High-precision dust removal method and system for optical lens

Abstract

The invention belongs to the technical field of optical lens dust removal, and particularly relates to a high-precision dust removal method and system for an optical lens. According to the invention, multiple imaging data are adopted for mutual verification, so that the dust detection accuracy is remarkably improved, the problem of missing detection or misjudgment possibly occurring in a traditional single detection mode is avoided, the reliability of the overall dust removal effect is further improved, the cleaning result can be monitored in real time, and dynamic strategy adjustment is performed according to feedback information; closed-loop feedback not only improves the dust removal efficiency, but also enables the cleaning process to have self-adaptive ability, can cope with changes of various environments and dust accumulation degrees, ensures that the cleaning quality can still be guaranteed under complex working conditions, provides customized cleaning schemes for different areas by utilizing compensation information, and improves the cleaning efficiency. Dust with different thicknesses and different accumulation conditions is treated in a targeted manner, the phenomenon of excessive cleaning or insufficient cleaning is avoided, and the purposes of protecting the surface of the lens and prolonging the service life of the lens are achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of optical lens dust removal, and particularly relates to a high-precision dust removal method and system for an optical lens. Background Art

[0002] With the wide application of precision optical devices, optical lenses have become key components in cameras, microscopes, telescopes, projection devices, and intelligent devices. The imaging quality of an optical lens depends to a large extent on the cleanliness of its surface. However, during long-term use, the lens surface is extremely vulnerable to dust particle contamination. Especially in special environments such as industry, medical, and aerospace, dust contamination will significantly reduce the imaging clarity, affect the device performance, and even lead to image recognition failure or precision measurement deviation. Therefore, how to efficiently and accurately remove dust on the lens surface has become an important issue in optical system maintenance.

[0003] In the prior art, common dust removal methods include mechanical wiping, air flow blowing, electrostatic adsorption, etc. Although these methods can achieve the dust removal effect to a certain extent, most rely on manual operation or simple physical mechanisms, and it is difficult to accurately locate the dust removal path according to the actual dust distribution. This often leads to repeated cleaning in some areas, while some dust-intensive areas are not effectively covered, resulting in limited dust removal effect. Most systems cannot identify the thickness distribution of dust, cannot distinguish between minor particles and strongly adherent heavy contamination, resulting in the inability to effectively match the dust removal intensity and mode. Most are single-execution dust removal, lacking a cleanliness detection and feedback mechanism, and unable to dynamically adjust the dust removal strategy according to the cleaning effect, resulting in the phenomena of "not cleaning thoroughly" or "over-cleaning". The current system has a fixed dust removal mode and cannot perform strategy compensation and path optimization according to different lens structures, pollution forms, or historical data, making it difficult to meet the requirements of high-precision optical devices. Summary of the Invention

[0004] The purpose of the present invention is to provide a high-precision dust removal method and system for an optical lens, which can perform partitioned, graded, and closed-loop controlled cleaning treatment on the surface of the optical lens to improve the lens cleanliness, imaging quality, and device stability.

[0005] The technical solutions adopted by the present invention are specifically as follows: A high-precision dust removal method for an optical lens, comprising: Obtaining surface image data of the optical lens and obtaining dust distribution information according to the surface image data; Obtaining side view data of the dust on the optical lens and obtaining dust thickness information according to the side view data of the dust; Obtaining dust area data of the optical lens and obtaining dust removal compensation information according to the dust area data; Obtain a dust removal strategy based on the dust distribution information, dust thickness information, and dust removal compensation information, and remove the dust on the optical lens according to the dust removal strategy; Obtain a dust removal path according to the dust removal strategy, and obtain the cleaning feedback area of the optical lens according to the dust distribution information, dust thickness information, and dust removal path; Obtain the cleanliness information within the cleaning feedback area, and determine whether the cleanliness information meets the preset conditions. If not, re-obtain the dust distribution information, dust thickness information, and dust removal compensation information, and combine the cleanliness information to obtain a new dust removal strategy, and execute the dust removal strategy until the preset conditions are met.

[0006] In a preferred solution, the steps of obtaining the dust distribution information according to the surface image data of the optical lens include: Obtain the surface image data of the optical lens, and obtain the surface dust image according to the surface image data; Construct a plane rectangular coordinate system on the surface dust image, and obtain the center coordinates of multiple dust regions according to the plane rectangular coordinate system; Obtain the dust region distribution value according to the center coordinates of multiple dust regions; Obtain a dust distribution table, where the dust distribution table includes multiple dust region distribution interval values and the corresponding dust distribution information for each dust region distribution interval value; Obtain the corresponding dust distribution information from the dust distribution table according to the dust region distribution interval value corresponding to the dust region distribution value.

[0007] In a preferred solution, the steps of obtaining the dust thickness information according to the dust side view data of the optical lens include: Obtain the dust side view data of the optical lens, and obtain the dust side view image of each dust region according to the dust side view data; Obtain a reference dust side view image, and overlap the dust side view image of each dust region with the reference dust side view image respectively to form a composite dust side view image; Construct a plane rectangular coordinate system in each composite dust side view image, where the X-axis of the plane rectangular coordinate system coincides with the plane of the optical lens, and obtain the Y-axis coordinate of each dust side view image and the Y-axis coordinate of the reference dust side view image respectively, and mark them as the dust side view value and the reference dust side view value; Obtain the ratio of the dust side view value to the reference dust side view value, and mark it as the thickness ratio; Obtain a thickness table, where the thickness table includes multiple thickness ratios and the corresponding dust thickness information for each thickness ratio; Obtain the corresponding dust thickness information from the thickness table according to the thickness ratio.

