Impact point area positioning method, device, electronic device and storage medium

Abstract

The embodiments of the present application provide a method, device, electronic device and storage medium for locating an impact point area, and relate to the technical field of non-destructive flaw detection. The method includes: determining the initial data of at least one surface of the sample to be tested according to the first point cloud coordinate data of the sample to be tested within a preset time length, determining the data to be compared of at least one surface of the sample to be tested according to the second point cloud coordinate data of the sample to be tested within a preset time length, determining the surface to be tested in at least one surface according to the initial data of at least one surface and the data to be compared of at least one surface, dividing the surface to be tested into multiple sub-areas, determining the first sub-area from each sub-area according to the first point cloud coordinate data corresponding to each sub-area of ​​the surface to be tested and the second point cloud coordinate data corresponding to each sub-area of ​​the surface to be tested, extracting the point cloud coordinate data corresponding to the impact point in the first sub-area to locate the position of the impact point on the sample to be tested. The embodiments of the present application achieve high-efficiency and high-precision positioning of defects in the sample to be tested.

Description

Technical Field

[0001] This application relates to the field of nondestructive testing technology, specifically to a method, apparatus, electronic device, and storage medium for locating impact point areas. Background Technology

[0002] The purpose of impact point identification is to determine the critical nodes or bottlenecks in a system or process in order to better manage and optimize it. By identifying impact points, we can understand which factors have the greatest impact on the efficiency and stability of a system or process, and thus take corresponding measures to improve efficiency, reduce costs, and mitigate risks. Furthermore, analyzing impact points can help predict future trends and changes, assisting businesses and organizations in making better decisions and plans.

[0003] Traditional methods for locating impact points include multi-point measurement, electromagnetic methods, and acoustic methods. All of these methods suffer from low efficiency and low accuracy. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a method, device, electronic device and storage medium for locating the impact point area, so as to achieve high-efficiency and high-precision location of defects in the sample under test.

[0005] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0006] In a first aspect, embodiments of this application provide a method for locating an impact point region, the method comprising:

[0007] Based on the first point cloud coordinate data of the sample to be tested within a preset time period, determine the initial data of at least one face of the sample to be tested;

[0008] Based on the second point cloud coordinate data of the sample under test within the preset time period, the comparison data of at least one face of the sample under test is determined; wherein, the second point cloud coordinate data is the point cloud coordinate data of the sample under test obtained within the preset time period after obtaining the first point cloud coordinate data at a first time interval.

[0009] Based on the initial data of the at least one face and the comparison data of the at least one face, a face to be tested is determined from the at least one face; wherein the comparison data of the face to be tested does not match the initial data of the face to be tested;

[0010] The surface to be tested is divided into multiple sub-regions. Based on the first point cloud coordinate data corresponding to each sub-region of the surface to be tested and the second point cloud coordinate data corresponding to each sub-region of the surface to be tested, a first sub-region is determined from each of the sub-regions; wherein, the first sub-region is the sub-region where the impact point is located.

[0011] Extract the point cloud coordinate data corresponding to the impact point in the first sub-region to locate the position of the impact point on the sample to be tested.

[0012] This application provides a method for locating impact point regions. Based on the first point cloud coordinate data of the sample under test within a preset time period, initial data for at least one surface of the sample is determined. Then, based on the second point cloud coordinate data of the sample under test within the preset time period, comparison data for at least one surface of the sample is determined. Finally, based on the initial data and comparison data of the at least one surface, the surface to be tested within that surface is determined, dividing the surface into multiple sub-regions. Based on the first point cloud coordinate data and the second point cloud coordinate data corresponding to the multiple sub-regions of the surface to be tested, a first sub-region is determined from the multiple sub-regions. This first sub-region is the sub-region where the impact point is located. Finally, the point cloud coordinate data corresponding to the impact point in the first sub-region is extracted to locate the position of the impact point on the sample under test. This method can achieve high-efficiency and high-precision location of defects in the sample under test. Furthermore, it eliminates the need to install sensors on the surface of the object under test, making it more convenient to locate the impact point region and further improving the efficiency of impact point region location.

[0013] In an optional implementation, the step of determining the test surface among at least one surface based on the initial data of the at least one surface and the comparison data of the at least one surface includes:

[0014] The test surface is determined one by one from the at least one surface based on the initial data, the comparison data, and a preset first threshold; wherein the difference between the initial data and the comparison data of the test surface is greater than the preset first threshold. This design determines the test surface by analyzing the initial data, the comparison data, and the preset first threshold, that is, by analyzing each surface, the test surface where the vibration amplitude changes is found, making the region of the impact point determined based on the test surface more accurate.

[0015] In an optional implementation, the step of dividing the surface to be measured into multiple sub-regions includes:

[0016] The first point cloud coordinate data corresponding to the surface to be measured is divided into multiple first point cloud coordinate data subsets to obtain multiple sub-regions, and the multiple first point cloud coordinate data subsets correspond one-to-one with the multiple sub-regions.

[0017] In an optional implementation, the step of determining the first sub-region from each of the sub-regions based on the first point cloud coordinate data corresponding to each sub-region of the surface to be measured and the second point cloud coordinate data corresponding to each sub-region of the surface to be measured includes:

[0018] Based on the first point cloud coordinate data corresponding to each sub-region of the surface to be tested, a first feature value corresponding to each sub-region of the surface to be tested is determined; wherein, the first feature value corresponding to each sub-region is the mean of the first point cloud coordinate data corresponding to each sub-region.

[0019] Based on the second point cloud coordinate data corresponding to each sub-region of the surface to be measured, the second feature value corresponding to each sub-region of the surface to be measured is determined; wherein, the second feature value corresponding to each sub-region is the mean value of the second point cloud coordinate data corresponding to each sub-region.

[0020] The first sub-region is determined from each of the sub-regions based on the first feature value corresponding to each of the sub-regions and the second feature value corresponding to each of the sub-regions. In this embodiment, the region where the impact point is located, i.e., the first sub-region, is determined based on each sub-region in the surface to be tested. The first sub-region is determined more accurately using the first feature value and the second feature value corresponding to each sub-region.

[0021] In an optional implementation, the step of determining the first sub-region from each of the sub-regions based on the first feature value corresponding to each of the sub-regions and the second feature value corresponding to each of the sub-regions includes:

[0022] The first sub-region is determined from each of the sub-regions based on the first feature value corresponding to each sub-region, the second feature value corresponding to each sub-region, and a preset second threshold; wherein the difference between the first feature value and the second feature value of the first sub-region is greater than the preset second threshold. This embodiment of the application uses the first feature value corresponding to each sub-region, the second feature value corresponding to each sub-region, and the preset second threshold to make the determined first sub-region more accurate.

