CN120190181B - A high-precision dust removal method and system for optical lenses - Google Patents
A high-precision dust removal method and system for optical lensesInfo
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- CN120190181B CN120190181B CN202510630027.7A CN202510630027A CN120190181B CN 120190181 B CN120190181 B CN 120190181B CN 202510630027 A CN202510630027 A CN 202510630027A CN 120190181 B CN120190181 B CN 120190181B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B08—CLEANING
- B08B—CLEANING IN GENERAL; PREVENTION OF FOULING IN GENERAL
- B08B11/00—Cleaning flexible or delicate articles by methods or apparatus specially adapted thereto
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
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Abstract
本发明属于光学镜头除尘技术领域，具体涉及一种光学镜头的高精度除尘方法及系统。该发明，采用多种成像数据相互印证，显著提高了尘埃检测的准确性，避免传统单一检测方式可能出现的漏检或误判问题，进而提高整体除尘效果的可靠性，能够实时监控清洁结果，并根据反馈信息进行动态策略调整，闭环反馈不仅提高了除尘效率，还使清洁过程具备自适应能力，能够应对各种环境和尘埃堆积程度的变化，确保清洁质量在复杂工况下仍能得到保障，利用补偿信息为不同区域提供定制化的清洁方案，有针对性地处理不同厚度、不同堆积情况的尘埃，避免了过度清洁或不足清洁的现象，达到保护镜头表面及延长镜头使用寿命的目标。
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. This invention employs multiple imaging data to cross-verify, 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. It enables real-time monitoring of cleaning results and dynamic strategy adjustments based on feedback information. The closed-loop feedback not only improves dust removal efficiency but also gives the cleaning process adaptive capabilities, enabling 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, avoiding over-cleaning or under-cleaning, and ultimately protecting the lens surface and extending its lifespan.
Description
Technical Field
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.
Background
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 degree of 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 treatment, aerospace and the like, which can significantly reduce imaging definition, affect device performance, and even cause image recognition failure or precision measurement deviation. Therefore, how to remove dust from the lens surface efficiently and accurately becomes an important issue in maintenance of the optical system.
In the prior art, common dust removal modes comprise mechanical wiping, airflow sweeping, electrostatic adsorption and the like. Although the dust removal effect can be achieved to a certain extent by the methods, most of the methods rely on manual operation or a simple physical mechanism, the dust removal paths are difficult to accurately position according to the actual distribution of dust, partial areas are often repeatedly cleaned, certain dust-dense areas are not effectively covered, the dust removal effect is limited, most of the systems cannot identify the thickness distribution of the dust, light particles cannot be distinguished and heavy pollution with strong adhesiveness is caused, the dust removal strength and the mode cannot be effectively matched, most of the methods are used for singly executing dust removal, the cleanliness detection and feedback mechanism is lacked, the dust removal strategy cannot be dynamically adjusted according to the cleaning effect, the phenomenon of 'incomplete cleaning' or 'excessive cleaning' exists, the current system dust removal mode is fixed, policy compensation and path optimization cannot be carried out according to different lens structures, pollution forms or historical data, and the requirements of high-precision optical equipment are difficult to meet.
Disclosure of Invention
The invention aims to provide a high-precision dust removing method and a high-precision dust removing system for an optical lens, which can carry out cleaning treatment of partition, grading and closed-loop control on the surface of the optical lens so as to improve the cleanliness, imaging quality and equipment stability of the lens.
The technical scheme adopted by the invention is as follows:
a high-precision dust removing method of an optical lens comprises the following steps:
acquiring surface image data of an optical lens, and acquiring dust distribution information according to the surface image data;
acquiring dust side-looking data of the optical lens, and acquiring dust thickness information according to the dust side-looking data;
acquiring dust area data of the optical lens, and acquiring dust removal compensation information according to the dust area data;
Acquiring a dust removal strategy according to the dust distribution information, the dust thickness information and the dust removal compensation information, and removing dust of the optical lens according to the dust removal strategy;
Acquiring a dust removing path according to a dust removing strategy, and acquiring a cleaning feedback area of the optical lens according to dust distribution information, dust thickness information and the dust removing path;
Acquiring cleanliness information in the cleaning feedback area, judging whether the cleanliness information accords with preset conditions, if not, re-acquiring dust distribution information, dust thickness information and dust removal compensation information, acquiring a new dust removal strategy by combining the cleanliness information, and executing the dust removal strategy until the preset conditions are met.