[0008] In a preferred solution, the steps of obtaining dust area data of an optical lens and obtaining dust removal compensation information according to the dust area data include: Obtain dust area data of the optical lens and obtain multiple dust area images according to the dust area data; Construct a rectangular coordinate system in the dust area image and obtain the coordinates of multiple inflection points of the dust area contour in each dust area according to the rectangular coordinate system; Obtain a dust area value according to the coordinates of multiple inflection points of the dust area contour in each dust area; Arrange multiple dust area values in descending order to obtain an arrangement list, and mark the dust area value in the first place in the arrangement list as the target dust area value; Obtain a dust removal compensation table, where the dust removal compensation table includes multiple dust area interval values and the corresponding dust removal compensation information for each dust area interval value; Obtain the corresponding dust removal compensation information from the dust removal compensation table according to the dust area interval value corresponding to the target dust area value.

[0009] In a preferred solution, the steps of obtaining a dust removal strategy according to the dust distribution information, dust thickness information, and dust removal compensation information and removing the dust on the optical lens according to the dust removal strategy include: Obtain the corresponding dust area distribution value, thickness ratio, and target dust area value according to the dust distribution information, dust thickness information, and dust removal compensation information respectively; Obtain a strategy value according to the dust area distribution value, thickness ratio, and target dust area value; Obtain a strategy table, where the strategy table includes multiple strategy interval values and the corresponding dust removal strategies for each strategy interval value; Obtain the corresponding dust removal strategy from the strategy table according to the strategy interval value corresponding to the strategy value, and remove the dust on the optical lens according to the dust removal strategy.

[0010] In a preferred solution, the steps of obtaining a dust removal path according to the dust removal strategy and obtaining the cleaning feedback area of the optical lens according to the dust distribution information, dust thickness information, and dust removal path include: Obtain a dust removal path according to the dust removal strategy and obtain the corresponding dust removal path vector according to the dust removal path; Obtain the corresponding dust area distribution value and thickness ratio based on the dust distribution information and dust thickness information respectively; Obtain a feedback value according to the dust area distribution value, thickness ratio, and dust removal path vector; Obtain an area table, where the area table includes multiple feedback interval values and the corresponding cleaning feedback areas for each feedback interval value; The feedback interval value corresponding to the root feedback value obtains the corresponding cleaning feedback area from the area table.

[0011] In a preferred solution, the steps of obtaining the cleanliness information in the cleaning feedback area, determining whether the cleanliness information meets the preset conditions, and if not, re-obtaining the dust distribution information, dust thickness information, and dust removal compensation information, and obtaining a new dust removal strategy in combination with the cleanliness information and executing the dust removal strategy until the preset conditions are met include: Obtain the cleanliness information in the cleaning feedback area, and obtain the corresponding cleanliness value according to the cleanliness information; Obtain the standard cleanliness threshold; Determine whether the cleanliness value is lower than the standard cleanliness threshold; If the cleanliness value is lower than the standard cleanliness threshold, it is determined that the dust removal strategy executed on the surface of the optical lens is abnormal and marked as an abnormal dust removal strategy; If the cleanliness value is not lower than the standard cleanliness threshold, it is determined that the dust removal strategy executed on the surface of the optical lens is normal; After obtaining the abnormal dust removal strategy, re-obtain the dust distribution information, dust thickness information, and dust removal compensation information, and obtain a new dust removal strategy in combination with the cleanliness information and execute the dust removal strategy until the preset conditions are met.

[0012] In a preferred solution, the steps of re-obtaining the dust distribution information, dust thickness information, and dust removal compensation information after obtaining the abnormal dust removal strategy, and obtaining a new dust removal strategy in combination with the cleanliness information and executing the dust removal strategy until the preset conditions are met include: After obtaining the abnormal dust removal strategy, re-obtain the dust distribution information, dust thickness information, and dust removal compensation information, and obtain the corresponding feedback dust distribution information, feedback dust thickness information, and feedback dust removal compensation information based on the re-obtained dust distribution information, dust thickness information, and dust removal compensation information, and mark them as the feedback dust area distribution value, feedback thickness ratio, and feedback dust area value respectively; Obtain a comprehensive value according to the feedback dust area distribution value, feedback thickness ratio, feedback dust area value, and cleanliness value; Obtain a reset table, where the reset table includes multiple comprehensive interval values and the reset dust removal strategy corresponding to each comprehensive interval value; Obtain the corresponding reset dust removal strategy from the reset table according to the comprehensive interval value corresponding to the comprehensive value, execute the reset dust removal strategy, obtain the reset dust removal path according to the reset dust removal strategy, and return the reset dust removal path as the dust removal path to the step of obtaining the cleaning feedback area of the optical lens according to the dust distribution information, dust thickness information, and dust removal path.

[0013] The present invention also provides a high-precision dust removal system for an optical lens, which is used for the high-precision dust removal method of the above optical lens, and includes: A dust distribution module, which is used to obtain the surface image data of the optical lens and obtain the dust distribution information according to the surface image data; A dust thickness module, which is used to obtain the side view data of the dust on the optical lens and obtain the dust thickness information according to the side view data of the dust; A dust removal compensation module, which is used to obtain the dust area data of the optical lens and obtain the dust removal compensation information according to the dust area data; A dust removal strategy module, which is used to obtain a dust removal strategy according to the dust distribution information, the dust thickness information and the dust removal compensation information, and remove the dust on the optical lens according to the dust removal strategy; A cleaning area module, which is used to obtain a dust removal path according to the dust removal strategy, and obtain the cleaning feedback area of the optical lens according to the dust distribution information, the dust thickness information and the dust removal path; A strategy feedback module, which is used to obtain the cleanliness information in the cleaning feedback area, and judge whether the cleanliness information meets the preset conditions. If not, re-obtain the dust distribution information, the dust thickness information and the dust removal compensation information, and obtain a new dust removal strategy in combination with the cleanliness information, and execute the dust removal strategy until the preset conditions are met.

[0014] And, a high-precision dust removal terminal for an optical lens, including: One or more processors; A storage device, on which one or more programs are stored; When one or more programs are executed by one or more processors, the one or more processors implement the high-precision dust removal method for the optical lens.

