Full-automatic focus fixing and positioning system based on corneal topography map

Abstract

The invention relates to the technical field of precise positioning, in particular to a corneal topographic map-based full-automatic fixed-focus positioning system, which comprises an optical imaging module for acquiring multi-angle optical signals on the surface of a cornea through an optical device, processing light reflection in different areas, determining the edge of the cornea by using the intensity difference of the reflected light, and calculating the reflection angle and intensity; and identifying the light signal of the key area to obtain light reflection characteristic data. According to the method, accurate analysis of the complex form of the cornea surface is achieved through accurate analysis of the light reflection characteristic data, the accuracy of focal length control is improved, a PID control algorithm is applied, the curvature change data is combined, the high matching degree of focal length adjustment and the cornea surface curvature is ensured, the stability and response speed of focal length adjustment are improved, and the accuracy of focal length adjustment is improved. Through light path deviation correction, imaging errors caused by light path deviation are reduced, and the accuracy, the stability and the imaging quality of automatic focus fixing and positioning of the corneal topography are improved.

Description

Technical Field

[0001] The present invention relates to the field of precision positioning technology, and in particular to a full-automatic focus positioning system based on corneal topography. Background Art

[0002] The field of precision positioning technology aims to achieve precise adjustment and control of the position, direction, and focus of objects through high-precision measurement, control, and regulation technologies, ensuring accurate positioning of objects within a small range.

[0003] The purpose of the fully automatic fixed-focus positioning system based on corneal topography is to use the three-dimensional surface data provided by the corneal topography to automatically adjust the focus and position of the optical system to ensure the clarity and accuracy of the imaging. It obtains precise corneal surface geometry data through optical scanning, adjusts the focus and position of the optical device, and ensures that the imaging system can focus quickly and accurately and provide high-quality images.

[0004] When dealing with complex morphology of the corneal surface, traditional systems rely on simple optical scanning and focus adjustment methods, and lack accurate analysis of changes in corneal curvature, resulting in large focus adjustment errors when the system faces irregular corneal morphology, affecting imaging clarity. The system is insufficient in optical path calibration and cannot effectively correct the deviation between the optical path and the corneal surface, resulting in unnecessary optical path errors in the imaging process, affecting imaging accuracy. There is a lack of correlation analysis between focal length and corneal surface curvature, and the focus adjustment is unstable and has a slow response speed, which affects the efficiency and stability of the equipment in actual operation and reduces accuracy and reliability. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a fully automatic focus positioning system based on corneal topography.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a fully automatic focus positioning system based on corneal topography comprises:

[0007] Optical imaging module: Based on an optical device, it collects multi-angle light signals from the corneal surface, processes light reflections from different areas of the corneal surface, uses the difference in reflected light intensity to determine the edge of the corneal area, calculates the light reflection angle and intensity, identifies the light signal characteristics of key areas, and obtains light reflection characteristic data;

[0008] Curvature analysis module: based on the light reflection feature data, the corneal surface area is segmented, and the local curvature information of the corneal surface is extracted by analyzing the relationship between the light reflection angle and the position. The overall curvature change is analyzed by combining the curvature values ​​of different areas, the curvature of the key position is calculated, and the curvature change distribution data is generated;

[0009] Focus control module: Based on the curvature change distribution data, a PID control algorithm is used to perform correlation analysis between the corneal curvature value and the focal length, and the focus adjustment point is determined through the curvature changes in each area, the focus offset range is calculated, the focus device is adjusted, the current focus value is corrected, and the relationship between the focal length and the curvature is ensured to be consistent, and a focus adjustment result is generated;

[0010] Imaging calibration module: based on the focus adjustment result, analyze the current light path state, determine the position of key points in the light path by measuring the light path offset after the focus adjustment, calculate the relative deviation between the light path and the corneal surface, adjust the angle and position of the optical sensor, and generate light path calibration parameters;

[0011] Focus adjustment module: Based on the optical path calibration parameters, focus adjustment is performed to determine the imaging quality of different areas on the corneal surface, and the deviation between the focal length and the imaging position is analyzed through the focusing intensity of the light signal, and the displacement and angle of the focus device are gradually corrected, the focus state is updated, and focus position correction data is generated;

[0012] Position correction module: based on the focus position correction data, analyze the displacement deviation between the imaging device and the corneal surface, calculate the position change of the device relative to the cornea through the displacement sensor data, select the correction point to adjust the device position, correct the displacement parameters of the imaging device, update the position state, and generate position adjustment data;

[0013] System self-test module: Based on the position adjustment data, perform system status detection, measure key parameters of focal length, optical path and displacement through the multi-point detection module, analyze the equipment operation status, judge the overall situation of corneal imaging, confirm the normality of all system parameters, and generate a system detection report.

[0014] As a further scheme of the present invention, the light reflection characteristic data include the reflection intensity of different areas of the corneal surface, the light incidence angle, and the reflection angle distribution; the curvature change distribution data include the curvature value of each partition, the curvature change amplitude of the corneal surface, and the curvature difference value of the key point; the focal length adjustment result includes the focal length offset value, the focal length correction parameter, and the change relationship between the focal length and the curvature; the optical path calibration parameters include the optical path offset angle, the corrected sensor position, and the optical path deviation correction value; the focus position correction data include the focus distance correction value, the focus adjustment amount of the imaging area, and the focal length correction parameter; the position adjustment data include the equipment displacement correction amount, the cornea relative position offset value, and the position calibration parameter; the system detection report includes the focal length calibration data, the optical path status detection result, and the equipment displacement correction result.

