High-precision dust removal method and system for optical lens

Abstract

The present application belongs to the technical field of optical lens dust removal, and specifically relates to a high-precision dust removal method and system for optical lenses. The present application uses multiple imaging data to verify each other, significantly improving the accuracy of dust detection, avoiding the missed detection or misjudgment problems that may occur in traditional single detection methods, and thereby improving the reliability of the overall dust removal effect. The present application can monitor the cleaning result in real time and dynamically adjust the 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 self-adaptive capability, which can cope with various environmental and dust accumulation changes, ensuring that the cleaning quality can still be guaranteed under complex working conditions. The present application uses compensation information to provide customized cleaning solutions for different regions, and specifically deals with dust of different thicknesses and different accumulation conditions, avoiding the phenomenon of excessive cleaning or insufficient cleaning, and achieving the goal of protecting the lens surface and prolonging the service life of the lens.

Description

Technical Field

[0001] This invention belongs to the field of optical lens dust removal technology, specifically relating to a high-precision dust removal method and system for optical lenses. Background Technology

[0002] With the widespread application of precision optical equipment, optical lenses have become key components in cameras, microscopes, telescopes, projection devices, and smart devices. The imaging quality of an optical lens largely depends on the cleanliness of its surface. However, during long-term use, lens surfaces are highly susceptible to dust particle contamination, especially in specialized environments such as industrial, medical, and aerospace fields. Dust contamination can significantly reduce image sharpness, affect equipment performance, and even lead to image recognition failures or precision measurement deviations. Therefore, how to efficiently and accurately remove dust from lens surfaces has become an important issue in optical system maintenance.

[0003] In existing technologies, commonly used dust removal methods include mechanical wiping, airflow purging, and electrostatic adsorption. While these methods can achieve dust removal to some extent, they mostly rely on manual operation or simple physical mechanisms, making it difficult to accurately locate the dust removal path based on the actual dust distribution. This often leads to repeated cleaning of some areas while some dust-dense areas remain uncovered, limiting the dust removal effect. Most systems cannot identify the thickness distribution of dust, nor can they distinguish between fine particles and heavily adhered contaminants, resulting in an ineffective match between dust removal intensity and mode. Most are single-execution dust removal systems, lacking cleanliness detection and feedback mechanisms, and unable to dynamically adjust dust removal strategies based on cleaning results, resulting in "incomplete removal" or "over-cleaning." Current systems have fixed dust removal modes and cannot perform strategy compensation and path optimization based on different lens structures, contamination patterns, or historical data, making it difficult to meet the needs of high-precision optical equipment. Summary of the Invention

[0004] The purpose of this invention is to provide a high-precision dust removal method and system for optical lenses, which can perform zoned, graded, and closed-loop controlled cleaning of the optical lens surface to improve lens cleanliness, imaging quality, and equipment stability.

[0005] The specific technical solution adopted by this invention is as follows:

[0006] A high-precision dust removal method for optical lenses includes:

[0007] Acquire surface image data of the optical lens, and obtain dust distribution information based on the surface image data;

[0008] Obtain dust side-view data from the optical lens, and obtain dust thickness information based on the dust side-view data;

[0009] Acquire dust area data of the optical lens, and obtain dust removal compensation information based on the dust area data;

[0010] A dust removal strategy is obtained based on dust distribution information, dust thickness information, and dust removal compensation information, and dust on the optical lens is removed according to the dust removal strategy.

[0011] The dust removal path is obtained based on the dust removal strategy, and the cleaning feedback area of ​​the optical lens is obtained based on the dust distribution information, dust thickness information, and dust removal path.

[0012] Obtain cleanliness information within the cleaning feedback area and determine whether the cleanliness information meets the preset conditions. If not, re-obtain 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.

[0013] In a preferred embodiment, the step of acquiring surface image data of the optical lens and obtaining dust distribution information based on the surface image data includes:

[0014] Acquire surface image data of the optical lens, and obtain surface dust images based on the surface image data;

[0015] A Cartesian coordinate system is constructed on the surface dust image, and the center coordinates of multiple dust regions are obtained based on the Cartesian coordinate system;

[0016] The dust area distribution value is obtained based on the center coordinates of multiple dust areas;

[0017] Obtain a dust distribution table, which includes multiple dust area distribution interval values ​​and dust distribution information corresponding to each dust area distribution interval value;

[0018] The dust distribution information is obtained from the dust distribution table based on the dust distribution range value corresponding to the dust distribution value.

[0019] In a preferred embodiment, the steps of acquiring dust side-view data of the optical lens and obtaining dust thickness information based on the dust side-view data include:

[0020] Acquire dust side-view data of the optical lens, and obtain dust side-view images of each dust area based on the dust side-view data;

[0021] Obtain a baseline dust side view image, and then overlay the dust side view image of each dust region with the baseline dust side view image to form a composite dust side view image;

[0022] A Cartesian coordinate system is constructed in each composite dust side view image, wherein the X-axis of the Cartesian coordinate system coincides with the plane of the optical lens, and the Y-axis coordinates of each dust side view image and the reference dust side view image are obtained respectively and labeled as dust side view value and reference dust side view value.

[0023] Obtain the ratio of the dust side view value to the reference dust side view value and mark it as the thickness ratio;

[0024] Obtain the thickness table, which includes multiple thickness ratios and the dust thickness information corresponding to each thickness ratio;

[0025] Obtain the corresponding dust thickness information from the thickness table based on the thickness ratio.

