Component light source adaptive search method, system, device, equipment and medium

Abstract

The present invention relates to the technical field of image light source calibration, and discloses a component light source adaptive search method, system, device, equipment, and medium. The method comprises: selecting a target area from a component coarse positioning image as a fine positioning image; calculating the difference in grayscale values ​​of the component coarse positioning image under adjacent light source brightness levels and the difference in grayscale values ​​of the fine positioning image under adjacent light source brightness levels; obtaining, based on the difference values, the light source brightness level L corresponding to the grayscale value with the largest change in the component coarse positioning image and the light source brightness level S corresponding to the grayscale value with the largest change in the fine positioning image; comparing the difference between L and S, and if the difference range is (1, 3], taking the mean of L and S as the light source adaptive search result; otherwise, taking the larger value of L and S as the light source adaptive search result. This solution realizes automatic adjustment of light source brightness, improves production efficiency, and the adjustment result has high accuracy and reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of image light source calibration, and in particular to a component light source adaptive search method, system, device, equipment and medium. Background Art

[0002] In the surface mount technology (SMT) production process, placement equipment must capture clear images of electronic components for precise positioning, search, and identification. This is a critical prerequisite for ensuring subsequent accurate placement operations. However, due to significant differences in the physical properties of different electronic components (such as size, shape, surface material, pin structure, and reflective properties), obtaining clear images that meet the requirements of image processing algorithms typically requires a specific light distribution scheme for each component type.

[0003] Currently, the configuration of light source distribution in placement equipment relies primarily on manual adjustment based on operator experience. Operators must repeatedly try different combinations of light source parameters based on the specific characteristics of the component until a relatively clear image is observed in the equipment's imaging system.

[0004] This method of manually debugging the light source distribution has the following significant disadvantages: 1. Inefficiency, time-consuming and labor-intensive: For a wide variety of electronic components, manual light source debugging one by one requires a lot of production preparation time and human resources, reducing equipment utilization and production line efficiency. 2. Unstable results and reliance on subjective experience: The light source configuration effect is highly dependent on the individual experience and judgment criteria of the operator. Even for components of the same model, the light source distribution schemes debugged by different operators on the same device may be significantly different. This subjectivity leads to a lack of consistency and repeatability in the light source configuration results. 3. Impact on the reliability of subsequent links: The quality of the light source distribution scheme directly determines the image quality, which in turn affects the accuracy and reliability of the component search and recognition algorithm. The instability of manual debugging results brings potential risks to the subsequent placement accuracy and yield rate.

[0005] Therefore, the existing technology of relying on manual experience to adjust the light source distribution has become one of the bottleneck problems restricting the improvement of SMT placement equipment efficiency, the improvement of automation level and the guarantee of process stability. Summary of the Invention

[0006] The purpose of the present invention is to overcome the deficiencies of the prior art. To achieve the above purpose, a component light source adaptive search method, system, device, equipment and medium are used.

[0007] A first aspect of the present invention provides a method for adaptively searching for a component light source, characterized by comprising the following steps:

[0008] Filter out the target area from the component coarse positioning image as the fine positioning image;

[0009] respectively calculating the difference in grayscale values ​​of the component coarse positioning image under adjacent light source brightness levels and the difference in grayscale values ​​of the fine positioning image under adjacent light source brightness levels;

[0010] According to the difference value, respectively, the light source brightness level L corresponding to the gray value with the largest change in the rough positioning image of the component and the light source brightness level S corresponding to the gray value with the largest change in the fine positioning image are obtained;

[0011] Compare the difference between L and S. If the difference range is (1, 3], take the average of L and S as the light source adaptive search result; otherwise, take the larger value of L and S as the light source adaptive search result.

[0012] A second aspect of the present invention provides a device for adaptively searching for a component light source, comprising:

[0013] The component fine positioning module is configured to use a Blob algorithm to filter out a target area from the component coarse positioning image as a fine positioning image;

[0014] an image grayscale value calculation module configured to calculate, using an MS-SSIM algorithm, a difference value of grayscale values ​​of the component coarse positioning image at adjacent light source brightness levels and a difference value of grayscale values ​​of the fine positioning image at adjacent light source brightness levels, where the light source brightness level is the sum of the main axis light source brightness level and the coaxial light source brightness level;

[0015] Light source brightness level search module, configured to obtain the light source brightness level L corresponding to the gray value with the largest change in the rough positioning image of the component and the light source brightness level S corresponding to the gray value with the largest change in the fine positioning image, respectively, according to the difference value;

[0016] The result output module is configured to compare the difference between L and S. If the difference interval is (1, 3], the average of L and S is taken as the light source adaptive search result; otherwise, the larger value of L and S is taken as the light source adaptive search result.