[0015] As a further solution of the present invention, the optical imaging module includes a light signal acquisition submodule, a light intensity analysis submodule, and a light reflection feature recognition submodule, wherein:

[0016] Light signal acquisition submodule: Based on the optical device, it collects multi-angle light signals from different areas on the corneal surface, collects and records the light reflection information of each area, and generates light signal data;

[0017] Light intensity analysis submodule: based on the light signal data, calculates the light reflection intensity of each corneal area, compares the light intensity difference at different angles, and generates light intensity distribution data;

[0018] Light reflection feature recognition submodule: Based on the light intensity distribution data, it recognizes the light reflection features of key areas on the corneal surface, extracts the edge features of each area, and generates light reflection feature data.

[0019] As a further solution of the present invention, the curvature analysis module includes a region division submodule, a curvature information extraction submodule, and a curvature change calculation submodule, wherein:

[0020] Region division submodule: based on the light reflection characteristic data, the region division of the corneal surface is performed, the boundaries of each region are divided according to the difference of light reflection characteristics, and the region division data is generated;

[0021] Curvature information extraction submodule: based on the area division data, extract the local curvature information in each partition, calculate the curvature variation of each area, and generate local curvature data;

[0022] Curvature change calculation submodule: Based on the local curvature data and combined with the curvature changes of different partitions, the overall curvature distribution of the cornea is analyzed to generate curvature change distribution data.

[0023] As a further solution of the present invention, the focal length control module includes a curvature analysis submodule, a focal length adjustment point judgment submodule, and a focal length offset calculation submodule, wherein:

[0024] Curvature analysis submodule: based on the curvature change distribution data, analyze the curvature changes of different areas of the corneal surface, extract key point information of the curvature change, establish a curvature change model of each area of ​​the corneal surface, and generate curvature key point information;

[0025] Focus adjustment point judgment submodule: based on the curvature key point information, compares the correlation between the curvature value and the focal length in different areas, determines the best position for focal length adjustment by calculating the influence range of the curvature change position, and generates focus adjustment point information;

[0026] Focus offset calculation submodule: Based on the focus adjustment point information, a PID control algorithm is used to calculate the offset that the focus device needs to adjust, and the focus offset value is determined in combination with the focus control parameters of the system, the focus device is adjusted, and a focus adjustment result is generated.

[0027] As a further solution of the present invention, the PID control algorithm is adopted according to the formula:

[0028]

[0029] Where: u(t) is the offset that the focal length device needs to adjust, K p is the proportional coefficient, e(t) is the error between the current focal length and the target focal length, K i It is the integral coefficient, which represents the impact of the accumulated error on the system adjustment and is used to compensate for the error that exists for a long time. is the error integral from time 0 to the current time t, K d is the differential coefficient, is the rate of change of error over time, K f is the external environment coefficient, f(v) is the ambient light change rate, K s is the system load factor, and s(θ) is the lens angle change rate.

[0030] As a further solution of the present invention, the imaging calibration module includes an optical path state analysis submodule, an optical path offset measurement submodule, and a sensor position adjustment submodule, wherein:

[0031] Light path state analysis submodule: based on the focus adjustment result, analyze the current state of the light path, determine the light path change after the focus adjustment, identify the key point offset in the light path, and generate light path state information;

[0032] Optical path offset measurement submodule: based on the optical path state information, measures the actual offset of each key point in the optical path, calculates the deviation value of each position in the optical path, obtains the overall offset information of the optical path, and generates optical path offset data;

[0033] Sensor position adjustment submodule: Based on the optical path offset data, adjust the angle and position of the optical sensor, correct the position of the sensor in the optical path, ensure that the sensor is aligned with the optical path, and generate optical path calibration parameters.

[0034] As a further solution of the present invention, the focus adjustment module includes an imaging quality judgment submodule, a focal length deviation analysis submodule, and a focus correction submodule, wherein:

[0035] Imaging quality judgment submodule: based on the optical path calibration parameters, judge the imaging quality of each area of ​​the cornea, analyze the imaging effects of different areas through focusing intensity measurement, identify poor imaging areas, and generate imaging quality evaluation data;

[0036] Focus deviation analysis submodule: based on the imaging quality evaluation data, analyzes the deviation between the imaging position and the focal length, evaluates the impact of the focal length change in each area on the imaging position, calculates the focus adjustment requirement, and generates the focus deviation information;

[0037] Focus correction submodule: Based on the focal length deviation information, adjust the displacement and angle of the focus device, correct the focal length and position of the imaging device, gradually update the focus state, and generate focus position correction data.

[0038] As a further solution of the present invention, the position correction module includes a displacement deviation analysis submodule, a position change assessment submodule, and an equipment position adjustment submodule, wherein:

[0039] Displacement deviation analysis submodule: based on the focus position correction data, analyzes the displacement deviation between the imaging device and the corneal surface, determines the deviation of the relative position of the device by comparing the data of the displacement sensor, and generates displacement deviation information;

[0040] Position change assessment submodule: based on the displacement deviation information, assess the specific position change of the imaging device relative to the corneal surface, calculate the correction amount required for the device movement, and generate position change assessment data;

[0041] Device position adjustment submodule: based on the position change evaluation data, adjust the position and angle of the device, correct the displacement parameters of the imaging device, ensure that the device is aligned with the cornea, and generate position adjustment data.