[0026] In a preferred embodiment, the steps of acquiring dust area data of the optical lens and obtaining dust removal compensation information based on the dust area data include:

[0027] Acquire dust area data of the optical lens, and obtain multiple dust area images based on the dust area data;

[0028] Construct a Cartesian coordinate system in the dust area image, and obtain the coordinates of multiple dust area contour inflection points in each dust area based on the Cartesian coordinate system;

[0029] The dust region value is obtained based on the coordinates of multiple dust region contour inflection points within each dust region.

[0030] Arrange multiple dust area values ​​in descending order to obtain a sorted list, and select the first dust area value from the sorted list as the target dust area value;

[0031] Obtain the dust removal compensation table, which includes multiple dust area interval values ​​and the corresponding dust removal compensation information for each dust area interval value;

[0032] The corresponding dust removal compensation information is obtained from the dust removal compensation table based on the dust area interval value corresponding to the target dust area value.

[0033] In a preferred embodiment, the steps of obtaining a dust removal strategy based on dust distribution information, dust thickness information, and dust removal compensation information, and removing dust from the optical lens according to the dust removal strategy, include:

[0034] Based on dust distribution information, dust thickness information, and dust removal compensation information, obtain the corresponding dust area distribution value, thickness ratio, and target dust area value, respectively.

[0035] The strategy value is obtained based on the dust area distribution value, thickness ratio, and target dust area value;

[0036] Obtain the strategy table, which includes multiple strategy interval values ​​and the dust removal strategy corresponding to each strategy interval value;

[0037] The corresponding dust removal strategy is obtained from the strategy table based on the strategy range value corresponding to the strategy value, and the dust on the optical lens is removed according to the dust removal strategy.

[0038] In a preferred embodiment, the steps of obtaining the dust removal path based on the dust removal strategy and obtaining the cleaning feedback area of ​​the optical lens based on dust distribution information, dust thickness information, and the dust removal path include:

[0039] The dust removal path is obtained based on the dust removal strategy, and the corresponding dust removal path vector is obtained based on the dust removal path.

[0040] Based on dust distribution information and dust thickness information, the corresponding dust area distribution value and thickness ratio value are obtained respectively;

[0041] Feedback values ​​are obtained based on dust area distribution values, thickness ratio values, and dust removal path vectors;

[0042] Obtain the area table, which includes multiple feedback interval values ​​and the cleaning feedback area corresponding to each feedback interval value;

[0043] The feedback interval value corresponding to the root feedback value is obtained from the area table to obtain the corresponding cleaning feedback area.

[0044] In a preferred embodiment, the steps of acquiring cleanliness information within the cleaning feedback area, determining whether the cleanliness information meets preset conditions, and if not, re-acquiring dust distribution information, dust thickness information, and dust removal compensation information, and combining this with the cleanliness information to obtain a new dust removal strategy, and executing the dust removal strategy until the preset conditions are met, include:

[0045] Obtain cleanliness information within the cleaning feedback area and obtain the corresponding cleanliness value based on the cleanliness information;

[0046] Obtain the standard cleanliness threshold;

[0047] Determine whether the cleanliness level is lower than the standard cleanliness threshold;

[0048] If the cleanliness value is lower than the standard cleanliness threshold, the dust removal strategy implemented on the surface of the optical lens is determined to be abnormal and marked as an abnormal dust removal strategy.

[0049] If the cleanliness value is not lower than the standard cleanliness threshold, the dust removal strategy applied to the surface of the optical lens is considered normal.

[0050] 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 by combining it with the cleanliness information. The dust removal strategy is then executed until the preset conditions are met.

[0051] In a preferred embodiment, after obtaining an abnormal dust removal strategy, the steps of re-obtaining dust distribution information, dust thickness information, and dust removal compensation information, and combining this with cleanliness information to obtain a new dust removal strategy, and then executing the dust removal strategy until preset conditions are met, include:

[0052] After obtaining the abnormal dust removal strategy, the dust distribution information, dust thickness information, and dust removal compensation information are re-obtained. Based on the re-obtained dust distribution information, dust thickness information, and dust removal compensation information, the corresponding feedback dust distribution information, feedback dust thickness information, and feedback dust removal compensation information are obtained and marked as feedback dust area distribution value, feedback thickness ratio, and feedback dust area value, respectively.

[0053] A comprehensive value is obtained based on the feedback dust area distribution value, feedback thickness ratio value, feedback dust area value, and cleanliness value.

[0054] Obtain the reset table, which includes multiple integrated interval values ​​and the corresponding reset dust removal strategy for each integrated interval value;

[0055] The corresponding reset dust removal strategy is obtained from the reset table based on the comprehensive interval value corresponding to the comprehensive value, and the reset dust removal strategy is executed. The reset dust removal path is obtained based on the reset dust removal strategy, and the reset dust removal path is used as the dust removal path to return to the step of obtaining the cleaning feedback area of ​​the optical lens based on the dust distribution information, dust thickness information and dust removal path.

[0056] The present invention also provides a high-precision dust removal system for optical lenses, used in the aforementioned high-precision dust removal method for optical lenses, comprising:

[0057] The dust distribution module is used to acquire surface image data of the optical lens and obtain dust distribution information based on the surface image data.

[0058] The dust thickness module is used to acquire dust side-view data of the optical lens and obtain dust thickness information based on the dust side-view data.

