Optical lens automatic focusing method and system

Abstract

The present invention relates to the field of automatic focusing technology, and in particular to an optical lens automatic focusing method and system. The method comprises the following steps: performing a stereoscopic environment scan on a moving path and performing waste optical environment analysis through an optical lens carried on a sweeping robot to obtain waste optical environment data; performing spatial optical axis offset calculation on the waste optical environment data, and performing focus offset coefficient calculation on the waste optical environment data to obtain the waste focus offset coefficient; performing focal plane curvature change analysis according to the waste focus offset coefficient to obtain focal plane curvature change data; performing focal plane multi-dimensional calibration processing according to the focal plane curvature change data and formulating an optical lens focusing strategy to obtain an optical lens focusing strategy; performing automated firmware design on the optical lens focusing strategy to obtain an optical lens focusing strategy firmware. The present invention makes the automatic focusing technology more perfect by optimizing the automatic focusing technology.

Description

Technical Field

[0001] The present invention relates to the field of automatic focusing technology, and in particular to an optical lens automatic focusing method and system. Background Art

[0002] In the past, sweeping robots mostly relied on simple sensors such as infrared or ultrasonic waves for obstacle detection and path planning. However, these methods have low accuracy and reaction speed when facing complex environments such as glass walls, small obstacles or dark areas, resulting in limited cleaning efficiency. The introduction of optical lenses has given sweeping robots more sophisticated environmental perception capabilities. However, fixed-focal-length optical lenses produce blurred images and cannot clearly capture details when identifying objects at different distances, which seriously affects the robot's environmental judgment and decision-making capabilities. Therefore, autofocus technology has become the key, which improves perception accuracy by adjusting the lens focal length in real time to ensure that objects can be clearly imaged regardless of distance. The realization of autofocus not only needs to solve the interference of dynamic factors such as light changes, environmental reflections, speed and vibration, but also needs to take into account limitations such as volume, cost and processing speed. Currently, autofocus technology is mostly used in smartphones and surveillance cameras, and its application in sweeping robots requires unique designs for low power consumption, high stability and intelligent control, especially in home scenes with fast movement and complex and changing environments. Advanced algorithms and optical adjustment mechanisms are combined to achieve real-time and accurate autofocus adjustment. The innovation of this method lies in the integration of depth perception, edge detection and intelligent control system, which enables the sweeping robot to adapt to scene changes in real time, significantly improves obstacle avoidance and cleaning effect, and promotes the performance optimization and popularization of household intelligent robots. However, the traditional automatic focusing method of optical lenses has the problem of large focal plane accuracy error and inaccurate analysis of the impact of aperture diffraction. Summary of the invention

[0003] Based on this, it is necessary to provide an optical lens automatic focusing method and system to solve at least one of the above technical problems.

[0004] To achieve the above object, an optical lens automatic focusing method is provided, the method comprising the following steps:

[0005] Step S1: performing a three-dimensional environment scan on the moving path through the optical lens carried by the sweeping robot to obtain three-dimensional environment scanning video data; performing waste optical environment analysis on the three-dimensional environment scanning video data to obtain waste optical environment data;

[0006] Step S2: performing spatial optical axis offset calculation on the waste optical environment data to obtain the waste spatial optical axis offset data; performing aperture diffraction intensity interval calculation based on the waste spatial optical axis offset data to obtain the aperture diffraction intensity interval; performing focus offset coefficient calculation on the waste optical environment data based on the aperture diffraction intensity interval to obtain the waste focus offset coefficient;

[0007] Step S3: performing focal plane curvature change analysis according to the waste focus offset coefficient to obtain focal plane curvature change data; performing focal plane multi-dimensional calibration processing according to the focal plane curvature change data to obtain focal plane multi-dimensional calibration data;

[0008] Step S4: Formulate an optical lens focusing strategy according to the focal plane multi-dimensional calibration data to obtain the optical lens focusing strategy; perform automated firmware design on the optical lens focusing strategy to obtain the optical lens focusing strategy firmware, and send the optical lens focusing strategy firmware to the sweeping robot management system to execute automatic focusing of the optical lens.

[0009] The present invention can obtain detailed three-dimensional data of the environment by using the optical lens on the sweeping robot to perform stereoscopic environment scanning. This detailed spatial data helps to accurately identify the form and position of waste. By analyzing the optical environment data obtained by scanning, the optical feature differences between the waste and the environment can be distinguished, thereby improving the accuracy of waste identification. This means that waste of different types and sizes can be detected more effectively. By calculating the spatial optical axis offset of the waste in the optical environment, the spatial information of the waste relative to the environment can be obtained. This helps to accurately identify the actual position of the waste during the classification and positioning process, thereby improving the classification accuracy. Calculating the aperture diffraction intensity interval can provide more information about the optical properties of the waste. These properties can be used to further analyze the material and size of the waste, thereby optimizing the classification strategy and treatment method. The focus offset coefficient calculated based on the aperture diffraction intensity interval can more accurately adjust the focus of the scanning and identification system, so that it can more effectively detect and process different types of waste. These steps replace manual processing through algorithms and data analysis, thereby improving the automation level of waste detection and processing. This not only reduces labor costs, but also improves the efficiency and consistency of processing. High-precision detection and classification can reduce missed detection and misjudgment, and improve overall processing efficiency. For example, being able to accurately identify and classify different wastes can ensure that each type of waste is properly handled and recycled, reducing resource waste and environmental pollution. Accurate optical and spatial data analysis provides more decision support for the sweeping robot, enabling it to make intelligent adjustments during the cleaning process to adapt to different environments and waste conditions. Analysis of the change in focal plane curvature can understand the change in curvature of the focal plane under different environmental conditions. This analysis helps identify and compensate for the focal plane position offset caused by environmental factors (such as light refraction, surface reflection, etc.), thereby improving focus accuracy. Multi-dimensional calibration processing based on focal plane curvature change data can further adjust and optimize the position of the focal plane to adapt to different environments and waste characteristics. This calibration improves the system's adaptability to different environmental conditions and ensures high accuracy in waste detection and classification. The focusing strategy developed based on the multi-dimensional calibration data of the focal plane can achieve the best optical lens focus for specific environments and waste conditions. An effective focusing strategy can improve the clarity and detail capture ability of the lens in actual operation, thereby improving the detection and identification of waste. Designing automated firmware to implement the focusing strategy of the optical lens makes the focusing process fully automated. The firmware can adjust the focal length of the lens in real time to cope with environmental changes and waste characteristics, thereby improving the intelligence level and ease of operation of the system. The automated optical lens focusing strategy can significantly reduce manual intervention and improve operational efficiency. This means that the system can autonomously adjust the lens focus in various environments to adapt to different waste detection needs. Through optimized focusing strategies and precise calibration processing, the system can capture the details of the waste more clearly.This helps to improve detection accuracy, reduce misjudgments and missed detections, and improve the accuracy of waste classification. Automated focusing strategies and multi-dimensional calibration processing enable the system to adapt to different environmental changes, such as light intensity, surface texture, etc. This allows the sweeping robot to maintain high performance and stability in a variety of environments. Accurate focal plane adjustment and focusing strategies enable the system to effectively handle waste of various types and sizes, including those tiny wastes that are ignored under traditional methods. Automated firmware and focusing strategies reduce reliance on manual adjustments, thereby reducing system maintenance requirements and failure rates. Automated adjustment and optimization can effectively extend the service life and stability of the equipment. Accurate automated adjustment and optimized focusing strategies improve the performance and reliability of the system, enhance the user experience, and make the sweeping robot more efficient and accurate when handling waste. Therefore, the present invention is an optimization process for a traditional optical lens automatic focusing method, which solves the problems of large focal plane accuracy error and inaccurate analysis of the impact caused by aperture diffraction in a traditional optical lens automatic focusing method, reduces the focal plane accuracy error, and improves the accuracy of the impact analysis caused by aperture diffraction.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: performing a stereoscopic environment scan on the moving path through an optical lens carried by the sweeping robot to obtain stereoscopic environment scan video data;

[0012] Step S12: converting the three-dimensional environment scanning video data to obtain environment format compatible video data;

[0013] Step S13: performing color space optimization on the environment format compatible video data to obtain environment color space optimized data;

[0014] Step S14: performing waste optical environment analysis on the environmental format compatible video data according to the environmental color space optimization data to obtain waste optical environment data.

[0015] The present invention can capture detailed three-dimensional data of the environment by performing stereoscopic environmental scanning on the moving path through the optical lens on the sweeping robot. This stereoscopic scanning can provide more accurate spatial information, which is helpful to fully understand the location and form of waste in the environment. Stereoscopic environmental scanning can generate depth maps and texture maps of the environment, providing rich background information for subsequent waste detection and analysis. This enables the system to better identify and locate waste, especially in complex and dynamic environments. The stereoscopic environmental scanning video data is converted to ensure that the data format is compatible with the analysis system. This step ensures that subsequent data processing and analysis can proceed smoothly, reducing data processing obstacles caused by format mismatch. The format conversion can convert the original data into a format that is more suitable for processing and analysis, thereby improving the efficiency and accuracy of data processing. It can reduce errors and information loss generated during data conversion. Color space optimization of environmental format compatible video data can improve the color performance of the image, making it more suitable for subsequent analysis. This optimization can enhance the contrast and details of the waste, making the identification of the waste clearer and more accurate. Color space optimization can reduce the impact of ambient light changes on image quality, thereby improving the robustness of the waste detection system under different lighting conditions. This is especially important for processing data under various lighting conditions in actual operations. Waste optical environmental analysis based on environmental format compatible video data based on environmental color space optimization data can accurately identify and classify waste. The optimized data can provide clearer waste features, thereby improving the accuracy of detection. Through waste optical environmental analysis, characteristic information of waste (such as color, shape, size, etc.) can be extracted and compared with the preset waste type. This enables better classification and processing of different types of waste.