[0042] As a further solution of the present invention, the system self-check module includes a focal length detection submodule, an optical path detection submodule, and a displacement detection submodule, wherein:

[0043] Focus detection submodule: Based on the position adjustment data, the focus of the imaging system is measured, the focus parameters of each point are collected, the deviation between the focus and the preset value is checked, and the focus detection data is generated;

[0044] Light path detection submodule: based on the focal length detection data, measures the light signal transmission status of each point in the light path, analyzes the light path deviation of key points, obtains the overall operation status of the light path, and generates light path detection data;

[0045] Displacement detection submodule: Based on the optical path detection data, the displacement between the imaging device and the corneal surface is measured, and the deviation between the device and the target position is determined using a displacement sensor to generate displacement detection data.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are:

[0047] In the present invention, accurate analysis of the complex morphology of the corneal surface is achieved through precise analysis of the light reflection characteristic data, thereby improving the accuracy of focus control. The PID control algorithm is applied in combination with the curvature change data to ensure a high degree of matching between the focus adjustment and the corneal surface curvature, thereby improving the stability and response speed of the focus adjustment. Through the correction of optical path deviation, the imaging error caused by the optical path deviation is reduced, thereby improving the accuracy, stability and imaging quality of the automatic focus positioning of the corneal topography. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a system flow chart of the present invention;

[0049] Figure 2 Schematic diagram of the system framework of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0051] Embodiment 1

[0052] See also Figure 1 The present invention provides a technical solution: a fully automatic focus positioning system based on corneal topography includes:

[0053] Optical imaging module: Based on an optical device, it collects multi-angle light signals from the corneal surface, processes light reflections from different areas of the corneal surface, uses the difference in reflected light intensity to determine the edge of the corneal area, calculates the light reflection angle and intensity, identifies the light signal characteristics of key areas, and obtains light reflection characteristic data;

[0054] Curvature analysis module: Based on the light reflection feature data, the corneal surface area is segmented, and the local curvature information of the corneal surface is extracted by analyzing the relationship between the light reflection angle and the position. The overall curvature change is analyzed by combining the curvature values ​​of different areas, the curvature of the key position is calculated, and the curvature change distribution data is generated;

[0055] Focus control module: Based on the curvature change distribution data, the PID control algorithm is used to perform correlation analysis between the corneal curvature value and the focal length. Through the curvature changes in each area, the focus adjustment point is determined, the focus offset range is calculated, the focus device is adjusted, the current focus value is corrected, the relationship between the focal length and the curvature is ensured to be consistent, and the focus adjustment result is generated;

[0056] Imaging calibration module: Based on the focus adjustment result, analyze the current optical path state, determine the position of key points in the optical path by measuring the optical path offset after the focus adjustment, calculate the relative deviation between the optical path and the corneal surface, adjust the angle and position of the optical sensor, and generate the optical path calibration parameters;

[0057] Focus adjustment module: Based on the optical path calibration parameters, focus adjustment is performed to determine the imaging quality of different areas on the corneal surface. Through the focusing intensity of the light signal, the deviation between the focal length and the imaging position is analyzed, the displacement and angle of the focus device are gradually corrected, the focus status is updated, and the focus position correction data is generated;

[0058] Position correction module: Based on the focus position correction data, analyze the displacement deviation between the imaging device and the corneal surface, calculate the position change of the device relative to the cornea through the displacement sensor data, select the correction point to adjust the device position, correct the displacement parameters of the imaging device, update the position status, and generate position adjustment data;

[0059] System self-test module: Based on the position adjustment data, the system status is tested. Through the multi-point detection module, the key parameters of focal length, optical path and displacement are measured, the equipment operation status is analyzed, the overall situation of corneal imaging is judged, the normality of all system parameters is confirmed, and a system test report is generated.

[0060] The light reflection characteristic data include the reflection intensity of different areas of the corneal surface, the incident angle of light, and the distribution of reflection angles. The curvature change distribution data include the curvature value of each partition, the curvature change amplitude of the corneal surface, and the curvature difference value of key points. The focal length adjustment result includes the focal length offset value, the focal length correction parameter, and the relationship between the focal length and the curvature change. The optical path calibration parameters include the optical path offset angle, the corrected sensor position, and the optical path deviation correction value. The focus position correction data include the focus distance correction value, the focus adjustment amount of the imaging area, and the focal length correction parameter. The position adjustment data include the device displacement correction amount, the cornea relative position offset value, and the position calibration parameters. The system detection report includes the focal length calibration data, the optical path status detection results, and the device displacement correction results.