[0059] The dust removal compensation module is used to acquire dust area data of the optical lens and obtain dust removal compensation information based on the dust area data.

[0060] The dust removal strategy module is used to obtain a dust removal strategy based on dust distribution information, dust thickness information, and dust removal compensation information, and remove dust from the optical lens according to the dust removal strategy.

[0061] The cleaning area module is used to obtain the dust removal path according to the dust removal strategy, and to obtain the cleaning feedback area of ​​the optical lens based on the dust distribution information, dust thickness information and dust removal path.

[0062] The strategy feedback module is used to obtain cleanliness information within the cleaning feedback area and determine whether the cleanliness information meets the preset conditions. If it does not meet the preset conditions, it re-obtains dust distribution information, dust thickness information, and dust removal compensation information, and combines the cleanliness information to obtain a new dust removal strategy, and executes the dust removal strategy until the preset conditions are met.

[0063] And, a high-precision dust removal terminal for optical lenses, comprising:

[0064] One or more processors;

[0065] A storage device on which one or more programs are stored;

[0066] When one or more programs are executed by one or more processors, the one or more processors implement a high-precision dust removal method for optical lenses.

[0067] The technical effects achieved by this invention are as follows:

[0068] This invention significantly improves the accuracy of dust detection by employing multiple imaging data for cross-verification, avoiding the potential for missed detections or misjudgments that can occur with traditional single-detection methods. This enhances the reliability of the overall dust removal effect. It enables real-time monitoring of cleaning results and dynamic strategy adjustments based on feedback information. This closed-loop feedback not only improves dust removal efficiency but also gives the cleaning process an adaptive capability, allowing it to cope with various environmental conditions and changes in dust accumulation levels. This ensures that cleaning quality remains consistent even under complex working conditions. By utilizing compensation information, it provides customized cleaning solutions for different areas, specifically addressing dust of varying thicknesses and accumulation levels. This avoids over- or under-cleaning, achieving the goal of protecting the lens surface and extending its lifespan. High-precision dust removal significantly reduces residual dust on the lens surface, improving light transmittance and image quality. It provides stable and accurate performance support for high-end optical equipment in scientific research, medical applications, and industrial testing. Attached Figure Description

[0069] Figure 1 This is a flowchart of the method provided by the present invention.

[0070] Figure 2 This is a system module diagram provided by the present invention. Detailed Implementation

[0071] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0072] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0073] Secondly, the term "an 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" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that mutually excludes other embodiments.

[0074] Furthermore, the present invention will be described in detail with reference to the schematic diagrams. When describing the embodiments of the present invention in detail, the schematic diagrams are merely examples for ease of explanation and should not limit the scope of protection of the present invention.

[0075] Please see the appendix Figure 1 As shown, a high-precision dust removal method for optical lenses is provided, including:

[0076] S1. Obtain surface image data of the optical lens and obtain dust distribution information based on the surface image data;

[0077] S2. Obtain dust side-view data of the optical lens, and obtain dust thickness information based on the dust side-view data;

[0078] S3. Obtain dust area data of the optical lens and obtain dust removal compensation information based on the dust area data;

[0079] S4. Obtain a dust removal strategy based on dust distribution information, dust thickness information, and dust removal compensation information, and remove dust from the optical lens according to the dust removal strategy;

[0080] S5. Obtain the 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.

[0081] S6. Obtain the cleanliness information in the cleaning feedback area and determine whether the cleanliness information meets the preset conditions. If it does not meet the preset conditions, 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.

[0082] As described in steps S1 to S6 above, a high-resolution camera system is used to image the lens surface. Image processing algorithms are used to detect the distribution of dust on the lens surface. The high-resolution camera system also acquires side-view images of the lens and obtains dust thickness information. Compared to simple surface distribution detection, side-view data can reveal the accumulation of dust on the lens surface and its embedding depth in the microstructure. Based on the dust area data, the specific characteristics of the dust area are determined, and dust removal compensation information is generated. Combining the previously acquired data (dust distribution information, thickness information, and compensation information), the algorithm comprehensively determines the optimal dust removal strategy. This strategy can customize the cleaning plan according to the degree of dust accumulation and distribution location, precisely control the working state of the cleaning equipment, and effectively remove dust. A specific dust removal path is generated based on the dust removal strategy. Simultaneously, the cleaned lens area (cleaning feedback area) is determined based on dust information feedback. After initial dust removal, the cleanliness of the cleaning feedback area is detected again. By comparing it with a preset cleanliness standard, it is determined whether the current cleaning effect meets the requirements. If the detection result does not meet the preset conditions, relevant data (dust...) will be collected again. By combining information on dust distribution, thickness, and compensation with obtained cleaning feedback data, the dust removal strategy and path are dynamically adjusted until cleanliness standards are met, ensuring that each cleaning achieves or exceeds the expected results. Multiple imaging data (frontal, lateral, and regional characteristic data) are used for cross-verification, significantly improving the accuracy of dust detection and avoiding potential missed detections or misjudgments that may occur with traditional single-detection methods. This enhances the reliability of the overall dust removal effect. The system can monitor cleaning results in real time and dynamically adjust strategies based on feedback information. This closed-loop feedback not only improves dust removal efficiency but also enables the cleaning process to adapt to various environmental conditions and changes in dust accumulation levels, ensuring cleaning quality remains consistent even under complex working conditions. Compensation information is used to provide customized cleaning solutions for different areas, specifically addressing dust of varying thicknesses and accumulation levels, avoiding over- or under-cleaning. This achieves the goal of protecting the lens surface and extending its lifespan. High-precision dust removal significantly reduces residual dust on the lens surface, improving light transmittance and image quality, providing stable and accurate performance support for high-end optical equipment in scientific research, medical, or industrial testing fields.

