Flatness detection method and device, equipment and storage medium

Abstract

The invention provides a flatness detection method and device, equipment and a storage medium, and relates to the technical field of circuits. The method comprises the steps that a three-dimensional point cloud image of a bonding pad surface is generated according to three-dimensional point cloud data of the bonding pad surface of a wireless communication module to be detected, and the gray value of each pixel point in the three-dimensional point cloud image is used for representing the height of the corresponding position of each pixel point; according to the gray value of each pixel point in the three-dimensional point cloud image, obtaining target height values of reference points in a plurality of unit areas on the bonding pad surface of the wireless communication module to be detected; according to the target height values of the reference points in the plurality of unit areas and the coordinates of the reference points in the plurality of unit areas, constructing a sample point set of the bonding pad surface; and according to the sample point set, carrying out flatness detection on the bonding pad surface. According to the method, the accuracy of the detection result is improved while the detection efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of circuit design technology, and more specifically, to a flatness detection method, apparatus, device, and storage medium. Background Technology

[0002] In the manufacturing process of wireless communication modules, the printed circuit board (PCB) has exposed solder pads (pads) on its surface used for soldering. The flatness of these pads directly affects the reliability of soldering and the stability of signal transmission. Therefore, it is of great significance to accurately inspect the flatness of the solder pads before the wireless communication module is packaged and shipped from the factory.

[0003] Currently, the industry commonly uses 3D camera technology to scan pad surfaces to obtain 3D point cloud data, thereby enabling the detection of pad surface flatness. However, this detection method has significant limitations. On the one hand, the 3D point cloud data obtained by 3D camera scanning contains a massive amount of sample points, typically ranging from tens of thousands to hundreds of thousands or even more. Processing such large-scale data requires substantial computing resources and time, significantly reducing detection efficiency. On the other hand, in the massive point cloud data, there are inevitably some outliers and extreme points. These data points can interfere with the flatness calculation, severely affecting the accuracy of the measurement results and leading to significant errors in the detection results, making it difficult to meet the high-quality production requirements of wireless communication modules. Summary of the Invention

[0004] This application addresses the shortcomings of the prior art by providing a flatness detection method, apparatus, device, and storage medium to solve the problems existing in the prior art.

[0005] The technical solution adopted in the embodiments of this application is as follows: In a first aspect, embodiments of this application provide a method for flatness detection, including: Based on the three-dimensional point cloud data of the pad surface of the wireless communication module to be tested, a three-dimensional point cloud image of the pad surface is generated. The gray value of each pixel in the three-dimensional point cloud image is used to characterize the height of the corresponding position of each pixel. Based on the grayscale value of each pixel in the three-dimensional point cloud image, the target height value of reference points in multiple unit regions on the pad surface of the wireless communication module to be detected is obtained; Based on the target height values ​​of reference points in multiple unit regions and the coordinates of the reference points in multiple unit regions, a set of sample points for the pad surface is constructed. The flatness of the pad surface is tested based on the set of sample points.

[0006] In one embodiment, obtaining the target height value of a reference point in a multiple unit region on the pad surface of the wireless communication module to be detected based on the grayscale value of each pixel in the three-dimensional point cloud image includes: The pad surface is divided into unit regions to obtain multiple unit regions; Based on the grayscale value of each pixel in the three-dimensional point cloud image, obtain the height value of all pixels in multiple unit regions; Based on the height values ​​of all pixels within the multiple unit regions, the target height values ​​of reference points within each of the multiple unit regions are determined.

[0007] In one embodiment, before performing flatness detection on the pad surface based on the sample point set, the method further includes: From the multiple unit regions, a unit region corresponding to a plurality of target boundary regions on the pad surface is determined, wherein the area of ​​the plurality of target boundary regions is the same, and each target boundary region corresponds to at least two unit regions; Based on the target height value of the reference point in the unit area corresponding to each target boundary area, a reference plane for the pad surface is constructed. The step of performing flatness detection on the pad surface based on the sample point set includes: The flatness of the pad surface is detected based on the set of sample points and the reference plane.