[0015] The technical effects achieved by the present invention are: In the present invention, by using multiple imaging data to corroborate each other, the accuracy of dust detection is significantly improved, avoiding the problems of missed detection or misjudgment that may occur in traditional single detection methods, and further improving the reliability of the overall dust removal effect. It can monitor the cleaning result in real time and adjust the dynamic strategy according to the feedback information. The closed-loop feedback not only improves the dust removal efficiency, but also enables the cleaning process to have an adaptive ability, capable of coping with various environmental and dust accumulation degree changes, ensuring that the cleaning quality can still be guaranteed under complex working conditions. Using the compensation information to provide customized cleaning solutions for different regions, specifically dealing with dust of different thicknesses and different accumulation situations, avoiding the phenomena of over-cleaning or under-cleaning, achieving the goal of protecting the lens surface and extending the service life of the lens. High-precision dust removal can significantly reduce the residual dust on the lens surface, improve the light transmittance and imaging quality, and provide stable and accurate performance support for high-end optical equipment in the fields of scientific research, medical treatment or industrial inspection. Description of the Drawings

[0016] Figure 1 It is a flow chart of the method provided by the present invention.

[0017] Figure 2 It is a system module diagram provided by the present invention. DETAILED DESCRIPTION

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0019] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0020] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" that appears in different places in this specification does not refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0021] Secondly, the present invention is described in detail in conjunction with schematic diagrams. When describing the embodiments of the present invention in detail, for the convenience of explanation, the schematic diagrams are only examples and should not limit the scope of protection of the present invention.

[0022] Please refer to the attached Figure 1 As shown, a high-precision dust removal method for an optical lens is provided, comprising: S1, obtaining surface image data of the optical lens, and obtaining dust distribution information according to the surface image data; S2, obtaining dust side view data of the optical lens, and obtaining dust thickness information according to the dust side view data; S3, obtaining dust area data of the optical lens, and obtaining dust removal compensation information according to the dust area data; S4, obtaining a dust removal strategy according to the dust distribution information, the dust thickness information, and the dust removal compensation information, and removing dust from the optical lens according to the dust removal strategy; S5, obtaining a dust removal path according to the dust removal strategy, and obtaining a cleaning feedback area of ​​the optical lens according to the dust distribution information, the dust thickness information, and the dust removal path; S6. Obtain the cleanliness information within the cleaning feedback area, and determine whether the cleanliness information meets the preset conditions. If not, re-obtain the dust distribution information, dust thickness information, and dust removal compensation information, and combine the cleanliness information to obtain a new dust removal strategy, and execute the dust removal strategy until the preset conditions are met.

[0023] In the above steps S1 to S6, the surface of the lens is imaged using a high-resolution imaging system, and the distribution of dust on the lens surface is detected through an image processing algorithm. The side view image of the lens is collected using the high-resolution imaging system to obtain the thickness information of the dust. Compared with the simple surface distribution detection, the side view data can reveal the accumulation of dust on the lens surface and the embedding depth in the micro-structure. According to the dust area data, the specific area characteristics where the dust is located are determined, and then the dust removal compensation information is generated. Combining the previously collected data (dust distribution information, thickness information, and compensation information), the optimal dust removal strategy is comprehensively determined through an algorithm. This strategy can customize the cleaning plan according to the dust accumulation degree and distribution position, accurately control the working state of the cleaning equipment, so as to effectively remove the dust. According to the dust removal strategy, a specific dust removal path is generated, and at the same time, the lens area (cleaning feedback area) after cleaning is determined according to the dust information feedback. After the preliminary dust removal is completed, the cleanliness within the cleaning feedback area is detected again. By comparing with the preset cleanliness standard, it is judged whether the current cleaning effect meets the requirements. If the detection result does not meet the preset conditions, the relevant data (dust distribution, thickness, compensation information) will be re-collected, and combined with the obtained cleaning feedback data, the dust removal strategy and path will be dynamically adjusted until the cleanliness standard is met, ensuring that each cleaning can achieve or exceed the expected effect. Using multiple imaging data (front view, side view, and regional characteristic data) to corroborate each other significantly improves the accuracy of dust detection, avoids the problems of missed detection or misjudgment that may occur in the traditional single detection method, and further improves the reliability of the overall dust removal effect. It can monitor the cleaning result in real time and adjust the dynamic strategy according to the feedback information. The closed-loop feedback not only improves the dust removal efficiency but also enables the cleaning process to have an adaptive ability, capable of coping with various environmental and dust accumulation degree changes, ensuring that the cleaning quality can still be guaranteed under complex working conditions. Using the compensation information to provide a customized cleaning plan for different regions, dealing with dust of different thicknesses and different accumulation situations in a targeted manner, avoiding the phenomena of over-cleaning or under-cleaning, achieving the goal of protecting the lens surface and extending the service life of the lens. High-precision dust removal can significantly reduce the residual dust on the lens surface, improve the light transmittance and imaging quality, and provide stable and accurate performance support for high-end optical equipment in the fields of scientific research, medical treatment, or industrial inspection.

[0024] In a preferred embodiment, the step of obtaining the dust distribution information according to the surface image data of the optical lens includes: S101. Obtain the surface image data of the optical lens, and obtain the surface dust image according to the surface image data; S102. Construct a rectangular coordinate system on the surface dust image, and obtain the central coordinates of multiple dust regions according to the rectangular coordinate system; S103. Obtain the dust region distribution value according to the central coordinates of multiple dust regions; S104. Obtain a dust distribution table, where the dust distribution table includes multiple dust region distribution interval values and the dust distribution information corresponding to each dust region distribution interval value; S105. Obtain the corresponding dust distribution information from the dust distribution table according to the dust region distribution interval value corresponding to the dust region distribution value.