[0016] Preferably, step S2 comprises the following steps:

[0017] Step S21: performing spatial optical scattering characteristic analysis on the waste optical environment data to obtain waste spatial optical scattering characteristic data;

[0018] Step S22: performing spatial optical axis offset calculation on the waste optical environment data according to the waste spatial optical scattering characteristic data to obtain waste spatial optical axis offset data;

[0019] Step S23: calculating the aperture diffraction intensity interval of the waste optical environment data according to the waste space optical axis offset data to obtain the aperture diffraction intensity interval;

[0020] Step S24: Calculate the focus shift coefficient of the waste optical environment data based on the aperture diffraction intensity interval to obtain the waste focus shift coefficient.

[0021] The present invention can extract the pattern and intensity of waste scattering in the optical sensor by analyzing the spatial optical scattering characteristics of waste optical environment data. This helps to understand how waste affects light propagation and scattering, so as to obtain detailed waste optical characteristics. Understanding the scattering characteristics can help the system adapt to different optical environments, including different lighting conditions and waste surface characteristics, thereby improving the reliability and stability of the system in a variable environment. The spatial optical axis offset calculation based on the spatial optical scattering characteristic data can correct the optical axis offset caused by the waste scattering characteristics. This step ensures the accuracy of the optical axis during the waste detection process and helps to reduce image blur or error caused by the optical axis offset. By accurately calculating the optical axis offset, the image clarity and detail capture ability of the system when processing waste can be improved, thereby improving the accuracy of detection and classification. The aperture diffraction intensity interval is calculated according to the spatial optical axis offset data, and the influence of the aperture on the image quality can be analyzed. The diffraction effect affects the clarity and details of the image. By calculating the diffraction intensity interval, the image quality under different aperture settings can be determined. Understanding the aperture diffraction intensity interval helps to adjust the aperture setting to obtain the best image clarity. This can reduce image blur caused by diffraction effects and improve the overall performance of the waste detection system. The focus shift factor is calculated based on the aperture diffraction intensity range, and the focus can be accurately adjusted to compensate for the effects of aperture diffraction and optical axis offset. This step allows the system to maintain the optimal focus position at different aperture settings, improving image clarity and accuracy. Accurate focus shift factors can improve image quality and ensure that waste features are more clearly distinguishable, thereby improving the system's capabilities in waste detection and classification.

[0022] Preferably, step S23 includes the following steps:

[0023] Step S231: calculating the optical axis offset angle of the waste space optical axis offset data to obtain optical axis offset angle data;

[0024] Step S232: reconstructing the optical axis offset vector field according to the optical axis offset angle data and the waste space optical axis offset data to obtain the optical axis offset vector field data;

[0025] Step S233: performing optical axis-waste interface effect analysis on the waste optical environment data according to the optical axis offset vector field data to obtain optical axis-waste interface effect data;

[0026] Step S234: performing a multi-level optical interference structure analysis on the optical axis-waste interface action data to obtain interface action optical interference structure data;

[0027] Step S235: Calculating the aperture diffraction intensity interval of the waste optical environment data according to the interface optical interference structure data and the optical axis-waste interface data to obtain the aperture diffraction intensity interval.

[0028] The present invention can accurately measure the actual degree of optical axis deviation in the waste optical environment by calculating the optical axis deviation angle. This provides accurate deviation angle data for subsequent analysis, which helps to adjust the optical system more accurately. Understanding the optical axis deviation angle helps to better understand the deviation effect of the optical axis, thereby improving the correction and processing of the image and improving the accuracy of waste detection. Reconstructing the optical axis deviation vector field based on the optical axis deviation angle data and the waste space optical axis deviation data can obtain a comprehensive description of the optical axis deviation. This provides detailed information about the optical axis in the entire field of view for subsequent analysis, which helps to fully understand the impact of the optical axis deviation on the image quality. By reconstructing the optical axis deviation vector field, the system can adapt to various optical axis deviation conditions, thereby maintaining high image quality and detection accuracy under different environments and conditions. By analyzing the interaction between the optical axis and the waste interface based on the optical axis deviation vector field data, it is possible to understand how light interacts with the waste surface. This helps to identify and correct optical errors caused by the surface characteristics of the waste. Analyzing the role of the optical axis-waste interface can improve the waste detection algorithm, especially when dealing with waste with complex surface features, enhancing the system's ability to recognize waste. Multi-level optical interference structure analysis of the optical axis-waste interface data can accurately measure and understand the optical interference effect. This helps to identify image distortion or errors caused by optical interference, thereby improving image quality. Through interference structure analysis, the impact of optical interference on images can be better understood and processed, and the system's ability and accuracy in processing images in complex environments can be improved. Based on the interface optical interference structure data and the optical axis-waste interface data, the aperture diffraction intensity range calculation can accurately evaluate the diffraction intensity under the aperture setting. This helps determine the optimal aperture setting to reduce the impact of diffraction effects on image quality. The calculated aperture diffraction intensity range can be used to optimize image clarity and contrast, thereby improving the accuracy and reliability of waste detection.

[0029] Preferably, step S24 comprises the following steps:

[0030] Step S241: performing aperture diffraction effect distribution fitting on the aperture diffraction intensity interval to obtain aperture diffraction effect distribution fitting data;

[0031] Step S242: performing optical axis offset interference mapping on the waste space optical axis offset data according to the aperture diffraction effect distribution fitting data to obtain optical axis offset interference mapping data;

[0032] Step S243: performing multi-gradient focus dynamic simulation on the waste optical environment data according to the optical axis offset interference mapping data to obtain multi-gradient focus dynamic simulation data;

[0033] Step S244: performing focus dynamic offset mean difference calculation on the multi-gradient focus dynamic simulation data to obtain focus dynamic offset mean difference data;

[0034] Step S245: Calculate the focus shift coefficient according to the focus dynamic shift mean difference data to obtain the waste focus shift coefficient.

[0035] The present invention performs distribution fitting on the aperture diffraction intensity interval, which can accurately describe the diffraction effect of the aperture under different settings. The data generated in this step helps to understand how the aperture affects the diffraction pattern of light, thereby revealing the specific reasons for image blur. By fitting the data, the aperture setting can be optimized to reduce the impact of the aperture diffraction effect on the image. This helps to improve the overall clarity and detail performance of the image. By using the aperture diffraction effect distribution fitting data to perform interference mapping on the waste space optical axis offset data, a detailed interference image of the optical axis offset can be created. This helps to fully understand the interference effect of the optical axis offset on the image, including the change of interference fringes and the distribution of light intensity. Through interference mapping, the specific impact of the optical axis offset on the imaging quality can be identified, and then the system can be adjusted to reduce these impacts, thereby improving the accuracy and clarity of the image. According to the optical axis offset interference mapping data, multi-gradient focus dynamic simulation can be performed to simulate the change of focus under different optical axis offset conditions. This helps to understand the dynamic performance of the focus under different conditions and predict the focus offset that occurs in actual operation. The simulation results provide an in-depth understanding of the dynamic behavior of the focus, so that the focus adjustment strategy can be optimized and the accuracy and stability of the waste detection system can be improved. By calculating the mean difference of multi-gradient focus dynamic simulation data, the offset error of different focus positions can be quantified. The mean difference calculation helps to identify the degree and regularity of focus offset under different conditions, and provides a detailed statistical description of the dynamic behavior of the focus. By understanding the mean difference of the focus dynamic offset, the focus adjustment algorithm can be optimized to ensure that the focus can be maintained in the optimal position under various working conditions, thereby improving the accuracy and clarity of the image. The focus offset coefficient can be calculated based on the focus dynamic offset mean difference data to obtain an accurate focus offset coefficient. This coefficient is used to correct the focus position to compensate for the focus offset caused by optical axis offset and other factors. Accurate focus offset coefficient calculation helps ensure that the waste detection system can maintain the optimal focus position under various environments and conditions, thereby enhancing the system's detection capabilities and image quality.

[0036] Preferably, step S3 comprises the following steps:

[0037] Step S31: performing focal plane curvature change analysis according to the waste focus offset coefficient and the waste space optical axis offset data to obtain focal plane curvature change data;

[0038] Step S32: performing optical aberration evaluation on the focal plane curvature change data to obtain curvature change optical aberration data;

[0039] Step S33: performing focal plane multi-dimensional calibration processing according to the curvature change optical aberration data and the focal plane curvature change data to obtain focal plane multi-dimensional calibration data.

[0040] The present invention can accurately depict the curvature change of the focal plane under different conditions by analyzing the focus shift coefficient and the optical axis shift data. This provides basic data for understanding the performance of the optical system under actual working conditions. The analysis of the change in focal plane curvature can reveal the deformation of the focal plane caused by the optical axis shift or other factors. This helps to identify the source of optical errors that affect the image quality. Understanding the change in focal plane curvature can help optimize the design and calibration of the optical system, thereby improving the overall performance and reliability of the system. By evaluating the focal plane curvature change data, the aberrations (such as spherical aberration, coma, etc.) caused by the curvature change in the optical system can be identified. This helps to understand the specific impact of these aberrations on image quality. Understanding and quantifying the nature and degree of optical aberrations can provide a basis for adjusting and calibrating the optical system, thereby reducing the negative impact of aberrations on imaging quality. By evaluating the aberrations, targeted adjustment strategies can be formulated to optimize the focal plane settings and improve the performance of the optical system under different focal plane conditions. Multidimensional calibration processing uses curvature change optical aberration data and focal plane curvature change data for complex calibration. This ensures accurate adjustment of the focal plane under various working conditions and minimizes optical errors. Through accurate calibration, the overall performance of the optical system can be significantly improved, including image clarity, contrast and accuracy. This is particularly important for waste detection systems because it directly affects the accuracy and reliability of detection. The multi-dimensional calibration process not only corrects the main optical errors in the focal plane, but also optimizes the system's performance under different environmental conditions, enhancing the system's robustness and stability.