[0061] See also Figure 2 The optical imaging module includes a light signal acquisition submodule, a light intensity analysis submodule, and a light reflection feature recognition submodule, wherein:

[0062] Light signal acquisition submodule: Based on the optical device, it collects multi-angle light signals from different areas on the corneal surface, collects and records the light reflection information of each area, and generates light signal data;

[0063] Light intensity analysis submodule: Based on the light signal data, calculate the light reflection intensity of each corneal area, compare the light intensity differences at different angles, and generate light intensity distribution data;

[0064] Light reflection feature recognition submodule: Based on the light intensity distribution data, it identifies the light reflection features of key areas on the corneal surface, extracts the edge features of each area, and generates light reflection feature data;

[0065] Light signal acquisition submodule: Based on the optical device, a multi-angle scanning strategy is used to collect light signals from different areas of the corneal surface. The acquisition angle range is set to 0 to 360 degrees, with an interval of 1 degree. The photoelectric detector is used to record the light signal intensity and corresponding angle position information at each angle. After the acquisition is completed, the data is stored in the memory buffer to generate light signal data;

[0066] Light intensity analysis submodule: Based on the light signal data, a linear regression algorithm is used to analyze the light reflection intensity of each corneal area. The acquisition angle is used as the input parameter and the light intensity is used as the target parameter. The input data set of the linear regression is extracted from the light signal data, and the relationship between the light intensity and the angle is determined by the fitting parameters. During the analysis process, the iterative optimization method with a step size of 0.01 is used to adjust the regression parameters to ensure that the light intensity of all areas is accurately calculated and the light intensity distribution data is generated;

[0067] Light reflection feature recognition submodule: Based on the light intensity distribution data, the Canny edge detection algorithm is used to identify the edge features of the corneal surface. First, the light intensity data is smoothed using a Gaussian filter. During the smoothing process, the standard deviation parameter is set to 1.4. Then, the intensity changes in each area are determined by gradient calculation, and the edge position is determined using the gradient direction. The non-maximum suppression operation is applied to ensure that only the edge information of the key area is retained. The edge is screened using the double threshold method to identify the key reflection features of the corneal surface and generate light reflection feature data.

[0068] See also Figure 2 The curvature analysis module includes a region division submodule, a curvature information extraction submodule, and a curvature change calculation submodule, wherein:

[0069] Region division submodule: Based on the light reflection feature data, the corneal surface is divided into regions, the boundaries of each region are divided according to the difference in light reflection features, and the region division data is generated;

[0070] Curvature information extraction submodule: Based on the regional division data, extract the local curvature information in each partition, calculate the curvature change range of each region, and generate local curvature data;

[0071] Curvature change calculation submodule: Based on local curvature data and combined with the curvature changes of different partitions, the overall curvature distribution of the cornea is analyzed to generate curvature change distribution data;

[0072] Region division submodule: Based on the light reflection feature data, the K-means clustering algorithm is used to divide the corneal surface into regions. The light reflection feature data is used as the input parameter, the initial cluster centers are set to 5, the number of iterations is 100, and the Euclidean distance is used as the clustering standard. The distance from each data point to the cluster center is calculated, and the cluster center position is adjusted through repeated iterations. The data point attribution of each region is updated until the distance converges and meets the set threshold of 0.01, and the region division data is generated;

[0073] Curvature information extraction submodule: Based on the regional division data, the finite difference method is used to calculate the local curvature in each partition, the coordinate data of each partition is extracted, the partition boundary points are differentially calculated, and the change in local curvature is calculated using the known height difference and level difference between the boundary points. The differential step size is set to 0.1 mm, and the local curvature analysis is performed in each partition. After calculation, the curvature change value is stored and the local curvature data is generated;

[0074] Curvature change calculation submodule: Based on local curvature data, the quadratic surface fitting method is used to perform an overall analysis of the curvature changes of different partitions. The local curvature value of each partition is used as an input parameter, and the quadratic surface fitting is performed using the least squares method. The error threshold used in the fitting process is set to 0.05, and the Gauss-Newton iterative algorithm is used to optimize and adjust the fitting results to gradually reduce the fitting error. The curvature change of each partition is calculated and stored, and the curvature data of all partitions are combined to generate the curvature change distribution data.

[0075] See also Figure 2 The focal length control module includes a curvature analysis submodule, a focal length adjustment point judgment submodule, and a focal length offset calculation submodule, wherein:

[0076] Curvature analysis submodule: Based on the curvature change distribution data, analyze the curvature changes in different areas of the corneal surface, extract the key point information of the curvature change, establish the curvature change model of each area of ​​the corneal surface, and generate the curvature key point information;

[0077] Focus adjustment point judgment submodule: Based on the curvature key point information, the correlation between the curvature value and the focal length in different areas is compared, and the influence range of the curvature change position is calculated to determine the optimal position for focal length adjustment and generate the focus adjustment point information;

[0078] Focus offset calculation submodule: Based on the focus adjustment point information, the PID control algorithm is used to calculate the offset that the focus device needs to adjust, and the focus offset value is determined in combination with the focus control parameters of the system, the focus device is adjusted, and the focus adjustment result is generated;

[0079] Curvature analysis submodule: Based on the curvature change distribution data, the cubic spline interpolation algorithm is used to analyze the curvature changes in different areas of the corneal surface. The segmented interpolation method is used to fit the curvature changes of each partition. The input is the curvature change data of each partition. The interval of the interpolation node is set to 0.05 mm. During the interpolation calculation process, the boundary conditions and smoothness parameters are set to smooth the curvature change trend. After the interpolation is completed, the key point information of the curvature change is extracted, the maximum and minimum positions of the curvature change are marked, and the curvature change model of each area of ​​the corneal surface is established to generate the curvature key point information;