[0083] In a preferred embodiment, the step of acquiring surface image data of the optical lens and obtaining dust distribution information based on the surface image data includes:

[0084] S101. Obtain surface image data of the optical lens, and obtain a surface dust image based on the surface image data;

[0085] S102. Construct a Cartesian coordinate system on the surface dust image, and obtain the center coordinates of multiple dust areas based on the Cartesian coordinate system;

[0086] S103. Obtain the dust area distribution value based on the center coordinates of multiple dust areas;

[0087] S104. Obtain a dust distribution table, wherein the dust distribution table includes multiple dust area distribution interval values ​​and dust distribution information corresponding to each dust area distribution interval value;

[0088] S105. Obtain the corresponding dust distribution information from the dust distribution table based on the dust distribution interval value corresponding to the dust area distribution value.

[0089] As described in steps S101 to S105 above, surface image data of the optical lens is acquired using a high-resolution imaging device. Image processing techniques (such as edge detection and binarization) are applied to the acquired image data to extract the morphology of dust on the lens surface, generating a clear surface dust image. A Cartesian coordinate system is constructed on the generated dust image, ensuring that each point in the image has an accurate spatial location. Based on the dust regions in the image, clustering or morphological methods are used to calculate the center coordinates of each dust region. These coordinates reflect the spatial distribution characteristics of dust on the lens surface. Based on the center coordinates of each dust region, the dust coverage or dust concentration within that region is further statistically analyzed and calculated, extracting the dust region distribution value for each region. The formula for calculating the dust region distribution value is as follows: In the formula, F represents the dust area distribution value, i represents the number of the X-axis coordinate of the center of multiple dust areas and the number of the Y-axis coordinate of the center of multiple dust areas, i=1,2,3…n, x i Let y be the X-axis coordinate of the center of the i-th dust region. i Let Y be the center coordinate of the i-th dust region. Based on different dust region distribution values, a dust distribution table is pre-constructed. This table divides multiple dust region distribution intervals and sets corresponding dust distribution information (such as concentration, area coverage, shape characteristics, etc.) for each interval. This distribution table plays a role in data mapping and classification, converting discrete dust information into a regularized data pattern. Using the obtained dust region distribution values, the pre-defined dust distribution information in the dust distribution table is retrieved according to the corresponding dust region distribution interval. Through image processing and coordinate system construction, the intuitive dust distribution is transformed 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 regions allows each dust region to be clearly located and identified, thereby achieving accurate control of the dust distribution on the lens surface. After constructing the dust distribution table, matching can be performed quickly based on the preset interval values, significantly improving the speed and efficiency of data processing.

[0090] In a preferred embodiment, the step of acquiring dust side-view data of the optical lens and obtaining dust thickness information based on the dust side-view data includes:

[0091] S201. Obtain dust side view data of the optical lens, and obtain dust side view images of each dust area based on the dust side view data;

[0092] 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 to form a composite dust side view image;

[0093] S203. Construct a Cartesian coordinate system in each composite dust side view image, wherein the X-axis of the Cartesian 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, which are respectively labeled as dust side view value and reference dust side view value.

[0094] S204. Obtain the ratio of the side-view dust value to the reference side-view dust value and mark it as the thickness ratio;

[0095] S205. Obtain a thickness table, wherein the thickness table includes multiple thickness ratios and dust thickness information corresponding to each thickness ratio;

[0096] S206. Obtain the corresponding dust thickness information from the thickness table based on the thickness ratio.

[0097] As described in steps S201 to S206 above, a dedicated side-view camera is used to acquire side image data of the optical lens in different dust areas. Based on the acquired data, a side view image of each dust area is extracted, thus clearly presenting the shape and outline of the dust in each area. A representative reference dust side view image is pre-acquired as a standard comparison. The side view images of each area are overlaid with the reference image to form a composite image. In each composite side view image, a Cartesian coordinate system is set, where the X-axis coincides with the lens plane to ensure that the positions in the image have an actual correspondence. The corresponding Y-axis coordinate values ​​are obtained from the dust side view image and the reference image, respectively labeled 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 is defined by a pre-designed thickness table containing multiple different thickness ratio ranges, each corresponding to a predetermined dust thickness. Based on the calculated thickness ratio, the corresponding dust thickness information is retrieved from the thickness table. In this way, the ratio data obtained from the side view image can directly correspond to the actual dust thickness value, providing accurate parameter support for dust removal operations. By using the numerical comparison method of the side view image, traditional visual judgment is transformed into quantitative calculation. By directly obtaining dust thickness information through ratio calculation, the measurement accuracy is significantly improved. This quantitative method avoids the uncertainty of traditional visual estimation, ensuring that dust removal work can be adjusted according to the actual dust thickness. The composite side view image overlays the actual acquired image and the standard reference image, which can not only capture the absolute value of dust thickness, but also reflect the relative change of dust state through the ratio.

[0098] In a preferred embodiment, the step of acquiring dust area data of the optical lens and obtaining dust removal compensation information based on the dust area data includes:

[0099] S301. Obtain dust area data of the optical lens, and obtain multiple dust area images based on the dust area data;

[0100] S302. Construct a Cartesian 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 Cartesian coordinate system.