[0025] In the above steps S101 to S105, the surface image data of the optical lens is obtained through a high-resolution imaging device, and image processing techniques (such as edge detection, binarization processing, etc.) are used for the collected image data to extract the shape of the dust on the lens surface and generate a clear surface dust image. A rectangular coordinate system is constructed on the generated dust image so that each point on the image has an accurate spatial position. According to the dust regions in the image, clustering or morphological methods are used to calculate the central coordinates of each dust region. These coordinates reflect the spatial distribution characteristics of the dust on the lens surface. According to the central coordinates of each dust region, the coverage or dust concentration of the dust in this region is further statistically calculated, and the dust region distribution value of each region is extracted. The calculation formula of the dust region distribution value is , where F represents the dust region distribution value, i represents the numbers of the X-axis coordinates and the Y-axis coordinates of the centers of multiple dust regions, i = 1, 2, 3…n, x i represents the X-axis coordinate of the center of the i-th dust region, y iDenoted as the Y-axis coordinate of the center of the i-th dust area, according to different dust area distribution values, a dust distribution table is pre-constructed. This distribution table divides the distribution intervals of multiple dust areas and sets corresponding dust distribution information (such as concentration, area coverage rate, shape characteristics, etc.) for each interval. This distribution table plays a role in data mapping and classification, and can convert discrete dust information into a regularized data pattern. Using the obtained dust area distribution value, according to the corresponding dust area distribution interval, look up and obtain the pre-set dust distribution information in the dust distribution table. Through image processing and coordinate system construction, the intuitive dust distribution is converted into quantified numerical data, making the dust information more refined and standardized, facilitating subsequent analysis and processing. The extraction of the center coordinates of the dust area enables each dust area to be clearly located and identified, thus achieving accurate control of the dust distribution on the lens surface. After constructing the dust distribution table, it can be quickly matched according to the preset interval values, greatly improving the speed and efficiency of data processing.

[0026] In a preferred embodiment, the steps of obtaining the dust thickness information according to the dust side view data of the optical lens include: S201. Obtain the dust side view data of the optical lens and obtain the dust side view image of each dust area according to the dust side view data; S202. Obtain a reference dust side view image and overlap the dust side view image of each dust area with the reference dust side view image respectively to form a composite dust side view image; S203. Construct a plane rectangular coordinate system in each composite dust side view image. Among them, the X-axis of the plane rectangular coordinate system coincides with the plane of the optical lens, and obtain the Y-axis coordinate of each dust side view image and the Y-axis coordinate of the reference dust side view image respectively, and mark them as the dust side view value and the reference dust side view value; S204. Obtain the ratio of the dust side view value to the reference dust side view value and mark it as the thickness ratio; S205. Obtain a thickness table, where the thickness table includes multiple thickness ratios and the dust thickness information corresponding to each thickness ratio; S206. Obtain the corresponding dust thickness information from the thickness table according to the thickness ratio.

[0027] In the above steps S201 to S206, by using a dedicated side-view imaging device, the side image data of the optical lens in different dust areas is obtained. According to the collected data, the side view images of each dust area are extracted, so that the shape and contour of the dust in each area are clearly presented. A representative reference dust side view image is obtained in advance as a standard comparison. The side view images of each area are overlapped with the reference image to form a composite image. In each composite side view image, a plane rectangular coordinate system is set, where the X-axis coincides with the lens plane to ensure that the positions in the image have an actual corresponding relationship. The corresponding Y-axis coordinate values are obtained from the dust side view image and the reference image respectively, and are marked as "dust side view value" and "reference dust side view value". The ratio of the dust side view value extracted from the actual dust side view image to the reference side view value is calculated to generate a thickness ratio. A thickness table is designed in advance, which contains multiple different thickness ratio intervals, and each ratio interval corresponds to predetermined dust thickness information. According to the calculated thickness ratio, the corresponding dust thickness information is found from the thickness table. In this way, the ratio data obtained from the side view image can be directly corresponding to the actual dust thickness value, providing accurate parameter support for the dust removal operation. By using the numerical comparison method of the side view image, the traditional visual judgment is transformed into quantitative calculation, and the dust thickness information is directly obtained by calculating the ratio, significantly improving the measurement accuracy. This quantitative method avoids the uncertainty of traditional visual estimation, ensuring that the dust removal work can be adjusted according to the actual dust thickness. The composite side view image superimposes the actual collected image and the standard reference image, which can not only capture the absolute value of the dust thickness, but also reflect the relative change of the dust state through the ratio.

[0028] In a preferred embodiment, the steps of obtaining the dust area data of the optical lens and obtaining the dust removal compensation information according to the dust area data include: S301. Obtain the dust area data of the optical lens, and obtain multiple dust area images according to the dust area data; S302. Construct a plane rectangular coordinate system in the dust area image, and obtain the coordinates of multiple dust area contour inflection points in each dust area according to the plane rectangular coordinate system; S303. Obtain the dust area value according to the coordinates of multiple dust area contour inflection points in each dust area; S304. Arrange the multiple dust area values in descending order to obtain an arrangement table, and select the dust area value in the first place from the arrangement table and mark it as the target dust area value; S305. Obtain a dust removal compensation table, where the dust removal compensation table includes multiple dust area interval values and the dust removal compensation information corresponding to each dust area interval value; S306. Obtain the corresponding dust removal compensation information from the dust removal compensation table according to the dust area interval value corresponding to the target dust area value.