[0023] In an optional implementation, the step of determining the initial data of at least one face of the sample to be tested based on the first point cloud coordinate data of the sample to be tested within a preset time period includes:

[0024] The first point cloud coordinate data of the sample under test within the preset time period are superimposed to obtain the first superimposed data;

[0025] The first superimposed data is fitted using the least squares method to obtain initial data for at least one surface of the sample to be tested.

[0026] In an optional implementation, before the step of determining the initial data of at least one face of the sample under test based on the first point cloud coordinate data of the sample under test within a preset time period, the method further includes:

[0027] The point cloud data of the sample to be tested is collected within the preset time period to obtain the first point cloud coordinate data of the sample to be tested within the preset time period.

[0028] In an optional implementation, the step of acquiring point cloud data of the sample under test within the preset time period and obtaining the first point cloud coordinate data of the sample under test within the preset time period includes:

[0029] Collect point cloud data of the sample to be tested within the preset time period to obtain a first point cloud dataset; wherein, the first point cloud dataset includes the first point cloud data of the sample to be tested within the preset time period, and the first point cloud data includes the first point cloud coordinate data.

[0030] The first point cloud coordinate data of the sample under test within the preset time period is extracted from the first point cloud dataset. This embodiment of the application ensures that the quality of the obtained point cloud coordinate data is sufficiently supported by collecting point cloud data of the sample under test within the preset time period.

[0031] In an optional implementation, the step of determining the comparison data for at least one face of the sample under test based on the second point cloud coordinate data of the sample under test within the preset time period includes:

[0032] The second point cloud coordinate data of the sample under test within the preset time period are superimposed to obtain the second superimposed data;

[0033] The second superimposed data is fitted using the least squares method to obtain comparison data for at least one face of the test sample. In this embodiment, the comparison data for at least one face of the test sample obtained by fitting using the least squares method is more accurate.

[0034] In an optional implementation, before the step of determining the comparison data of at least one face of the sample under test based on the second point cloud coordinate data of the sample under test within the preset time period, the method further includes:

[0035] Within the preset time period, point cloud data of the sample to be tested is collected. After the first time interval, point cloud data of the sample to be tested is collected again within the preset time period to obtain the second point cloud coordinate data of the sample to be tested within the preset time period.

[0036] In an optional implementation, the step of acquiring point cloud data of the sample under test within the preset time period, and then, after an interval of the first time period, acquiring point cloud data of the sample under test again within the preset time period to obtain second point cloud coordinate data of the sample under test within the preset time period includes:

[0037] Within the preset time period, point cloud data of the sample to be tested is collected. After the first time period, point cloud data of the sample to be tested is collected again within the preset time period to obtain a second point cloud dataset. The second point cloud dataset includes the second point cloud data of the sample to be tested within the preset time period, and the second point cloud data includes the second point cloud coordinate data.

[0038] The second point cloud coordinate data of the sample under test within the preset time period is extracted from the second point cloud dataset. In this embodiment, by collecting point cloud data of the sample under test within the preset time period, and then, after a first time interval, collecting point cloud data of the sample under test again within the preset time period, point cloud coordinate data of the corresponding number of frames is obtained, ensuring that the quality of the point cloud coordinate data has sufficient data support.

[0039] Secondly, embodiments of this application provide an impact point area positioning device, including a first processing unit and a second processing unit;

[0040] The first processing unit is used to determine the initial data of at least one surface of the sample under test based on the first point cloud coordinate data of the sample under test within a preset time period.

[0041] The first processing unit is further configured to determine comparison data for at least one face of the sample under test based on the second point cloud coordinate data of the sample under test within the preset time period; wherein, the second point cloud coordinate data is the point cloud coordinate data of the sample under test obtained within the preset time period after obtaining the first point cloud coordinate data at a first time interval.

[0042] The second processing unit is configured to determine the surface to be tested from at least one surface based on the initial data of the at least one surface and the comparison data of the at least one surface; wherein the comparison data of the surface to be tested does not match the initial data of the surface to be tested.

[0043] The second processing unit is further configured to divide the surface to be tested into multiple sub-regions, and determine a first sub-region from the multiple sub-regions based on the first point cloud coordinate data corresponding to the multiple sub-regions of the surface to be tested and the second point cloud coordinate data corresponding to the multiple sub-regions of the surface to be tested; wherein, the first sub-region is the sub-region where the impact point is located;

[0044] The second processing unit is further configured to extract the point cloud coordinate data corresponding to the impact point in the first sub-region, so as to locate the position of the impact point on the sample to be tested.

[0045] The apparatus provided in the second aspect can execute the method described in any implementation of the first aspect, which will not be elaborated here. Optionally, the first processing unit and the second processing unit can be set separately, or they can be integrated into one module, i.e., a processing module, or they can be integrated into two or more modules. This application does not specifically limit the specific implementation of the first processing unit and the second processing unit.

[0046] Optionally, the apparatus provided in the second aspect may further include a storage unit storing a program or instructions. When the first processing unit and the second processing unit execute the program or instructions, the apparatus provided in the second aspect can perform any of the possible implementations of the first aspect.

[0047] The device provided in the second aspect may include a lidar, which is used to collect point cloud data of the sample to be tested within a preset time period. For example, the device provided in the second aspect may be a flaw detector or other detection equipment, or it may be a chip (system) or other component or assembly that can be set in a flaw detector or other detection equipment. This application does not limit this.

[0048] Furthermore, the technical effects of the device described in the second aspect can be referred to the technical effects of the method described in any implementation of the first aspect, and will not be repeated here.

[0049] Thirdly, embodiments of this application provide an electronic device, including a memory and a processor;

[0050] The memory is used to store computer programs;

[0051] The processor is used to execute the computer program to implement the impact point area localization method provided as described in the first aspect embodiment and / or in combination with possible implementations of the first aspect embodiment.

[0052] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon. When the computer program is executed (by a device or computer, etc.), it implements the impact point region localization method provided in the first aspect embodiment and / or in combination with possible implementations of the first aspect embodiment.

[0053] Fifthly, this application provides a computer program product comprising: a computer program (also referred to as code or instructions) that, when run, causes a computer to perform the method described in any possible implementation of the first aspect.