[0101] S303. Obtain the dust area value based on the coordinates of multiple dust area outline inflection points within each dust area;

[0102] S304. Arrange multiple dust area values ​​in descending order to obtain a sorted list, and select the first dust area value from the sorted list as the target dust area value.

[0103] S305. Obtain the dust removal compensation table, wherein the dust removal compensation table includes multiple dust area interval values ​​and dust removal compensation information corresponding to each dust area interval value;

[0104] S306. Obtain the corresponding dust removal compensation information from the dust removal compensation table based on the dust area interval value corresponding to the target dust area value.

[0105] As described in steps S301 to S306 above, a high-precision imaging system is used to acquire dust area data from the surface of the optical lens, and the data is converted into multiple dust area images. A Cartesian coordinate system is constructed in each dust area image to ensure that each pixel in the image has a clear spatial location. Based on this coordinate system, edge detection or contour extraction algorithms are 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 acquired contour inflection point coordinates are digitized to calculate the dust area value. The formula for calculating the dust area value is as follows: Where Q represents the dust area value, g represents the coordinates of multiple dust area contour inflection points, g=1,2,3…h, U g U is represented as the X-axis coordinate of the inflection point of the g-th dust region contour. g+1 V is represented by the X-axis coordinate of the inflection point of the (g+1)th dust region contour. g V is represented by the Y-axis coordinate of the inflection point of the g-th dust region contour. g+1 Let g+1 be the Y-axis coordinate of the inflection point of the (g+1)th dust area contour. When g is h, h+1 is 1. Arrange all dust area values ​​in descending order to obtain a sorting list. Select the dust area value with the largest value (first one) in the sorting list 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 and cleaning path adjustment plan. According to the interval where the target dust area value is located, the corresponding dust removal compensation information is obtained from the dust removal compensation table. The dust area data is quantified, and the contour inflection point is extracted through the Cartesian coordinate system. This achieves accurate measurement of the geometric features of the dust area. Quantifying the dust area value helps to accurately identify and locate more serious dust areas, ensuring that key areas are effectively cleaned.

[0106] In a preferred embodiment, the step of obtaining a dust removal strategy based on 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:

[0107] S401. Based on the dust distribution information, dust thickness information, and dust removal compensation information, obtain the corresponding dust area distribution value, thickness ratio, and target dust area value, respectively.

[0108] S402. Obtain the strategy value based on the dust area distribution value, thickness ratio value, and target dust area value;

[0109] S403. Obtain the strategy table, wherein the strategy table includes multiple strategy interval values ​​and the dust removal strategy corresponding to each strategy interval value;

[0110] 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 from the optical lens according to the dust removal strategy.

[0111] As described in steps S401 to S404 above, dust area distribution values ​​are extracted using the previously obtained dust distribution information. By analyzing dust thickness information, thickness ratios are extracted. Combined with pre-determined dust area data from the dust removal compensation information, the most representative or critical area value in the dust area is selected, i.e., the target dust area value. Using the obtained dust area distribution value, thickness ratio, and target dust area value, a strategy value is calculated. The formula for calculating 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. A strategy table is pre-established, which will include... The dust removal task is divided into different strategy intervals. Each strategy interval value corresponds to a set of dust removal strategies suitable for the current dust conditions. Based on the generated strategy values, the corresponding strategy interval value is searched in the strategy table to obtain the specific dust removal strategy. After obtaining the corresponding dust removal strategy, the dust on the optical lens is removed according to the preset dust removal strategy's cleaning path, cleaning method, and compensation parameters, achieving efficient and precise cleaning operations. The dust distribution information, thickness information, and area compensation information are integrated and quantified to generate a comprehensive strategy value, making dust removal decisions based on objective and quantitative data, avoiding errors caused by a single data source, and making the decisions more accurate.

[0112] In a preferred embodiment, the steps of obtaining a dust removal path based on a dust removal strategy and obtaining a cleaning feedback area for the optical lens based on dust distribution information, dust thickness information, and the dust removal path include:

[0113] 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;

[0114] S502. Based on dust distribution information and dust thickness information, obtain the corresponding dust area distribution value and thickness ratio value respectively;

[0115] S503. Obtain feedback values ​​based on dust area distribution values, thickness ratio values, and dust removal path vectors;

[0116] S504. Obtain the area table, wherein the area table includes multiple feedback interval values ​​and the cleaning feedback area corresponding to each feedback interval value;

[0117] S505. The feedback interval value corresponding to the root feedback value is obtained from the area table to obtain the corresponding cleaning feedback area.

[0118] As described in steps S501 to S505 above, based on the previously established dust removal strategy, a dust removal path is extracted. After the dust removal path is generated, the path is converted into a path vector. Based on the pre-collected front view image data, the dust area distribution value is extracted. Combined with the side view data, the height ratio of each dust area to the corresponding reference image is calculated to obtain the quantified value of the dust thickness. Based on the dust area distribution value, the thickness ratio, and the dust removal path vector, a feedback value is calculated. The formula for the feedback value is: In the formula, 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, dividing the feedback value into multiple feedback intervals. Each interval corresponds to a cleaning feedback area. Based on 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 dust distribution and thickness quantification data, the feedback value can be calculated in real time, thereby realizing the instant monitoring of the cleaning effect.