In a preferred embodiment, the step of acquiring surface image data of the optical lens and acquiring dust distribution information from the surface image data includes:
Acquiring surface image data of an optical lens, and acquiring a surface dust image according to the surface image data;
constructing a plane rectangular coordinate system on the surface dust image, and acquiring center coordinates of a plurality of dust areas according to the plane rectangular coordinate system;
Acquiring dust area distribution values according to the central coordinates of the dust areas;
Acquiring a dust distribution table, wherein the dust distribution table comprises a plurality of dust area distribution interval values and dust distribution information corresponding to each dust area distribution interval value;
And acquiring corresponding dust distribution information from the dust distribution table according to the dust area distribution interval value corresponding to the dust area distribution value.
In a preferred embodiment, the step of acquiring dust side view data of the optical lens and acquiring dust thickness information according to the dust side view data includes:
acquiring dust side-looking data of the optical lens, and acquiring dust side-looking images of each dust area according to the dust side-looking data;
Acquiring a reference dust side view image, and overlapping 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 respectively acquiring the Y axis coordinate of each dust side view image and the Y axis coordinate of the reference dust side view image, and respectively marking the Y axis coordinate as a dust side view value and a reference dust side view value;
acquiring the ratio of the dust side view value to the reference dust side view value, and marking the ratio as a thickness ratio;
Acquiring a thickness table, wherein the thickness table comprises a plurality of thickness ratios and dust thickness information corresponding to each thickness ratio;
and acquiring corresponding dust thickness information from the thickness table according to the thickness ratio.
In a preferred embodiment, the step of acquiring dust area data of the optical lens and acquiring dust removal compensation information according to the dust area data includes:
acquiring dust area data of the optical lens, and acquiring a plurality of dust area images according to the dust area data;
constructing a plane rectangular coordinate system in the dust area image, and acquiring a plurality of dust area outline inflection point coordinates in each dust area according to the plane rectangular coordinate system;
acquiring dust area values according to coordinates of inflection points of outlines of a plurality of dust areas in each dust area;
arranging a plurality of dust area values in a sequence from big to small to obtain a ranking list, and selecting a first dust area value from the ranking list to be marked as a target dust area value;
acquiring a dust removal compensation table, wherein the dust removal compensation table comprises a plurality of dust area interval values and dust removal compensation information corresponding to each dust area interval value;
and acquiring 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.
In a preferred embodiment, the step of acquiring a dust removal policy according to the dust distribution information, the dust thickness information, and the dust removal compensation information, and removing dust of the optical lens according to the dust removal policy includes:
Respectively acquiring a corresponding dust area distribution value, a thickness ratio and a target dust area value according to the dust distribution information, the dust thickness information and the dust removal compensation information;
acquiring a strategy value according to the dust area distribution value, the thickness ratio and the target dust area value;
obtaining a policy table, wherein the policy table comprises a plurality of policy interval values and dust removal policies corresponding to each policy interval value;
And acquiring a corresponding dust removing strategy from the strategy table according to a strategy interval value corresponding to the strategy value, and removing dust of the optical lens according to the dust removing strategy.
In a preferred embodiment, the step of acquiring a dust removal path according to a dust removal policy, and acquiring a cleaning feedback area of the optical lens according to dust distribution information, dust thickness information, and the dust removal path includes:
Acquiring a dust removing path according to a dust removing strategy, and acquiring a corresponding dust removing path vector according to the dust removing path;
respectively acquiring corresponding dust area distribution values and thickness ratios based on dust distribution information and dust thickness information;
acquiring a feedback value according to the dust area distribution value, the thickness ratio and the dust removal path vector;
acquiring an area table, wherein the area table comprises multiple feedback interval values and a cleaning feedback area corresponding to each feedback interval value;
and acquiring a corresponding cleaning feedback area from the area table by the feedback interval value corresponding to the root feedback value.
In a preferred scheme, acquiring cleanliness information in a cleaning feedback area, judging whether the cleanliness information accords with preset conditions, if not, re-acquiring dust distribution information, dust thickness information and dust removal compensation information, acquiring a new dust removal strategy by combining the cleanliness information, and executing the dust removal strategy until the preset conditions are met, wherein the method comprises the following steps of:
acquiring cleanliness information in a cleaning feedback area, and acquiring a corresponding cleanliness value according to the cleanliness information;
acquiring a standard cleanliness threshold;
Judging whether the cleanliness value is lower than a standard cleanliness threshold value or not;
If the cleanliness value is lower than the standard cleanliness threshold, judging that the dust removal strategy executed on the surface of the optical lens is abnormal and marking the dust removal strategy as an abnormal dust removal strategy;
If the cleanliness value is not lower than the standard cleanliness threshold, judging that the dust removal strategy executed on the surface of the optical lens is normal;
And after acquiring the abnormal dust removal strategy, acquiring dust distribution information, dust thickness information and dust removal compensation information again, acquiring a new dust removal strategy by combining the cleanliness information, and executing the dust removal strategy until the preset condition is met.