[0017] A third aspect of the present invention provides a component light source adaptive search system, comprising:

[0018] Cameras, including bottom mirror cameras and flying cameras, are used to obtain component images;

[0019] The controller is configured to execute the above-mentioned component light source adaptive search method.

[0020] A fourth aspect of the present invention provides an electronic device, comprising:

[0021] one or more processors; and

[0022] The memory is used to store one or more programs, and when the one or more programs are executed by the one or more processors, the electronic device implements the above method.

[0023] A fifth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the above method when the program is executed by a processor.

[0024] Compared with the prior art, the present invention has the following technical effects:

[0025] By employing this technical solution, the brightness levels of the coarse and fine component positioning images are determined based on the grayscale variation characteristics of the images at adjacent light source brightness levels. The brightness level is then optimized based on the difference between the two. This effectively eliminates the effects of factors such as nozzle material, reflective material of the component itself, and the overall machine structure, improving the accuracy of the light source brightness level. This solution automatically adjusts the light source brightness, improving production efficiency and achieving highly accurate and reliable adjustment results. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings:

[0027] Figure 1 This is a flowchart of the component light source adaptive search disclosed in this application;

[0028] Figure 2 This is a schematic diagram of the multi-search box search process disclosed in this application;

[0029] Figure 3 A schematic diagram of the multi-search box disclosed in this application;

[0030] Figure 4 This is a comparison chart of the search results for CHIP components and light sources in the embodiment of this application;

[0031] Figure 5 This is a comparison chart of the search results for BGA components and light sources in the embodiment of this application;

[0032] Figure 6 This is a comparison chart of search results for special-shaped components and light sources in the embodiment of the present application. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0034] A first aspect of the present invention provides a method for adaptively searching for a component light source. Figure 1 As shown, the following steps are included:

[0035] S1, screening the target area from the component coarse positioning image as the fine positioning image;

[0036] Embodiments of the present invention preferably use a Blob algorithm to filter target areas from a coarse component positioning image as a fine positioning image. The Blob algorithm extracts feature regions, removes smaller regions and discrete points, and preserves other feature regions. The Blob algorithm's binary threshold determination scheme effectively optimizes light source brightness parameters, resulting in more accurate component recognition results.

[0037] The main axis light source brightness level and the coaxial light source brightness level each include 10 brightness levels, and preferably use the same number of levels. Components include BGA components, chip components, multi-pin components, and special-shaped components. When the component is a BGA component, the light source brightness level refers to the photometric light source brightness level.

[0038] This embodiment of the present invention uses a main light brightness level of 5 and a coaxial light brightness level of 5 to capture a rough positioning image, from which the component can be precisely located. Various combinations can also be used, such as 3 main lights + 4 coaxial lights; 4 main lights + 3 coaxial lights; 3 main lights + 5 coaxial lights; and 5 main lights + 3 coaxial lights. For example, the combinations of 3 main lights + 5 coaxial lights, 5 main lights + 3 coaxial lights, and 4 main lights + 4 coaxial lights are considered equivalent. The technical solution of the present invention allows brightness level searches and light source adjustment strategies to be performed on different image types.

[0039] Furthermore, the component rough positioning image is positioned by a multi-search box search algorithm. The multi-search box is a plurality of search boxes of different sizes selected according to the component type and size. The positioning process is as follows:

[0040] After filtering out the rough position of the component from the component image based on the actual component, Figure 2 As shown, with the rough position as the center, the search boxes are used to search in ascending order until the component target area is all within the search box, and the search is stopped to obtain the component rough positioning image.