[0054] In a sixth aspect, this application provides a chip system comprising: a processor and a memory for storing one or more programs; when the one or more programs are executed by the processor, the method described in any possible implementation of the first aspect is implemented.

[0055] The beneficial effects of the embodiments of this application include, for example:

[0056] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0057] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 An exemplary structural block diagram of an electronic device provided in an embodiment of this application is shown;

[0059] Figure 2 A flowchart illustrating an impact point region localization method provided in an embodiment of this application is shown.

[0060] Figure 3 This is a second schematic flowchart of an impact point area localization method provided in an embodiment of this application;

[0061] Figure 4 The third schematic flowchart of an impact point area localization method provided in this application is shown;

[0062] Figure 5 The fourth schematic flowchart illustrates a method for locating an impact point region according to an embodiment of this application.

[0063] Figure 6 The fifth illustration shows a flowchart of an impact point area localization method provided in an embodiment of this application;

[0064] Figure 7 This paper illustrates a flowchart of an impact point region localization method provided in an embodiment of this application. Figure 6 ;

[0065] Figure 8 This paper illustrates a flowchart of an impact point region localization method provided in an embodiment of this application. Figure 7 ;

[0066] Figure 9This paper illustrates a flowchart of an impact point region localization method provided in an embodiment of this application. Figure 8 ;

[0067] Figure 10 This paper illustrates a flowchart of an impact point region localization method provided in an embodiment of this application. Figure 9 ;

[0068] Figure 11 This paper illustrates a flowchart of an impact point region localization method provided in an embodiment of this application. Figure 10 ;

[0069] Figure 12 An exemplary structural block diagram of an impact point area positioning device provided in an embodiment of this application is shown.

[0070] Icons: 110-Electronic device; 1101-Memory; 1102-Processor; 1103-Communication interface; 300-Impact point area positioning device; 301-First processing unit; 302-Second processing unit. Detailed Implementation

[0071] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can be arranged and designed in various different configurations.

[0072] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0073] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0074] It should be noted that, where there is no conflict, the features in the embodiments of this application can be combined with each other.

[0075] The purpose of impact point identification is to determine the critical nodes or bottlenecks in a system or process in order to better manage and optimize it. By identifying impact points, we can understand which factors have the greatest impact on the efficiency and stability of a system or process, and thus take corresponding measures to improve efficiency, reduce costs, and mitigate risks. Furthermore, analyzing impact points can help predict future trends and changes, assisting businesses and organizations in making better decisions and plans.

[0076] Traditional methods for locating impact points include multi-point measurement, electromagnetic methods, and acoustic methods. Multi-point measurement, in particular, involves determining a physical quantity or phenomenon by taking multiple measurements at different locations or time points. It is commonly used to study complex systems or processes to gain a more comprehensive understanding of their properties and behavior. Multi-point measurement requires selecting appropriate measurement points and times, as well as using suitable measuring tools and techniques. For certain physical quantities, such as temperature, pressure, and electric potential, sensors can be used for measurement; for other physical quantities, such as velocity, acceleration, and flow rate, different measurement methods are required.

[0077] Electromagnetic methods are a non-contact detection and positioning method that utilizes the propagation characteristics of electromagnetic waves in a medium. In shock wave positioning, electromagnetic methods can be used to determine the location of the impact point. Electromagnetic waves are fluctuations generated by alternating electric and magnetic fields and can propagate in a medium. During the propagation of a shock wave, electromagnetic waves are generated, and their propagation speed is generally faster than that of sound waves; therefore, electromagnetic methods can be used to locate the impact point. Specifically, the electromagnetic method involves placing electromagnetic sensors along the propagation path of the shock wave to measure the propagation time and intensity of the electromagnetic wave at different locations. Based on the speed and distance of electromagnetic wave propagation in the medium, the location of the impact point can be calculated.

[0078] The acoustic method is a non-contact detection and positioning method that utilizes the propagation characteristics of sound waves in a medium. In shock wave positioning, the acoustic method can be used to determine the location of the impact point. Sound waves are fluctuations generated by the vibration of molecules or particles in a medium and can propagate within the medium. Sound waves are also generated during the propagation of a shock wave, and since the propagation speed of sound waves is generally slower than that of electromagnetic waves, the acoustic method can be used to locate the impact point. Specifically, the acoustic method involves placing acoustic wave sensors along the propagation path of the shock wave to measure the propagation time and intensity of the sound wave at different locations. Based on the speed and distance of sound wave propagation in the medium, the location of the impact point can be calculated.

[0079] The above methods have the following drawbacks: First, most of these methods are based on threshold-based identification techniques. This method determines the presence of an impact point based on whether the signal amplitude or slope exceeds a certain threshold. While simple and easy to implement, it also suffers from a high false positive rate. Second, these methods require the installation of several contact sensors. This approach necessitates attaching the contact sensors to the surface of the object being measured, collecting data, and then performing calculations to locate the impact point. The calculation results can contain significant errors.

[0080] Furthermore, contact sensors also have the following drawbacks: First, limited accuracy: Since the sensor is attached to the surface of the object being measured, it can only measure the data at the location of the impact point, and cannot accurately determine the specific coordinates of the impact point, thus limiting accuracy. Second, susceptible to interference: Because the sensor is attached to the surface of the object being measured, it is more susceptible to environmental and external interference, such as temperature changes and electromagnetic interference, which can affect the accuracy and precision of the measurement. It also affects the surface of the object being measured: The sensor needs to be attached to the surface of the object being measured, which may have a certain impact on the surface, such as adhesive residue or wear, affecting the surface quality and lifespan of the object being measured. Third, only applicable to certain materials: The sensor is only applicable to certain materials. For certain materials, such as liquids and gases, the sensor's effectiveness is limited. Fourth, unable to achieve real-time monitoring: Since the sensor is attached to the surface of the object being measured, data processing and analysis are required, making real-time monitoring impossible and unsuitable for scenarios requiring real-time monitoring.

[0081] Based on this, embodiments of this application provide a method for locating the impact point region to solve the above problems.

[0082] Please see Figure 1 , Figure 1 An exemplary structural block diagram of an electronic device provided in an embodiment of this application is shown, such as... Figure 1 As shown, the electronic device 110 includes a memory 1101 and a processor 1102, and may also include a communication interface 1103. The memory 1101, processor 1102, and communication interface 1103 are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines.

[0083] The memory 1101 can be used to store software programs and modules. The processor 1102 executes various functional applications and data processing by executing the software programs and modules stored in the memory 1101. The communication interface 1103 can be used to communicate with other node devices for signaling or data.