[0029] In the above steps S301 to S306, using a high-precision imaging system, data of the dust areas are obtained from the surface of the optical lens, and the data are converted into multiple dust area images. In each dust area image, a plane rectangular coordinate system is constructed to ensure that each pixel point on the image has a clear spatial position. Based on this coordinate system, an edge detection or contour extraction algorithm is used to identify the coordinates of multiple contour inflection points within each dust area. These inflection points can accurately describe the geometric contour of the dust area. The obtained coordinates of the contour inflection points are digitally processed to calculate the dust area value. The calculation formula for the dust area value is , where Q represents the dust area value, g represents the number of the coordinates of the contour inflection points of multiple dust areas, g = 1, 2, 3... h, U g represents the X-axis coordinate point of the g-th dust area contour inflection point, U g+1 represents the X-axis coordinate point of the (g + 1)-th dust area contour inflection point, V g represents the Y-axis coordinate point of the g-th dust area contour inflection point, V g+1 represents the Y-axis coordinate point of the (g + 1)-th dust area contour inflection point. When g takes the value of h, h + 1 represents 1. Arrange all the dust area values in descending order to obtain an arrangement table, and select the largest (the first) dust area value in the arrangement table as the target dust area value. Usually, this part of the area has the greatest impact on the overall dust removal effect. A dust removal compensation table is established in advance. This table contains multiple dust area interval values (i.e., segments of different dust ranges) and corresponding dust removal compensation information, such as compensation intensity, cleaning path adjustment scheme, etc. According to the interval where the target dust area value is located, obtain the corresponding dust removal compensation information from the dust removal compensation table. Quantify the dust area data and extract the contour inflection points through the plane rectangular coordinate system, which realizes the accurate measurement of the geometric characteristics of the dust area. The quantitative dust area value helps to accurately identify and locate relatively serious dust areas, ensuring that key areas are effectively cleaned.

[0030] In a preferred embodiment, the steps of obtaining a dust removal strategy according to the dust distribution information, dust thickness information, and dust removal compensation information, and removing the dust on the optical lens according to the dust removal strategy include: S401. Respectively obtain the corresponding dust area distribution value, thickness ratio, and target dust area value according to the dust distribution information, dust thickness information, and dust removal compensation information; S402. Obtain a strategy value according to the dust area distribution value, thickness ratio, and target dust area value; S403. Obtain a strategy table, where the strategy table includes multiple strategy interval values and the dust removal strategies corresponding to each strategy interval value; S404. Obtain the corresponding dust removal strategy from the strategy table according to the strategy interval value corresponding to the strategy value, and remove the dust on the optical lens according to the dust removal strategy.

[0031] In the above steps S401 to S404, using the dust distribution information obtained in the early stage, extract the dust area distribution value, analyze the dust thickness information, extract the thickness ratio, combine with the dust area data predetermined in the dust removal compensation information, and select the most representative or critical area value in the dust area, that is, the target dust area value. Use the obtained dust area distribution value, thickness ratio, and target dust area value to calculate the strategy value. The calculation formula of the strategy value is L = F * H * Q, where L represents the strategy value, F represents the dust area distribution value, H represents the thickness ratio, and Q represents the dust area value. Pre-establish a strategy table, which divides the dust removal tasks into different strategy intervals, and each strategy interval value corresponds to a set of dust removal strategies suitable for the current dust condition. According to the previously generated strategy value, find the corresponding strategy interval value in the strategy table, and then match to obtain the specific dust removal strategy. After obtaining the corresponding dust removal strategy, remove the dust on the optical lens according to the cleaning path, cleaning method, and compensation parameters of the preset dust removal strategy, realizing efficient and accurate cleaning operations. Integrate, quantify the dust distribution information, thickness information, and area compensation information, and generate a comprehensive strategy value, so that the dust removal decision is based on objective and quantified data, avoiding errors caused by a single data source and making the decision more accurate.

[0032] In a preferred embodiment, the steps of obtaining the cleaning feedback area of the optical lens according to the dust removal strategy, the dust distribution information, and the dust thickness information include: S501. Obtain the dust removal path according to the dust removal strategy, and obtain the corresponding dust removal path vector according to the dust removal path. S502. Obtain the corresponding dust area distribution value and thickness ratio based on the dust distribution information and the dust thickness information respectively. S503. Obtain the feedback value according to the dust area distribution value, the thickness ratio, and the dust removal path vector. S504. Obtain the area table, where the area table includes multiple feedback interval values and the cleaning feedback area corresponding to each feedback interval value. S505. Obtain the corresponding cleaning feedback area from the area table according to the feedback interval value corresponding to the feedback value.

[0033] In the above steps S501 to S505, according to the dust removal strategy established in the early stage, the dust removal path is extracted. After the dust removal path is generated, the route is converted into a path vector. Based on the frontal image data collected in advance, the dust area distribution value is extracted. Combining with the side view data, the height ratio of each dust area corresponding to the reference image is calculated to obtain the thickness quantization value of the dust. According to the dust area distribution value, the thickness ratio, and the dust removal path vector, the feedback value is calculated. The formula for the feedback value is , where K represents the feedback value, F represents the dust area distribution value, H represents the thickness ratio, and J represents the dust removal path vector. A region table is pre-constructed, and the feedback value is divided into multiple feedback intervals. Each interval corresponds to a cleaning feedback area. According to the feedback interval corresponding to the currently calculated feedback value, the corresponding cleaning feedback area is searched and matched in the region table. By using the dust removal path vector and the dust distribution and thickness quantization data, the feedback value can be calculated in real time, so as to realize the instant monitoring of the cleaning effect.

[0034] In a preferred embodiment, the cleanliness information in the cleaning feedback area is obtained, and it is judged whether the cleanliness information meets the preset conditions. If not, the dust distribution information, the dust thickness information, and the dust removal compensation information are re-obtained, and a new dust removal strategy is obtained in combination with the cleanliness information, and the dust removal strategy is executed until the preset conditions are met. The steps include: S601. Obtain the cleanliness information in the cleaning feedback area and obtain the corresponding cleanliness value according to the cleanliness information; S602. Obtain the standard cleanliness threshold; S603. Judge whether the cleanliness value is lower than the standard cleanliness threshold; If the cleanliness value is lower than the standard cleanliness threshold, it is determined that the dust removal strategy executed on the surface of the optical lens is abnormal and marked as an abnormal dust removal strategy; If the cleanliness value is not lower than the standard cleanliness threshold, it is determined that the dust removal strategy executed on the surface of the optical lens is normal; S604. After obtaining the abnormal dust removal strategy, re-obtain the dust distribution information, the dust thickness information, and the dust removal compensation information, and obtain a new dust removal strategy in combination with the cleanliness information, and execute the dust removal strategy until the preset conditions are met.