[0041] Preferably, step S31 includes the following steps:

[0042] Step S311: performing focus dynamic shift trajectory identification according to the waste focus shift coefficient and the waste space optical axis shift data to obtain focus dynamic shift trajectory data;

[0043] Step S312: calculating the trajectory offset angle of the focus dynamic offset trajectory data to obtain trajectory offset angle data;

[0044] Step S313: constructing a focus offset trajectory dynamic three-dimensional plane according to the trajectory offset angle data and the focus dynamic offset trajectory data to obtain a focus offset trajectory dynamic three-dimensional plane data set;

[0045] Step S314: performing offset circular curvature calculation on the focus offset trajectory dynamic three-dimensional plane data set to obtain offset circular curvature data;

[0046] Step S315: calculating the steering angle of the dynamic three-dimensional plane data set of the focus offset trajectory to obtain dynamic plane steering angle data;

[0047] Step S316: performing a focus position agglomeration effect analysis according to the offset circular curvature data and the dynamic plane steering angle data to obtain focus position agglomeration effect data;

[0048] Step S317: performing focal plane curvature change analysis based on the focus position concentration effect data, the dynamic plane steering angle data, and the offset circular curvature data to obtain focal plane curvature change data.

[0049] The present invention identifies the dynamic offset trajectory of the focus and provides the movement path data of the focus in space. This helps to understand how the focus changes with time and optical axis offset, which is the basis for subsequent analysis. Clear offset trajectory data enables optical system designers to identify and understand the dynamic behavior of the optical system under different conditions, so as to carry out targeted optimization and adjustment. Calculating the trajectory offset angle can quantify the angle change of the focus at different positions, which is crucial for analyzing how the focus shifts in the dynamic process. Accurate offset angle data helps to identify potential defects in the system, such as alignment problems of optical components or installation errors of lenses. Through three-dimensional plane construction, the dynamic offset trajectory of the focus in space can be intuitively visualized. This helps to understand the spatial variation characteristics of the focus at different times and conditions. The dynamic three-dimensional plane data set provides a detailed spatial information basis for subsequent offset calculations and focal plane calibration, supporting further error analysis and correction. The circular curvature calculation provides the curvature degree data of the focus offset trajectory, which is crucial for evaluating the offset mode and dynamic behavior of the focus in the trajectory. By understanding the curvature, a targeted correction strategy can be formulated to correct the curvature problem in the offset trajectory, thereby improving the accuracy of the system. Calculating the steering angle helps to analyze the steering change of the focus in the three-dimensional plane. This helps understand the complexity of the dynamic behavior of the focus and identify asymmetries or inconsistencies in the system. Steering angle data helps optimize the design and adjustment of the optical system to ensure the stability and accuracy of the focus under different conditions. Focus position agglomeration effect analysis provides data on the concentration of the focus at different positions. This helps to identify the concentration effect of the focus within the working area to evaluate the performance of the system. By understanding the agglomeration effect of the focus position, the system settings can be adjusted and optimized to improve the overall accuracy and consistency of the waste detection or imaging system. Focal plane curvature change analysis helps understand how the focal plane changes under different dynamic conditions and provides data for accurately calibrating the focal plane. With the focal plane curvature change data, the focal plane settings can be effectively adjusted to improve the image quality and detection capabilities of the system and ensure that the focus can maintain the optimal position under different conditions.

[0050] Preferably, step S33 includes the following steps:

[0051] Step S331: performing imaging distortion analysis on the curvature variation optical aberration data to obtain optical aberration imaging distortion data;

[0052] Step S332: calculating the distortion value interval according to the optical aberration imaging distortion data and the focal plane curvature change data to obtain the imaging distortion value interval;

[0053] Step S333: performing spatial interpolation processing on the imaging distortion value interval to obtain the imaging distortion optimization value interval;

[0054] Step S334: performing focal plane multi-dimensional calibration processing according to the imaging distortion optimization value interval to obtain focal plane multi-dimensional calibration data.

[0055] The present invention can reduce the distortion in the image by calculating and optimizing the distortion value interval, thereby making the image clearer and more realistic. The multi-dimensional calibration processing of the focal plane can more accurately adjust the imaging system, reduce the error of the focal plane, and ensure the geometric and optical accuracy of the image. Spatial interpolation processing and optimizing the value interval can improve the overall performance of the imaging system and enhance the details and quality of the image. Reducing distortion and optimizing calibration can extend the service life of the equipment and improve its stability and reliability in practical applications.

[0056] Preferably, step S4 comprises the following steps:

[0057] Step S41: performing normalization processing on the focal plane multi-dimensional calibration data to obtain focal plane multi-dimensional calibration normalized data;

[0058] Step S42: performing calibration iterative processing on the focal plane multidimensional calibration normalization data based on the memory network to obtain multidimensional calibration iterative data;

[0059] Step S43: formulating an optical lens focusing strategy according to the multi-dimensional calibration iteration data to obtain an optical lens focusing strategy;

[0060] Step S44: Automated firmware design is performed on the optical lens focusing strategy to obtain the optical lens focusing strategy firmware, and the optical lens focusing strategy firmware is sent to the sweeping robot management system to execute automatic focusing of the optical lens.

[0061] The normalization process of the present invention converts data of different scales into a unified standard, making subsequent processing more stable and efficient. Standardized data can reduce errors caused by differences in data magnitude and improve the accuracy of calibration processing. Normalized data facilitates more complex calibration iterative processing and improves the convergence speed and effect of the algorithm. The memory network can optimize the calibration data through iterative learning and gradually improve the calibration accuracy. Iterative processing can adapt to various complex calibration situations and improve the system's adaptability to different environmental conditions. Automated calibration iteration reduces the need for manual adjustment and improves efficiency and consistency. Formulating an accurate focusing strategy can optimize the focusing effect of the lens and ensure the clarity and quality of the image. Through strategy formulation, the focusing mode of the lens can be adjusted to adapt to different shooting conditions and requirements. The determined focusing strategy helps to improve the imaging performance and reliability of the overall system. Firmware design and deployment enable the optical lens focusing strategy to be automatically executed, reducing the complexity of manual operation. The automated firmware can adjust the lens focus in real time to ensure that the best imaging effect can be maintained in various operating environments. The automatic focusing strategy is applied in the sweeping robot to enable it to maintain efficient image capture and navigation capabilities under different environmental and lighting conditions.

[0062] Preferably, the present invention further provides an optical lens automatic focusing system, which is used to execute the optical lens automatic focusing method as described above, and the optical lens automatic focusing system comprises:

[0063] The waste environment optical analysis module is used to perform a three-dimensional environment scan on the moving path through the optical lens carried by the sweeping robot to obtain three-dimensional environment scanning video data; perform waste optical environment analysis on the three-dimensional environment scanning video data to obtain waste optical environment data;

[0064] A focus shift calculation module is used to perform spatial optical axis shift calculation on waste optical environment data to obtain waste spatial optical axis shift data; perform aperture diffraction intensity interval calculation based on the waste spatial optical axis shift data to obtain aperture diffraction intensity interval; perform focus shift coefficient calculation on waste optical environment data based on the aperture diffraction intensity interval to obtain waste focus shift coefficient;

[0065] A focal plane calibration module is used to analyze the change of focal plane curvature according to the waste focus offset coefficient to obtain focal plane curvature change data; and to perform focal plane multi-dimensional calibration processing according to the focal plane curvature change data to obtain focal plane multi-dimensional calibration data;

[0066] The automatic focusing strategy formulation module is used to formulate an optical lens focusing strategy according to the multi-dimensional calibration data of the focal plane to obtain the optical lens focusing strategy; to perform automated firmware design on the optical lens focusing strategy to obtain the optical lens focusing strategy firmware, and to send the optical lens focusing strategy firmware to the sweeping robot management system to execute automatic focusing of the optical lens.

[0067] The beneficial effect of the present invention is that by using the optical lens on the sweeping robot to perform stereoscopic environmental scanning, detailed three-dimensional data of the environment can be obtained. This detailed spatial data helps to accurately identify the form and position of waste. By analyzing the optical environment data obtained by scanning, the optical characteristic differences between the waste and the environment can be distinguished, thereby improving the accuracy of waste identification. This means that waste of different types and sizes can be detected more effectively. By calculating the spatial optical axis offset of the waste in the optical environment, the spatial information of the waste relative to the environment can be obtained. This helps to accurately identify the actual position of the waste during the classification and positioning process, thereby improving the classification accuracy. Calculating the aperture diffraction intensity interval can provide more information about the optical properties of the waste. These characteristics can be used to further analyze the material and size of the waste, thereby optimizing the classification strategy and treatment method. The focus offset coefficient calculated based on the aperture diffraction intensity interval can more accurately adjust the focus of the scanning and recognition system, so that it can more effectively detect and process different types of waste. These steps replace manual processing with algorithms and data analysis, thereby improving the automation level of waste detection and processing. This not only reduces labor costs, but also improves the efficiency and consistency of processing. High-precision detection and classification can reduce missed detection and misjudgment, and improve overall processing efficiency. For example, being able to accurately identify and classify different wastes can ensure that each type of waste is properly handled and recycled, reducing resource waste and environmental pollution. Accurate optical and spatial data analysis provides more decision support for the sweeping robot, enabling it to make intelligent adjustments during the cleaning process to adapt to different environments and waste conditions. Analysis of the change in focal plane curvature can understand the change in curvature of the focal plane under different environmental conditions. This analysis helps identify and compensate for the focal plane position offset caused by environmental factors (such as light refraction, surface reflection, etc.), thereby improving focus accuracy. Multi-dimensional calibration processing based on focal plane curvature change data can further adjust and optimize the position of the focal plane to adapt to different environments and waste characteristics. This calibration improves the system's adaptability to different environmental conditions and ensures high accuracy in waste detection and classification. The focusing strategy developed based on the multi-dimensional calibration data of the focal plane can achieve the best optical lens focus for specific environments and waste conditions. An effective focusing strategy can improve the clarity and detail capture ability of the lens in actual operation, thereby improving the detection and identification of waste. Designing automated firmware to implement the focusing strategy of the optical lens makes the focusing process fully automated. The firmware can adjust the focal length of the lens in real time to cope with environmental changes and waste characteristics, thereby improving the intelligence level and ease of operation of the system. The automated optical lens focusing strategy can significantly reduce manual intervention and improve operational efficiency. This means that the system can autonomously adjust the lens focus in various environments to adapt to different waste detection needs. Through optimized focusing strategies and precise calibration processing, the system can capture the details of the waste more clearly.This helps to improve detection accuracy, reduce misjudgments and missed detections, and improve the accuracy of waste classification. Automated focusing strategies and multi-dimensional calibration processing enable the system to adapt to different environmental changes, such as light intensity, surface texture, etc. This allows the sweeping robot to maintain high performance and stability in a variety of environments. Accurate focal plane adjustment and focusing strategies enable the system to effectively handle waste of various types and sizes, including those tiny wastes that are ignored under traditional methods. Automated firmware and focusing strategies reduce reliance on manual adjustments, thereby reducing system maintenance requirements and failure rates. Automated adjustment and optimization can effectively extend the service life and stability of the equipment. Accurate automated adjustment and optimized focusing strategies improve the performance and reliability of the system, enhance the user experience, and make the sweeping robot more efficient and accurate when handling waste. Therefore, the present invention is an optimization process for a traditional optical lens automatic focusing method, which solves the problems of large focal plane accuracy error and inaccurate analysis of the impact caused by aperture diffraction in a traditional optical lens automatic focusing method, reduces the focal plane accuracy error, and improves the accuracy of the impact analysis caused by aperture diffraction. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A schematic diagram of the steps of an automatic focusing method for an optical lens;