[0008] In one embodiment, constructing the reference plane of the pad surface based on the target height value of the reference point in the unit region corresponding to each target boundary region includes: The height value of each target boundary region is determined based on the target height value of the reference point in the unit region corresponding to each target boundary region. Based on the height values ​​of each target boundary region, a reference plane for the pad surface is constructed.

[0009] In one embodiment, the step of performing flatness detection on the pad surface based on the sample point set and the reference plane includes: Based on the reference plane and the target height values ​​of reference points in multiple unit regions of the sample point set, determine the distance parameters between multiple unit regions of the pad surface and the reference plane in the vertical direction; The flatness of the pad surface is determined based on the distance parameters between the multiple unit regions and the reference plane.

[0010] In one embodiment, determining the flatness of the pad surface based on the distance parameters between the plurality of unit regions and the reference plane includes: The maximum and minimum distance parameters are determined from the distance parameters between the multiple unit regions and the reference plane; The flatness of the pad surface is determined based on the distance difference between the maximum distance parameter and the minimum distance parameter.

[0011] In one embodiment, constructing a reference plane for the pad surface based on the height values ​​of each of the target boundary regions includes: Based on the height values ​​of each target boundary region, the least squares method is used to perform plane fitting to obtain the reference plane of the pad surface.

[0012] Secondly, embodiments of this application provide a flatness detection device, comprising: The generation module is used to generate a three-dimensional point cloud image of the pad surface based on the three-dimensional point cloud data of the pad surface of the wireless communication module to be tested. The gray value of each pixel in the three-dimensional point cloud image is used to characterize the height of the corresponding position of each pixel. The acquisition module is used to acquire the target height value of reference points in multiple unit areas on the pad surface of the wireless communication module to be detected based on the grayscale value of each pixel in the three-dimensional point cloud image. A construction module is used to construct a set of sample points on the pad surface based on the target height values ​​of reference points in multiple unit regions and the coordinates of the reference points in multiple unit regions. The detection module is used to perform flatness detection on the pad surface based on the set of sample points.

[0013] Thirdly, embodiments of this application provide an electronic device, including: a processor, a storage medium, and a bus. The storage medium stores program instructions executable by the processor. When the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the program instructions to implement the flatness detection method described in any of the above embodiments.

[0014] Fourthly, embodiments of this application provide a readable storage medium storing program instructions, which, when executed by a processor, implement the flatness detection method described in any of the above embodiments.

[0015] The beneficial effects of this application are as follows: This application provides a flatness detection method that can convert three-dimensional point cloud data into three-dimensional point cloud images, and obtain the target height values ​​of reference points in multiple unit areas on the pad surface based on the grayscale values ​​of the three-dimensional point cloud images. Compared with the prior art that uses point cloud data to determine the flatness of the pad surface, this application only needs to use the data of reference points, which reduces the amount of calculation and improves the detection efficiency. In addition, using the data of reference points also filters out abnormal points and extreme points in the massive amount of data, ensuring the accuracy of the measurement results. Attached Figure Description

[0016] 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.

[0017] Figure 1 This is one of the flowcharts illustrating the flatness detection method provided in the embodiments of this application; Figure 2 One of the schematic diagrams of a three-dimensional point cloud image provided in an embodiment of this application; Figure 3 The second schematic flowchart of the flatness detection method provided in the embodiments of this application; Figure 4 The third schematic flowchart of the flatness detection method provided in the embodiments of this application; Figure 5 A second schematic diagram of a three-dimensional point cloud image provided in an embodiment of this application; Figure 6 The fourth schematic flowchart of the flatness detection method provided in the embodiments of this application; Figure 7 Fifth schematic flowchart of the flatness detection method provided in the embodiments of this application; Figure 8 A schematic diagram of the reference plane provided in the embodiments of this application; Figure 9 Sixth schematic flowchart of the flatness detection method provided in the embodiments of this application; Figure 10 This is a schematic diagram of the flatness detection device provided in the embodiments of this application; Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, 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 some embodiments of this application, but not all embodiments.