[0035] In the above steps S601 to S604, a dedicated detection device (such as a high-resolution camera or sensor) is used to collect the actual cleanliness data within the cleaning feedback area. According to the acquired data, the corresponding cleanliness value is extracted through image processing. A standard cleanliness threshold is preset or read as the benchmark for judging whether the cleaning effect meets the requirements. This threshold can be customized according to the requirements of the device, the working environment, and the application standard to ensure that the cleaning effect meets the actual needs. The actually collected cleanliness value is compared with the preset standard cleanliness threshold. If the cleanliness value is lower than the standard threshold, it is determined that the dust removal strategy for the current optical lens surface has not achieved the expected effect and is marked as an abnormal dust removal strategy; otherwise, it is determined that the executed dust removal strategy is normal. When an abnormal dust removal strategy is detected, a closed-loop feedback mechanism is activated to re-obtain the latest dust distribution information, dust thickness information, and dust removal compensation information. Using the current cleanliness information and the newly collected data, a new dust removal strategy is recalculated and generated, and then the cleaning operation is adjusted. This process is continuously executed until the obtained cleanliness value meets or exceeds the standard cleanliness threshold, thereby ensuring that the final cleaning effect meets the preset conditions, being able to quickly judge the execution effect of the dust removal strategy and promptly detect abnormalities. The closed-loop feedback mechanism has the ability of self-detection and self-correction to ensure that the dust removal operation is continuously in the best state. The dynamic optimization strategy reduces manual intervention and at the same time reduces the risk of repeated operations due to incomplete cleaning.

[0036] In a preferred embodiment, after obtaining the abnormal dust removal strategy, the steps of re-obtaining the dust distribution information, dust thickness information, and dust removal compensation information, and obtaining a new dust removal strategy in combination with the cleanliness information, and executing the dust removal strategy until it meets the preset conditions include: S6041. After obtaining the abnormal dust removal strategy, re-obtain the dust distribution information, dust thickness information, and dust removal compensation information. Based on the re-obtained dust distribution information, dust thickness information, and dust removal compensation information, obtain the corresponding feedback dust distribution information, feedback dust thickness information, and feedback dust removal compensation information, and mark them as the feedback dust area distribution value, feedback thickness ratio, and feedback dust area value respectively; S6042. Obtain a comprehensive value according to the feedback dust area distribution value, feedback thickness ratio, feedback dust area value, and cleanliness value; S6043. Obtain a reset table, where the reset table includes multiple comprehensive interval values and the reset dust removal strategy corresponding to each comprehensive interval value; S6044. Obtain the corresponding reset dust removal strategy from the reset table according to the comprehensive interval value corresponding to the comprehensive value, and execute the reset dust removal strategy. Obtain the reset dust removal path according to the reset dust removal strategy, and return the reset dust removal path as the dust removal path to the step of obtaining the cleaning feedback area of the optical lens according to the dust distribution information, dust thickness information, and dust removal path.

[0037] In the above steps S6041 to S6044, when it is determined that the current dust removal strategy is abnormal, the dust distribution information, dust thickness information, and dust removal compensation information in the current environment are re-collected. Based on the newly collected data, through corresponding image processing and numerical calculations, they are converted into feedback indicators, and the dust area distribution value, feedback thickness ratio, and dust area value are fed back. The various feedback indicators re-obtained are fused with the current cleanliness value to calculate a comprehensive value. The calculation formula for the comprehensive value is , where Z represents the feedback value, represents the feedback dust area distribution value, represents the feedback thickness ratio, represents the feedback dust removal path vector, D represents the cleanliness value. A reset table is pre-established. This table divides the comprehensive value into multiple intervals, and for each interval, a set of predefined reset dust removal strategies is corresponding. According to the strategy found in the reset table, a new reset dust removal strategy is obtained, and a new dust removal path is planned according to this strategy. The new dust removal path is calculated and then returned to the subsequent cleaning feedback steps to verify the cleaning effect until the final cleanliness index reaches or exceeds the preset conditions. This process forms a closed-loop feedback system. When it is found that the dust removal effect does not meet the standard, it can automatically re-collect data, update the feedback indicators, and calculate the comprehensive value, so as to achieve intelligent decision-making and formulate a new dust removal strategy. This automation and adaptive adjustment mechanism significantly improves the ability to respond to different working environments and dust changes. When the environment or dust characteristics change, it can quickly respond until the set cleanliness standard is reached, ensuring that the equipment is always in the best working state. The efficient feedback closed-loop and dynamic compensation strategy ensure thorough cleaning of the optical lens, reducing imaging quality problems and equipment wear caused by dust residue.

[0038] Please refer to the appendix Figure 2 As shown, the present invention also provides a high-precision dust removal system for an optical lens, which is used for the high-precision dust removal method of the above optical lens, including: A dust distribution module, which is used to obtain the surface image data of the optical lens and obtain the dust distribution information according to the surface image data; A dust thickness module, which is used to obtain the side view data of the dust on the optical lens and obtain the dust thickness information according to the side view data of the dust; A dust removal compensation module, which is used to obtain the dust area data of the optical lens and obtain the dust removal compensation information according to the dust area data; A dust removal strategy module, which is used to obtain the dust removal strategy according to the dust distribution information, dust thickness information, and dust removal compensation information, and remove the dust on the optical lens according to the dust removal strategy; A cleaning area module, which is used to obtain a dust removal path according to a dust removal strategy, and obtain a cleaning feedback area of an optical lens according to dust distribution information, dust thickness information, and the dust removal path. A strategy feedback module, which is used to obtain the cleanliness information within the cleaning feedback area, and determine whether the cleanliness information meets a preset condition. If not, it re-obtains the dust distribution information, dust thickness information, and dust removal compensation information, combines the cleanliness information to obtain a new dust removal strategy, and executes the dust removal strategy until the preset condition is met.