In a preferred embodiment, after acquiring the abnormal dust removal policy, re-acquiring dust distribution information, dust thickness information and dust removal compensation information, acquiring a new dust removal policy by combining the cleanliness information, and executing the dust removal policy until the abnormal dust removal policy meets a preset condition, including:
After acquiring an abnormal dust removal strategy, re-acquiring dust distribution information, dust thickness information and dust removal compensation information, acquiring corresponding feedback dust distribution information, feedback dust thickness information and feedback dust removal compensation information based on the re-acquired dust distribution information, dust thickness information and dust removal compensation information, and marking the corresponding feedback dust distribution information, feedback dust thickness information and feedback dust removal compensation information as a feedback dust area distribution value, a feedback thickness ratio and a feedback dust area value respectively;
Acquiring a comprehensive value according to the feedback dust area distribution value, the feedback thickness ratio, the feedback dust area value and the cleanliness value;
acquiring a reset table, wherein the reset table comprises a plurality of comprehensive interval values and a reset dust removal strategy corresponding to each comprehensive interval value;
And acquiring a corresponding reset dust removing strategy from the reset table according to the comprehensive interval value corresponding to the comprehensive value, executing the reset dust removing strategy, acquiring a reset dust removing path according to the reset dust removing strategy, and returning the reset dust removing path as a dust removing path to a cleaning feedback area of the optical lens according to the dust distribution information, the dust thickness information and the dust removing path.
The invention also provides a high-precision dust removing system of the optical lens, which is used for the high-precision dust removing method of the optical lens and comprises the following steps:
the dust distribution module is used for acquiring surface image data of the optical lens and acquiring dust distribution information according to the surface image data;
the dust thickness module is used for acquiring dust side-looking data of the optical lens and acquiring dust thickness information according to the dust side-looking data;
the dust removal compensation module is used for acquiring dust area data of the optical lens and acquiring dust removal compensation information according to the dust area data;
the dust removal strategy module is used for acquiring a dust removal strategy according to the dust distribution information, the dust thickness information and the dust removal compensation information and removing dust of the optical lens according to the dust removal strategy;
The cleaning area module is used for acquiring a dust removing path according to a dust removing strategy and acquiring a cleaning feedback area of the optical lens according to dust distribution information, dust thickness information and the dust removing path;
the strategy feedback module is used for acquiring the cleanliness information in the cleaning feedback area, judging whether the cleanliness information accords with preset conditions, if not, acquiring the dust distribution information, the dust thickness information and the dust removal compensation information again, acquiring a new dust removal strategy by combining the cleanliness information, and executing the dust removal strategy until the preset conditions are met.
And, a high accuracy dust removal terminal of optical lens, includes:
One or more processors;
a storage device having one or more programs stored thereon;
when the one or more programs are executed by the one or more processors, the one or more processors implement a high-precision dust removal method for the optical lens.
The invention has the technical effects that:
According to the invention, when a plurality of imaging data are adopted for mutual verification, the accuracy of dust detection is obviously improved, the problem of missed detection or misjudgment possibly occurring in the 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, dynamic strategy adjustment is carried out according to feedback information, the dust removal efficiency is improved, the cleaning process has self-adaptive capability, the change of various environments and dust accumulation degree can be dealt with, the cleaning quality can be ensured under complex working conditions, customized cleaning schemes are provided for different areas by utilizing compensation information, dust with different thicknesses and different accumulation conditions can be processed in a targeted manner, the phenomenon of excessive cleaning or insufficient cleaning is avoided, the aims of protecting the lens surface and prolonging the service life of the lens are achieved, the residual dust on the lens surface can be obviously reduced by high-precision dust removal, the light transmittance and the imaging quality are improved, and stable and accurate performance support is provided for high-end optical equipment in the fields of scientific research, medical treatment or industrial detection and the like.
Drawings
FIG. 1 is a flow chart of a method provided by the present invention.
Fig. 2 is a block diagram of a system provided by the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one preferred embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Further, the present invention will be described in detail with reference to the drawings, which are only examples for convenience of illustration, and should not limit the scope of the present invention.
Referring to fig. 1, a high-precision dust removing method for an optical lens is provided, including:
S1, acquiring surface image data of an optical lens, and acquiring dust distribution information according to the surface image data;
s2, acquiring dust side view data of the optical lens, and acquiring dust thickness information according to the dust side view data;
s3, acquiring dust area data of the optical lens, and acquiring dust removal compensation information according to the dust area data;
s4, acquiring a dust removal strategy according to the dust distribution information, the dust thickness information and the dust removal compensation information, and removing dust of the optical lens according to the dust removal strategy;
s5, acquiring a dust removing path according to a dust removing strategy, and acquiring a cleaning feedback area of the optical lens according to dust distribution information, dust thickness information and the dust removing path;
S6, acquiring cleanliness information in the cleaning feedback area, judging whether the cleanliness information accords with preset conditions, if not, acquiring dust distribution information, dust thickness information and dust removal compensation information again, acquiring a new dust removal strategy by combining the cleanliness information, and executing the dust removal strategy until the preset conditions are met.