[0019] 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.

[0020] Furthermore, the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Additionally, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] It should be noted that, where there is no conflict, the features in the embodiments of this application can be combined with each other.

[0022] This application provides a flatness detection method, which can be generated by any electronic device with computing and processing capabilities. The electronic device can be, for example, a terminal-facing computer or a backend server. The following, in conjunction with the accompanying drawings, provides specific examples illustrating the flatness detection method provided in this application.

[0023] Figure 1 This is one of the flowcharts illustrating the flatness detection method provided in the embodiments of this application, such as... Figure 1 As shown, the method includes: S101. Generate a three-dimensional point cloud image of the pad surface based on the three-dimensional point cloud data of the pad surface of the wireless communication module to be tested.

[0024] First, to acquire the 3D point cloud data of the pad surface of the wireless communication module under test, high-precision 3D scanning equipment, such as laser scanners and structured light scanners, is typically used. These devices emit lasers or project structured light patterns onto the pad surface, and use sensors to receive the reflected light signals. Based on principles such as the time of flight and phase difference of light, they accurately calculate the 3D coordinates (X, Y, Z) of each point on the pad surface, thus obtaining a large amount of discrete 3D point data. These data are collected together to form the 3D point cloud data of the pad surface.

[0025] Raw 3D point cloud data often contains noise and redundancy, making it unsuitable for direct observation and analysis. Therefore, preprocessing is necessary. Preprocessing includes removing outliers, smoothing the point cloud data, and reducing the number of points. Removing outliers prevents erroneous data from interfering with subsequent analysis; smoothing the point cloud data eliminates fluctuations caused by scanning errors or minor surface unevenness; and reducing the number of points reduces the data volume without losing crucial information, thus improving subsequent processing efficiency.

[0026] After preprocessing, specialized 3D visualization software or programming libraries are used to convert the processed 3D point cloud data into intuitive 3D point cloud images. During image generation, the software or library determines the position of each point in virtual 3D space based on the coordinate information in the point cloud data. By setting appropriate parameters such as color, transparency, and rendering mode, the 3D structure of the solder pad surface is clearly presented. For example, to highlight the height differences on the solder pad surface, different colors can be assigned based on the Z-coordinate of the points; to facilitate observation from different angles, interactive functions such as rotation, scaling, and translation can be added. The final generated 3D point cloud image allows inspection personnel to intuitively view information such as the shape, flatness, and solder joint height of the solder pad surface, thus providing an important basis for judging whether the soldering quality is acceptable. The grayscale value of each pixel in the 3D point cloud image is used to represent the height of the corresponding position of each pixel.

[0027] S102. Based on the grayscale values ​​of each pixel in the three-dimensional point cloud image, obtain the target height values ​​of reference points in multiple unit areas on the pad surface of the wireless communication module to be tested.

[0028] To obtain the target height value of reference points in multiple unit areas on the pad surface, the pad surface first needs to be divided into regions. Figure 2 One of the schematic diagrams of the three-dimensional point cloud image provided in the embodiments of this application is shown below. Figure 2 As shown, a 3D point cloud image contains multiple unit regions. In each region, one or more representative points are selected as reference points. The selection of these reference points should reflect the height characteristics of the region. For example, the point at the center of the unit region or the point with obvious height changes can be selected.

[0029] Once the reference point is determined, the target height value of the reference point in multiple unit areas can be determined by reading the grayscale value of the pixel where the reference point is located.

[0030] S103. Based on the target height values ​​of reference points in multiple unit regions and the coordinates of reference points in multiple unit regions, construct a set of sample points for the pad surface.

[0031] After obtaining the target height values ​​and coordinates of reference points in multiple unit regions, constructing a sample point set for the pad surface is a crucial foundation for further data analysis and processing. The construction of the sample point set aims to integrate discrete, isolated reference point data into a structured dataset for subsequent comprehensive and in-depth analysis.