[0041] For example, Figure 3 As shown, four search boxes are set, with the smallest search box serving as the first box for the search. If all components are within the search box, the component's rough position information, i.e., the component's coarse positioning image, is output. Otherwise, the second box is searched, and so on. The SMT placement machine in this embodiment of the present invention primarily uses floor mirror cameras and flying cameras (floor mirror cameras are line scan cameras, and flying cameras are area scan cameras). Image acquisition requires selecting the appropriate camera and lighting conditions based on component size and the specified placement requirements. The multiple search boxes are manually preset based on the expected size of the target component and the common size range of similar components. Specifically, the size screening rules for the search boxes are determined based on the component's inherent dimensional characteristics and the spacing parameters between multiple positioning axes, thereby creating multiple candidate search areas of varying sizes centered around the coarse position. Specifically, floor mirror cameras can search the entire image without specific size restrictions, so the maximum search box size can be the entire image. However, flying cameras, due to physical structural limitations, have limited effective imaging areas. To ensure recognition reliability, the maximum search box size must not exceed half the image width. This restriction is intended to avoid interference issues caused by overlapping image areas when oversized components exist on both the left and right sides of the image.

[0042] Using multiple search boxes can improve overall image processing efficiency. Testing has shown that using multiple search boxes reduces search time by up to 58% compared to searching the entire image. While processing speeds vary for different image types, using multiple search boxes for initial processing can significantly improve the time required for the same image.

[0043] S2. Calculate the difference in grayscale values ​​of the component coarse positioning image and the difference in grayscale values ​​of the fine positioning image at adjacent light source brightness levels, respectively. The light source brightness level is the sum of the main axis light source brightness level and the coaxial light source brightness level.

[0044] In an embodiment of the present invention, the MS-SSIM algorithm (the SSIM method or the MSE (Mean Squared Error) method may also be used, and MS-SSIM is an improved method of SSIM) is used to calculate the difference in grayscale values ​​of the coarse positioning image of the component under adjacent light source brightness levels and the difference in grayscale values ​​of the fine positioning image under adjacent light source brightness levels, respectively. The calculation formula is as follows:

[0045] , where x and y represent two image data at adjacent light source brightness levels, 1 (x,y) is the brightness comparison of the two images, c(x,y) is the contrast comparison of the two images (the degree of brightness change of the image, that is, the standard deviation of the pixels), s (x,y) is the structure comparison of two images, α M >0, β j >0,γ j >0 respectively means 1 (x,y), c (x,y) and s The weights of the three metrics (x, y). M represents the division of the entire image into M parts, and j represents the sequence number of each part. In each part, the brightness, contrast, and structure of image x and image y are compared sequentially, simulating the human eye's careful discrimination of different parts.

[0046] The image calculation method corresponding to SSIM on the image is as follows:

[0047]

[0048] µ x , µ y : the mean (brightness) of the image in the local window,

[0049] σ x , σ y : Standard deviation (contrast) of the image within the local window,

[0050] σ xy : covariance of two images (structural similarity),

[0051] c 1 , c 2 : A constant that prevents the denominator from being zero, usually c 1 =( K 1 L ) 2 , c 2 =( K 2 L ) 2 , L is the dynamic range of pixel values ​​(such as 255). K 1 is a constant, and K 1 <1, preferably 0.01, K 2 is a constant, preferably 0.03.

[0052] S3, respectively, according to the difference value to obtain the component coarse positioning image of the largest change in gray value corresponding to the light source brightness level L and the fine positioning image of the largest change in gray value corresponding to the light source brightness level S;

[0053] For example, if the difference in grayscale values ​​of the component coarse positioning images under adjacent light source brightness levels is the largest between the light source brightness level 2 and the light source brightness level 1, the corresponding light source brightness level S is 2.

[0054] S4. Compare the difference between L and S. If the difference is in the range of (1, 3], take the average of L and S as the light source adaptive search result; otherwise, take the larger value of L and S as the light source adaptive search result.

[0055] If the difference is greater than 3 brightness levels, it indicates significant influence from outside the component, possibly from the nozzle area. The larger brightness level, L or S, should be selected as the final brightness level. If the difference is between 1 and 3, select the brightness level between L and S for processing. This balances external influences and facilitates subsequent feature searches. Otherwise, subsequent feature searches may result in missing features or interference that cannot be removed. If the difference between the two brightness levels is less than or equal to 1, select the larger brightness level for output.

[0056] The coarse component positioning image has a large image area. Due to the influence of the nozzle material, the reflective material of the component itself, or the overall machine structure, the overall brightness of the coarse component positioning image may fluctuate. Obtaining a fine component positioning image can reduce external environmental interference on the component itself. When there are no component reflections, oxidation, or structural influences such as the nozzle, the light source brightness results of the two images are basically consistent and require no adjustment. When the component itself is oxidized, the light source brightness level calculated using the coarse positioning image will be underestimated due to the decreasing difference in the MS-SSIM method, which will result in an earlier output of the peak position. In this case, using the fine component positioning image will increase the light source brightness in this area and improve the accuracy of the calculation result (in this case, the difference in brightness between the two light sources is less than or equal to 2). When the component itself is highly reflective, such as with lamp beads, the light source level structure calculated using the fine component positioning image may be excessively large, with a difference of 3 or more from the light source in the coarse component positioning image. If the coarse positioning light source is less than 5, the coarse positioning light source is output; otherwise, the average of the two light sources is output.