[0069] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0070] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0071] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0072] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0073] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0074] To achieve this, please refer to Figure 1 to Figure 2 , an optical lens automatic focusing method, the method comprising the following steps:

[0075] Step S1: performing a three-dimensional environment scan on the moving path through the optical lens carried by the sweeping robot to obtain three-dimensional environment scanning video data; performing waste optical environment analysis on the three-dimensional environment scanning video data to obtain waste optical environment data;

[0076] Step S2: performing spatial optical axis offset calculation on the waste optical environment data to obtain the waste spatial optical axis offset data; performing aperture diffraction intensity interval calculation based on the waste spatial optical axis offset data to obtain the aperture diffraction intensity interval; performing focus offset coefficient calculation on the waste optical environment data based on the aperture diffraction intensity interval to obtain the waste focus offset coefficient;

[0077] Step S3: performing focal plane curvature change analysis according to the waste focus offset coefficient to obtain focal plane curvature change data; performing focal plane multi-dimensional calibration processing according to the focal plane curvature change data to obtain focal plane multi-dimensional calibration data;

[0078] Step S4: Formulate an optical lens focusing strategy according to the focal plane multi-dimensional calibration data to obtain the optical lens focusing strategy; perform automated firmware design on the optical lens focusing strategy to obtain the optical lens focusing strategy firmware, and send the optical lens focusing strategy firmware to the sweeping robot management system to execute automatic focusing of the optical lens.

[0079] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of an optical lens automatic focusing method of the present invention. In this example, the optical lens automatic focusing method includes the following steps:

[0080] Step S1: performing a three-dimensional environment scan on the moving path through the optical lens carried by the sweeping robot to obtain three-dimensional environment scanning video data; performing waste optical environment analysis on the three-dimensional environment scanning video data to obtain waste optical environment data;

[0081] In an embodiment of the present invention, a high-resolution optical lens carried by a sweeping robot is used to perform a stereoscopic environment scan on the moving path to obtain detailed stereoscopic environment scanning video data. The specific operation process includes: first, the sweeping robot is placed in the area to be scanned, and the automatic rotation and tilting function of the optical lens is activated so that it can capture all kinds of objects and spatial details in the environment in all directions. When the lens is scanning, the exposure, contrast, and aperture size of the lens need to be finely adjusted according to the intensity of the ambient light to ensure the clarity and accuracy of the video data. After the data acquisition is completed, the acquired video stream is preprocessed by the real-time imaging algorithm of the optical lens to filter out background noise and unnecessary image interference, thereby obtaining high-precision stereoscopic environment scanning video data. Next, the stereoscopic environment scanning video data is subjected to waste optical environment analysis. Image recognition technology is combined with spectral analysis methods to classify and mark the waste in the video, and its position, shape, optical characteristics, etc. in space are identified. In the specific implementation process, the spectral feature analysis tool is first used to analyze the reflectance spectrum of each pixel in the video to distinguish waste of different materials; then, the edge detection and region segmentation technology is used to finely depict the outline of the waste to ensure that the edge of the waste is clear and correct. The final output waste optical environment data covers the type of waste, location coordinates and its optical response characteristics in the scanning environment, laying the foundation for subsequent optical adjustments and calculations.

[0082] Step S2: performing spatial optical axis offset calculation on the waste optical environment data to obtain the waste spatial optical axis offset data; performing aperture diffraction intensity interval calculation based on the waste spatial optical axis offset data to obtain the aperture diffraction intensity interval; performing focus offset coefficient calculation on the waste optical environment data based on the aperture diffraction intensity interval to obtain the waste focus offset coefficient;

[0083] In the embodiment of the present invention, when calculating the spatial optical axis offset of the waste optical environment data, it is necessary to first construct a three-dimensional coordinate system, with the position of the waste as the center, combined with the imaging angle and distance parameters of the optical lens, to accurately calculate the offset degree of the waste relative to the optical axis in space. During the calculation process, a laser ranging module is used to accurately measure the distance between the waste and the lens; then the spatial optical axis correction algorithm is used to input the measurement data into the calculation program, and the spatial optical axis offset value of the waste is output in real time. During this operation, the reflection characteristics of the waste at each angle are analyzed to ensure the accuracy of the offset data and provide support for subsequent optical adjustments. The calculated waste spatial optical axis offset data records in detail the spatial offset direction of the waste and its specific offset value relative to the optical axis. Subsequently, the aperture diffraction intensity interval is calculated based on the waste spatial optical axis offset data. This step requires the use of a diffraction optical analyzer to measure the intensity of the waste reflected light under the influence of the optical axis offset, and to determine the distribution interval of different diffraction intensities by controlling the aperture opening. In the specific implementation process, the aperture opening range is first set, and the aperture is adjusted multiple times with the waste as the center, so that the distribution law of light in each diffraction intensity range can be clearly presented. During the measurement process, special attention should be paid to the accuracy and stability of the aperture adjustment to avoid measurement data errors caused by small aperture changes. The obtained aperture diffraction intensity interval data covers the intensity changes of light at different offset angles, providing a quantitative basis for focus adjustment. When calculating the focus shift coefficient of the waste optical environment data based on the aperture diffraction intensity interval, it is first necessary to input the aforementioned diffraction intensity interval data into the focus shift calculation module. Using the multi-point focusing algorithm, the diffraction paths of light with different intensities are tracked and calculated to determine the optimal focus point position. In the specific process, by dynamically adjusting the focal length of the lens, the imaging position of the optical lens is adjusted in real time, so that it can focus quickly under different diffraction intensities and accurately track the optical response changes of the waste. The calculation of the focus shift coefficient needs to comprehensively consider multiple parameters such as the material reflection characteristics of the waste and the influence of the aperture change on the focus, to ensure that the final shift coefficient accurately reflects the characteristics of the optical environment changes of the waste. The final output waste focus offset coefficient provides an accurate numerical reference for subsequent automatic focus adjustment.

[0084] Step S3: performing focal plane curvature change analysis according to the waste focus offset coefficient to obtain focal plane curvature change data; performing focal plane multi-dimensional calibration processing according to the focal plane curvature change data to obtain focal plane multi-dimensional calibration data;

[0085] In the embodiment of the present invention, when analyzing the change of focal plane curvature according to the waste focus offset coefficient, it is first necessary to construct an accurate three-dimensional spatial model of the focal plane, and based on the waste focus offset coefficient, measure the specific change of focal plane curvature during lens imaging by high-precision laser scanning and optical ranging technology. During the operation, an optical curvature analyzer is used to scan different focal positions point by point to accurately record the change in curvature of light on the focal plane. To ensure the accuracy of the data, the focal plane scanning needs to be repeated for many times, and the error correction is performed on the measurement results to eliminate interference caused by external light sources, lens shaking and other factors. Finally, the focal plane curvature change data will present the curvature characteristics of the focal plane at different focus positions in detail, providing necessary data support for subsequent multi-dimensional calibration. Next, the focal plane multi-dimensional calibration process is performed according to the focal plane curvature change data. The specific operation steps include: first, using the focal plane calibration device, inputting and parsing the curvature change data, and automatically generating the calibration parameters that need to be adjusted; then, through the electric control mechanism, the internal lens group of the optical lens is fine-tuned, and the position and angle of the lens group are adjusted to correct the deviation of the focal plane curvature during the imaging process. During the adjustment process, the calibration equipment will continuously monitor the calibration effect of the lens group and provide real-time feedback on the accuracy of the calibration parameters. After the multi-dimensional calibration process is completed, the output focal plane multi-dimensional calibration data contains the specific adjustment parameters of the lens group and the final calibration effect, which is the basis for further optimization of focusing accuracy.

[0086] Step S4: Formulate an optical lens focusing strategy according to the focal plane multi-dimensional calibration data to obtain the optical lens focusing strategy; perform automated firmware design on the optical lens focusing strategy to obtain the optical lens focusing strategy firmware, and send the optical lens focusing strategy firmware to the sweeping robot management system to execute automatic focusing of the optical lens.

[0087] In an embodiment of the present invention, when formulating the optical lens focusing strategy according to the multi-dimensional calibration data of the focal plane, it is necessary to comprehensively consider the lens group adjustment parameters of the lens, the trend of the focal plane curvature change and the imaging accuracy requirements. First, the multi-dimensional calibration data is imported into the focusing strategy generation system, and the system generates a set of dynamic focusing adjustment schemes by automatically analyzing the weights and mutual influence relationships of each calibration parameter. The scheme specifically specifies the operation details such as the focusing adjustment path, lens group movement rate and angle change of the lens in different environments and waste positions. In order to ensure the stability of the focusing strategy, the system will perform multiple strategy simulation tests, adjust the execution order and strength of each operation step to achieve the best focusing effect, and finally generate the optical lens focusing strategy. When designing the automated firmware for the optical lens focusing strategy, it is first necessary to convert the strategy content into machine executable instruction code. Using the firmware writing platform, each step of the focusing strategy adjustment instruction is encoded through programmatic design, and the adapted hardware interface command is embedded to ensure the precise control of the optical lens of the sweeping robot. In the firmware design process, in order to ensure the execution efficiency and response speed of the instruction, the instruction path needs to be optimized to reduce unnecessary instruction delays. After the design is completed, the firmware execution effect is verified through the firmware simulation test platform to detect the firmware's response and focusing accuracy under different ambient light conditions. The optical lens focusing strategy firmware finally generated will be loaded into the management system of the sweeping robot. The optical lens focusing strategy firmware is sent to the sweeping robot management system to execute the automatic focusing of the optical lens. During the actual execution process, the management system automatically adjusts the firmware's operating instructions by monitoring the focusing effect in real time to ensure the focusing stability and accuracy of the optical lens in different scanning environments. The entire focusing process is fully automated. The firmware directly communicates with the robot's optical lens assembly to control the focusing action of the lens in real time, ensuring that each focusing step is executed according to the strategy, and ultimately achieving efficient and accurate automatic focusing of the optical lens.

