding to the 3D array sensors are obtained. The acoustic wave propagation characteristic data includes the propagation speed, propagation attenuation, and propagation reflection characteristics of sound waves on vehicle parts.

3. The layout and imaging method for acoustic imaging 3D array sensors for vehicle parts according to claim 1, characterized in that, The spacing between sensors within the 3D array sensor is optimized based on the geometric features of vehicle components and the acoustic wave propagation characteristics data corresponding to the 3D array sensor. Specifically: Based on the geometric features of vehicle components, the initial spacing between sensors in the 3D array sensor is set to obtain different initial sensor spacing layout conditions. Based on the propagation characteristics data of the sound wave in the vehicle parts corresponding to the 3D array sensor, the propagation speed and propagation attenuation of the sound wave are obtained under different sensor spacing conditions. The signal acquisition capability of the sensor under the corresponding initial spacing between each sensor in the 3D array sensor is evaluated based on the acoustic wave propagation efficiency of the 3D array sensor under different sensor spacing conditions. The acoustic wave propagation characteristics of the sensor under different layout spacing conditions are obtained. Based on the acoustic wave propagation characteristics of the sensors and the acoustic signal capture performance under different layout spacing conditions, the spacing between the sensors in the 3D array sensor is optimized iteratively to determine the optimal acoustic signal capture capability target corresponding to the sensor layout conditions. The particle swarm optimization algorithm is used to iteratively design the sensor spacing, gradually adjusting the spacing and layout between the sensors to achieve the optimal acoustic signal capture effect, generating a 3D array sensor spacing optimization design scheme, including the spacing between the sensors, the sensor installation position, and the signal capture configuration parameters.

4. The 3D array sensor layout and imaging method for acoustic imaging of vehicle parts according to claim 1, characterized in that, By using the parameterized function model of sensor tilt angle, the propagation attenuation and reflection characteristics of the sound wave corresponding to the 3D array sensor on vehicle parts are statistically analyzed to obtain the sound wave reflection intensity and sound wave attenuation rate of the 3D array sensor under different tilt angle conditions. Based on the acoustic wave reflection intensity and acoustic wave attenuation rate of the 3D array sensor under different tilt angles, the signal receiving capability of each sensor in the 3D array sensor is evaluated and analyzed, and the influence factor of the sensor acoustic wave propagation characteristics on the signal receiving capability under different tilt angles is obtained. The specific formula for calculating the signal reception capability impact factor is as follows: ； In the formula, I s (θ) is the signal receiving capability influence factor of the 3D array sensor under the tilt angle condition θ, Ω is the sensor signal propagation space region, x is the spatial position point corresponding to the sensor sound wave, x' is the receiving position point corresponding to the sensor sound wave, S(x,θ) is the sound wave reflection intensity at spatial position point x under the tilt angle condition θ, S0(x) is the ideal sound wave intensity at spatial position point x, A(x,θ) is the reception influence efficiency of reflection intensity at spatial position point x under the tilt angle condition θ, β is the reflection intensity influence weight factor, exp is the exponential function, ε(x, x') is the sound wave attenuation rate from spatial position point x to receiving position point x' on the sensor sound wave propagation path, and δ is the sound wave attenuation ratio coefficient. Based on the influence factors of signal reception capability under different tilt angle conditions according to the acoustic wave propagation characteristics of the sensor, the tilt angle of each sensor in the 3D array sensor is optimized. Specifically, the tilt angle of each sensor in the 3D array sensor is iteratively adjusted using the particle swarm optimization algorithm to maximize the signal reception capability. The initial tilt angle of each sensor and the corresponding signal reception capability influence factor are input, and the parameters are continuously adjusted by the particle swarm optimization algorithm until the final tilt angle and layout information of each sensor are found, thus generating the tilt angle optimization design scheme of the 3D array sensor.