In the steps S1 to S6, the high-resolution imaging system is used to image the lens surface, the distribution of dust on the lens surface is detected by the image processing algorithm, the high-resolution imaging system is used to collect the side view image of the lens and obtain the thickness information of dust, compared with the pure surface distribution detection, the side view data can reveal the accumulation of dust on the lens surface and the embedded depth in the microstructure, according to the dust area data, the specific area characteristics of dust are judged, and then dust removal compensation information is generated, the collected data (dust distribution information, thickness information and compensation information) are combined, the optimal dust removal strategy is comprehensively determined by the algorithm, the strategy can customize the cleaning scheme according to the dust accumulation degree and distribution position, thereby effectively removing dust, a specific dust removal path is generated according to the dust removal strategy, meanwhile, the cleaned lens area (cleaning feedback area) is determined according to the dust information feedback, after dust removal is completed, the cleanliness in the cleaning feedback area is detected again, whether the current cleaning effect is required is judged by comparing with the preset cleanliness standard, if the detection result is not preset, the re-related thickness data (dust distribution information, thickness information and thickness information) is reset, the expected to be matched with the preset cleanliness data is obtained, the expected to be better than the expected, the dynamic data can be obtained, the optimal, the cleaning effect can be achieved, and the optimal performance is achieved, or the optimal, and has the optimal performance is achieved, and has the advantages that can be achieved, or has the optimal and has the advantages, and has the advantages that the performance and has improved, and has the advantages compared with the conventional cleaning and the dust cleaning method, the method further improves the reliability of the overall dust removal effect, can monitor the cleaning result in real time, and carries out dynamic strategy adjustment according to feedback information, closed loop feedback not only improves the dust removal efficiency, but also enables the cleaning process to have self-adaptive capacity, can cope with various environments and the change of dust accumulation degree, ensures that the cleaning quality can still be ensured under complex working conditions, provides customized cleaning schemes for different areas by using compensation information, pertinently processes dust with different thickness and different accumulation conditions, avoids the phenomenon of excessive cleaning or insufficient cleaning, achieves the aims of protecting the lens surface and prolonging the service life of the lens, can obviously reduce the residual dust on the lens surface by high-precision dust removal, improves the light transmittance and imaging quality, and provides stable and accurate performance support for high-end optical equipment in the fields of scientific research, medical treatment or industrial detection and the like.
In a preferred embodiment, the step of acquiring surface image data of the optical lens and acquiring dust distribution information from the surface image data includes:
S101, acquiring surface image data of an optical lens, and acquiring a surface dust image according to the surface image data;
S102, constructing a plane rectangular coordinate system on the surface dust image, and acquiring center coordinates of a plurality of dust areas according to the plane rectangular coordinate system;
S103, acquiring dust area distribution values according to central coordinates of a plurality of dust areas;
s104, acquiring a dust distribution table, wherein the dust distribution table comprises a plurality of dust area distribution interval values and dust distribution information corresponding to each dust area distribution interval value;
S105, acquiring corresponding dust distribution information from a dust distribution table according to the dust area distribution interval value corresponding to the dust area distribution value.
In the steps S101 to S105, the surface image data of the optical lens is obtained by the high resolution imaging device, the image processing technology (such as edge detection, binarization processing, etc.) is used to extract the shape of the dust on the surface of the lens, a clear surface dust image is generated, a rectangular planar coordinate system is constructed on the generated dust image, each point on the image has an accurate spatial position, the central coordinates of each dust area are calculated by clustering or morphology according to the dust area in the image, the coordinates reflect the spatial distribution characteristics of the dust on the surface of the lens, the coverage degree or dust concentration of the dust in each dust area is further counted and calculated according to the central coordinates of the dust area, the dust area distribution value of each area is extracted, and the calculation formula of the dust area distribution value isIn the formula, F represents a dust area distribution value, i represents a number of a plurality of dust area center X-axis coordinates and a number of a plurality of dust area center Y-axis coordinates, i=1, 2, 3..n, X i represents an ith dust area center X-axis coordinate, Y i represents an ith dust area center Y-axis coordinate, according to different dust area distribution values, a dust distribution table is constructed in advance, the distribution table is divided into a plurality of dust area distribution intervals, corresponding dust distribution information (such as concentration, area coverage, shape characteristics and the like) is set for each interval, the distribution table plays a role of data mapping and classification, discrete dust information can be converted into a regularized data pattern, the obtained dust area distribution values are utilized, dust distribution information preset in the dust distribution table is searched and obtained according to the corresponding dust area distribution interval, visual dust distribution is converted into quantized numerical data through image processing and coordinate system construction, the dust information is further refined and quantized according to different dust area distribution values, the distribution information is convenient to divide the distribution intervals of the dust area into the distribution intervals of the dust distribution table, the dust distribution table is convenient to realize the subsequent extraction of the dust distribution information, the dust distribution table is accurately matched with the preset dust distribution values, and the dust distribution table is accurately positioned according to the preset dust distribution values, and the dust distribution values can be accurately positioned, and the dust distribution can be well aligned.