[0119] In a preferred embodiment, the steps of acquiring cleanliness information within the cleaning feedback area, determining whether the cleanliness information meets preset conditions, and if not, re-acquiring dust distribution information, dust thickness information, and dust removal compensation information, and combining this with the cleanliness information to obtain a new dust removal strategy, and executing the dust removal strategy until the preset conditions are met, include:

[0120] S601. Obtain the cleanliness information within the cleaning feedback area and obtain the corresponding cleanliness value based on the cleanliness information;

[0121] S602. Obtain the standard cleanliness threshold;

[0122] S603. Determine whether the cleanliness value is lower than the standard cleanliness threshold.

[0123] If the cleanliness value is lower than the standard cleanliness threshold, the dust removal strategy implemented on the surface of the optical lens is determined to be abnormal and marked as an abnormal dust removal strategy.

[0124] If the cleanliness value is not lower than the standard cleanliness threshold, the dust removal strategy applied to the surface of the optical lens is considered normal.

[0125] S604. After obtaining the abnormal dust removal strategy, re-obtain the dust distribution information, dust thickness information, and dust removal compensation information, and combine them with the cleanliness information to obtain a new dust removal strategy, and execute the dust removal strategy until the preset conditions are met.

[0126] As described in steps S601 to S604 above, specialized detection equipment (such as a high-resolution camera or sensor) is used to collect actual cleanliness data within the cleaning feedback area. Based on the acquired data, the corresponding cleanliness value is extracted through image processing. A preset or read standard cleanliness threshold is used as a benchmark to determine whether the cleaning effect meets the requirements. This threshold can be customized according to the equipment requirements, working environment, and application standards to ensure that the cleaning effect meets actual needs. The actual 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 current dust removal strategy on the optical lens surface has not achieved the expected effect and is marked as an abnormal dust removal strategy; otherwise, the executed dust removal strategy is determined to be effective. In normal operation, when an abnormal dust removal strategy is detected, a closed-loop feedback mechanism is activated to reacquire the latest dust distribution, dust thickness, and dust removal compensation information. Using the current cleanliness information and the latest collected data, a new dust removal strategy is recalculated and generated, and the cleaning operation is adjusted accordingly. This process is continuously repeated until the acquired cleanliness value meets or exceeds the standard cleanliness threshold, thereby ensuring that the final cleaning effect meets the preset conditions. The mechanism can quickly judge the execution effect of the dust removal strategy and promptly detect abnormalities. The closed-loop feedback mechanism has the ability to self-detect and self-correct, ensuring that the dust removal operation is always in the best state. The dynamic optimization strategy reduces human intervention and lowers the risk of repeated operations due to incomplete cleaning.

[0127] In a preferred embodiment, after obtaining an abnormal dust removal strategy, the steps of re-obtaining dust distribution information, dust thickness information, and dust removal compensation information, and combining them with cleanliness information to obtain a new dust removal strategy, and executing the dust removal strategy until preset conditions are met, include:

[0128] 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 feedback dust area distribution value, feedback thickness ratio, and feedback dust area value, respectively.

[0129] S6042. Obtain a comprehensive value based on the feedback dust area distribution value, feedback thickness ratio value, feedback dust area value, and cleanliness value.

[0130] S6043. Obtain the reset table, wherein the reset table includes multiple comprehensive interval values ​​and the corresponding reset dust removal strategy for each comprehensive interval value;

[0131] S6044. 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 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 as the dust removal path.

[0132] As described in steps S6041 to S6044 above, when an anomaly is detected in the current dust removal strategy, the dust distribution information, dust thickness information, and dust removal compensation information in the current environment are re-collected. Based on the latest collected data, through corresponding image processing and numerical calculations, feedback indicators are generated, including dust area distribution value, thickness ratio, and dust area value. These newly acquired feedback indicators are then integrated with the current cleanliness value to calculate a comprehensive value. The formula for calculating the comprehensive value is as follows: In the formula, Z represents the feedback value. This is represented as the feedback dust area distribution value. This is expressed as the feedback thickness ratio. The feedback dust removal path vector is represented by D, where D represents the cleanliness value. A reset table is pre-established, which divides the comprehensive value into multiple intervals and assigns a predefined reset dust removal strategy to each interval. Based on 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. After calculation, the new dust removal path is 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 the dust removal effect is found to be substandard, data can be automatically re-collected, feedback indicators updated, and comprehensive values ​​calculated, thereby achieving intelligent decision-making and formulating new dust removal strategies. This automated 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 respond quickly 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 and reduce imaging quality problems and equipment wear caused by dust residue.

[0133] Please see the appendix Figure 2 As shown, the present invention also provides a high-precision dust removal system for optical lenses, used in the aforementioned high-precision dust removal method for optical lenses, comprising:

[0134] The dust distribution module is used to acquire surface image data of the optical lens and obtain dust distribution information based on the surface image data.

[0135] The dust thickness module is used to acquire dust side-view data of the optical lens and obtain dust thickness information based on the dust side-view data.

[0136] The dust removal compensation module is used to acquire dust area data of the optical lens and obtain dust removal compensation information based on the dust area data.

[0137] The dust removal strategy module is used to obtain a dust removal strategy based on dust distribution information, dust thickness information, and dust removal compensation information, and remove dust from the optical lens according to the dust removal strategy.

[0138] The cleaning area module is used to obtain the dust removal path according to the dust removal strategy, and to obtain the cleaning feedback area of ​​the optical lens based on the dust distribution information, dust thickness information and dust removal path.