[0032] The first step in constructing a sample point set is to organize and standardize the data. Each reference point corresponds to a position in three-dimensional space, and its coordinate information (usually represented by X, Y, and Z coordinates in a Cartesian coordinate system) describes the point's planar position (X and Y coordinates) and height position (Z coordinate, i.e., the target height value) on the pad surface. We need to summarize these reference point data, which are scattered across various unit areas, according to certain rules. For example, we can use a table to record the X coordinate, Y coordinate, and target height value (Z coordinate) of each reference point, forming a data table containing information on all reference points.

[0033] S104. Based on the sample point set, perform flatness detection on the pad surface.

[0034] After obtaining the sample point set, the flatness of the pad surface can be detected based on the sample point set. The flatness of the pad surface is one of the key indicators for measuring the quality of the pad and plays a decisive role in the soldering quality.

[0035] When the flatness of the solder pads is poor, the solder will be unevenly distributed on the pads, resulting in soldering defects such as cold solder joints and poor soldering. This seriously affects the electrical connection performance and mechanical stability between electronic components and the solder pads, and consequently affects the reliability and lifespan of the entire electronic device. Therefore, in the field of electronic manufacturing, strictly controlling the flatness of the solder pads is a crucial step in ensuring product quality.

[0036] In summary, this embodiment provides a flatness detection method that can convert three-dimensional point cloud data into three-dimensional point cloud images and obtain the target height values ​​of reference points in multiple unit areas on the pad surface based on the grayscale values ​​of the three-dimensional point cloud images. Compared with the prior art that uses point cloud data to determine the flatness of the pad surface, this embodiment only needs to use the data of reference points, which reduces the amount of calculation and improves the detection efficiency. Furthermore, using the data of reference points also filters out abnormal points and extreme points in the massive amount of data, ensuring the accuracy of the measurement results.

[0037] Optionally, after determining the flatness of the solder pads, the obtained flatness data can be compared and analyzed with the pre-set error range standard in the design requirements. For example, if the design requirement is that the flatness error of the solder pads be controlled within ±0.05 mm, but the actual test results show that the error in a certain area reaches 0.1 mm, it means that the flatness of the solder pads does not meet the design requirements. In addition, it is also necessary to consider the specific requirements of the soldering process on the flatness of the solder pads. For example, different soldering methods such as wave soldering and reflow soldering have different effects on the flatness of the solder pads. A comprehensive judgment should be made on whether the flatness of the solder pads truly meets the dual requirements of product design and subsequent soldering processes, thereby ensuring the overall performance and reliability of electronic products.

[0038] If the flatness of the solder pads does not meet the design requirements, the pads can be re-pressed to achieve the desired flatness. The causes of insufficient flatness during production can also be analyzed to improve the manufacturing process. For example, if the flatness is due to a mismatch between the material's coefficient of thermal expansion and the circuit board substrate, subsequent production processes should select pad materials with a high coefficient of thermal expansion matching the substrate. For instance, on ceramic circuit boards, tungsten-copper alloy pads with similar coefficients of thermal expansion can be used to reduce the risk of deformation after soldering due to differences in material coefficients. Simultaneously, the purity of the pad material should be strictly controlled to prevent impurities from affecting material performance and causing unevenness on the pad surface.

[0039] In one embodiment, Figure 3 This is a second schematic flowchart of the flatness detection method provided in the embodiments of this application, as shown below. Figure 3 As shown, step S102, which involves obtaining the target height value of reference points in multiple unit regions on the pad surface of the wireless communication module to be detected based on the grayscale values ​​of each pixel in the three-dimensional point cloud image, includes: S201. Divide the pad surface into unit regions to obtain multiple unit regions.

[0040] Based on the actual testing requirements and the size of the pads, the entire pad surface is evenly divided into several unit areas of the same size, such as square or rectangular areas, according to a certain step size.

[0041] For example, assuming the pad surface is 1060×975 units in the image and the step size is 20 pixels, the sample points are sharply reduced from 1060×975 to (1060 / 20)*(975 / 20), which is approximately 2544. That is, the 3D point cloud image of the pad surface is divided into 2544 square unit regions.