[0039] As described above, the dust distribution module obtains image data on the surface of the optical lens through a high-definition imaging device, and uses image processing algorithms (such as edge detection, grayscale enhancement, etc.) to extract the dust positions and generate dust distribution information. The dust thickness module extracts the vertical thickness information of the dust accumulation by obtaining an image at the side view angle of the lens or using technical means such as structured light, and generates dust thickness information. The dust removal compensation module obtains dust removal compensation information according to the area of the dust region. The dust removal strategy module constructs a comprehensive index (such as a strategy value) using the dust distribution information, dust thickness information, and dust removal compensation information, looks up a table or obtains a suitable dust removal strategy (such as air flow intensity, static charge magnitude, path planning method, etc.) through a model, and applies this strategy to an actual dust removal execution device (such as a jet head, mechanical brush, etc.). The cleaning area module generates a specific dust removal path according to the dust removal strategy, and then combines the dust distribution and thickness to calculate the area feedback value covered by this path, and matches the cleaning feedback area from a preset area table to achieve a regional evaluation of the dust removal effect. The strategy feedback module performs a cleanliness detection on the cleaning feedback area, obtains a cleanliness value, and compares it with a set standard cleanliness threshold. If it is lower than the threshold, it is marked as an abnormal dust removal strategy, and the dust information is re-collected, and combined with this cleanliness value, a set of adaptable dust removal strategies (reset strategies) are regenerated, and the execution is repeated until the preset condition is met. If the threshold is reached, it is marked as a normal dust removal strategy, and this round of cleaning task is completed, breaking through the limitations of traditional single-image judgment, making the dust removal strategy more targeted, dynamically generating a cleaning path according to the complexity of the dust distribution, and realizing differential dust removal treatment for different parts of the optical lens. With the help of the strategy feedback module, real-time cleanliness monitoring and abnormal strategy identification are realized. Once the detection effect does not meet the standard, the strategy can be quickly reset to form a closed-loop control mechanism.

[0040] And, a high-precision dust removal terminal for an optical lens, including: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the high-precision dust removal method for the optical lens.

[0041] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention. The structures, devices, and operation methods not specifically described and explained in the present invention are implemented according to the conventional means in the art without special instructions and limitations.

Claims

1. A high-precision dust removal method for an optical lens, characterized in that: include: Acquire surface image data of the optical lens, and acquire dust distribution information according to the surface image data; Obtain dust side view data of the optical lens, and obtain dust thickness information according to the dust side view data; Obtain dust area data of the optical lens, and obtain dust removal compensation information according to the dust area data; Obtaining a dust removal strategy according to the dust distribution information, the dust thickness information, and the dust removal compensation information, and removing dust from the optical lens according to the dust removal strategy; Obtain a dust removal path according to the dust removal strategy, and obtain a cleaning feedback area of ​​the optical lens according to the dust distribution information, the dust thickness information, and the dust removal path; Obtain the cleanliness information in the cleaning feedback area and determine whether the cleanliness information meets the preset conditions. If not, re-obtain the dust distribution information, dust thickness information and dust removal compensation information, and obtain a new dust removal strategy in combination with the cleanliness information, and execute the dust removal strategy until the preset conditions are met.

2. The high-precision dust removal method for an optical lens according to claim 1, characterized in that: The steps of obtaining surface image data of the optical lens and obtaining dust distribution information according to the surface image data include: Acquire surface image data of the optical lens, and acquire a surface dust image according to the surface image data; Constructing a plane rectangular coordinate system on the surface dust image, and obtaining the center coordinates of multiple dust regions according to the plane rectangular coordinate system; Obtain dust area distribution values ​​according to center coordinates of multiple dust areas; Obtaining a dust distribution table, wherein the dust distribution table includes a plurality of dust area distribution interval values ​​and dust distribution information corresponding to each dust area distribution interval value; The corresponding dust distribution information is obtained from the dust distribution table according to the dust area distribution interval value corresponding to the dust area distribution value.

3. The high-precision dust removal method for an optical lens according to claim 1, characterized in that: The steps of obtaining dust side view data of the optical lens and obtaining dust thickness information according to the dust side view data include: Acquire dust side view data of the optical lens, and acquire a dust side view image of each dust area according to the dust side view data; Acquire a reference dust side view image, and overlap the dust side view image of each dust area with the reference dust side view image to form a composite dust side view image; Constructing a plane rectangular coordinate system in each composite dust side view image, wherein the X axis of the plane rectangular coordinate system coincides with the plane of the optical lens, and obtaining the Y axis coordinate of each dust side view image and the Y axis coordinate of the reference dust side view image, respectively, and marking them as dust side view value and reference dust side view value, respectively; Obtain a ratio of the dust side view value to the reference dust side view value, and mark it as a thickness ratio; Obtaining a thickness table, wherein the thickness table includes a plurality of thickness ratios and dust thickness information corresponding to each thickness ratio; The corresponding dust thickness information is obtained from the thickness table according to the thickness ratio.

4. The high-precision dust removal method for an optical lens according to claim 1, characterized in that: The steps of obtaining dust area data of the optical lens and obtaining dust removal compensation information according to the dust area data include: Acquire dust area data of the optical lens, and acquire multiple dust area images according to the dust area data; Constructing a plane rectangular coordinate system in the dust region image, and obtaining coordinates of a plurality of dust region contour inflection points in each dust region according to the plane rectangular coordinate system; Obtaining a dust region value according to the coordinates of a plurality of dust region contour inflection points in each dust region; Arrange the multiple dust area values ​​in descending order to obtain an arrangement table, and select the first dust area value from the arrangement table and mark it as the target dust area value; Obtaining a dust removal compensation table, wherein the dust removal compensation table includes a plurality of dust area interval values ​​and dust removal compensation information corresponding to each dust area interval value; The corresponding dust removal compensation information is obtained from the dust removal compensation table according to the dust area interval value corresponding to the target dust area value.