[0088] Preferably, step S1 comprises the following steps:

[0089] Step S11: performing a three-dimensional environment scan on the moving path through an optical lens carried by the sweeping robot to obtain three-dimensional environment scanning video data;

[0090] Step S12: converting the three-dimensional environment scanning video data to obtain environment format compatible video data;

[0091] Step S13: performing color space optimization on the environment format compatible video data to obtain environment color space optimized data;

[0092] Step S14: performing waste optical environment analysis on the environmental format compatible video data according to the environmental color space optimization data to obtain waste optical environment data.

[0093] In an embodiment of the present invention, when the optical lens mounted on the sweeping robot is used to perform a stereoscopic environment scan on the moving path, a high-resolution fisheye lens or a panoramic camera is required to ensure that the scanning range covers the entire stereoscopic environment of the robot's forward path. The camera needs to be precisely adjusted in angle to ensure that clear environmental images can be captured at different heights and tilt angles. The acquisition of environmental scanning video data depends on the real-time imaging and rapid response capabilities of the optical lens. Through multi-frame synthesis technology, image frames at different time points and angles are integrated to generate a seamless stereoscopic environmental scanning video. The acquisition of video data also requires synchronous data compression processing to reduce data delays during real-time transmission, ensure that all environmental features are fully captured, and finally form clear and continuous stereoscopic environmental scanning video data. When converting the stereoscopic environmental scanning video data, the original video format needs to be standardized to ensure the compatibility and transmission efficiency of the video data. The specific operation steps include using a video encoding conversion device to convert the stereoscopic environmental scanning video data from the original format (such as YUV, AVI) to a more widely used standard video format (such as MP4, H.264). The conversion process must speed up the processing of video data through hardware acceleration encoding technology to avoid delays. During the conversion process, parameters such as frame rate and resolution need to be adjusted to maintain visual consistency between the video quality and the actual environment. The video format compatibility test equipment is used to detect the compatibility of the output video to ensure that the converted environment format compatible video data can be recognized and processed by different analysis devices. When optimizing the color space of the environment format compatible video data, the color correction module needs to be used to automatically adjust and optimize the color of each frame in the video. The specific operation is to analyze the color models such as RGB and HSV in the video through the color analyzer to identify color deviation and uneven exposure. Subsequently, through automatic color balance processing, parameters such as color saturation, brightness and contrast are adjusted to make the color value of each pixel in the video closer to the optical performance of the real environment. Special attention should be paid to the adaptive processing of ambient light during operation. Through frame-by-frame adjustment and optimization, the color of the entire video is ensured to be uniform and natural, and the environment color space optimization data without color difference is formed. To ensure the effect, the optimized video data needs to be verified multiple times to eliminate color deviation. When performing waste optical environment analysis on the environment format compatible video data according to the environment color space optimization data, the color space optimized video data needs to be imported into the optical environment analysis module first. During the analysis process, optical filtering technology and edge detection algorithms are used to accurately identify waste features in the video, such as shape, texture, and color differences. Each frame of the video data is automatically extracted through optical comparison to extract optical features related to the waste, such as reflected light intensity and surface roughness. Then, spectral analysis technology is combined to detect the material characteristics of the waste and determine its reflection and absorption characteristics under different light.The analysis results are output as waste optical environment data, which records the optical response characteristics of the waste in detail and provides an accurate optical basis for the focusing of optical lenses and waste detection.

[0094] Preferably, step S2 comprises the following steps:

[0095] Step S21: performing spatial optical scattering characteristic analysis on the waste optical environment data to obtain waste spatial optical scattering characteristic data;

[0096] Step S22: performing spatial optical axis offset calculation on the waste optical environment data according to the waste spatial optical scattering characteristic data to obtain waste spatial optical axis offset data;

[0097] Step S23: calculating the aperture diffraction intensity interval of the waste optical environment data according to the waste space optical axis offset data to obtain the aperture diffraction intensity interval;

[0098] Step S24: Calculate the focus shift coefficient of the waste optical environment data based on the aperture diffraction intensity interval to obtain the waste focus shift coefficient.

[0099] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0100] Step S21: performing spatial optical scattering characteristic analysis on the waste optical environment data to obtain waste spatial optical scattering characteristic data;

[0101] In an embodiment of the present invention, when performing spatial optical scattering characteristic analysis on waste optical environment data, an optical scattering analysis device, such as a multi-angle light scatterer or an optical coherence tomography (OCT) instrument, is required. First, the scattered light distribution in the waste optical environment data is measured pixel by pixel by the analysis device, and the intensity and angle of each light scattering direction are recorded. Subsequently, the optical scattering coefficient, phase function and depolarization phenomenon on the surface and inside of the waste are accurately determined by angle-resolved spectral measurement. During the analysis process, the optical response characteristics under different scattering conditions are obtained by adjusting the wavelength and incident angle of the light source to maximize the description of the spatial scattering behavior of the waste. The data processing process removes background noise and extracts key scattering characteristics through Fourier transform and spectral deconvolution technology, and finally generates detailed waste spatial optical scattering characteristic data.

[0102] Step S22: performing spatial optical axis offset calculation on the waste optical environment data according to the waste spatial optical scattering characteristic data to obtain waste spatial optical axis offset data;

[0103] In an embodiment of the present invention, when calculating the spatial optical axis offset of the waste optical environment data based on the waste spatial optical scattering characteristic data, a precision optical axis detector is required to monitor the optical axis offset of the waste in real time by adjusting the relative position of the waste and the detector. During the detection process, a spatial optical axis modulator is used to accurately align the light source beam with the center of the optical axis of the waste, and the offset area is scanned step by step to record the offset angle and position change of the optical axis. The calculation of the optical axis offset is based on the difference in optical paths. The degree of optical axis offset is accurately quantified by the optical path difference calculation formula, and the offset result is calibrated in combination with the optical scattering characteristic data. During the operation, each test result needs to be measured repeatedly multiple times to ensure the measurement accuracy and avoid error accumulation. The final output of the waste spatial optical axis offset data records in detail the axial offset of the waste under optical conditions.

[0104] Step S23: calculating the aperture diffraction intensity interval of the waste optical environment data according to the waste space optical axis offset data to obtain the aperture diffraction intensity interval;

[0105] In an embodiment of the present invention, when calculating the aperture diffraction intensity interval of the waste optical environment data according to the waste space optical axis offset data, it is necessary to first normalize the optical axis offset data to ensure the availability of the data during the calculation process. Subsequently, the optical environment data calibrated by the optical axis offset is imported into the device through a diffraction intensity analyzer to perform step-by-step diffraction intensity measurements. During the measurement process, the change in optical diffraction intensity needs to be based on the diffraction theory formula, and the incident light beam is adjusted to different diameters through a variable aperture, and the intensity change after the light passes through the aperture is recorded. During the calculation of the aperture diffraction intensity interval, special attention should be paid to compensating for changes in ambient light, and the consistency of experimental environment lighting should be ensured through synchronous light source control technology. The calculation result is determined by the maximum and minimum values ​​of the aperture diffraction intensity, and finally the aperture diffraction intensity interval is generated as an important basis for subsequent focus offset calibration.

[0106] Step S24: Calculate the focus shift coefficient of the waste optical environment data based on the aperture diffraction intensity interval to obtain the waste focus shift coefficient.

[0107] In an embodiment of the present invention, when calculating the focus shift coefficient of the waste optical environment data based on the aperture diffraction intensity interval, it is necessary to use an automatic focusing algorithm module to perform a detailed correction on the actual focus position in combination with the aperture diffraction intensity interval data. First, the waste optical environment data is matched with the aperture diffraction intensity, and the degree and direction of the focus shift are determined by an optical interferometer. During the measurement process, the focal length and aperture opening of the lens are adjusted and gradually adjusted to the minimum focus shift state. A precise optical rangefinder is used in the calculation process, and an accurate focus shift is obtained through multiple measurements and error correction. Subsequently, the focus shift coefficient of the waste is calculated based on the imaging formula of the optical lens and the actual measurement results. This coefficient is an important reference indicator for the automatic focusing adjustment of the optical lens, which directly affects the final imaging quality and automatic focusing efficiency.

[0108] Preferably, step S23 includes the following steps:

[0109] Step S231: calculating the optical axis offset angle of the waste space optical axis offset data to obtain optical axis offset angle data;

[0110] Step S232: reconstructing the optical axis offset vector field according to the optical axis offset angle data and the waste space optical axis offset data to obtain the optical axis offset vector field data;

[0111] Step S233: performing optical axis-waste interface effect analysis on the waste optical environment data according to the optical axis offset vector field data to obtain optical axis-waste interface effect data;

[0112] Step S234: performing a multi-level optical interference structure analysis on the optical axis-waste interface action data to obtain interface action optical interference structure data;

[0113] Step S235: Calculating the aperture diffraction intensity interval of the waste optical environment data according to the interface optical interference structure data and the optical axis-waste interface data to obtain the aperture diffraction intensity interval.