[0084] The memory 1101 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0085] The processor 1102 can be an integrated circuit chip with signal processing capabilities. The processor 1102 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0086] It should be noted that the aforementioned electronic device 110 can be a flaw detector or other detection device.

[0087] The following description uses electronic device 110 as the execution subject to illustrate an impact point area localization method provided in this application. Please refer to [link to relevant documentation]. Figure 2 , Figure 2 A flowchart illustrating an impact point area localization method provided in an embodiment of this application is shown.

[0088] like Figure 2 As shown, the above-described impact point area localization method is applied to electronic device 110, which includes a lidar, or the electronic device and the lidar are communicatively connected. The above-described impact point area localization method may include the following steps:

[0089] S210, Based on the first point cloud coordinate data of the sample to be tested within a preset time period, determine the initial data of at least one surface of the sample to be tested.

[0090] For example, at least one surface of the sample under test may be one or more surfaces of the sample under test within the field of view of the lidar. At least one surface of the sample under test may be, but is not limited to, a plane or a curved surface, and this application does not limit it in this regard.

[0091] S220, based on the second point cloud coordinate data of the sample to be tested within a preset time period, determine the comparison data of at least one surface of the sample to be tested.

[0092] The second point cloud coordinate data is the point cloud coordinate data of the sample to be tested obtained within a preset time period after the first point cloud coordinate data is obtained.

[0093] Optionally, the first time period is preset.

[0094] S230, based on the initial data of at least one face and the comparison data of at least one face, determine the face to be tested in at least one face.

[0095] In this case, the comparison data of the surface to be tested does not match the initial data of the surface to be tested.

[0096] S240, the surface to be measured is divided into multiple sub-regions, and the first sub-region is determined from each sub-region based on the first point cloud coordinate data corresponding to each sub-region of the surface to be measured and the second point cloud coordinate data corresponding to each sub-region of the surface to be measured.

[0097] The first sub-region is the sub-region where the impact point is located.

[0098] Optionally, the number of first sub-regions can be one or more.

[0099] S250: Extract the point cloud coordinate data corresponding to the impact point in the first sub-region to locate the sub-region where the impact point is located.

[0100] The above steps realize the process of determining the initial data of at least one face of the test sample based on the first point cloud coordinate data of the test sample within a preset time period, and determining the comparison data of at least one face of the test sample based on the second point cloud coordinate data of the test sample within a preset time period. Based on the initial data of at least one face and the comparison data of at least one face, the analysis is performed to finally determine the sub-region where the impact point is located and locate the sub-region where the impact point is located.

[0101] Specifically, step S210 is the process of determining the initial data of at least one face of the sample to be tested based on the first point cloud coordinate data of the sample to be tested within a preset time period.

[0102] In this embodiment, the aforementioned first point cloud coordinate data can be obtained by an electronic device collecting point cloud data of the sample under test within a preset time period. Specifically, the electronic device can collect the aforementioned point cloud data through a lidar connected to it. The electronic device may include a lidar, or the electronic device and the lidar may be separately configured; this embodiment does not limit this.

[0103] Furthermore, when setting up a lidar in an electronic device, the lidar needs to be placed in a suitable position so that it can detect the sample to be tested within its field of view. The lidar scans the sample to be tested to obtain the point cloud data of the sample. The electronic device will collect the point cloud data to obtain the first point cloud coordinate data of the sample to be tested within a preset time period.

[0104] Furthermore, the preset duration can be 10 seconds, that is, 10 seconds of point cloud data are collected. Since sufficient point cloud data is needed to ensure the quality of the point cloud data during subsequent data processing, the quality of the point cloud data is better when there are 100 frames of point cloud data. Based on the above setting, it is necessary to collect 10 seconds of point cloud data to ensure that 10 frames of point cloud data are collected per second, for a total of 100 frames of point cloud data are collected to obtain a point cloud dataset. This point cloud dataset includes the point cloud data of the sample to be tested within the preset duration. After that, the point cloud coordinate data of the sample to be tested within the preset duration can be extracted from the point cloud dataset (that is, collect the point cloud data of the sample to be tested within the preset duration to obtain the first point cloud coordinate data of the sample to be tested within the preset duration).

[0105] In this embodiment, after obtaining the first point cloud coordinate data, in step S210, the first point cloud coordinate data of the sample to be tested within a preset time period can be superimposed and fitted using the least squares method to finally obtain the initial data of at least one face of the sample to be tested. This initial data can be represented by an initial plane equation. After obtaining the initial data of at least one face, step S220 is then executed to determine the comparison data of at least one face of the sample to be tested based on the second point cloud coordinate data of the sample to be tested within the preset time period.

[0106] Specifically, step S220 is the process of continuing to obtain data for at least one face of the sample under test. This process can be initiated either by electronic devices or by collecting point cloud data of the sample under test within a preset time period. After a first time interval, point cloud data of the sample under test is collected again within the preset time period to obtain the second point cloud coordinate data of the sample under test within the preset time period. Similarly, the determination of the comparison data for at least one face of the sample under test in step S220 can be achieved by superimposing the second point cloud coordinate data of the sample under test within the preset time period and fitting it using the least squares method.

[0107] In this embodiment of the application, after determining the initial data of at least one face of the sample to be tested and the comparison data of at least one face of the sample to be tested, steps S230 and S240 are executed. Based on the initial data of at least one face and the comparison data of at least one face, the face to be tested in at least one face is determined, and the face to be tested is divided into multiple sub-regions. Based on the first point cloud coordinate data corresponding to each sub-region of the face to be tested and the second point cloud coordinate data corresponding to each sub-region of the face to be tested, the first sub-region is determined from each sub-region to determine the sub-region where the impact point is located.

[0108] Specifically, the test surface in at least one surface determined in step S230 can be one or more. If the comparison data corresponding to the test surface does not match its initial data, it indicates that there may be an impact point on the test surface. In order to further determine the region where the impact point is located, the test surface can be divided into multiple sub-regions. Based on the first point cloud coordinate data and the second point cloud coordinate data corresponding to each sub-region of the test surface, the first sub-region is determined from each sub-region. The first sub-region is the sub-region where the impact point is located. This process can make the final determined position of the impact point on the test sample more accurate.