[0139] The strategy feedback module is used to obtain cleanliness information within the cleaning feedback area and determine whether the cleanliness information meets the preset conditions. If it does not meet the preset conditions, it re-obtains dust distribution information, dust thickness information, and dust removal compensation information, and combines the cleanliness information to obtain a new dust removal strategy, and executes the dust removal strategy until the preset conditions are met.

[0140] The dust distribution module acquires image data of the optical lens surface using a high-definition camera and extracts dust locations using image processing algorithms (such as edge detection and grayscale enhancement) to generate dust distribution information. The dust thickness module extracts the vertical thickness information of dust accumulation by acquiring images from the lens side view angle or using techniques such as structured light to generate dust thickness information. The dust removal compensation module obtains dust removal compensation information based on the area of ​​the dust region. The dust removal strategy module constructs a comprehensive index (such as a strategy value) using dust distribution, dust thickness, and dust removal compensation information, and obtains a suitable dust removal strategy (such as airflow intensity, static charge magnitude, path planning method, etc.) by looking up a table or using a model, and applies the strategy to the actual dust removal execution device (such as a jet nozzle, mechanical brush, etc.). The cleaning area module generates a specific dust removal path based on the dust removal strategy, and then calculates the cleaning area of ​​the path based on the dust distribution and thickness. The system uses a coverage area feedback value to match cleaning feedback areas from a preset area table, enabling regional evaluation of dust removal effectiveness. The strategy feedback module detects the cleanliness of the feedback area, obtains a cleanliness value, and compares it with a set standard cleanliness threshold. If the value is below the threshold, it is marked as an abnormal dust removal strategy. Dust information is then re-collected, and combined with the cleanliness value, a new adaptive dust removal strategy is generated (strategy reset). This process is repeated until the preset conditions are met. If the threshold is reached, it is marked as a normal dust removal strategy, completing the current cleaning task. This overcomes the limitations of traditional single-image judgment, making dust removal strategies more targeted. It can dynamically generate cleaning paths based on the complexity of dust distribution, enabling differentiated dust removal treatment for different parts of the optical lens. The strategy feedback module enables real-time monitoring of cleanliness and identification of abnormal strategies. If the detection effect fails to meet the standard, the strategy can be quickly reset, forming a closed-loop control mechanism.

[0141] And, a high-precision dust removal terminal for optical lenses, comprising:

[0142] One or more processors;

[0143] A storage device on which one or more programs are stored;

[0144] When one or more programs are executed by one or more processors, the one or more processors implement a high-precision dust removal method for optical lenses.

[0145] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited.

Claims

1. A high-precision dust removal method for optical lenses, characterized in that, include: Acquire surface image data of the optical lens, and obtain dust distribution information based on the surface image data; Obtain dust side-view data from the optical lens, and obtain dust thickness information based on the dust side-view data; Acquire dust area data of the optical lens, and obtain dust removal compensation information based on the dust area data; A dust removal strategy is obtained based on dust distribution information, dust thickness information, and dust removal compensation information, and dust on the optical lens is removed according to the dust removal strategy. The dust removal path is obtained based on the dust removal strategy, and the cleaning feedback area of ​​the optical lens is obtained based on the dust distribution information, dust thickness information, and dust removal path. Obtain cleanliness information within the cleaning feedback area and determine whether the cleanliness information meets the preset conditions. If not, re-obtain 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. The steps of acquiring surface image data of an optical lens and obtaining dust distribution information based on the surface image data include: Acquire surface image data of the optical lens, and obtain surface dust images based on the surface image data; A Cartesian coordinate system is constructed on the surface dust image, and the center coordinates of multiple dust regions are obtained based on the Cartesian coordinate system; The dust area distribution value is obtained based on the center coordinates of multiple dust areas; Obtain a dust distribution table, which includes multiple dust area distribution interval values ​​and dust distribution information corresponding to each dust area distribution interval value; Obtain the corresponding dust distribution information from the dust distribution table based on the dust distribution range value corresponding to the dust area distribution value; The steps for acquiring dust area data of the optical lens and obtaining dust removal compensation information based on the dust area data include: Acquire dust area data of the optical lens, and obtain multiple dust area images based on the dust area data; Construct a Cartesian coordinate system in the dust area image, and obtain the coordinates of multiple dust area contour inflection points in each dust area based on the Cartesian coordinate system; The dust region value is obtained based on the coordinates of multiple dust region contour inflection points within each dust region. Arrange multiple dust area values ​​in descending order to obtain a sorted list, and select the first dust area value from the sorted list as the target dust area value; Obtain the dust removal compensation table, which includes multiple dust area interval values ​​and the corresponding dust removal compensation information for each dust area interval value; Based on the dust area interval value corresponding to the target dust area value, obtain the corresponding dust removal compensation information from the dust removal compensation table. The dust removal compensation information is the cleaning path adjustment plan. The steps of obtaining the dust removal path based on the dust removal strategy, and obtaining the cleaning feedback area of ​​the optical lens based on dust distribution information, dust thickness information, and the dust removal path, include: The dust removal path is obtained based on the dust removal strategy, and the corresponding dust removal path vector is obtained based on the dust removal path. Based on dust distribution information and dust thickness information, the corresponding dust area distribution value and thickness ratio value are obtained respectively; Feedback values ​​are obtained based on dust area distribution values, thickness ratio values, and dust removal path vectors; Obtain the area table, which includes multiple feedback interval values ​​and the cleaning feedback area corresponding to each feedback interval value; The corresponding cleaning feedback area is obtained from the area table based on the feedback interval value corresponding to the feedback value.