[0114] In an embodiment of the present invention, when calculating the optical axis offset angle of the waste space optical axis offset data, a precision angle measuring instrument and a laser interferometer measurement system are used, and the angle is measured by inputting the waste space optical axis offset data into the measuring instrument. First, the light beam is irradiated through a calibrated reference optical axis, and the actual offset trajectory of the optical axis is recorded in real time. Using a three-dimensional angle measurement method, the offset trajectory of the optical axis is projected onto a two-dimensional plane, and the offset angle of the optical axis is calculated by a geometric optical formula. This process requires repeated measurements to ensure the accuracy of the data, and finally outputs accurate optical axis offset angle data by correcting noise interference and system errors. When reconstructing the optical axis offset vector field based on the optical axis offset angle data and the waste space optical axis offset data, an optical vector field reconstruction algorithm and an image processing system are used to integrate and process the data. First, by importing the optical axis offset angle data and the space offset data into the reconstruction system, a vector distribution field of the optical axis is established based on the vector superposition principle. During the reconstruction process, Gaussian interpolation is used to smooth discrete data points to accurately reflect the continuous offset state of the optical axis. Each offset angle and position is fully expressed by the vector field through the vector direction and magnitude. The reconstructed optical axis offset vector field data shows in detail the distribution characteristics of the optical axis on the surface and inside of the waste. When analyzing the optical axis-waste interface effect of the waste optical environment data based on the optical axis offset vector field data, it is necessary to use an optical interface analyzer to jointly process the vector field data with the optical environment data of the waste. First, each vector direction in the optical axis offset vector field is finely matched with the waste interface to analyze the refraction, reflection and scattering characteristics of the optical axis when passing through the interface. During the operation, the interference fringes generated by the interaction between the optical axis and the waste interface are captured by the interferometer, and the light intensity, displacement and phase changes are quantitatively analyzed. Combined with the physical properties of the interface, the specific impact of the optical axis offset on the optical behavior of the waste is calculated, and finally the optical axis-waste interface effect data is output. When performing multi-level optical interference structure analysis on the optical axis-waste interface effect data, multi-level interference analysis technology is used to perform layered measurement of the optical interaction between the optical axis and the waste interface. First, the interface action data is input into the optical interferometer, and the interference phenomena at each level are scanned layer by layer. The optical structure characteristics on the interface are analyzed by the distribution of optical path difference and the intensity change of interference fringes. This process uses the double-beam interference method to accurately locate the phase difference and light intensity change in each layer of the optical structure to ensure the accuracy of the interference measurement. By analyzing the obtained interference pattern, the interaction between the optical axis and the waste is quantitatively described, and finally the interface action optical interference structure data is output. When the aperture diffraction intensity interval of the waste optical environment data is calculated based on the interface action optical interference structure data and the optical axis-waste interface action data, the interference structure data is analyzed and processed using an optical calculator. First, the interference structure and interface action data are fused to analyze the diffraction intensity changes at different aperture openings after the optical axis passes through the interface.The aperture adjustment system is used to measure the light intensity under different size conditions by changing the aperture size, and the maximum and minimum diffraction intensity under each aperture state are recorded. During the calculation process, the aperture needs to be adjusted repeatedly to obtain the full range of diffraction intensity distribution, and finally the aperture diffraction intensity range is accurately calculated to provide reliable reference data for subsequent focus calibration.

[0115] Preferably, step S24 includes the following steps:

[0116] Step S241: performing aperture diffraction effect distribution fitting on the aperture diffraction intensity interval to obtain aperture diffraction effect distribution fitting data;

[0117] Step S242: performing optical axis offset interference mapping on the waste space optical axis offset data according to the aperture diffraction effect distribution fitting data to obtain optical axis offset interference mapping data;

[0118] Step S243: performing multi-gradient focus dynamic simulation on the waste optical environment data according to the optical axis offset interference mapping data to obtain multi-gradient focus dynamic simulation data;

[0119] Step S244: performing focus dynamic offset mean difference calculation on the multi-gradient focus dynamic simulation data to obtain focus dynamic offset mean difference data;

[0120] Step S245: Calculate the focus shift coefficient according to the focus dynamic shift mean difference data to obtain the waste focus shift coefficient.

[0121] In an embodiment of the present invention, when fitting the aperture diffraction effect distribution for the aperture diffraction intensity interval, an optical data fitting instrument and a high-precision calculation algorithm are used to import the aperture diffraction intensity data into the fitting system, and accurate mathematical fitting is performed on different diffraction intensity regions by a polynomial fitting method. During the operation, the parameters of the fitting function are gradually adjusted to best fit the changing trend of the diffraction intensity interval. The fitting process strictly controls the error, and by comparing the fitting degree of the fitting curve with the actual measured data, it is ensured that the obtained distribution curve accurately reflects the diffraction effect. The aperture diffraction effect distribution fitting data finally outputted characterizes the diffraction effect characteristics of the aperture in each intensity interval in detail, providing data support for the subsequent optical axis interference mapping analysis. When optical axis offset interference mapping is performed on the waste space optical axis offset data according to the aperture diffraction effect distribution fitting data, the optical axis interference mapping instrument is used to combine and analyze the data. The fitted aperture diffraction effect distribution and the spatial optical axis offset data are input into the mapping system, and the interference image of the optical axis under the influence of different diffraction effects is generated by the interference mapping algorithm. This operation reconstructs the dynamic interference trajectory of the optical axis offset by comparing the light intensity changes of each data point, focusing on the phase change and interference fringe distribution of the optical axis in a specific environment. During the operation, the diffraction effect on the offset of the optical axis is calculated point by point, and accurate optical axis offset interference mapping data is output, laying the foundation for dynamic focus simulation. When performing multi-gradient focus dynamic simulation on the waste optical environment data according to the optical axis offset interference mapping data, a dynamic optical simulation system is used to simulate the multi-gradient changes of the focus by establishing a virtual optical environment. The optical axis offset interference mapping data is imported into the simulation system, and the movement trajectory of the focus under different optical axis interference conditions is gradually simulated using a real-time calculation method. During the operation, the offset angle, light intensity, and environmental optical characteristic parameters of the optical axis are adjusted, and the change law of the focus on the surface and inside of the waste is calculated frame by frame. The simulation system outputs the dynamic position and intensity distribution of the focus according to the changes in different gradient optical characteristics, thereby obtaining multi-gradient focus dynamic simulation data, accurately describing the behavior pattern of the focus in a complex environment. When calculating the mean difference of the focus dynamic offset for the multi-gradient focus dynamic simulation data, a data analysis instrument is used to perform statistical calculations on the dynamic changes of the focus. First, the simulation data is imported into the analysis system, and the focus position offset is compared frame by frame to calculate the focus offset of each frame, and the average offset and fluctuation range in the entire dynamic process are counted. During the calculation process, the weighted mean method is used to correct the focus offset under different conditions to eliminate the influence of environmental disturbance factors on the focus position. Finally, the obtained focus dynamic offset mean difference data reflects in detail the focus offset stability under different gradient environments, and provides reliable basic data for the accurate calculation of the focus offset coefficient. When calculating the focus offset coefficient based on the focus dynamic offset mean difference data, a precise optical offset analyzer and calculation tools are used to analyze and process the mean difference data.The mean difference data is used as input, combined with the dynamic change law of the focus, and the correction coefficient of the focus offset is calculated through linear regression and offset coefficient fitting algorithm. During the operation, the parameters of the regression equation are gradually optimized so that it can accurately match the actual change of the focus offset. In order to ensure the accuracy of the calculation, the fitting results are checked multiple times, and the rationality of the fitting error is evaluated through residual analysis. The final output of the waste focus offset coefficient data provides an accurate numerical reference for the automatic focus adjustment of the optical lens, which can effectively improve the focusing accuracy and response speed of the lens.

[0122] Preferably, step S3 comprises the following steps:

[0123] Step S31: performing focal plane curvature change analysis according to the waste focus offset coefficient and the waste space optical axis offset data to obtain focal plane curvature change data;

[0124] Step S32: performing optical aberration evaluation on the focal plane curvature change data to obtain curvature change optical aberration data;

[0125] Step S33: performing focal plane multi-dimensional calibration processing according to the curvature change optical aberration data and the focal plane curvature change data to obtain focal plane multi-dimensional calibration data.

[0126] In an embodiment of the present invention, when analyzing the change of focal plane curvature according to the waste focus offset coefficient and the waste space optical axis offset data, a focal plane curvature analyzer is used to analyze the comprehensive effect of focus offset and optical axis offset in detail. First, the waste focus offset coefficient and the space optical axis offset data are input into the analyzer, and the system converts these data into the change parameters of the focal plane curvature. Through precise optical calculation and curvature measurement, the curvature of the focal plane at different optical axis positions is obtained. The analyzer compares the actual measured curvature data with the theoretical model and calculates the curvature change value of the focal plane under specific offset conditions. These focal plane curvature change data provide basic data support for subsequent optical aberration evaluation and calibration. When performing optical aberration evaluation on the focal plane curvature change data, a high-precision optical aberration measuring instrument is used to process the data. First, the focal plane curvature change data is input into the optical aberration evaluation system, and the system calculates various aspects of optical aberrations, such as spherical aberration, chromatic aberration and coma, based on the input data. The optical aberration measuring instrument adopts beam testing and interference technology to measure the optical aberration of the focal plane under different curvatures through multi-angle beam scanning and interference image analysis. The obtained curvature change optical aberration data reflects in detail the impact of the focal plane curvature change on the optical image quality, including the distribution and intensity of the aberration. This process ensures an accurate evaluation of the optical aberration of the focus under different conditions, and provides an important basis for further multi-dimensional calibration. When the focal plane multi-dimensional calibration is performed according to the curvature change optical aberration data and the focal plane curvature change data, a multi-dimensional optical calibration device is used for processing. The focal plane curvature change data and curvature change optical aberration data are input into the calibration system, and the system performs multi-dimensional calibration through high-precision optical adjustment devices and data processing algorithms. During the calibration process, the system adjusts multiple parameters of the optical lens (such as aperture, focal length, optical axis position, etc.) to correct the curvature and aberration of the focal plane in real time. The calibration equipment uses adjustable lenses and beam interferometers to dynamically adjust the focal plane to ensure that the focal plane reaches the optimal state under all measurement conditions. Ultimately, the obtained multi-dimensional calibration data of the focal plane accurately describes the optimized state of the focal plane under actual operating conditions, providing a detailed calibration basis for the automatic focusing adjustment of the optical lens.