[0080] Focus adjustment point judgment submodule: Based on the curvature key point information, a multivariate linear regression algorithm is used to compare the correlation between the curvature value and the focal length in different areas. The curvature key point information is input as the independent variable, and the historical data of the focal length adjustment is used as the dependent variable. The influence range of the curvature change position is calculated, and the least squares method is used to estimate the influence weight of the curvature change on the focal length. The key parameter values ​​required for focal length adjustment are obtained through multivariate regression analysis, the optimal position of focal length adjustment is determined, and the focal length adjustment point information is generated;

[0081] Focal offset calculation submodule: Based on the focal adjustment point information, the PID control algorithm is used to calculate the offset that the focal device needs to adjust, and the PID control parameters are set, the proportional coefficient Kp=1.5, the integral coefficient Ki=0.1, the differential coefficient Kd=0.05, and the focal adjustment point information is used as input. Combined with the focal control parameters in the system, the offset is calculated in real time, and the deviation is further corrected using integral and differential calculations to determine the focal offset value, adjust the physical position of the focal device, and generate the focal adjustment result.

[0082] Using PID control algorithm, according to the formula:

[0083]

[0084] Where: u(t) is the offset that the focal length device needs to adjust, K p is the proportional coefficient, e(t) is the error between the current focal length and the target focal length, K i is the integral coefficient, which represents the impact of the accumulated error on the system adjustment and is used to compensate for the error that exists for a long time; is the error integral from time 0 to the current time t, K d is the differential coefficient, is the rate of change of error over time, K f is the external environment coefficient, f(v) is the ambient light change rate, K s is the system load factor, s(θ) is the lens angle change rate;

[0085] Execution process: First, calculate the focal length error e(t) of the system, that is, the difference between the current focal length value and the target focal length value. The proportional control part is controlled by K p e(t) generates a preliminary offset, representing the effect of the instantaneous error. The integral control part calculates the cumulative integral of the error Generate compensation to correct the cumulative effect of long-term errors, differential control part Calculate the error change rate, reduce the error change trend, and add the external environment coefficient K f f(v) correction Due to the influence of external light changes on the focal length, the light intensity change rate f(v) can be collected in real time by the light sensor. After the predetermined filtering and normalization processing, it reflects the dynamic response of light to the focal length device. The newly added system load factor K s ·s(θ) corrects the error caused by the change of lens angle. The angle change rate s(θ) is calculated by the built-in angle sensor to reflect the impact of the lens on the focus adjustment at various angles. s and K f The weight coefficient can be determined through experimental calibration, and the optimal coefficient value can be determined by fitting and analyzing the test data of focus adjustment accuracy under different environments.

[0086] See also Figure 2 The imaging calibration module includes an optical path state analysis submodule, an optical path offset measurement submodule, and a sensor position adjustment submodule, wherein:

[0087] Optical path state analysis submodule: Based on the focus adjustment result, analyze the current state of the optical path, determine the change of the optical path after the focus adjustment, identify the offset of key points in the optical path, and generate optical path state information;

[0088] Optical path offset measurement submodule: Based on the optical path status information, measure the actual offset of each key point in the optical path, calculate the deviation value of each position in the optical path, obtain the overall offset information of the optical path, and generate optical path offset data;

[0089] Sensor position adjustment submodule: Based on the optical path offset data, adjust the angle and position of the optical sensor, correct the position of the sensor in the optical path, ensure that the sensor is aligned with the optical path, and generate optical path calibration parameters;

[0090] Optical path state analysis submodule: Based on the focal length adjustment result, the ray tracing algorithm is used to analyze the current optical path state. The input parameters include the coordinate data of each point in the optical path and the result of the focal length adjustment. The ray tracing method is used to track the path of each light ray between each optical element, and the refraction angle and offset of the light ray passing through each medium are calculated step by step. The offset of the key points in the optical path is identified, and the optical path change is determined by calculating the intersection of the light ray and the optical element, and the optical path state information is generated;

[0091] Optical path offset measurement submodule: Based on the optical path status information, the least square method is used to measure the actual offset of each key point in the optical path. The optical path status information is input as the initial data, and the least square method is used to calculate the offset of each key point. The error threshold of 0.001 is used in the calculation process, and the offset value of the key point position is iteratively updated. By accumulating the deviation of each position, the comprehensive data of the overall optical path offset is obtained to generate the optical path offset data;

[0092] Sensor position adjustment submodule: Based on the optical path offset data, the servo control algorithm is used to adjust the angle and position of the optical sensor. First, the optical path offset data is input as the target parameter, and the angle adjustment range of the servo control system is set to 0 to 15 degrees and the displacement adjustment range is set to 0 to 10 mm. The angle and displacement of the sensor are adjusted using the feedback control of the servo motor. The feedback error is reduced to within 0.001 degrees through continuous adjustment, the sensor is adjusted to the appropriate position, and the optical path calibration parameters are generated.