[0042] S202. Based on the grayscale values ​​of each pixel in the 3D point cloud image, obtain the height values ​​of all pixels in multiple unit regions.

[0043] In each unit region, there are multiple other points besides the reference point, and it is necessary to obtain the height values ​​of all pixels within each unit region.

[0044] S203. Based on the height values ​​of all pixels in multiple unit regions, determine the target height values ​​of reference points in each of the multiple unit regions.

[0045] The next task is to determine the target height value of the reference point in each unit area. The method used here is to take the average height value of all pixels in each unit area, and replace the original height value of the reference point in each unit area with the calculated average value to obtain the target height value of the reference point.

[0046] Choosing to take the average value as the method for determining the target height has several advantages. From a statistical perspective, the average value can comprehensively reflect the central tendency of the height values ​​of all pixels within a unit area, effectively eliminating the influence of individual abnormal pixel height values ​​(such as extreme values ​​caused by noise interference or special local structures on the object's surface) on the results, making the obtained target height value more representative and stable. In practice, for each unit area, the height values ​​of all pixels within that area are added together and then divided by the total number of pixels to obtain the average height of that area.

[0047] For example, if a unit area contains M pixels with height values ​​h1, h2, ..., hM, then the average height of this area is H = (h1 + h2 + ... + hM) / M. Assigning this average value H to a pre-defined reference point within this unit area, replacing its original height value, yields the target height value of the reference point within that unit area. By performing the same operation on each unit area, the target height value of the reference point in each unit area can be determined. These target height values ​​will be used in subsequent applications such as pad flatness analysis, providing crucial data support for evaluating the flatness of the pad surface.

[0048] Figure 4 This is the third flowchart illustrating the flatness detection method provided in the embodiments of this application. Figure 4As shown, before S104 performs flatness detection on the pad surface based on the sample point set, the method of this application further includes: S301. Determine the unit regions corresponding to multiple target boundary regions on the pad surface from multiple unit regions.

[0049] like Figure 5 As shown, multiple target boundary regions on the pad surface are determined from multiple unit regions, wherein the area of ​​each target boundary region is the same, and each target boundary region corresponds to at least two unit regions.

[0050] S302. Construct a reference plane for the pad surface based on the target height value of the reference point in the unit area corresponding to each target boundary area.

[0051] After obtaining the unit regions corresponding to multiple target boundary regions, the reference plane of the pad surface can be constructed based on the target height values ​​of the reference points in the unit regions corresponding to each target boundary region. Specifically, as follows... Figure 6 As shown, it includes: S401. Determine the height value of each target boundary region based on the target height value of the reference point in the unit region corresponding to each target boundary region.

[0052] In the actual operation of pad inspection, in order to accurately determine the height value of each target boundary region, it is necessary to calculate the target height value based on the reference point in the unit region corresponding to the target boundary region. Each target boundary region consists of at least two unit regions, and within each unit region, representative reference points (such as the region center point or feature points determined by the centroid algorithm) are selected. The height values ​​of these reference points reflect the height of the region.

[0053] Common methods for determining the height of a target boundary region include the average method and the weighted average method. The average method involves adding the target height values ​​of all reference points within the target boundary region and then dividing by the number of reference points. The resulting average value is the height of the target boundary region. This method is simple and direct, and suitable for situations where each reference point has a similar degree of influence on the region's height. For example, if a target boundary region contains three unit regions, and one reference point is selected for each unit region, with height values ​​of 1.2mm, 1.3mm, and 1.1mm respectively, the height of the target boundary region calculated using the average method is (1.2 + 1.3 + 1.1) / 3 = 1.2mm.