[0127] Preferably, step S31 includes the following steps:

[0128] Step S311: performing focus dynamic shift trajectory identification according to the waste focus shift coefficient and the waste space optical axis shift data to obtain focus dynamic shift trajectory data;

[0129] Step S312: calculating the trajectory offset angle of the focus dynamic offset trajectory data to obtain trajectory offset angle data;

[0130] Step S313: constructing a focus offset trajectory dynamic three-dimensional plane according to the trajectory offset angle data and the focus dynamic offset trajectory data to obtain a focus offset trajectory dynamic three-dimensional plane data set;

[0131] Step S314: performing offset circular curvature calculation on the focus offset trajectory dynamic three-dimensional plane data set to obtain offset circular curvature data;

[0132] Step S315: calculating the steering angle of the dynamic three-dimensional plane data set of the focus offset trajectory to obtain dynamic plane steering angle data;

[0133] Step S316: performing a focus position agglomeration effect analysis according to the offset circular curvature data and the dynamic plane steering angle data to obtain focus position agglomeration effect data;

[0134] Step S317: performing focal plane curvature change analysis based on the focus position concentration effect data, the dynamic plane steering angle data, and the offset circular curvature data to obtain focal plane curvature change data.

[0135] In an embodiment of the present invention, when the dynamic focus offset trajectory is identified according to the waste focus offset coefficient and the waste space optical axis offset data, a high-precision optical motion tracking system is used. The system inputs the waste focus offset coefficient and the space optical axis offset data into the trajectory identification module. The system collects real-time data through the motion sensor installed on the optical lens, and the system records the position change of the focus during the movement. Then, the trajectory analysis algorithm is applied to process these data to identify the movement trajectory of the focus at different time points. In this process, the system will dynamically track the actual movement path of the focus and generate the dynamic focus offset trajectory data to accurately describe the movement characteristics of the focus. When the trajectory offset angle is calculated for the dynamic focus offset trajectory data, the trajectory data is analyzed using an angle measurement tool. First, the dynamic focus offset trajectory data is input into the angle calculation device. The device measures the angle of each point in the trajectory through a precise optical angle measuring instrument and calculates the offset angle of the focus between different points. The specific operation includes measuring the direction change of the focus offset through the beam projection technology and converting these data into angle values. The obtained trajectory offset angle data accurately reflects the offset angle of the focus on the trajectory, thereby providing the necessary data support for the subsequent dynamic three-dimensional plane construction. When constructing a dynamic three-dimensional plane of the focus offset trajectory according to the trajectory offset angle data and the focus dynamic offset trajectory data, a three-dimensional data processing system is used. The trajectory offset angle data and the dynamic offset trajectory data are input into the three-dimensional modeling tool. The tool generates a three-dimensional dynamic plane of the focus offset by using the trajectory data by establishing a three-dimensional coordinate system. All trajectory points are stereoscopically processed by three-dimensional scanning and data point fitting technology to form a dynamic three-dimensional plane data set of the focus offset trajectory. This data set describes in detail the dynamic change trajectory of the focus in space, and provides visual data support for further analysis of the motion characteristics of the focus. The dynamic three-dimensional plane data set of the focus offset trajectory constructed in step S313 is analyzed to calculate the curvature of the offset circle. The circular part is extracted from the three-dimensional plane data by using the surface fitting technology, and the curvature radius is calculated by analyzing the tangent of the local surface. The specific operation includes performing local surface analysis on each data point, and applying the curvature formula (for example, the calculation of Gaussian curvature and principal curvature) to obtain the curvature data of the offset circle. By analyzing the three-dimensional plane data set constructed in step S313, the turning angle of the plane is calculated. The angle calculation method between the plane normal vector and the reference coordinate system is used to determine the orientation of the plane in space. The specific operation includes calculating the normal vector of each plane, and then using the vector dot product method to calculate the angle with the normal vector of the standard coordinate axis to obtain the steering angle data of the dynamic plane. The offset circular curvature data obtained in step S314 and the dynamic plane steering angle data obtained in step S315 are used to analyze the focus position agglomeration effect.The data aggregation technology is used to combine the position data of the focus at different time points with the curvature and steering angle data to evaluate the focusing effect of the focus at each position. The analysis method includes calculating the statistical distribution of the focus position, combining the curvature and steering angle data to determine the degree and regularity of the focus in a specific area. According to the focus position agglomeration effect data, dynamic plane steering angle data and offset circular curvature data obtained in step S316, the focal plane curvature change analysis is performed. By summarizing the agglomeration effect of the focus position and combining the performance of the focus under different curvatures and angles, the curvature change of the focal plane under different conditions is evaluated. The specific operation includes using data fitting technology to perform regression analysis on the focus position agglomeration effect and curvature data, generate a data set of focal plane curvature changes, and describe in detail how the curvature of the focal plane changes with time and focus offset.

[0136] Preferably, step S33 includes the following steps:

[0137] Step S331: performing imaging distortion analysis on the curvature variation optical aberration data to obtain optical aberration imaging distortion data;

[0138] Step S332: calculating the distortion value interval according to the optical aberration imaging distortion data and the focal plane curvature change data to obtain the imaging distortion value interval;

[0139] Step S333: performing spatial interpolation processing on the imaging distortion value interval to obtain the imaging distortion optimization value interval;

[0140] Step S334: performing focal plane multi-dimensional calibration processing according to the imaging distortion optimization value interval to obtain focal plane multi-dimensional calibration data.

[0141] In an embodiment of the present invention, when performing imaging distortion analysis on curvature-varying optical aberration data, a high-precision imaging system and a distortion analysis instrument are used. First, the curvature-varying optical aberration data is input into the distortion analyzer. The instrument captures the actual imaging result through an optical sensor, compares it with the theoretical model, and identifies the imaging distortion caused by the optical aberration. The specific operation includes projecting a standard pattern onto the optical lens, recording the imaging effect through a camera system, and then analyzing the degree of distortion using image processing technology. The optical aberration imaging distortion data finally obtained reflects the distortion characteristics in lens imaging, and provides basic data for subsequent optimization. The optical aberration imaging distortion data obtained in step S331 is combined with the focal plane curvature change data, and the distortion value interval is obtained by mathematical calculation. First, the change in the focal plane curvature is quantified and corresponds to the optical aberration data one by one. Then, a data analysis tool (such as Matlab) is used to calculate the distortion value interval within each curvature change range. This process involves statistical analysis and numerical calculation, the purpose of which is to determine the degree of distortion under different curvature change conditions in the optical system, thereby obtaining a distortion interval data suitable for various focal plane curvature conditions. The imaging distortion value interval obtained in step S332 is processed using spatial interpolation technology. The specific operation includes using an interpolation algorithm (such as cubic spline interpolation or Kriging interpolation) to perform spatial interpolation on the distortion data. These algorithms can smoothly transition in space according to the known distortion value interval to generate a more refined imaging distortion optimization value interval. The purpose of this step is to improve the resolution of the distortion data through spatial interpolation so that the imaging distortion optimization value interval more accurately reflects the performance of the actual optical system. The imaging distortion optimization value interval obtained in step S333 is used to perform multi-dimensional calibration on the focal plane of the optical system. First, a multi-dimensional calibration experiment scheme is designed according to the optimized value interval, the focal plane setting in the optical system is adjusted, and the optimized value is applied in the actual imaging process. The calibration process includes adjusting the position and angle of the lens and monitoring the imaging effect in real time. Through a series of repeated adjustments and measurements, multi-dimensional calibration data of the focal plane are generated, which are used to finally adjust the settings of the optical system to ensure the best imaging quality and minimum distortion.

[0142] Preferably, step S4 comprises the following steps:

[0143] Step S41: performing normalization processing on the focal plane multi-dimensional calibration data to obtain focal plane multi-dimensional calibration normalized data;

[0144] Step S42: performing calibration iterative processing on the focal plane multidimensional calibration normalization data based on the memory network to obtain multidimensional calibration iterative data;

[0145] Step S43: formulating an optical lens focusing strategy according to the multi-dimensional calibration iteration data to obtain an optical lens focusing strategy;

[0146] Step S44: Automated firmware design is performed on the optical lens focusing strategy to obtain the optical lens focusing strategy firmware, and the optical lens focusing strategy firmware is sent to the sweeping robot management system to execute automatic focusing of the optical lens.

[0147] In an embodiment of the present invention, a normalization processing tool is used when the focal plane multidimensional calibration data is normalized. First, the focal plane multidimensional calibration data is imported into a processing device, which is equipped with a data normalization module. The module is used to perform a linear transformation on the data, and the data range is mapped to an interval of 0 to 1. The specific process includes setting the minimum and maximum values ​​of the data as normalized boundary values, and adjusting each data point by a linear ratio. During the processing, the standard deviation and mean of the data are monitored to ensure that the normalized data maintains consistent distribution characteristics. The normalized data is used to standardize subsequent calibration steps to improve processing accuracy and consistency. When the focal plane multidimensional calibration normalized data is calibrated iteratively based on a memory network, a memory network processing device is used. The focal plane multidimensional calibration normalized data is input into a memory network module of the device. The module includes multiple storage units for recording and analyzing historical calibration data. Through an iterative algorithm, the calibration parameters are gradually adjusted to optimize the matching accuracy of the data. Each iteration process includes comparing the current data with the historical data and adjusting the calibration strategy to reduce errors. During the iterative process, the data after each adjustment is recorded, and the optimal multi-dimensional calibration iteration data is output at the end of the process. This data is used to develop a more accurate optical lens focusing strategy. When developing an optical lens focusing strategy based on multi-dimensional calibration iteration data, a strategy development tool is used. First, the multi-dimensional calibration iteration data is imported into the tool, and the tool develops a specific focusing strategy by analyzing the patterns and trends in the data. The specific operation includes analyzing the data to determine the optimal focus adjustment range and step size. The strategy development process needs to consider the calibration requirements of different focal plane positions, and use the iteration data to optimize the focus position and adjustment strategy. The resulting optical lens focusing strategy includes optimization solutions for different environments and conditions, and is applied to the actual lens adjustment operation. When automating the firmware design of the optical lens focusing strategy, a firmware development tool is used. First, the developed optical lens focusing strategy is input into the firmware design system, which converts the strategy into executable firmware code through the firmware programming module. The specific operation includes writing firmware code to implement the automatic focusing function according to the requirements of the strategy. After the code is written, the firmware is tested to ensure its reliability and stability under different working conditions. After the firmware design is completed, the firmware is uploaded to the sweeping robot management system and sent to the sweeping robot through wireless transmission technology. The robot then performs the automatic focusing task of the optical lens according to the firmware.