[0057] Furthermore, before comparing the difference between L and S in step S4, the light source brightness level S is adjusted and calculated as follows:

[0058] S41. Calculate the threshold based on the light source brightness level S. The process is as follows: take the light source brightness level S and the preset maximum light source brightness level as the interval, and calculate the average value of the difference in grayscale values ​​of the precise positioning image under adjacent light source brightness levels as the threshold.

[0059] S42, calculating a difference value Diff1 between the grayscale value of the precise positioning image at the light source brightness level S and the previous light source brightness level, and a difference value Diff2 between the grayscale value of the precise positioning image at the light source brightness level S and the next light source brightness level;

[0060] The maximum brightness level range for both the main light and the coaxial light is 0-10. For the method for categorizing light source brightness levels, refer to the dimming method for improving camera light source linearity disclosed in patent CN115657404A. For the method for calibrating the linear relationship between light source brightness level and grayscale value, refer to the calibration method for image light source illumination parameters disclosed in patent CN116309863A. If the current brightness level S has reached the maximum brightness level, calculate the average grayscale difference between each image at all brightness levels. Take the average of all average grayscale differences and use it as the threshold for brightness level adjustment. By slightly adjusting some of the calculated data in the MS-SSIM algorithm to obtain a suitable threshold, more accurate image brightness levels can be selected to meet application requirements.

[0061] S43. Compare Diff1 and Diff2:

[0062] If Diff1>Diff2, then starting from the light source brightness level S and moving backward, the difference values ​​of the grayscale values ​​of the precise positioning image at adjacent light source brightness levels are compared with the threshold value in sequence, until the difference value is greater than the threshold value, and the corresponding light source brightness level is adjusted to the current light source brightness level S, otherwise no adjustment is made;

[0063] When Diff1 is greater than Diff2, move backward from S; compare the difference between the two images at subsequent adjacent light source brightness levels to see if it is less than the threshold; if so, continue moving backward; if it exceeds the threshold, stop and update the current brightness level S; if the brightness level of the last light source still does not exceed the threshold, do not change the value of S. In this application, the forward-to-backward direction refers to the order of light source brightness levels from smallest to largest.

[0064] If Diff1 ≤ Diff2, then starting from light source brightness level S, the grayscale difference of the precise positioning image at adjacent light source brightness levels is compared with the threshold value. If the difference is greater than the threshold, the corresponding light source brightness level is adjusted to the current light source brightness level S. Otherwise, no further adjustment is made. If the light source brightness level is still less than or equal to the threshold after moving to level 1, the value of S is not changed.

[0065] In summary, determining the light source brightness levels for both the coarse and fine component positioning images based on the grayscale value variations at adjacent light source brightness levels, and optimizing the brightness level based on the difference between the two, effectively eliminates the effects of factors such as nozzle material, reflective material of the component itself, and the overall machine structure, improving the accuracy of the light source brightness level. This solution automatically adjusts light source brightness, improving production efficiency, and achieving highly accurate and reliable adjustment results.

[0066] Test example:

[0067] The light source search method of this application is used and the light source search of the Yamaha YS12F placement machine is used for comparative testing.

[0068] Comparison of component light source search results: On the one hand, we can intuitively feel the difference between the light source images searched by this application method and the Yamaha YS12F placement machine; on the other hand, we verify the subsequent component recognition operations based on the light source search results of this application method to see if there is any impact. The results are as follows: Figure 4 The CHIP components shown, Figure 5 The BGA components shown and Figure 6 The special-shaped components shown. Figure 4-Figure 6 The left image is the search result of the present invention, the middle image is the search result of Yamaha YS12F, and the right image is the verification result of the light source search result based on the present invention for subsequent component identification. Figure 4-Figure 6 It can be seen intuitively that for different types of components, the light source search results of this application method and the light source search results of Yamaha YS12F are relatively similar. Moreover, the light source search results of this application method will not affect subsequent recognition, which largely solves the problem of manual automatic setting of light sources. In addition, using the solution of this application, the light source parameters can be universal under different machines under the same type of camera; the same type of camera can also be universal between different axes of the same machine, greatly improving production efficiency.