In a preferred embodiment, the step of acquiring dust side view data of the optical lens and acquiring dust thickness information based on the dust side view data includes:
S201, acquiring dust side view data of an optical lens, and acquiring dust side view images of each dust area according to the dust side view data;
S202, acquiring a reference dust side view image, and overlapping the dust side view image of each dust area with the reference dust side view image to form a composite dust side view image;
s203, constructing a plane rectangular coordinate system in each composite dust side view image, wherein an X-axis of the plane rectangular coordinate system coincides with the plane of the optical lens, and respectively acquiring Y-axis coordinates of each dust side view image and Y-axis coordinates of a reference dust side view image, and respectively marking the Y-axis coordinates as a dust side view value and a reference dust side view value;
s204, obtaining the ratio of the dust side view value to the reference dust side view value, and marking the ratio as a thickness ratio;
S205, acquiring a thickness table, wherein the thickness table comprises a plurality of thickness ratios and dust thickness information corresponding to each thickness ratio;
s206, acquiring corresponding dust thickness information from the thickness table according to the thickness ratio.
In the steps S201 to S206, the special side view image pickup device is used to obtain the side image data of the optical lens in different dust areas, and the side image of each dust area is extracted according to the collected data, so that the form and outline of dust in each area are clearly presented, a representative reference dust side image is obtained in advance, as a standard reference, the side image of each area is overlapped with the reference image to form a composite image, a rectangular planar coordinate system is set in each composite side image, wherein the X axis coincides with the lens plane, the positions in the images are ensured to have actual corresponding relation, the corresponding Y axis coordinate values are obtained from the dust side image and the reference image respectively, the dust side image is marked as a dust side value and a reference dust side value respectively, a thickness ratio is generated by calculating the ratio of the dust side value extracted from the actual dust side image to the reference value, a thickness table is designed in advance, the table contains a plurality of different thickness ratio sections, each ratio section corresponds to predetermined thickness information, a corresponding thickness table is searched in the thickness table according to the calculated thickness ratio, the calculated thickness ratio value is found in the thickness table, the corresponding thickness value is directly overlapped with the conventional side image, the actual dust side image is directly calculated by the conventional side image, the quantitative dust side image has high accuracy, the actual dust removing accuracy is improved, the actual dust side image is directly compared to the actual dust side image is obtained by the actual dust side image, the actual dust side image is directly, the actual dust image is obtained, and the actual dust is compared, and the actual dust is directly has high by the actual dust value is compared, and the actual dust is obtained, and the actual dust is directly, and has high by the actual dust is directly and has high by the value and has high accuracy and high compared, not only can the absolute value of the dust thickness be captured, but also the relative change of the dust state can be reflected by the ratio.
In a preferred embodiment, the step of acquiring dust area data of the optical lens and acquiring dust removal compensation information according to the dust area data includes:
S301, acquiring dust area data of an optical lens, and acquiring a plurality of dust area images according to the dust area data;
s302, constructing a plane rectangular coordinate system in the dust area image, and acquiring outline inflection point coordinates of a plurality of dust areas in each dust area according to the plane rectangular coordinate system;
s303, acquiring dust area values according to coordinates of inflection points of outlines of a plurality of dust areas in each dust area;
S304, arranging a plurality of dust area values in a sequence from large to small to obtain a ranking list, and selecting a first dust area value from the ranking list to be marked as a target dust area value;
S305, acquiring a dust removal compensation table, wherein the dust removal compensation table comprises a plurality of dust area interval values and dust removal compensation information corresponding to each dust area interval value;
s306, acquiring 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.