2. The high-precision dust removal method for optical lenses according to claim 1, characterized in that, The steps for acquiring dust side-view data of the optical lens and obtaining dust thickness information based on the dust side-view data include: Acquire dust side-view data of the optical lens, and obtain dust side-view images of each dust area based on the dust side-view data; Obtain a baseline dust side view image, and then overlay the dust side view image of each dust region with the baseline dust side view image to form a composite dust side view image; A Cartesian coordinate system is constructed in each composite dust side view image, wherein the X-axis of the Cartesian coordinate system coincides with the plane of the optical lens, and the Y-axis coordinates of each dust side view image and the reference dust side view image are obtained respectively and labeled as dust side view value and 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 the thickness table, which includes multiple thickness ratios and the dust thickness information corresponding to each thickness ratio; Obtain the corresponding dust thickness information from the thickness table based on the thickness ratio.

3. The high-precision dust removal method for optical lenses according to claim 1, characterized in that, The steps of obtaining a dust removal strategy based on dust distribution information, dust thickness information, and dust removal compensation information, and removing dust from the optical lens according to the dust removal strategy, include: Based on dust distribution information, dust thickness information, and dust removal compensation information, obtain the corresponding dust area distribution value, thickness ratio, and target dust area value, respectively. The strategy value is obtained based on the dust area distribution value, thickness ratio, and target dust area value; Obtain the strategy table, which includes multiple strategy interval values ​​and the dust removal strategy corresponding to each strategy interval value; The corresponding dust removal strategy is obtained from the strategy table based on the strategy range value corresponding to the strategy value, and the dust on the optical lens is removed according to the dust removal strategy.

4. The high-precision dust removal method for optical lenses according to claim 1, characterized in that, The process of acquiring cleanliness information within the cleaning feedback area and determining whether the cleanliness information meets preset conditions, and if not, re-acquiring dust distribution information, dust thickness information, and dust removal compensation information, and combining this with the cleanliness information to obtain a new dust removal strategy, and executing the dust removal strategy until the preset conditions are met, includes the following steps: Obtain cleanliness information within the cleaning feedback area and obtain the corresponding cleanliness value based on the cleanliness information; Obtain the standard cleanliness threshold; Determine whether the cleanliness level is lower than the standard cleanliness threshold; If the cleanliness value is lower than the standard cleanliness threshold, the dust removal strategy implemented 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, the dust removal strategy applied to the surface of the optical lens is considered 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 by combining it with the cleanliness information. The dust removal strategy is then executed until the preset conditions are met.

5. The high-precision dust removal method for optical lenses according to claim 4, characterized in that, After obtaining the abnormal dust removal strategy, the following steps are taken: re-acquire dust distribution information, dust thickness information, and dust removal compensation information, and combine them with cleanliness information to obtain a new dust removal strategy. This new dust removal strategy is then executed until the preset conditions are met. After obtaining the abnormal dust removal strategy, the dust distribution information, dust thickness information, and dust removal compensation information are re-obtained. Based on the re-obtained dust distribution information, dust thickness information, and dust removal compensation information, the corresponding feedback dust distribution information, feedback dust thickness information, and feedback dust removal compensation information are obtained and marked as feedback dust area distribution value, feedback thickness ratio, and feedback dust area value, respectively. A comprehensive value is obtained based on the feedback dust area distribution value, feedback thickness ratio value, feedback dust area value, and cleanliness value. Obtain the reset table, which includes multiple comprehensive interval values ​​and the corresponding dust removal strategy for each comprehensive interval value; The corresponding reset dust removal strategy is obtained from the reset table based on the comprehensive interval value corresponding to the comprehensive value, and the reset dust removal strategy is executed. The reset dust removal path is obtained based on the reset dust removal strategy, and the reset dust removal path is used as the dust removal path to return to the step of obtaining the cleaning feedback area of ​​the optical lens based on the dust distribution information, dust thickness information and dust removal path.

6. A high-precision dust removal system for optical lenses, applied to the high-precision dust removal method for optical lenses according to any one of claims 1 to 5, characterized in that, include: The dust distribution module is used to acquire surface image data of the optical lens and obtain dust distribution information based on the surface image data. The dust thickness module is used to acquire dust side-view data of the optical lens and obtain dust thickness information based on the dust side-view data. The dust removal compensation module is used to acquire dust area data of the optical lens and obtain dust removal compensation information based on the dust area data. The dust removal strategy module is used to obtain a dust removal strategy based on dust distribution information, dust thickness information, and dust removal compensation information, and remove dust from the optical lens according to the dust removal strategy. The cleaning area module is used to obtain the dust removal path according to the dust removal strategy, and to obtain the cleaning feedback area of ​​the optical lens based on the dust distribution information, dust thickness information and dust removal path. The strategy feedback module is used to obtain cleanliness information within the cleaning feedback area and determine whether the cleanliness information meets the preset conditions. If it does not meet the preset conditions, it re-obtains dust distribution information, dust thickness information, and dust removal compensation information, and combines the cleanliness information to obtain a new dust removal strategy, and executes the dust removal strategy until the preset conditions are met.

7. A high-precision dust removal terminal for optical lenses, characterized in that, include: 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 according to any one of claims 1 to 5.

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