[0109] Furthermore, the process of determining the first sub-region described above can be achieved by determining the first feature value corresponding to each sub-region of the surface to be measured, and determining the second feature value corresponding to each sub-region of the surface to be measured, and setting corresponding rules based on the first feature value, the second feature value, and a preset threshold. Moreover, each of the first feature values ​​can be the mean of the first point cloud coordinate data corresponding to each sub-region, and each of the second feature values ​​can be the mean of the second point cloud coordinate data corresponding to each sub-region. Each first feature value and each second feature value characterizes the vibration amplitude of the corresponding sub-region.

[0110] Furthermore, after determining the first and second characteristic values ​​corresponding to each sub-region, the first sub-region can be determined by setting corresponding rules. For example, it can be set that if the difference between the first and second characteristic values ​​of a certain sub-region is greater than a preset threshold, it indicates that the vibration amplitude represented by the characteristic value of the sub-region is large, indicating that the sub-region is the region where the impact point is located. Then the sub-region can be determined as the first sub-region, that is, the sub-region where the impact point is located.

[0111] In this embodiment, after determining the sub-region where the impact point is located (i.e., the first sub-region), step S250 is executed to extract the point cloud coordinate data corresponding to the impact point in the first sub-region, so as to locate the position of the impact point on the sample under test. The above process locates the area where the sample under test has defects (e.g., the sample under test is damaged), and can provide an alarm prompt based on the located area for subsequent processing to eliminate potential hazards in the sample under test.

[0112] This application provides a method for locating impact point regions. Based on the first point cloud coordinate data of the sample under test within a preset time period, initial data for at least one surface of the sample is determined. Then, based on the second point cloud coordinate data of the sample under test within the preset time period, comparison data for at least one surface of the sample is determined. Finally, based on the initial data and comparison data of the at least one surface, the surface to be tested within that surface is determined, dividing the surface into multiple sub-regions. Based on the first point cloud coordinate data and the second point cloud coordinate data corresponding to the multiple sub-regions of the surface to be tested, a first sub-region is determined from the multiple sub-regions. This first sub-region is the sub-region where the impact point is located. Finally, the point cloud coordinate data corresponding to the impact point in the first sub-region is extracted to locate the position of the impact point on the sample under test. This method can achieve high-efficiency and high-precision location of defects in the sample under test. Furthermore, it eliminates the need to install sensors on the surface of the object under test, making it more convenient to locate the impact point region and further improving the efficiency of impact point region location.

[0113] Optionally, the process of determining the test surface in at least one surface based on initial data of at least one surface and comparison data of at least one surface can be achieved through the following steps:

[0114] exist Figure 2 Based on this, please refer to Figure 3 , Figure 3 The second schematic flowchart of the impact point region localization method provided in this application embodiment is shown. Step S230, which involves determining the test surface of at least one surface based on initial data of at least one surface and comparison data of at least one surface, includes:

[0115] S231, determine the test surface of at least one surface one by one based on the initial data, comparison data and preset first threshold of each surface in at least one surface.

[0116] Among them, the difference between the initial data of the surface to be tested and the data to be compared is greater than the preset first threshold.

[0117] Optionally, the preset first threshold can be 0 or greater than 0 (allowed error).

[0118] The above steps realize the process of determining the test surface in at least one surface based on the initial data of at least one surface and the comparison data of at least one surface.

[0119] Taking a plane as an example, in this embodiment, the initial data of each surface can be the initial plane equation of each surface, and the comparison data of each surface can be the comparison plane equation of each surface. For example, if the obtained comparison plane equations are set as B1, B2, B3..., and the initial plane equation is A1, if it is found during the judgment process that B1-A1 is greater than a preset first threshold, then the surface whose difference between the initial data and the comparison data is greater than the preset first threshold can be determined as the surface to be tested. That is, the comparison data of the surface to be tested does not match the initial data of the surface to be tested. It should be noted that the above-mentioned first threshold can be freely configured according to the actual use scenario.

[0120] Alternatively, the process of dividing the surface to be measured into multiple sub-regions can be achieved through the following steps:

[0121] exist Figure 2 or Figure 3 Based on this, please refer to Figure 4 , Figure 4 This illustrates the third flowchart of an impact point region location method provided in an embodiment of this application. Step S240, which involves dividing the surface to be tested into multiple sub-regions, includes:

[0122] S241, the first point cloud coordinate data corresponding to the surface to be measured is divided into multiple first point cloud coordinate data subsets to obtain multiple sub-regions, and the multiple first point cloud coordinate data subsets correspond one-to-one with the multiple sub-regions.

[0123] The above steps realize the process of dividing the surface to be measured into multiple sub-regions.

[0124] Optionally, the specific process of determining the first sub-region from multiple sub-regions based on the first point cloud coordinate data corresponding to the multiple sub-regions of the surface to be measured and the second point cloud coordinate data corresponding to the multiple sub-regions of the surface to be measured can be achieved through the following steps:

[0125] exist Figure 2 or Figure 3 Based on this, please refer to Figure 5 , Figure 5 The fourth flowchart of the impact point region positioning method provided in this application embodiment is shown. Step S240, which involves determining the first sub-region from each sub-region based on the first point cloud coordinate data corresponding to each sub-region of the surface to be measured and the second point cloud coordinate data corresponding to each sub-region of the surface to be measured, includes:

[0126] S242, Based on the first point cloud coordinate data corresponding to each sub-region of the surface to be measured, determine the first feature value corresponding to each sub-region of the surface to be measured.

[0127] Among them, the first feature value corresponding to each sub-region is the mean of the first point cloud coordinate data corresponding to each sub-region.

[0128] S243, Based on the second point cloud coordinate data corresponding to each sub-region of the surface to be measured, determine the second feature value corresponding to each sub-region of the surface to be measured.

[0129] Among them, the second feature value corresponding to each sub-region is the mean of the second point cloud coordinate data corresponding to each sub-region.

[0130] S244, determine the first sub-region from each sub-region based on the first feature value and the second feature value corresponding to each sub-region.

[0131] The above steps realize the process of determining the first sub-region from each sub-region based on the first point cloud coordinate data corresponding to each sub-region of the surface to be measured and the second point cloud coordinate data corresponding to each sub-region of the surface to be measured.

[0132] Optionally, the process of determining the first sub-region from each sub-region based on the first feature value and the second feature value corresponding to each sub-region can be achieved through the following steps:

[0133] exist Figure 5 Based on this, please refer to Figure 6 , Figure 6 The fifth flowchart of the impact point region localization method provided in this application embodiment is shown. Step S244, which involves determining the first sub-region from each sub-region based on the first feature value and the second feature value corresponding to each sub-region, includes:

[0134] S2441, determine the first sub-region from each sub-region based on the first feature value corresponding to each sub-region, the second feature value corresponding to each sub-region, and the preset second threshold.