[0054] The weighted average method takes into account that different reference points may have different weights influencing the height of the target boundary region. For example, reference points closer to the pad edge have a higher weight in assessing the flatness of the pad edge. Assuming that the two reference points closer to the edge have weights of 0.4 and the third has a weight of 0.2, the weighted average height of the target boundary region is calculated as 1.2 × 0.4 + 1.3 × 0.4 + 1.1 × 0.2 = 1.22 mm. Determining the height of each target boundary region in this way more accurately reflects the actual height of the region, providing a reliable data foundation for subsequent construction of the reference plane.

[0055] S402. Based on the height values ​​of each target boundary region, construct the reference plane for the pad surface.

[0056] Then, based on the height values ​​of each target boundary region, the least squares method is used for plane fitting to obtain the reference plane of the pad surface. The core idea of ​​the least squares method is to find the best function match for the data by minimizing the sum of squared errors.

[0057] When constructing the reference plane for the solder pad surface, each target boundary region is treated as a data point in three-dimensional space. The height value of each region corresponds to the Z coordinate, while its position coordinates on the solder pad surface correspond to the X and Y coordinates. Assuming there are n target boundary regions, each with coordinates (Xi, Yi, Zi) (i = 1, 2, n, where n is the number of reference points within the region), we need to find a plane equation Z = aX + bY + c such that the sum of the squares of the perpendicular distances from all points in the target boundary regions to this plane is S = i = Minimum.

[0058] By solving for the values ​​of parameters a, b, and c in S, the plane equation can be determined, and the reference plane of the pad surface can be constructed.

[0059] S303. Based on the sample point set and the reference plane, perform flatness detection on the pad surface.

[0060] After obtaining the sample point set and the reference plane, the flatness of the pad surface can be checked based on the sample point set and the reference plane, such as... Figure 7 As shown, it specifically includes: S501. Based on the target height values ​​of reference points in multiple unit regions of the reference plane and the sample point set, determine the distance parameters between multiple unit regions of the pad surface and the reference plane in the vertical direction.

[0061] like Figure 8As shown, first, a plane sectional view is determined according to the target height values of the reference points in multiple unit areas in the sample point set. In the vertical direction of the solder pad surface (which is also the vertical direction of the reference plane), each point on the plane sectional view represents the Z coordinate of the reference point of a unit area.

[0062] Then, in the vertical direction of the solder pad surface, the distance parameter between each point on the plane sectional view (i.e., multiple unit areas) and the reference plane is determined.

[0063] S502. Determine the flatness of the solder pad surface according to the distance parameters between multiple unit areas and the reference plane.

[0064] After obtaining the distance parameters between multiple unit areas and the reference plane, the flatness of the solder pad surface can be determined according to these distance parameters. Specifically, as Figure 9 shown, it can include: S601. Determine the maximum distance parameter and the minimum distance parameter from the distance parameters between multiple unit areas and the reference plane.

[0065] After obtaining the distance parameters between multiple unit areas and the reference plane, these parameters are analyzed and screened. The maximum distance parameter dmax represents the farthest deviation from the reference plane among all unit areas, and the minimum distance parameter dmin represents the minimum deviation.

[0066] By traversing all the distance parameter data, the initial maximum distance value can be set as a very small value (such as dmax = 0), and the minimum distance value can be set as a very large value (such as dmin = ∞). During the traversal process, each distance parameter dj is compared with the current dmax and dmin. If dj > dmax, then update dmax = dj; if dj < dmin, then update dmin = dj. In this way, the maximum distance parameter and the minimum distance parameter can be quickly and accurately determined from numerous distance parameters, and these two parameters are the key indicators for evaluating the flatness of the solder pad surface.

[0067] S602. Determine the flatness of the solder pad surface according to the distance difference between the maximum distance parameter and the minimum distance parameter.

[0068] The flatness of the solder pad surface can be measured by the distance difference Δd = dmax - dmin between the maximum distance parameter and the minimum distance parameter. The smaller the distance difference Δd, the smaller the height difference between each unit area on the solder pad surface relative to the reference plane, that is, the higher the flatness of the solder pad surface; on the contrary, the larger the distance difference, the more significant the height difference between different areas of the solder pad surface, and the poorer the flatness.