In the steps S301 to S306, the high-precision imaging system is used to acquire dust area data from the surface of the optical lens, convert the data into a plurality of dust area images, construct a rectangular planar coordinate system in each dust area image, ensure that each pixel point on the image has definite spatial positioning, identify a plurality of contour inflection point coordinates in each dust area by adopting an edge detection or contour extraction algorithm based on the coordinate system, accurately describe the geometric contour of the dust area by using the inflection points, digitize the acquired contour inflection point coordinates, and calculate dust area values according to the calculation formula of the dust area values as followsWherein Q represents a dust area value, g represents a number of coordinates of a plurality of dust area contour inflection points, g=1, 2, 3..h, U g represents a g-th dust area contour inflection point X-axis coordinate point, U g+1 represents a g+1th dust area contour inflection point X-axis coordinate point, V g represents a g-th dust area contour inflection point Y-axis coordinate point, V g+1 represents a g+1th dust area contour inflection point Y-axis coordinate point, h+1 represents 1 when g represents a value h, all dust area values are arranged in order from large to small to obtain an arrangement table, a (first-position) dust area value with the largest value in the arrangement table is selected as a target dust area value, generally, a dust removal compensation table is established in advance, the table contains a plurality of dust area interval values (i.e., segments of different dust areas) and corresponding dust removal compensation information, such as compensation force, cleaning path adjustment scheme and the like, the dust area value is accurately positioned in accordance with the target dust area value, the corresponding compensation table is obtained from the dust area contour coordinate system, the dust area is accurately positioned, the dust area is accurately identified, the dust area is accurately measured, the dust feature is accurately identified, and the dust area is accurately measured, and the dust feature is accurately identified.
In a preferred embodiment, the step of acquiring a dust removal policy according to the dust distribution information, the dust thickness information, and the dust removal compensation information, and removing dust of the optical lens according to the dust removal policy includes:
S401, respectively acquiring a corresponding dust area distribution value, a thickness ratio and a target dust area value according to dust distribution information, dust thickness information and dust removal compensation information;
s402, acquiring a strategy value according to a dust area distribution value, a thickness ratio and a target dust area value;
s403, acquiring a policy table, wherein the policy table comprises a plurality of policy interval values and dust removal policies corresponding to each policy interval value;
S404, acquiring a corresponding dust removing strategy from the strategy table according to a strategy interval value corresponding to the strategy value, and removing dust of the optical lens according to the dust removing strategy.
In the steps S401 to S404, the dust area distribution information obtained earlier is used to extract the dust area distribution value, the thickness ratio is extracted by analyzing the dust thickness information, the area value most representative or critical in the dust area, that is, the target dust area value, is selected in combination with the dust area data predetermined in the dust removal compensation information, the policy value is calculated by using the obtained dust area distribution value, thickness ratio and target dust area value, the calculation formula of the policy value is l=f×h×q, where L is represented as the policy value, F is represented as the dust area distribution value, H is represented as the thickness ratio, Q is represented as the dust area value, a policy table is pre-established, which divides the dust removal task according to different policy intervals, each policy interval value corresponds to a set of dust removal policy suitable for the current dust condition, the corresponding policy interval value is searched in a policy table according to the generated policy value, the specific dust removal policy is obtained by matching, after the corresponding dust removal policy is obtained, the dust on the optical lens is removed according to a cleaning path, a cleaning mode and compensation parameters of the preset dust removal policy, the efficient and accurate cleaning operation is realized, the distribution information, the thickness information and the area compensation information of the dust are fused and quantized, and the comprehensive policy value is generated, so that the dust removal decision is based on objective and quantized data, errors caused by a single data source are avoided, and the decision is more accurate.
In a preferred embodiment, the step of acquiring the dust removal path according to the dust removal policy, and acquiring the cleaning feedback area of the optical lens according to the dust distribution information, the dust thickness information, and the dust removal path includes:
S501, acquiring a dust removal path according to a dust removal strategy, and acquiring a corresponding dust removal path vector according to the dust removal path;
S502, respectively acquiring a corresponding dust area distribution value and a corresponding thickness ratio based on dust distribution information and dust thickness information;
s503, acquiring a feedback value according to the dust area distribution value, the thickness ratio and the dust removal path vector;
S504, acquiring an area table, wherein the area table comprises multiple feedback interval values and cleaning feedback areas corresponding to each feedback interval value;
S505, acquiring a corresponding cleaning feedback area from the area table by the feedback interval value corresponding to the root feedback value.
In the steps S501 to S505, the dust removing path is extracted according to the dust removing policy established in the previous stage, the path is converted into the path vector after the dust removing path is generated, the dust area distribution value is extracted based on the front view image data collected in advance, the height ratio of each dust area to the reference image is calculated by combining the side view data, the thickness quantization value of the dust is obtained, the feedback value is calculated according to the dust area distribution value, the thickness ratio and the dust removing path vector, and the formula of the feedback value is thatIn the formula, K is represented as a feedback value, F is represented as a dust area distribution value, H is represented as a thickness ratio, J is represented as a dust removal path vector, an area table is constructed in advance, the feedback value is divided into a plurality of feedback intervals, each interval corresponds to a cleaning feedback area, the corresponding cleaning feedback area is searched and matched in the area table according to the feedback interval corresponding to the feedback value obtained by current calculation, and the dust removal path vector, dust distribution and thickness quantization data are adopted, so that the feedback value can be calculated in real time, and the cleaning effect can be monitored in real time.