[0135] Among them, the difference between the first feature value and the second feature value of the first sub-region is greater than the preset second threshold.

[0136] Optionally, the preset second threshold can be 0 or greater than 0 (allowable error).

[0137] The above steps realize the process of determining the first sub-region from each sub-region based on the first feature value and the second feature value corresponding to each sub-region.

[0138] It should be noted that the first sub-region determined in step S2441 can be one or more, which will not be elaborated further in this embodiment.

[0139] Optionally, the specific process of determining the initial data of at least one surface of the sample to be tested based on the first point cloud coordinate data of the sample to be tested within a preset time period can be achieved through the following steps:

[0140] exist Figure 2 or Figure 3 Based on this, please refer to Figure 7 , Figure 7 This illustration shows a flowchart of an impact point region positioning method provided in an embodiment of this application. Step S210, which involves determining the initial data of at least one surface of the sample under test based on the first point cloud coordinate data of the sample under test within a preset time period, includes:

[0141] S211, superimpose the first point cloud coordinate data of the sample to be tested within a preset time period to obtain the first superimposed data.

[0142] S212, the first superimposed data is fitted by the least squares method to obtain the initial data of at least one surface of the sample to be tested.

[0143] The above steps realize the process of determining the initial data of at least one surface of the sample under test based on the first point cloud coordinate data of the sample under test within a preset time period.

[0144] Taking a plane as an example, in this embodiment of the application, the initial data of at least one surface can be obtained by superimposing the first point cloud coordinate data of the sample under test within a preset time period and fitting it using the least squares method, so as to finally obtain the initial plane equation of at least one surface.

[0145] Optionally, the first point cloud coordinate data in step S210 can be obtained by electronic equipment collecting point cloud data of the sample to be tested within a preset time period. Therefore, this process can be implemented through the following steps:

[0146] exist Figure 2 or Figure 3 Based on this, please refer to Figure 8 , Figure 8 This illustration shows a flowchart of an impact point region localization method according to an embodiment of this application. Before step S210, which determines the initial data of at least one surface of the sample to be tested based on the first point cloud coordinate data of the sample to be tested within a preset time period, the impact point region localization method further includes:

[0147] S190: Collect point cloud data of the sample to be tested within a preset time period to obtain the first point cloud coordinate data of the sample to be tested within the preset time period.

[0148] The above steps realize the process of collecting point cloud data of the sample to be tested within a preset time period to obtain the first point cloud coordinate data of the sample to be tested within the preset time period.

[0149] It should be noted that the above steps can be achieved by acquiring point cloud data of the sample under test within a preset time period using an electronic device, thereby obtaining the first point cloud coordinate data of the sample under test within that preset time period. Specifically, the electronic device can acquire the aforementioned point cloud data using a lidar connected to it. Therefore, this method replaces the traditional positioning method that uses contact sensors for impact point localization, eliminates the need to install sensors on the surface of the object being tested, and ensures that the lidar and the sample under test are separate and non-contact in position.

[0150] Optionally, the process of collecting point cloud data of the sample to be tested within a preset time period and obtaining the first point cloud coordinate data of the sample to be tested within the preset time period can be achieved through the following steps:

[0151] exist Figure 8 Based on this, please refer to Figure 9 , Figure 9 This illustrates the eighth step of a flowchart illustrating an impact point region localization method provided in this application. Step S190, which involves acquiring point cloud data of the sample to be tested within a preset time period and obtaining the first point cloud coordinate data of the sample to be tested within that preset time period, includes:

[0152] S191, Collect point cloud data of the sample to be tested within a preset time period to obtain the first point cloud dataset.

[0153] The first point cloud dataset includes the first point cloud data of the sample to be tested within a preset time period, and the first point cloud data includes the first point cloud coordinate data.

[0154] S192, extract the first point cloud coordinate data of the sample to be tested within a preset time period from the first point cloud dataset.

[0155] The above steps realize the specific process of collecting point cloud data of the sample to be tested within a preset time period and obtaining the first point cloud coordinate data of the sample to be tested within the preset time period.

[0156] It should be noted that the first point cloud data in this application embodiment includes not only the corresponding point cloud coordinate data, but also data such as color, reflectivity, and / or normal vector.

[0157] Optionally, the second point cloud coordinate data in step S220 can be obtained by collecting point cloud data of the sample under test within a preset time period using an electronic device, and then collecting point cloud data of the sample under test again within the preset time period after a first time interval. Therefore, this process can be implemented through the following steps:

[0158] exist Figure 9Based on this, please refer to Figure 10 , Figure 10 This document illustrates a flowchart of an impact point region localization method according to an embodiment of this application. Prior to step S220, which determines the comparison data for at least one surface of the sample under test based on the second point cloud coordinate data of the sample under test within a preset time period, the impact point region localization method further includes:

[0159] S200: After collecting point cloud data of the sample under test within a preset time period, and after an interval of the first time period, collect point cloud data of the sample under test again within the preset time period to obtain the second point cloud coordinate data of the sample under test within the preset time period.

[0160] The above steps realize the process of collecting point cloud data of the sample to be tested within a preset time period, and collecting point cloud data of the sample to be tested again within a preset time period after the first time interval, so as to obtain the second point cloud coordinate data of the sample to be tested within the preset time period.

[0161] Optionally, the specific process of acquiring point cloud data of the sample under test within a preset time period, and then acquiring point cloud data of the sample under test again within a preset time period after a first time interval, to obtain the second point cloud coordinate data of the sample under test within the preset time period, can be achieved through the following steps:

[0162] exist Figure 10 Based on this, please refer to Figure 11 , Figure 11 The tenth step of the flowchart of an impact point area localization method provided in this application embodiment is shown. Step S200 involves collecting point cloud data of the sample under test within a preset time period, and after a first time interval, collecting point cloud data of the sample under test again within the preset time period to obtain the second point cloud coordinate data of the sample under test within the preset time period. This step includes:

[0163] S201: Collect point cloud data of the sample to be tested within a preset time period. After a first time interval, collect point cloud data of the sample to be tested again within a preset time period to obtain a second point cloud dataset.

[0164] The second point cloud dataset includes the second point cloud data of the sample to be tested within a preset time period, and the second point cloud data includes the second point cloud coordinate data.

[0165] S202, extract the second point cloud coordinate data of the sample to be tested within a preset time period from the second point cloud dataset.