[0069] Based on the same inventive concept, a second aspect of an embodiment of the present invention provides an adaptive search device for a component light source, comprising:

[0070] The component fine positioning module is configured to use a Blob algorithm to filter out a target area from the component coarse positioning image as a fine positioning image;

[0071] an image grayscale value calculation module configured to calculate, using an MS-SSIM algorithm, a difference value of grayscale values ​​of the component coarse positioning image at adjacent light source brightness levels and a difference value of grayscale values ​​of the fine positioning image at adjacent light source brightness levels, where the light source brightness level is the sum of the main axis light source brightness level and the coaxial light source brightness level;

[0072] Light source brightness level search module, configured to obtain the light source brightness level L corresponding to the gray value with the largest change in the rough positioning image of the component and the light source brightness level S corresponding to the gray value with the largest change in the fine positioning image, respectively, according to the difference value;

[0073] The result output module is configured to compare the difference between L and S. If the difference interval is (1, 3], the average of L and S is taken as the light source adaptive search result; otherwise, the larger value of L and S is taken as the light source adaptive search result.

[0074] Furthermore, it also includes a light source brightness level adjustment module, which is configured to be executed before the result output module and adjust the light source brightness level S by the following method:

[0075] Calculating a difference value Diff1 between the grayscale value of the precise positioning image at the light source brightness level S and the previous light source brightness level, and a difference value Diff2 between the grayscale value of the precise positioning image at the light source brightness level S and the next light source brightness level;

[0076] Compare Diff1 and Diff2:

[0077] If Diff1>Diff2, then starting from the light source brightness level S and moving backward, the difference values ​​of the grayscale values ​​of the precise positioning image at adjacent light source brightness levels are compared with the threshold value in sequence, until the difference value is greater than the threshold value, and the corresponding light source brightness level is adjusted to the current light source brightness level S, otherwise no adjustment is made;

[0078] If Diff1≤Diff2, then starting from the light source brightness level S and moving forward, the difference values ​​of the grayscale values ​​of the precise positioning image under adjacent light source brightness levels are compared with the threshold values ​​in sequence until the difference value is greater than the threshold value. The corresponding light source brightness level is adjusted to the current light source brightness level S, otherwise no adjustment is made.

[0079] A third aspect of an embodiment of the present invention provides a component light source adaptive search system, including:

[0080] Cameras, including bottom mirror cameras and flying cameras, are used to obtain component images;

[0081] The controller is configured to execute the above-mentioned component light source adaptive search method.

[0082] A fourth aspect of an embodiment of the present invention provides an electronic device, comprising:

[0083] one or more processors; and

[0084] The memory is used to store one or more programs, and when the one or more programs are executed by the one or more processors, the electronic device implements the above method.

[0085] A fifth aspect of an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the above method when the program is executed by a processor.

[0086] Although the embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents, and all should be included within the scope of protection of the present invention.

Claims

1. A component light source adaptive search method, characterized in that: The following steps are involved: Filter out the target area from the component coarse positioning image as the fine positioning image; respectively calculating the difference in grayscale values ​​of the component coarse positioning image under adjacent light source brightness levels and the difference in grayscale values ​​of the fine positioning image under adjacent light source brightness levels; According to the difference value, respectively, the light source brightness level L corresponding to the gray value with the largest change in the rough positioning image of the component and the light source brightness level S corresponding to the gray value with the largest change in the fine positioning image are obtained; The light source brightness level S is adjusted and calculated as follows: calculate the difference value Diff1 between the light source brightness level S and the grayscale value of the precise positioning image at the previous light source brightness level, and the difference value Diff2 between the light source brightness level S and the grayscale value of the precise positioning image at the next light source brightness level; compare Diff1 and Diff2: if Diff1>Diff2, then starting from the light source brightness level S and moving backward, successively compare the difference values ​​of the grayscale values ​​of the precise positioning image at adjacent light source brightness levels with the threshold value, until the difference value is greater than the threshold value, and adjust the corresponding light source brightness level to the current light source brightness level S, otherwise no adjustment is made; if Diff1≤Diff2, then starting from the light source brightness level S and moving forward, successively compare the difference values ​​of the grayscale values ​​of the precise positioning image at adjacent light source brightness levels with the threshold value, until the difference value is greater than the threshold value, and adjust the corresponding light source brightness level to the current light source brightness level S, otherwise no adjustment is made; Compare the difference between L and S. If the difference range is (1, 3], take the average of L and S as the light source adaptive search result; otherwise, take the larger value of L and S as the light source adaptive search result.