5. The 3D array sensor layout and imaging method for acoustic imaging of vehicle parts according to claim 1, characterized in that, The method involves optimizing the layout of each sensor in the 3D sensor array to emit ultrasonic signals of different time frequencies to vehicle components, and using these ultrasonic signals for sound wave reflection monitoring and reception to obtain the sound wave propagation time inside the vehicle components and the 3D sound wave reflection reception signal of the vehicle components. Specifically: The signal frequency propagation characteristics of each sensor in the 3D layout optimization sensor array are analyzed to obtain the acoustic wave propagation characteristic data of each sensor under different signal frequencies. Based on the acoustic wave propagation characteristic data of each sensor at different signal frequencies, the signal transmission optimization design of each corresponding sensor in the 3D layout optimization sensor array is carried out. The genetic algorithm or particle swarm optimization algorithm is used to optimize the signal transmission timing and frequency combination by inputting the corresponding acoustic wave propagation characteristic data and the preset target imaging resolution parameters of each sensor. Based on the signal transmission timing and frequency combination of each sensor, the sensor array is optimized using 3D layout to transmit ultrasonic signals of different times and frequencies to vehicle components. The sensor array is then optimized using 3D layout to monitor and receive the ultrasonic signals of different times and frequencies, thereby obtaining the propagation time of sound waves inside the vehicle components and the 3D sound wave reflection and reception signals of the vehicle components.

6. The layout and imaging method for acoustic imaging 3D array sensors for vehicle components according to claim 1, characterized in that, The acoustic time-domain inversion signal processing of the vehicle components, based on the propagation time of sound waves inside the components and the 3D sound wave reflection received signals of the components, yields the 3D acoustic reflection signal characteristics inside the components. Specifically: Based on the propagation time of sound waves inside vehicle parts and the 3D sound wave reflection received signal of vehicle parts, acoustic time-domain inversion simulation analysis is performed on vehicle parts. The finite element method or time-domain finite difference method is used to generate the time-domain inversion propagation path of the 3D sound wave signal of vehicle parts. Based on the propagation path of the 3D acoustic wave signal time domain inversion of vehicle parts, the corresponding acoustic signal in the vehicle parts is modeled by time domain signal inversion to generate a mathematical model of the 3D acoustic wave signal time domain inversion of vehicle parts. Based on the time-domain inversion mathematical model of 3D acoustic wave signals of vehicle parts, the acoustic reflection signal characteristics of the corresponding acoustic signals inside the vehicle parts are analyzed to obtain the 3D acoustic reflection signal characteristics inside the vehicle parts.

7. The layout and imaging method for acoustic imaging 3D array sensors for vehicle parts according to claim 6, characterized in that, The acoustic time-domain inversion simulation analysis of vehicle components, based on the propagation time of sound waves inside the components and the 3D sound wave reflection received signals of the components, generates the time-domain inversion propagation path of the 3D sound wave signals of the components. Specifically: The propagation time difference of sound waves inside vehicle parts from transmission to reception is obtained, and Kalman filtering is used to optimize the propagation time interference of sound waves inside vehicle parts based on the propagation time difference, so as to obtain the actual propagation time of sound waves inside vehicle parts. A method combining fixed and dynamic durations was used to divide the actual propagation time of sound waves within vehicle components into time periods, resulting in different segments of sound wave propagation time for different vehicle components. Based on the different propagation time segments of sound waves of different vehicle parts, the 3D sound wave reflection and reception signals of vehicle parts are synchronously divided. The reflection signal features in each time segment are extracted by using short-time Fourier transform or wavelet transform, and then the 3D sound wave reflection and reception sub-signals of vehicle parts under different propagation time segments are obtained. Based on the 3D acoustic wave reflection receiving sub-signals corresponding to vehicle components at different propagation time periods, the acoustic time-domain propagation law of vehicle components is analyzed. The acoustic propagation refraction and reflection law of 3D acoustic wave reflection signals inside vehicle components is calculated by using the time-domain reflection method and acoustic propagation model. Based on the acoustic propagation, refraction, and reflection laws of 3D acoustic wave reflection signals inside vehicle components, an inversion algorithm is used to perform time-domain inversion simulation calculations of the corresponding acoustic wave propagation process inside vehicle components, generating the time-domain inversion propagation path of 3D acoustic wave signals in vehicle components.