[0069] The following will continue to explain the apparatus, equipment, and storage medium for implementing the flatness detection method provided in any of the above embodiments of this application. The specific implementation process and the resulting technical effects are the same as those in the corresponding method embodiments. For the sake of brevity, the parts not mentioned in the following embodiments can be referred to the corresponding content in the method embodiments.

[0070] Figure 10 The diagram shown is a structural schematic of the flatness testing device provided in an embodiment of this application. Figure 10 As shown, the device includes: The generation module 10 is used to generate a three-dimensional point cloud image of the pad surface based on the three-dimensional point cloud data of the pad surface of the wireless communication module to be tested. The gray value of each pixel in the three-dimensional point cloud image is used to characterize the height of the corresponding position of each pixel.

[0071] The acquisition module 20 is used to acquire the target height value of reference points in multiple unit areas on the pad surface of the wireless communication module to be detected based on the grayscale value of each pixel in the three-dimensional point cloud image.

[0072] The construction module 30 is used to construct a set of sample points on the pad surface based on the target height values ​​of reference points in multiple unit regions and the coordinates of the reference points in multiple unit regions.

[0073] The detection module 40 is used to perform flatness detection on the pad surface based on the set of sample points.

[0074] Optionally, the acquisition module 20 is further configured to divide the pad surface into unit regions to obtain multiple unit regions; obtain the height values ​​of all pixels in the multiple unit regions based on the grayscale values ​​of each pixel in the three-dimensional point cloud image; and determine the target height values ​​of reference points in the multiple unit regions based on the height values ​​of all pixels in the multiple unit regions.

[0075] Optionally, the apparatus further includes a determining module for determining a unit region corresponding to a plurality of target boundary regions on the pad surface from the plurality of unit regions, wherein the area of ​​the plurality of target boundary regions is the same, and each target boundary region corresponds to at least two unit regions.

[0076] The construction module 30 is further configured to construct a reference plane for the pad surface based on the target height value of the reference point in the unit area corresponding to each target boundary area; the detection module 40 is further configured to perform flatness detection on the pad surface based on the sample point set and the reference plane.

[0077] Optionally, the construction module 30 is further configured to determine the height value of each target boundary region based on the target height value of the reference point in the unit region corresponding to each target boundary region; and to construct the reference plane of the pad surface based on the height value of each target boundary region.

[0078] Optionally, the detection module 40 is further configured to determine the distance parameters between the multiple unit regions and the reference plane in the vertical direction of the pad surface based on the reference plane and the target height values ​​of reference points in the multiple unit regions of the sample point set; and to determine the flatness of the pad surface based on the distance parameters between the multiple unit regions and the reference plane.

[0079] Optionally, the detection module 40 is further configured to determine a maximum distance parameter and a minimum distance parameter from the distance parameters between the plurality of unit regions and the reference plane; and to determine the flatness of the pad surface based on the distance difference between the maximum distance parameter and the minimum distance parameter.

[0080] Optionally, the construction module 30 is further configured to perform plane fitting using the least squares method based on the height values ​​of each of the target boundary regions to obtain the reference plane of the pad surface.

[0081] The above-described device is used to execute the method provided in the foregoing embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0082] These modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more microprocessors, or one or more Field Programmable Gate Arrays (FPGAs). Alternatively, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a system-on-a-chip (SOC).

[0083] Figure 11 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application, such as... Figure 11As shown, this application also provides an electronic device, including a processor 100, a storage medium 200 and a bus 300. The storage medium stores program instructions executable by the processor. When the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the program instructions to implement the flatness detection method described in any of the above embodiments.

[0084] This application also provides a readable storage medium storing program instructions, which, when executed by a processor, implement the flatness detection method described in any of the above embodiments.

[0085] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0086] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0087] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0088] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some 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.