[0148] Preferably, the present invention further provides an optical lens automatic focusing system, which is used to execute the optical lens automatic focusing method as described above, and the optical lens automatic focusing system comprises:

[0149] The waste environment optical analysis module is used to perform a three-dimensional environment scan on the moving path through the optical lens carried by the sweeping robot to obtain three-dimensional environment scanning video data; perform waste optical environment analysis on the three-dimensional environment scanning video data to obtain waste optical environment data;

[0150] A focus shift calculation module is used to perform spatial optical axis shift calculation on waste optical environment data to obtain waste spatial optical axis shift data; perform aperture diffraction intensity interval calculation based on the waste spatial optical axis shift data to obtain aperture diffraction intensity interval; perform focus shift coefficient calculation on waste optical environment data based on the aperture diffraction intensity interval to obtain waste focus shift coefficient;

[0151] A focal plane calibration module is used to analyze the change of focal plane curvature according to the waste focus offset coefficient to obtain focal plane curvature change data; and to perform focal plane multi-dimensional calibration processing according to the focal plane curvature change data to obtain focal plane multi-dimensional calibration data;

[0152] The automatic focusing strategy formulation module is used to formulate an optical lens focusing strategy according to the multi-dimensional calibration data of the focal plane to obtain the optical lens focusing strategy; to perform automated firmware design on the optical lens focusing strategy to obtain the optical lens focusing strategy firmware, and to send the optical lens focusing strategy firmware to the sweeping robot management system to execute automatic focusing of the optical lens.

[0153] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0154] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An optical lens automatic focusing method, characterized in that: The following steps are involved: Step S1: performing a three-dimensional environment scan on the moving path through the optical lens carried by the sweeping robot to obtain three-dimensional environment scanning video data; performing waste optical environment analysis on the three-dimensional environment scanning video data to obtain waste optical environment data; Step S2: performing spatial optical axis offset calculation on the waste optical environment data to obtain waste spatial optical axis offset data; performing aperture diffraction intensity interval calculation based on the waste spatial optical axis offset data to obtain the aperture diffraction intensity interval; The focus shift coefficient of the waste optical environment data is calculated based on the aperture diffraction intensity interval to obtain the waste focus shift coefficient; wherein step S2 includes: Step S21: performing spatial optical scattering characteristic analysis on the waste optical environment data to obtain waste spatial optical scattering characteristic data; Step S22: performing spatial optical axis offset calculation on the waste optical environment data according to the waste spatial optical scattering characteristic data to obtain waste spatial optical axis offset data; Step S23: calculating the aperture diffraction intensity interval of the waste optical environment data according to the waste space optical axis offset data to obtain the aperture diffraction intensity interval; wherein step S23 includes: Step S231: calculating the optical axis offset angle of the waste space optical axis offset data to obtain optical axis offset angle data; Step S232: reconstructing the optical axis offset vector field according to the optical axis offset angle data and the waste space optical axis offset data to obtain the optical axis offset vector field data; Step S233: performing optical axis-waste interface effect analysis on the waste optical environment data according to the optical axis offset vector field data to obtain optical axis-waste interface effect data; Step S234: performing a multi-level optical interference structure analysis on the optical axis-waste interface action data to obtain interface action optical interference structure data; Step S235: calculating the aperture diffraction intensity interval of the waste optical environment data according to the interface optical interference structure data and the optical axis-waste interface data to obtain the aperture diffraction intensity interval; Step S24: Calculating the focus shift coefficient of the waste optical environment data based on the aperture diffraction intensity interval to obtain the waste focus shift coefficient; wherein step S24 includes: Step S241: performing aperture diffraction effect distribution fitting on the aperture diffraction intensity interval to obtain aperture diffraction effect distribution fitting data; Step S242: performing optical axis offset interference mapping on the waste space optical axis offset data according to the aperture diffraction effect distribution fitting data to obtain optical axis offset interference mapping data; Step S243: performing multi-gradient focus dynamic simulation on the waste optical environment data according to the optical axis offset interference mapping data to obtain multi-gradient focus dynamic simulation data; Step S244: performing focus dynamic offset mean difference calculation on the multi-gradient focus dynamic simulation data to obtain focus dynamic offset mean difference data; Step S245: Calculating the focus shift coefficient according to the focus dynamic shift mean difference data to obtain the waste focus shift coefficient; Step S3: performing focal plane curvature change analysis according to the waste focus offset coefficient to obtain focal plane curvature change data; performing focal plane multi-dimensional calibration processing according to the focal plane curvature change data to obtain focal plane multi-dimensional calibration data; Step S4: Formulate an optical lens focusing strategy according to the focal plane multi-dimensional calibration data to obtain the optical lens focusing strategy; perform automated firmware design on the optical lens focusing strategy to obtain the optical lens focusing strategy firmware, and send the optical lens focusing strategy firmware to the sweeping robot management system to execute automatic focusing of the optical lens.

2. The optical lens automatic focusing method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: performing a three-dimensional environment scan on the moving path through an optical lens carried by the sweeping robot to obtain three-dimensional environment scanning video data; Step S12: converting the three-dimensional environment scanning video data to obtain environment format compatible video data; Step S13: performing color space optimization on the environment format compatible video data to obtain environment color space optimized data; Step S14: performing waste optical environment analysis on the environmental format compatible video data according to the environmental color space optimization data to obtain waste optical environment data.

3. The optical lens automatic focusing method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing focal plane curvature change analysis according to the waste focus offset coefficient and the waste space optical axis offset data to obtain focal plane curvature change data; Step S32: performing optical aberration evaluation on the focal plane curvature change data to obtain curvature change optical aberration data; Step S33: performing focal plane multi-dimensional calibration processing according to the curvature change optical aberration data and the focal plane curvature change data to obtain focal plane multi-dimensional calibration data.

4. The optical lens automatic focusing method according to claim 3, characterized in that: Step S31 includes the following steps: Step S311: performing focus dynamic shift trajectory identification according to the waste focus shift coefficient and the waste space optical axis shift data to obtain focus dynamic shift trajectory data; Step S312: calculating the trajectory offset angle of the focus dynamic offset trajectory data to obtain trajectory offset angle data; Step S313: constructing a focus offset trajectory dynamic three-dimensional plane according to the trajectory offset angle data and the focus dynamic offset trajectory data to obtain a focus offset trajectory dynamic three-dimensional plane data set; Step S314: performing offset circular curvature calculation on the focus offset trajectory dynamic three-dimensional plane data set to obtain offset circular curvature data; Step S315: calculating the steering angle of the dynamic three-dimensional plane data set of the focus offset trajectory to obtain dynamic plane steering angle data; Step S316: performing a focus position agglomeration effect analysis according to the offset circular curvature data and the dynamic plane steering angle data to obtain focus position agglomeration effect data; Step S317: performing focal plane curvature change analysis based on the focus position concentration effect data, the dynamic plane steering angle data, and the offset circular curvature data to obtain focal plane curvature change data.

5. The optical lens automatic focusing method according to claim 3, characterized in that: Step S33 includes the following steps: Step S331: performing imaging distortion analysis on the curvature variation optical aberration data to obtain optical aberration imaging distortion data; Step S332: calculating the distortion value interval according to the optical aberration imaging distortion data and the focal plane curvature change data to obtain the imaging distortion value interval; Step S333: performing spatial interpolation processing on the imaging distortion value interval to obtain the imaging distortion optimization value interval; Step S334: performing focal plane multi-dimensional calibration processing according to the imaging distortion optimization value interval to obtain focal plane multi-dimensional calibration data.

6. The optical lens automatic focusing method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing normalization processing on the focal plane multi-dimensional calibration data to obtain focal plane multi-dimensional calibration normalized data; Step S42: performing calibration iterative processing on the focal plane multidimensional calibration normalization data based on the memory network to obtain multidimensional calibration iterative data; Step S43: formulating an optical lens focusing strategy according to the multi-dimensional calibration iteration data to obtain an optical lens focusing strategy; Step S44: Automated firmware design is performed on the optical lens focusing strategy to obtain the optical lens focusing strategy firmware, and the optical lens focusing strategy firmware is sent to the sweeping robot management system to execute automatic focusing of the optical lens.

7. An optical lens automatic focusing system, characterized in that: Used to perform the optical lens automatic focusing method as claimed in claim 1, the optical lens automatic focusing system comprises: The waste environment optical analysis module is used to perform a three-dimensional environment scan on the moving path through the optical lens carried by the sweeping robot to obtain three-dimensional environment scanning video data; perform waste optical environment analysis on the three-dimensional environment scanning video data to obtain waste optical environment data; A focus shift calculation module is used to perform spatial optical axis shift calculation on waste optical environment data to obtain waste spatial optical axis shift data; perform aperture diffraction intensity interval calculation based on the waste spatial optical axis shift data to obtain aperture diffraction intensity interval; perform focus shift coefficient calculation on waste optical environment data based on the aperture diffraction intensity interval to obtain waste focus shift coefficient; A focal plane calibration module is used to analyze the change of focal plane curvature according to the waste focus offset coefficient to obtain focal plane curvature change data; and to perform focal plane multi-dimensional calibration processing according to the focal plane curvature change data to obtain focal plane multi-dimensional calibration data; The automatic focusing strategy formulation module is used to formulate an optical lens focusing strategy according to the multi-dimensional calibration data of the focal plane to obtain the optical lens focusing strategy; to perform automated firmware design on the optical lens focusing strategy to obtain the optical lens focusing strategy firmware, and to send the optical lens focusing strategy firmware to the sweeping robot management system to execute automatic focusing of the optical lens.

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