2. The method according to claim 1, characterized in that Before adjusting and calculating the light source brightness level S, the threshold is calculated based on the light source brightness level S, and the process is as follows: Taking the light source brightness level S and the preset maximum light source brightness level as an interval, the average value of the difference in grayscale values ​​of the precise positioning image under adjacent light source brightness levels is calculated as the threshold.

3. The method according to claim 1, characterized in that The components include BGA components, CHIP components, multi-pin components and special-shaped components.

4. The method according to claim 1, wherein The difference value is calculated using the MS-SSIM algorithm, and the formula is as follows: , where x and y represent two image data at adjacent light source brightness levels, l (x,y) is the brightness comparison of the two images, c (x,y) is the contrast comparison of two images, s (x,y) is the structure comparison of two images, α M >0, β j >0,γ j >0 respectively means l (x,y), c (x,y) and s (x, y) are the weights of the three indicators, M means that the entire image is divided into M parts, and j means the serial number of each part.

5. The method according to any one of claims 1 to 4, characterized in that: The light source brightness level range is [0,10]. When the component is a CHIP component, a multi-pin component or a special-shaped component, the light source brightness level is the sum of the main axis light source brightness level and the coaxial light source brightness level. The main axis light source brightness level and the coaxial light source brightness level use the same level number. When the component is a BGA component, the light source brightness level refers to the photometric light source brightness level.

6. The method according to claim 5, characterized in that The component rough positioning image is located using a multi-search box search algorithm. The multi-search box is a number of search boxes of different sizes selected according to the component type and size. The positioning process is as follows: After the rough position of the component is screened out from the component image according to the actual component, the search box is used to search in ascending order with the rough position as the center until the target area of ​​the component is within the search box. The search is stopped and the component rough positioning image is obtained.

7. A component light source adaptive search device, characterized in that: include: The component fine positioning module is configured to use a Blob algorithm to filter out a target area from the component coarse positioning image as a fine positioning image; an image grayscale value calculation module configured to calculate, using an MS-SSIM algorithm, a difference value of grayscale values ​​of the component coarse positioning image at adjacent light source brightness levels and a difference value of grayscale values ​​of the fine positioning image at adjacent light source brightness levels, where the light source brightness level is the sum of the main axis light source brightness level and the coaxial light source brightness level; Light source brightness level search module, configured to obtain the light source brightness level L corresponding to the gray value with the largest change in the rough positioning image of the component and the light source brightness level S corresponding to the gray value with the largest change in the fine positioning image, respectively, according to the difference value; A result output module is configured to compare the difference between L and S. If the difference is in the range of (1, 3], the average of L and S is taken as the light source adaptive search result; otherwise, the larger value of L and S is taken as the light source adaptive search result; The light source brightness level adjustment module is configured to be executed before the result output module, and adjust the light source brightness level S by calculating the following method: calculating the difference value Diff1 between the light source brightness level S and the grayscale value of the precise positioning image under the previous light source brightness level, and the difference value Diff2 between the light source brightness level S and the grayscale value of the precise positioning image under the next light source brightness level; comparing Diff1 and Diff2: if Diff1>Diff2, then starting from the light source brightness level S and moving backward, the difference values ​​of the grayscale values ​​of the precise positioning image under adjacent light source brightness levels are compared with the threshold value until the difference value is greater than the threshold value, and the corresponding light source brightness level is adjusted to the current light source brightness level S, otherwise no adjustment is made; if Diff1≤Diff2, then starting from the light source brightness level S and moving forward, the difference values ​​of the grayscale values ​​of the precise positioning image under adjacent light source brightness levels are compared with the threshold value until the difference value is greater than the threshold value, and the corresponding light source brightness level is adjusted to the current light source brightness level S, otherwise no adjustment is made.

8. A component light source adaptive search system, characterized in that: include: Cameras, including bottom mirror cameras and flying cameras, are used to obtain component images; A controller is configured to execute the component light source adaptive search method according to any one of claims 1-6.

9. An electronic device, comprising: one or more processors; as well as The memory is used to store one or more programs, and when the one or more programs are executed by the one or more processors, the electronic device implements the method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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