[0093] See also Figure 2 The focus adjustment module includes an imaging quality judgment submodule, a focal length deviation analysis submodule, and a focus correction submodule, wherein:

[0094] Imaging quality judgment submodule: Based on the optical path calibration parameters, the imaging quality of each area of ​​the cornea is judged, the imaging effects of different areas are analyzed by focusing intensity measurement, poor imaging areas are identified, and imaging quality evaluation data is generated;

[0095] Focus deviation analysis submodule: Based on the imaging quality assessment data, analyze the deviation between the imaging position and the focal length, evaluate the impact of the focal length change in each area on the imaging position, calculate the focal length adjustment requirements, and generate focal length deviation information;

[0096] Focus correction submodule: Based on the focal length deviation information, adjust the displacement and angle of the focus device, correct the focal length and position of the imaging device, gradually update the focus state, and generate focus position correction data;

[0097] Imaging quality judgment submodule: Based on the optical path calibration parameters, the imaging quality of each area of ​​the cornea is judged by Fourier transform analysis. The input parameter is the optical signal after optical path calibration. The imaging signal is converted from the time domain to the frequency domain through fast Fourier transform. The frequency distribution of the imaging signal is analyzed and the focus intensity is extracted. The frequency resolution is set to 0.01 Hz. The imaging effect is calculated based on the frequency peak of each area, and the areas with abnormal frequency or poor focus are identified to generate imaging quality evaluation data.

[0098] Focus deviation analysis submodule: Based on the imaging quality assessment data, the least squares error analysis method is used to analyze the deviation between the imaging position and the focal length. The imaging position data is used as input, the focal length change is used as the target parameter, the initial condition is set to zero focal length deviation, and the impact of the focal length change of each area on the imaging position is evaluated step by step. The focus adjustment requirement of each area is calculated through error analysis, the deviation range is calculated and the calculation results are stored, and the focal length deviation information is generated;

[0099] Focus correction submodule: Based on the focal length deviation information, the fuzzy control algorithm is used to adjust the displacement and angle of the focus device. The input is set to the focal length deviation information, and the output is the displacement and angle adjustment of the focus device. The fuzzy control rules are set to a three-layer rule set, which are used to fine-tune the displacement, angle and focal length respectively. The fuzzy inference engine is used to calculate the step size of each adjustment. The step size range is set to 0.1 mm to 0.5 mm. The position and angle of the focus device are gradually adjusted to generate focus position correction data.

[0100] See also Figure 2 The position correction module includes a displacement deviation analysis submodule, a position change assessment submodule, and an equipment position adjustment submodule, among which:

[0101] Displacement deviation analysis submodule: Based on the focus position correction data, analyze the displacement deviation between the imaging device and the corneal surface, determine the deviation of the relative position of the device by comparing the data of the displacement sensor, and generate displacement deviation information;

[0102] Position change assessment submodule: Based on the displacement deviation information, it assesses the specific position change of the imaging device relative to the corneal surface, calculates the correction required for the device movement, and generates position change assessment data;

[0103] Device position adjustment submodule: Based on the position change evaluation data, adjust the position and angle of the device, correct the displacement parameters of the imaging device, ensure that the device is aligned with the cornea, and generate position adjustment data;

[0104] Displacement deviation analysis submodule: Based on the focus position correction data, the Kalman filter algorithm is used to analyze the displacement deviation between the imaging device and the corneal surface. The input parameters include the focus position correction data and the real-time displacement sensor data. The sensor data is smoothed using the Kalman filter. The noise covariance matrix set in the filtering process is Q = 0.001, R = 0.01. The displacement deviation of the device relative to the corneal position is calculated, and the estimated error is iteratively updated to generate displacement deviation information.

[0105] Position change assessment submodule: Based on the displacement deviation information, the Newton iteration method is used to assess the specific position change of the imaging device relative to the corneal surface. The displacement deviation information is input as the initial data, and the distance change between the imaging device and the cornea is calculated. The initial error is set to 0.01 mm, and the iteration step is 0.005 mm. The actual displacement correction amount is gradually approached, and the correction amount is calculated through multiple iterations to generate position change assessment data;

[0106] Device position adjustment submodule: Based on the position change evaluation data, the stepper motor control algorithm is used to adjust the position and angle of the imaging device. The position change evaluation data is input as the control target, the step length of the stepper motor is set to 0.01 mm, and the angle adjustment range is 0 to 10 degrees. The feedback control mechanism is used to monitor the changes in the device position in real time, gradually adjust the stepping speed of the motor, and correct the angle and position to generate position adjustment data.

[0107] See also Figure 2 The system self-test module includes a focal length detection submodule, an optical path detection submodule, and a displacement detection submodule, wherein:

[0108] Focus detection submodule: Based on the position adjustment data, the focus of the imaging system is measured, the focus parameters of each point are collected, the deviation between the focus and the preset value is checked, and the focus detection data is generated;

[0109] Optical path detection submodule: Based on the focal length detection data, measure the optical signal transmission status of each point in the optical path, analyze the optical path deviation of key points, obtain the overall operation status of the optical path, and generate optical path detection data;

[0110] Displacement detection submodule: Based on the optical path detection data, the displacement between the imaging device and the corneal surface is measured, and the deviation between the device and the target position is determined using a displacement sensor to generate displacement detection data;

[0111] Focus detection submodule: Based on the position adjustment data, the phase detection autofocus algorithm is used to measure the focus of the imaging system. The input parameters are the position adjustment data and the preset focus value. The focus parameters of each point are measured by the phase difference method. The optical sensor is used to collect the phase difference information in the image in real time. The accuracy of the phase difference detection is set to 0.01 mm. The difference between the current focal length and the preset focal length is calculated. The sampling frequency of the sensor is gradually adjusted to 100 times per second. The complete focal length data is collected and the focal length deviation of each point is recorded to generate the focal length detection data.