5. The high-precision dust removal method for an optical lens according to claim 1, characterized in that: The step of obtaining a dust removal strategy according to dust distribution information, dust thickness information, and dust removal compensation information, and removing dust from the optical lens according to the dust removal strategy includes: Obtaining corresponding dust area distribution value, thickness ratio value and target dust area value respectively according to dust distribution information, dust thickness information and dust removal compensation information; Obtaining a strategy value according to a dust area distribution value, a thickness ratio value, and a target dust area value; Obtaining a strategy table, wherein the strategy table includes a plurality of strategy interval values ​​and a dust removal strategy corresponding to each strategy interval value; The corresponding dust removal strategy is obtained from the strategy table according to the strategy interval value corresponding to the strategy value, and the dust on the optical lens is removed according to the dust removal strategy.

6. The high-precision dust removal method for an optical lens according to claim 1, characterized in that: The steps of obtaining a dust removal path according to a dust removal strategy, and obtaining a cleaning feedback area of ​​an optical lens according to dust distribution information, dust thickness information, and the dust removal path include: Obtaining a dust removal path according to the dust removal strategy, and obtaining a corresponding dust removal path vector according to the dust removal path; Based on the dust distribution information and the dust thickness information, respectively obtain the corresponding dust area distribution value and thickness ratio; Obtaining feedback values ​​according to dust area distribution values, thickness ratios, and dust removal path vectors; Obtaining a region table, wherein the region table includes multiple feedback interval values ​​and a cleaning feedback region corresponding to each feedback interval value; The feedback interval value corresponding to the root feedback value obtains the corresponding cleaning feedback area from the area table.

7. The high-precision dust removal method for an optical lens according to claim 1, characterized in that: The steps of obtaining cleanliness information in the cleaning feedback area and determining whether the cleanliness information meets the preset conditions, and if not, re-obtaining dust distribution information, dust thickness information, and dust removal compensation information, and obtaining a new dust removal strategy in combination with the cleanliness information, and executing the dust removal strategy until the preset conditions are met include: Obtain cleanliness information in the cleaning feedback area, and obtain corresponding cleanliness values ​​according to the cleanliness information; Obtain standard cleanliness threshold; Determine whether the cleanliness value is lower than the standard cleanliness threshold; If the cleanliness value is lower than the standard cleanliness threshold, the dust removal strategy executed on the surface of the optical lens is determined to be abnormal and marked as an abnormal dust removal strategy; If the cleanliness value is not lower than the standard cleanliness threshold, it is determined that the dust removal strategy implemented on the surface of the optical lens is normal; After obtaining the abnormal dust removal strategy, the dust distribution information, dust thickness information and dust removal compensation information are re-obtained, and a new dust removal strategy is obtained in combination with the cleanliness information, and the dust removal strategy is executed until the preset conditions are met.

8. The high-precision dust removal method for an optical lens according to claim 7, characterized in that: After obtaining the abnormal dust removal strategy, re-obtaining dust distribution information, dust thickness information, and dust removal compensation information, and obtaining a new dust removal strategy in combination with the cleanliness information, and executing the dust removal strategy until the preset conditions are met, including: After obtaining the abnormal dust removal strategy, re-obtain dust distribution information, dust thickness information and dust removal compensation information, obtain corresponding feedback dust distribution information, feedback dust thickness information and feedback dust removal compensation information based on the re-obtained dust distribution information, dust thickness information and dust removal compensation information, and mark them as feedback dust area distribution value, feedback thickness ratio and feedback dust area value respectively; Obtain a comprehensive value based on the feedback dust area distribution value, feedback thickness ratio value, feedback dust area value and cleanliness value; Obtaining a reset table, wherein the reset table includes multiple comprehensive interval values ​​and a reset dust removal strategy corresponding to each comprehensive interval value; According to the comprehensive interval value corresponding to the comprehensive value, the corresponding reset dust removal strategy is obtained from the reset table, and the reset dust removal strategy is executed. According to the reset dust removal strategy, the reset dust removal path is obtained, and the reset dust removal path is returned as the dust removal path to the step of obtaining the cleaning feedback area of ​​the optical lens according to the dust distribution information, dust thickness information and dust removal path.

9. A high-precision dust removal system for an optical lens, applied to the high-precision dust removal method for an optical lens as claimed in any one of claims 1 to 8, characterized in that: include: A dust distribution module is used to obtain surface image data of the optical lens and obtain dust distribution information according to the surface image data; A dust thickness module is used to obtain dust side view data of the optical lens and obtain dust thickness information according to the dust side view data; A dust removal compensation module is used to obtain dust area data of the optical lens and obtain dust removal compensation information according to the dust area data; A dust removal strategy module, used to obtain a dust removal strategy according to dust distribution information, dust thickness information and dust removal compensation information, and remove dust from the optical lens according to the dust removal strategy; A cleaning area module is used to obtain a dust removal path according to a dust removal strategy, and obtain a cleaning feedback area of ​​the optical lens according to dust distribution information, dust thickness information, and the dust removal path; The strategy feedback module is used to obtain the cleanliness information in the cleaning feedback area and determine whether the cleanliness information meets the preset conditions. If not, the dust distribution information, dust thickness information and dust removal compensation information are re-obtained, and a new dust removal strategy is obtained in combination with the cleanliness information, and the dust removal strategy is executed until the preset conditions are met.

10. A high-precision dust removal terminal for an optical lens, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon; When one or more programs are executed by one or more processors, the one or more processors implement the high-precision dust removal method for the optical lens as described in any one of claims 1 to 8.

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