[0089] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for detecting flatness, characterized in that, include: Based on the three-dimensional point cloud data of the pad surface of the wireless communication module to be tested, a three-dimensional point cloud image of the pad surface is generated. The gray value of each pixel in the three-dimensional point cloud image is used to characterize the height of the corresponding position of each pixel. Based on the grayscale value of each pixel in the three-dimensional point cloud image, the target height value of reference points in multiple unit regions on the pad surface of the wireless communication module to be detected is obtained; Based on the target height values ​​of reference points in multiple unit regions and the coordinates of the reference points in multiple unit regions, a set of sample points for the pad surface is constructed. The flatness of the pad surface is tested based on the set of sample points.

2. The method according to claim 1, characterized in that, The step of obtaining the target height value of reference points in multiple unit regions on the pad surface of the wireless communication module to be detected based on the grayscale value of each pixel in the three-dimensional point cloud image includes: The pad surface is divided into unit regions to obtain multiple unit regions; Based on the grayscale value of each pixel in the three-dimensional point cloud image, obtain the height value of all pixels in multiple unit regions; Based on the height values ​​of all pixels within the multiple unit regions, the target height values ​​of reference points within each of the multiple unit regions are determined.

3. The method according to claim 1, characterized in that, Before performing flatness detection on the pad surface based on the sample point set, the method further includes: From the multiple unit regions, a unit region corresponding to a plurality of target boundary regions on the pad surface is determined, wherein the area of ​​the plurality of target boundary regions is the same, and each target boundary region corresponds to at least two unit regions; Based on the target height value of the reference point in the unit area corresponding to each target boundary area, a reference plane for the pad surface is constructed. The step of performing flatness detection on the pad surface based on the sample point set includes: The flatness of the pad surface is detected based on the set of sample points and the reference plane.

4. The method according to claim 3, characterized in that, The step of constructing the reference plane for the pad surface based on the target height values ​​of reference points in the unit region corresponding to each target boundary region includes: The height value of each target boundary region is determined based on the target height value of the reference point in the unit region corresponding to each target boundary region. Based on the height values ​​of each target boundary region, a reference plane for the pad surface is constructed.

5. The method according to claim 3, characterized in that, The step of performing flatness detection on the pad surface based on the sample point set and the reference plane includes: Based on the reference plane and the target height values ​​of reference points in multiple unit regions of the sample point set, determine the distance parameters between multiple unit regions of the pad surface and the reference plane in the vertical direction; The flatness of the pad surface is determined based on the distance parameters between the multiple unit regions and the reference plane.

6. The method according to claim 5, characterized in that, Determining the flatness of the pad surface based on the distance parameters between the multiple unit regions and the reference plane includes: The maximum and minimum distance parameters are determined from the distance parameters between the multiple unit regions and the reference plane; The flatness of the pad surface is determined based on the distance difference between the maximum distance parameter and the minimum distance parameter.

7. The method according to claim 4, characterized in that, The step of constructing a reference plane for the pad surface based on the height values ​​of each target boundary region includes: Based on the height values ​​of each target boundary region, the least squares method is used to perform plane fitting to obtain the reference plane of the pad surface.

8. A flatness detection device, characterized in that, include: The generation module is used to generate a three-dimensional point cloud image of the pad surface based on the three-dimensional point cloud data of the pad surface of the wireless communication module to be tested. The gray value of each pixel in the three-dimensional point cloud image is used to characterize the height of the corresponding position of each pixel. The acquisition module is used to acquire the target height value of reference points in multiple unit areas on the pad surface of the wireless communication module to be detected based on the grayscale value of each pixel in the three-dimensional point cloud image. A construction module is used to construct a set of sample points on the pad surface based on the target height values ​​of reference points in multiple unit regions and the coordinates of the reference points in multiple unit regions. The detection module is used to perform flatness detection on the pad surface based on the set of sample points.

9. An electronic device, characterized in that, include: The device includes a processor, a storage medium, and a bus. The storage medium stores program instructions executable by the processor. When the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the program instructions to implement the flatness detection method according to any one of claims 1 to 7.

10. A readable storage medium, characterized in that, The readable storage medium stores program instructions, which, when executed by a processor, implement the flatness detection method according to any one of claims 1 to 7.

Images