[0112] Optical path detection submodule: Based on the focal length detection data, the ray tracing method is used to measure the transmission status of the optical signal at each point in the optical path. The focal length detection data is input as the initial parameter, and the ray tracing model is used to calculate the transmission path of each light ray. The reflectivity of each optical path point is set to 0.95, and the refraction and reflection process of the light between each optical element is calculated. The transmission intensity of the light at each position is tracked, the optical path deviation of the key points is analyzed, the optical signal intensity and deviation information is recorded, the overall operation status of the optical path is obtained, and the optical path detection data is generated;

[0113] Displacement detection submodule: Based on the optical path detection data, a laser displacement sensor is used to measure the displacement between the imaging device and the corneal surface. The optical path detection data is input as the calibration parameter, and the sampling frequency of the laser displacement sensor is set to 500 times per second. The three-point measurement method is used to determine the precise position of the device relative to the corneal surface, and the displacement difference of each point is calculated. The noise data is filtered through the internal filtering algorithm of the sensor to generate displacement detection data.

[0114] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A fully automatic focus positioning system based on corneal topography, characterized in that: The system comprises: Optical imaging module: Based on an optical device, it collects multi-angle light signals from the corneal surface, processes light reflections from different areas of the corneal surface, uses the difference in reflected light intensity to determine the edge of the corneal area, calculates the light reflection angle and intensity, identifies the light signal characteristics of key areas, and obtains light reflection characteristic data; Curvature analysis module: based on the light reflection feature data, the corneal surface area is segmented, and the local curvature information of the corneal surface is extracted by analyzing the relationship between the light reflection angle and the position. The overall curvature change is analyzed by combining the curvature values ​​of different areas, the curvature of the key position is calculated, and the curvature change distribution data is generated; Focus control module: Based on the curvature change distribution data, a PID control algorithm is used to perform correlation analysis between the corneal curvature value and the focal length, and the focus adjustment point is determined through the curvature changes in each area, the focus offset range is calculated, the focus device is adjusted, the current focus value is corrected, and the relationship between the focal length and the curvature is ensured to be consistent, and a focus adjustment result is generated; Imaging calibration module: based on the focus adjustment result, analyze the current light path state, determine the position of key points in the light path by measuring the light path offset after the focus adjustment, calculate the relative deviation between the light path and the corneal surface, adjust the angle and position of the optical sensor, and generate light path calibration parameters; Focus adjustment module: Based on the optical path calibration parameters, focus adjustment is performed to determine the imaging quality of different areas on the corneal surface, and the deviation between the focal length and the imaging position is analyzed through the focusing intensity of the light signal, and the displacement and angle of the focus device are gradually corrected, the focus state is updated, and focus position correction data is generated; Position correction module: based on the focus position correction data, analyze the displacement deviation between the imaging device and the corneal surface, calculate the position change of the device relative to the cornea through the displacement sensor data, select the correction point to adjust the device position, correct the displacement parameters of the imaging device, update the position state, and generate position adjustment data; System self-test module: Based on the position adjustment data, perform system status detection, measure key parameters of focal length, optical path and displacement through the multi-point detection module, analyze the equipment operation status, judge the overall situation of corneal imaging, confirm the normality of all system parameters, and generate a system detection report.

2. The fully automatic focus positioning based on corneal topography according to claim 1, characterized in that: The light reflection characteristic data includes the reflection intensity of different areas of the corneal surface, the incident angle of light, and the reflection angle distribution; the curvature change distribution data includes the curvature value of each partition, the curvature change amplitude of the corneal surface, and the curvature difference value of the key point; the focal length adjustment result includes the focal length offset value, the focal length correction parameter, and the change relationship between the focal length and the curvature; the optical path calibration parameter includes the optical path offset angle, the corrected sensor position, and the optical path deviation correction value; the focus position correction data includes the focus distance correction value, the focus adjustment amount of the imaging area, and the focal length correction parameter; the position adjustment data includes the equipment displacement correction amount, the cornea relative position offset value, and the position calibration parameter; the system detection report includes the focal length calibration data, the optical path status detection result, and the equipment displacement correction result.

3. The fully automatic focus positioning based on corneal topography according to claim 1, characterized in that: The optical imaging module includes a light signal acquisition submodule, a light intensity analysis submodule, and a light reflection feature recognition submodule, wherein: Light signal acquisition submodule: Based on the optical device, it collects multi-angle light signals from different areas on the corneal surface, collects and records the light reflection information of each area, and generates light signal data; Light intensity analysis submodule: based on the light signal data, calculates the light reflection intensity of each corneal area, compares the light intensity difference at different angles, and generates light intensity distribution data; Light reflection feature recognition submodule: Based on the light intensity distribution data, it recognizes the light reflection features of key areas on the corneal surface, extracts the edge features of each area, and generates light reflection feature data.