In a preferred embodiment, acquiring cleanliness information in a cleaning feedback area, judging whether the cleanliness information meets preset conditions, if not, re-acquiring dust distribution information, dust thickness information and dust removal compensation information, acquiring a new dust removal strategy by combining the cleanliness information, and executing the dust removal strategy until the preset conditions are met, wherein the method comprises the following steps of:
S601, acquiring cleanliness information in a cleaning feedback area, and acquiring a corresponding cleanliness value according to the cleanliness information;
S602, acquiring a standard cleanliness threshold;
S603, judging whether the cleanliness value is lower than a standard cleanliness threshold value;
If the cleanliness value is lower than the standard cleanliness threshold, judging that the dust removal strategy executed on the surface of the optical lens is abnormal and marking the dust removal strategy as an abnormal dust removal strategy;
If the cleanliness value is not lower than the standard cleanliness threshold, judging that the dust removal strategy executed on the surface of the optical lens is normal;
S604, after acquiring the abnormal dust removal strategy, acquiring dust distribution information, dust thickness information and dust removal compensation information again, acquiring a new dust removal strategy by combining the cleanliness information, and executing the dust removal strategy until the preset condition is met.
In the steps S601 to S604, the actual cleanliness data in the cleaning feedback area is collected by using a special detection device (such as a high-resolution camera or a sensor), the corresponding cleanliness value is extracted by image processing according to the obtained data, the standard cleanliness threshold is preset or read as a standard for judging whether the cleaning effect reaches the requirement, the threshold can be customized according to the requirement of the device, the working environment and the application standard, the cleaning effect is ensured to meet the actual requirement, the actual collected cleanliness value is compared with the preset standard cleanliness threshold, if the cleanliness value is lower than the standard threshold, namely, the dust removal strategy on the surface of the current optical lens is judged not to reach the expected effect and marked as an abnormal dust removal strategy, otherwise, the executed dust removal strategy is judged to be normal, after the abnormal dust removal strategy is detected, the closed loop feedback mechanism is started, the latest dust distribution information, the dust thickness information and the dust removal compensation information are acquired again, the new dust removal strategy is calculated and generated by using the current cleanliness information and the latest collected data, and then the cleaning operation is regulated until the acquired cleanliness value meets or exceeds the preset standard cleanliness threshold, the self-detection condition is reduced, and the self-cleaning performance is ensured, the self-cleaning performance is not optimized, and the risk is completely reduced, and the self-cleaning performance is guaranteed, and the dust removal performance is guaranteed is improved, and the self-cleaning performance is guaranteed, and the dust is better is guaranteed.
In a preferred embodiment, after acquiring the abnormal dust removal policy, acquiring the dust distribution information, the dust thickness information and the dust removal compensation information again, acquiring a new dust removal policy by combining the cleanliness information, and executing the dust removal policy until the abnormal dust removal policy meets the preset condition, including:
s6041, after acquiring an abnormal dust removal strategy, re-acquiring dust distribution information, dust thickness information and dust removal compensation information, acquiring corresponding feedback dust distribution information, feedback dust thickness information and feedback dust removal compensation information based on the re-acquired dust distribution information, dust thickness information and dust removal compensation information, and marking the corresponding feedback dust distribution information, feedback dust thickness information and feedback dust removal compensation information as a feedback dust area distribution value, a feedback thickness ratio and a feedback dust area value respectively;
s6042, acquiring a comprehensive value according to the feedback dust area distribution value, the feedback thickness ratio, the feedback dust area value and the cleanliness value;
s6043, acquiring a reset table, wherein the reset table comprises a plurality of comprehensive interval values and a reset dust removal strategy corresponding to each comprehensive interval value;
S6044, acquiring a corresponding reset dust removing strategy from the reset table according to the comprehensive interval value corresponding to the comprehensive value, executing the reset dust removing strategy, acquiring a reset dust removing path according to the reset dust removing strategy, and returning the reset dust removing path as a dust removing path to a cleaning feedback area of the optical lens according to the dust distribution information, the dust thickness information and the dust removing path.