8. The 3D array sensor layout and imaging method for acoustic imaging of vehicle parts according to claim 1, characterized in that, The process of performing 3D acoustic imaging reconstruction calculations on the corresponding vehicle components based on the internal 3D acoustic reflection signal characteristics of the vehicle components to generate 3D acoustic images of the vehicle components is as follows: The 3D acoustic reflection signal features inside vehicle parts are subjected to feature standardization processing to obtain the 3D acoustic standardized signal features of vehicle parts. Signal feature corner point detection is performed on the 3D acoustic standardized signal features of vehicle parts to obtain the 3D acoustic signal feature corner point set of vehicle parts; Based on the feature corner point set of 3D acoustic signals of vehicle parts, spatial image reconstruction and transformation calculations are performed on the corresponding vehicle parts, and interpolation algorithms are applied to generate 3D acoustic reconstruction spatial models of vehicle parts. The 3D acoustic reconstruction spatial model of vehicle parts is divided into 3D meshes using the octree decomposition method or the Delaunay triangulation method to obtain the 3D acoustic reconstruction mesh sub-model of each vehicle part; acoustic reflection and feature iteration calculations are performed on the 3D acoustic reconstruction mesh sub-model of each vehicle part to obtain the 3D acoustic reflection intensity and feature value corresponding to each vehicle part mesh sub-model. Based on the 3D acoustic reflection intensity and eigenvalues ​​corresponding to the mesh sub-models of each vehicle component, the corresponding 3D acoustic reconstruction mesh sub-models of the vehicle components are subjected to 3D rendering and imaging processing to generate 3D acoustic images of the vehicle components.

9. The layout and imaging method for acoustic imaging 3D array sensors for vehicle parts according to claim 8, characterized in that, The process involves performing spatial image reconstruction and transformation calculations on the corresponding vehicle parts based on the 3D acoustic signal feature corner point set of the vehicle parts, generating a 3D acoustic reconstruction spatial model of the vehicle parts. Specifically: The derivative calculation method is used to calculate the local extrema of the feature corner point set of the 3D acoustic signal of vehicle parts. At the same time, the rate of change between adjacent acoustic signals is calculated, and the sliding window technique is used to smooth the acoustic signal to obtain the local extrema and the rate of change of the 3D acoustic signal of vehicle parts. Based on the local extrema and rate of change of the 3D acoustic signals of vehicle components, a filter is constructed using the local extrema and rate of change of the characteristic corner points of the 3D acoustic signals of vehicle components to perform corner smoothing optimization processing. , thereby obtaining the optimized corner point set of 3D acoustic signals for vehicle components; The spatial position relationship of corner points in the 3D acoustic signal optimization corner point set of vehicle parts is mapped by the geometric transformation method, so as to obtain the spatial position relationship of acoustic features corresponding to the corner points of the 3D acoustic signal of each vehicle part. Based on the spatial positional relationship of the acoustic features corresponding to the corner points of the 3D acoustic signals of each vehicle component, 3D acoustic reconstruction conversion calculations are performed on the corresponding vehicle components. By establishing a mesh model, each acoustic corner point is transformed into a three-dimensional acoustic reconstruction space model, generating a 3D acoustic reconstruction space model of the vehicle components.

10. A 3D array sensor layout and imaging system for acoustic imaging of vehicle parts, characterized in that, The method for acoustic imaging 3D array sensor layout and imaging of vehicle parts according to any one of claims 1-9 includes a data acquisition module, an optimized sensor array generation module, an acoustic wave reflection monitoring and receiving module, an acoustic time-domain inversion signal processing module, and a 3D acoustic imaging reconstruction module. The data acquisition module is used to acquire the geometric features of vehicle parts and the acoustic wave propagation characteristics data corresponding to the 3D array sensor. The optimized sensor array generation module optimizes the spacing and tilt angle between each sensor in the 3D array sensor based on the geometric features of vehicle parts and the acoustic wave propagation characteristics data corresponding to the 3D array sensor, thereby generating a 3D layout optimized sensor array. The acoustic wave reflection monitoring and receiving module utilizes a 3D layout to optimize the transmission of ultrasonic signals of different frequencies to each sensor in the sensor array to the vehicle components, and uses the ultrasonic signals to perform acoustic wave reflection monitoring and receiving to obtain the propagation time of acoustic waves inside the vehicle components and the 3D acoustic wave reflection receiving signal of the vehicle components. The acoustic time-domain inversion signal processing module performs acoustic time-domain inversion signal processing on the vehicle parts based on the propagation time of sound waves inside the vehicle parts and the 3D sound wave reflection received signal of the vehicle parts, and obtains the 3D acoustic reflection signal characteristics inside the vehicle parts. The 3D acoustic imaging reconstruction module performs 3D acoustic imaging reconstruction calculations on the corresponding vehicle parts based on the characteristics of the 3D acoustic reflection signals inside the vehicle parts, and generates 3D acoustic images of the vehicle parts.

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