4. The fully automatic focus positioning based on corneal topography according to claim 1, characterized in that: The curvature analysis module includes a region division submodule, a curvature information extraction submodule, and a curvature change calculation submodule, wherein: Region division submodule: based on the light reflection characteristic data, the region division of the corneal surface is performed, the boundaries of each region are divided according to the difference of light reflection characteristics, and the region division data is generated; Curvature information extraction submodule: based on the area division data, extract the local curvature information in each partition, calculate the curvature variation of each area, and generate local curvature data; Curvature change calculation submodule: Based on the local curvature data and combined with the curvature changes of different partitions, the overall curvature distribution of the cornea is analyzed to generate curvature change distribution data.

5. The fully automatic focus positioning based on corneal topography according to claim 1, characterized in that: The focal length control module includes a curvature analysis submodule, a focal length adjustment point judgment submodule, and a focal length offset calculation submodule, wherein: Curvature analysis submodule: based on the curvature change distribution data, analyze the curvature changes of different areas of the corneal surface, extract key point information of the curvature change, establish a curvature change model of each area of ​​the corneal surface, and generate curvature key point information; Focus adjustment point judgment submodule: based on the curvature key point information, compares the correlation between the curvature value and the focal length in different areas, determines the best position for focal length adjustment by calculating the influence range of the curvature change position, and generates focus adjustment point information; Focus offset calculation submodule: Based on the focus adjustment point information, a PID control algorithm is used to calculate the offset that the focus device needs to adjust, and the focus offset value is determined in combination with the focus control parameters of the system, the focus device is adjusted, and a focus adjustment result is generated.

6. The fully automatic focus positioning based on corneal topography according to claim 1, characterized in that: The PID control algorithm is adopted according to the formula: Where: u(t) is the offset that the focal length device needs to adjust, K p is the proportional coefficient, e(t) is the error between the current focal length and the target focal length, K i It is the integral coefficient, which represents the impact of the accumulated error on the system adjustment and is used to compensate for the error that exists for a long time. is the error integral from time 0 to the current time t, K d is the differential coefficient, is the rate of change of error over time, K f is the external environment coefficient, f(v) is the ambient light change rate, K s is the system load factor, and s(θ) is the lens angle change rate.

7. The fully automatic focus positioning based on corneal topography according to claim 1, characterized in that: The imaging calibration module includes an optical path state analysis submodule, an optical path offset measurement submodule, and a sensor position adjustment submodule, wherein: Light path state analysis submodule: based on the focus adjustment result, analyze the current state of the light path, determine the light path change after the focus adjustment, identify the key point offset in the light path, and generate light path state information; Optical path offset measurement submodule: based on the optical path state information, measures the actual offset of each key point in the optical path, calculates the deviation value of each position in the optical path, obtains the overall offset information of the optical path, and generates optical path offset data; Sensor position adjustment submodule: Based on the optical path offset data, adjust the angle and position of the optical sensor, correct the position of the sensor in the optical path, ensure that the sensor is aligned with the optical path, and generate optical path calibration parameters.

8. The fully automatic focus positioning based on corneal topography according to claim 1, characterized in that: The focus adjustment module includes an imaging quality judgment submodule, a focal length deviation analysis submodule, and a focus correction submodule, wherein: Imaging quality judgment submodule: based on the optical path calibration parameters, judge the imaging quality of each area of ​​the cornea, analyze the imaging effects of different areas through focusing intensity measurement, identify poor imaging areas, and generate imaging quality evaluation data; Focus deviation analysis submodule: based on the imaging quality evaluation data, analyzes the deviation between the imaging position and the focal length, evaluates the impact of the focal length change in each area on the imaging position, calculates the focus adjustment requirement, and generates the focus deviation information; Focus correction submodule: Based on the focal length deviation information, adjust the displacement and angle of the focus device, correct the focal length and position of the imaging device, gradually update the focus state, and generate focus position correction data.

9. The fully automatic focus positioning based on corneal topography according to claim 1, characterized in that: The position correction module includes a displacement deviation analysis submodule, a position change assessment submodule, and an equipment position adjustment submodule, wherein: Displacement deviation analysis submodule: based on the focus position correction data, analyzes the displacement deviation between the imaging device and the corneal surface, determines the deviation of the relative position of the device by comparing the data of the displacement sensor, and generates displacement deviation information; Position change assessment submodule: based on the displacement deviation information, assess the specific position change of the imaging device relative to the corneal surface, calculate the correction amount required for the device movement, and generate position change assessment data; Device position adjustment submodule: based on the position change evaluation data, adjust the position and angle of the device, correct the displacement parameters of the imaging device, ensure that the device is aligned with the cornea, and generate position adjustment data.

10. The fully automatic focus positioning based on corneal topography according to claim 1, characterized in that: The system self-check module includes a focal length detection submodule, an optical path detection submodule, and a displacement detection submodule, wherein: Focus detection submodule: Based on the position adjustment data, the focus of the imaging system is measured, the focus parameters of each point are collected, the deviation between the focus and the preset value is checked, and the focus detection data is generated; Light path detection submodule: based on the focal length detection data, measures the light signal transmission status of each point in the light path, analyzes the light path deviation of key points, obtains the overall operation status of the light path, and generates light path detection data; Displacement detection submodule: Based on the optical path detection data, the displacement between the imaging device and the corneal surface is measured, and the deviation between the device and the target position is determined using a displacement sensor to generate displacement detection data.

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