In the steps S6041 to S6044, when it is determined that the current dust removal strategy is abnormal, the dust distribution information, the dust thickness information and the dust removal compensation information in the current environment are collected again, and the collected data are converted into feedback indexes, the dust area distribution value, the feedback thickness ratio and the dust area value are fed back through corresponding image processing and numerical calculation, the obtained feedback indexes and the current cleanliness value are fused, a comprehensive value is calculated, and the calculation formula of the comprehensive value is thatWhere Z is expressed as a feedback value,Represented as a feedback dust area distribution value,Expressed as a feedback thickness ratio,The method is characterized in that a feedback dust removal path vector is expressed, D is expressed as a cleanliness value, a reset table is established in advance, the comprehensive value is divided into a plurality of intervals, a set of predefined reset dust removal strategies are corresponding to each interval, a new reset dust removal strategy is obtained according to the strategies found in the reset table, a new dust removal path is planned according to the strategies, the new dust removal path is calculated and then returned to a subsequent cleaning feedback step for verifying the cleaning effect until the final cleanliness index reaches or exceeds a preset condition, the process forms a closed loop feedback system, when the dust removal effect is found to be not up to standard, the data can be automatically collected again, the feedback index is updated, the comprehensive value is calculated, thereby realizing intelligent decision making, making a new dust removal strategy, and the automatic and self-adaptive adjustment mechanism remarkably improves the coping capability for different working environments and dust changes.
Referring to fig. 2, the present invention further provides a high-precision dust removal system for an optical lens, which is used for the high-precision dust removal method of the optical lens, and includes:
the dust distribution module is used for acquiring surface image data of the optical lens and acquiring dust distribution information according to the surface image data;
the dust thickness module is used for acquiring dust side-looking data of the optical lens and acquiring dust thickness information according to the dust side-looking data;
the dust removal compensation module is used for acquiring dust area data of the optical lens and acquiring dust removal compensation information according to the dust area data;
the dust removal strategy module is used for acquiring a dust removal strategy according to the dust distribution information, the dust thickness information and the dust removal compensation information and removing dust of the optical lens according to the dust removal strategy;
The cleaning area module is used for acquiring a dust removing path according to a dust removing strategy and acquiring a cleaning feedback area of the optical lens according to dust distribution information, dust thickness information and the dust removing path;
the strategy feedback module is used for acquiring the cleanliness information in the cleaning feedback area, judging whether the cleanliness information accords with preset conditions, if not, acquiring the dust distribution information, the dust thickness information and the dust removal compensation information again, acquiring a new dust removal strategy by combining the cleanliness information, and executing the dust removal strategy until the preset conditions are met.
The dust distribution module acquires image data of the surface of the optical lens through the high-definition camera, extracts dust positions through an image processing algorithm (such as edge detection, gray enhancement and the like), generates dust distribution information, the dust thickness module extracts vertical thickness information of dust accumulation through acquiring an image under a side view angle of the lens or using technical means such as structured light and the like, generates dust thickness information, the dust removal compensation module acquires dust removal compensation information according to the area of a dust area, the dust removal strategy module utilizes the dust distribution information, the dust thickness information and the dust removal compensation information to construct comprehensive indexes (such as strategy values), looks up a table or acquires proper dust removal strategies (such as airflow intensity, static charge size, path planning modes and the like) through a model, applies the strategies to actual dust removal execution devices (such as jet heads, mechanical brushes and the like), a cleaning area module for generating a specific dust removing path according to a dust removing strategy, calculating an area feedback value covered by the path by combining dust distribution and thickness, matching a cleaning feedback area from a preset area table to realize regional assessment of dust removing effect, a strategy feedback module for detecting cleanliness of the cleaning feedback area, acquiring the cleanliness value, comparing the cleanliness value with a set standard cleanliness threshold, marking as an abnormal dust removing strategy if the cleanliness value is lower than the threshold, re-acquiring dust information, combining the cleanliness value, re-generating an adaptive dust removing strategy (reset strategy), repeatedly executing until the preset condition is met, marking as a normal dust removing strategy if the threshold is met, completing the cleaning task of the round, breaking through the limitation of traditional single image judgment, the dust removing strategy is more targeted, a cleaning path can be dynamically generated according to the complex condition of dust distribution, differential dust removing treatment on different parts of the optical lens is realized, real-time monitoring of cleanliness and abnormal strategy identification are realized by means of the strategy feedback module, and once the detection effect does not reach the standard, the strategy can be quickly reset, so that a closed-loop control mechanism is formed.
And, a high accuracy dust removal terminal of optical lens, includes:
One or more processors;
a storage device having one or more programs stored thereon;
when the one or more programs are executed by the one or more processors, the one or more processors implement a high-precision dust removal method for the optical lens.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention. Structures, devices and methods of operation not specifically described and illustrated herein, unless otherwise indicated and limited, are implemented according to conventional means in the art.
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