[0166] The above steps realize the process of collecting point cloud data of the sample to be tested within a preset time period, and collecting point cloud data of the sample to be tested again within a preset time period after the first time interval, so as to obtain the second point cloud coordinate data of the sample to be tested within the preset time period.

[0167] Based on the above method, the application of lidar in the field of non-destructive testing has changed the detection method, enabling non-contact acquisition and rapid location of impact points. Since no contact is required, the surface of the object being tested can be well protected from impact. Lidar obtains the position and shape information of a target object by emitting a laser beam and receiving its reflected signal. In impact point localization, lidar typically scans the surrounding environment at a very high frequency to quickly detect and locate potential impact points. Furthermore, lidar can also help robots or automated systems accurately locate and manipulate impact points by precisely measuring the distance and position of the target object.

[0168] Furthermore, the lidar used in this embodiment for impact point localization has the following advantages: First, high precision: lidar can accurately detect the distance and position of objects, achieving millimeter-level measurement accuracy. Second, high speed: lidar can achieve very high measurement speeds, enabling real-time measurement of fast-moving objects. Third, long range: lidar can detect objects at considerable distances, achieving a detection range of hundreds of meters. Fourth, high reliability: the detection results of lidar are unaffected by the environment and can be used in harsh weather conditions. Fifth, high safety: lidar will not cause harm to humans or the environment and can be used in densely populated and hazardous environments.

[0169] Furthermore, lidar has a large field of view (FOV), while traditional sensors can only measure the impact point at the sensor's location, failing to accurately determine the specific coordinates of the impact point. The lidar combined with the impact point area localization method provided in this application embodiment can comprehensively cover the detected area and accurately pinpoint the specific coordinates of the impact point.

[0170] It should be noted that the embodiments of this application can also be used in the following flaw detection scenarios:

[0171] Aerospace: This flaw detection technology is used in the aerospace industry to inspect aircraft, missiles, spacecraft, etc., to ensure the safety and reliability of the aircraft during use.

[0172] Automotive Industry: This flaw detection technology is used in the automotive industry to inspect the quality and damage of key components such as engines, transmissions, wheels, and frames, in order to improve vehicle performance and safety.

[0173] Petrochemical industry: This flaw detection technology is used in the petrochemical industry to detect damage and corrosion in equipment such as pipelines, storage tanks, and reactors to ensure the safety and stability of the production process.

[0174] Railway Industry: This flaw detection technology is used in the railway industry to detect damage and defects in key components such as railway tracks, wheels, and vehicles to ensure the safety and stability of railway transportation.

[0175] This application embodiment also provides an impact point area positioning device 300, including a first processing unit 301 and a second processing unit 302;

[0176] The first processing unit 301 is used to determine the initial data of at least one surface of the sample under test based on the first point cloud coordinate data of the sample under test within a preset time period.

[0177] The first processing unit 301 is further configured to determine comparison data for at least one face of the sample to be tested based on the second point cloud coordinate data of the sample to be tested within the preset time period; wherein, the second point cloud coordinate data is the point cloud coordinate data of the sample to be tested obtained within the preset time period after the first point cloud coordinate data is obtained at a first time interval.

[0178] The second processing unit 302 is configured to determine the surface to be tested in at least one surface based on the initial data of the at least one surface and the comparison data of the at least one surface; wherein the comparison data of the surface to be tested does not match the initial data of the surface to be tested.

[0179] The second processing unit 302 is further configured to divide the surface to be tested into multiple sub-regions, and determine a first sub-region from the multiple sub-regions based on the first point cloud coordinate data corresponding to the multiple sub-regions of the surface to be tested and the second point cloud coordinate data corresponding to the multiple sub-regions of the surface to be tested; wherein, the first sub-region is the sub-region where the impact point is located;

[0180] The second processing unit 302 is further configured to extract the point cloud coordinate data corresponding to the impact point in the first sub-region, so as to locate the position of the impact point on the sample to be tested.

[0181] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the impact point area positioning device 300 described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0182] Optionally, the first processing unit 301 and the second processing unit 302 can be set separately, or they can be integrated into one module, i.e., a processing module, or they can be integrated into two or more modules. This application does not specifically limit the specific implementation of the first processing unit 301 and the second processing unit 302.

[0183] Optionally, the impact point area positioning device 300 may also include a storage unit ( Figure 12(Not shown in the image), the storage unit stores a program or instructions. When the first processing unit and the second processing unit execute the program or instructions, the impact point area positioning device 300 can execute any possible implementation of the method embodiments of this application.

[0184] It should be noted that the impact point area positioning device 300 may include a lidar, which is used to collect point cloud data of the sample to be tested within a preset time period. For example, the impact point area positioning device may be a flaw detector or other detection equipment, or it may be a chip (system) or other component or assembly that can be set in a flaw detector or other detection equipment. This application does not limit this.

[0185] Furthermore, the technical effect of the impact point area positioning device 300 can be the same as the technical effect of the method shown in the above method embodiments, which will not be repeated here.

[0186] This application also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed (e.g., by a device or computer), it causes the device or computer to implement the impact point area localization method provided in the above embodiments.

[0187] This application also provides a computer program product, which includes a computer program (also referred to as code or instructions) that, when run, causes a computer to perform the method described in any possible implementation of the method embodiments of this application.

[0188] This application also provides a chip system, which includes a processor and a memory for storing one or more programs; when the processor executes the one or more programs, it implements the method described in any possible implementation of the method embodiments of this application.

[0189] The steps executed by the aforementioned computer program during runtime will not be described in detail here, but can be found in the explanation of the impact point area location method described above.

[0190] In the several embodiments provided in this application, it should be understood that the disclosed methods can also be implemented in other ways. The method embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0191] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0192] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0193] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for locating an impact point area, characterized in that: The method comprises: Superimposing the first point cloud coordinate data of the sample to be tested within a preset time length to obtain first superimposed data; Fitting the first superposition data by the least square method to obtain initial data of at least one surface of the sample to be tested; Superimposing the second point cloud coordinate data of the sample to be tested within the preset time length to obtain second superimposed data; wherein the second point cloud coordinate data is the point cloud coordinate data of the sample to be tested obtained within the preset time length after the first time period has elapsed from the first point cloud coordinate data; Fitting the second superposition data by the least square method to obtain the data to be compared of at least one surface of the sample to be tested; Determine a surface to be measured in the at least one surface according to the initial data of each surface in the at least one surface, the data to be compared and a preset first threshold value one by one; wherein the data to be compared of the surface to be measured does not match the initial data of the surface to be measured, and the difference between the initial data of the surface to be measured and the data to be compared is greater than the preset first threshold value; The surface to be measured is divided into a plurality of sub-areas, and according to the first point cloud coordinate data respectively corresponding to the plurality of sub-areas of the surface to be measured, the first eigenvalues ​​respectively corresponding to the sub-areas of the surface to be measured are determined; wherein the first eigenvalues ​​corresponding to the sub-areas are the average values ​​of the first point cloud coordinate data respectively corresponding to the sub-areas; Determine the second eigenvalues ​​corresponding to the sub-regions of the surface to be measured according to the second point cloud coordinate data corresponding to the multiple sub-regions of the surface to be measured; wherein the second eigenvalues ​​corresponding to the sub-regions are the average values ​​of the second point cloud coordinate data corresponding to the sub-regions; Determine a first sub-region from each of the sub-regions according to the first characteristic values ​​respectively corresponding to each of the sub-regions and the second characteristic values ​​respectively corresponding to each of the sub-regions; wherein the first sub-region is the sub-region where the impact point is located; The point cloud coordinate data corresponding to the impact point in the first sub-area is extracted to locate the position of the impact point on the sample to be tested.

2. The impact point area positioning method according to claim 1, characterized in that: The step of dividing the surface to be measured into a plurality of sub-areas comprises: The first point cloud coordinate data corresponding to the surface to be measured is divided into a plurality of first point cloud coordinate data subsets to obtain the plurality of sub-regions, and the plurality of first point cloud coordinate data subsets correspond to the plurality of sub-regions in a one-to-one manner.

3. The impact point area positioning method according to claim 1, characterized in that: The step of determining the first sub-region from each of the sub-regions according to the first characteristic values ​​respectively corresponding to each of the sub-regions and the second characteristic values ​​respectively corresponding to each of the sub-regions comprises: The first sub-region is determined from each of the sub-regions according to the first eigenvalues ​​corresponding to each of the sub-regions, the second eigenvalues ​​corresponding to each of the sub-regions, and a preset second threshold value; wherein a difference between the first eigenvalue and the second eigenvalue of the first sub-region is greater than the preset second threshold value.

4. The impact point area positioning method according to claim 1, characterized in that: Before the step of superimposing the first point cloud coordinate data of the sample to be tested within a preset time period to obtain first superimposed data, the method further includes: The point cloud data of the sample to be tested within the preset time length is collected to obtain the first point cloud coordinate data of the sample to be tested within the preset time length.

5. The impact point area positioning method according to claim 4, characterized in that: The step of collecting the point cloud data of the sample to be tested within the preset time length to obtain the first point cloud coordinate data of the sample to be tested within the preset time length includes: Collecting point cloud data of the sample to be tested within the preset time length to obtain a first point cloud data set; wherein the first point cloud data set includes first point cloud data of the sample to be tested within the preset time length, and the first point cloud data includes the first point cloud coordinate data; The first point cloud coordinate data of the sample to be tested within the preset time length is extracted from the first point cloud data set.

6. The impact point area positioning method according to claim 5, characterized in that: Before the step of superimposing the second point cloud coordinate data of the sample to be tested within the preset time length to obtain second superimposed data, the method further includes: After collecting the point cloud data of the sample to be tested within the preset time length, after the first time period, collecting the point cloud data of the sample to be tested within the preset time length again, and obtaining the second point cloud coordinate data of the sample to be tested within the preset time length.

7. The impact point area positioning method according to claim 6, characterized in that: The step of collecting the point cloud data of the sample to be tested within the preset time length, collecting the point cloud data of the sample to be tested within the preset time length again after the first time period, and obtaining the second point cloud coordinate data of the sample to be tested within the preset time length includes: After collecting the point cloud data of the sample to be tested within the preset time length, after the first time period, collecting the point cloud data of the sample to be tested within the preset time length again to obtain a second point cloud data set; wherein the second point cloud data set includes the second point cloud data of the sample to be tested within the preset time length, and the second point cloud data includes second point cloud coordinate data; The second point cloud coordinate data of the sample to be tested within the preset time length is extracted from the second point cloud data set.

8. An impact point area positioning device, characterized in that: comprising a first processing unit and a second processing unit; The first processing unit is used to superimpose the first point cloud coordinate data of the sample to be tested within a preset time length to obtain first superimposed data; The first processing unit is further used to fit the first superposition data by a least square method to obtain initial data of at least one surface of the sample to be tested; The first processing unit is further used to superimpose the second point cloud coordinate data of the sample to be tested within the preset time length to obtain second superimposed data; wherein the second point cloud coordinate data is the point cloud coordinate data of the sample to be tested obtained within the preset time length after the first time period is interval from obtaining the first point cloud coordinate data; The first processing unit is further used to fit the second superposition data by the least square method to obtain the data to be compared of at least one surface of the sample to be tested; The second processing unit is used to determine the surface to be measured in the at least one surface according to the initial data of each surface in the at least one surface, the data to be compared and the preset first threshold value one by one; wherein the data to be compared of the surface to be measured does not match the initial data of the surface to be measured, and the difference between the initial data of the surface to be measured and the data to be compared is greater than the preset first threshold value; The second processing unit is further used to divide the surface to be measured into multiple sub-areas, and determine the first eigenvalues ​​corresponding to each sub-area of ​​the surface to be measured according to the first point cloud coordinate data corresponding to the multiple sub-areas of the surface to be measured; wherein the first eigenvalue corresponding to each sub-area is the average value of the first point cloud coordinate data corresponding to each sub-area; The second processing unit is further used to determine the second eigenvalues ​​corresponding to the sub-regions of the surface to be measured according to the second point cloud coordinate data corresponding to the multiple sub-regions of the surface to be measured; wherein the second eigenvalues ​​corresponding to the sub-regions are the average values ​​of the second point cloud coordinate data corresponding to the sub-regions; The second processing unit is further configured to determine a first sub-region from each of the sub-regions according to the first characteristic values ​​respectively corresponding to each of the sub-regions and the second characteristic values ​​respectively corresponding to each of the sub-regions; wherein the first sub-region is the sub-region where the impact point is located; The second processing unit is further used to extract the point cloud coordinate data corresponding to the impact point in the first sub-area to locate the position of the impact point on the sample to be tested.

9. An electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is used to execute the computer program to implement the impact point area positioning method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the impact point area positioning method according to any one of claims 1 to 7 is implemented.

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