Elliptical single capillary lens cutting area selection method and device

Abstract

The application relates to an ellipsoidal single capillary lens cutting region selection method, which comprises the following steps: collecting profile data of an ellipsoidal single capillary to be cut; selecting a plurality of lens interested cutting regions according to the profile data and preset cutting data, and constructing a profile equation for each interested cutting region; solving the surface shape error of each interested cutting region based on the profile equation by using an improved cosine crow search algorithm, and updating the individual position according to Levy flight during calculation; until the surface shape errors of all the interested cutting regions are calculated, the minimum surface shape error value and the corresponding interested cutting region are output as the best cutting region. When the individual position is updated, the Levy flight is used to replace the fixed step length, the blindness of the cosine crow search algorithm is reduced, the problems that the traditional capillary cutting is prone to falling into local optimization and the convergence speed is slow are solved, and the calculation of the single capillary surface shape error is more efficient and stable.

Description

Technical Field

[0001] This invention relates to the field of optical lens technology, and in particular to a method and apparatus for selecting the cutting area of ​​an ellipsoidal single capillary lens. Background Technology

[0002] An X-ray ellipsoidal single capillary lens is a key component of an X-ray focusing system, characterized by high transmission efficiency, high light flux density, and controllable divergence angle after focusing. Therefore, it is widely used in the X-ray field, including X-ray diffraction imaging, X-ray absorption spectroscopy, and X-ray fluorescence analysis. This lens can efficiently focus and concentrate the X-ray beam, significantly improving the imaging quality and detection sensitivity of X-rays within materials. Furthermore, this lens shows broad application prospects in micro / nanostructure analysis, surface morphology measurement, and material composition analysis.

[0003] Surface figure error is one of the most critical factors affecting the focusing performance of single capillary lenses, and many institutions are researching how to reduce the surface figure error of capillaries. Currently, there are two methods widely used to improve the optical quality of capillaries. The first method is to optimize the design parameters of the capillary to enhance the focusing performance of the capillary. In 2018, Sun Xuepeng et al. of Beijing Normal University designed a twice-reflection single capillary suitable for copper target laboratory X-ray source, which mainly consists of ellipsoidal and conical parts; optical tests show that the surface figure error of the manufactured capillary is 54 μrad (Sun X, Zhu Y, Wang Y, et al. 13.1 micrometers hard X-ray focusing by a new type monocapillary X-ray optic designed for common laboratory X-ray source [J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2018, 888: 13-17.). The second solution is to improve the manufacturing technology of the capillary to reduce the surface figure error inside the capillary; for example, in 2017, Cordier Mark et al. of Sigray company manufactured a single capillary with parabolic shape, whose surface figure error is only 0.07 μm (Cordier M, Stripe B, Yun W, et al. Advances toward submicron resolution optics for X-ray instrumentation and applications [C] / / Advances in X-Ray / EUV Optics and Components XII. SPIE, 2017, 10386: 56-62.).

[0004] The contour data of a single capillary often deviates from the design value due to errors in the manufacturing process. To reduce the surface shape error of a single capillary lens, Zhou Peng et al. from Beijing Normal University proposed an effective method based on particle swarm optimization in 2019, which can effectively select the cutting position of the single capillary lens. This method can achieve a capillary surface shape error of 0.83 μm. (Zhou P, Ma X, Zhang S, et al. Application of particle swarm optimization in the design of a mono-capillary X-ray lens[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2020, 953: 163077.). However, this method does not consider the problem that optimization algorithms often get trapped in local optima. According to the analysis, the standard deviation of the statistical results of this method is relatively high, thus it suffers from slow convergence speed and insufficient search accuracy. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the problem that optimization algorithms in the prior art often get stuck in local optima, resulting in slow convergence speed and insufficient search accuracy.

[0006] To solve the above-mentioned technical problems, the present invention provides a method for selecting the cutting region of an ellipsoidal single capillary lens, comprising:

[0007] Collect the contour data of the ellipsoidal single capillary to be cut;

[0008] Based on the contour data and preset cutting data, select multiple lens cutting regions of interest, and construct a contour equation for each cutting region of interest;

[0009] An improved sine and cosine crow search algorithm is used to solve the surface error of each region of interest based on the contour equation, and the individual position is updated according to Levy flight during the calculation.

[0010] The process continues until the surface shape error of all regions of interest is calculated, and the minimum surface shape error value and its corresponding region of interest are output as the optimal cutting region for the ellipsoidal single capillary to be cut.

[0011] In one embodiment of the present invention, the acquisition of contour data of the ellipsoidal single capillary to be cut includes:

[0012] cleaning the surface of the ellipsoidal single capillary to be cut with the three-dimensional optical profiler;

[0013] placing the ellipsoidal single capillary to be cut on the measuring platform of the three-dimensional optical profiler;

[0014] adjusting the position of the ellipsoidal single capillary to be cut until the three-dimensional optical profiler can measure the profile data of the entire ellipsoidal single capillary to be cut, starting measurement, acquiring and storing the profile data of the ellipsoidal single capillary to be cut.

[0015] In an embodiment of the present application, the selecting a plurality of lens interest cutting regions according to the profile data and preset cutting data comprises:

[0016] mapping the profile data to a two-dimensional coordinate, the X-axis corresponding to the long axis direction of the ellipsoidal single capillary to be cut, and the Y-axis corresponding to the short axis direction of the ellipsoidal single capillary to be cut;

[0017] selecting a plurality of pairs of long axis boundary points in the long axis direction of the ellipsoidal single capillary according to the profile data and preset cutting data;

[0018] each pair of long axis boundary points corresponds to four short axis boundary points on the short axis, and the region surrounded by the six boundary points constitutes a lens interest cutting region.

[0019] In an embodiment of the present application, the difference between the horizontal coordinate distance of each pair of long axis boundary points on the X-axis and the horizontal coordinate distance in the preset cutting data is not greater than a preset threshold value.

[0020] In an embodiment of the present application, the constructing a profile equation for each interest cutting region comprises:

[0021] collecting M points between the horizontal coordinates of the two long axis boundary points x P and x Q in the interest cutting region, and constructing a profile equation, which is expressed as an Mx3 profile matrix, and the expression is:

[0022]

[0023] wherein x P , x P+1 …x Q represent the horizontal coordinates of the two long axis boundary points and the M points; y Upper_P , y Upper_P+1 …y Upper_Q represent the vertical coordinates of the two long axis boundary points and the M points on the curve in the Y-axis; and y Lower_P , y Lower_P+1 …y Lower_Q represent the vertical coordinates of the two long axis boundary points and the M points on the curve below the Y-axis.

[0024] In one embodiment of the present application, the improved cosine crow search algorithm is used to solve the surface shape error of each cutting region of interest based on the contour equation, and in the calculation, the individual position is updated according to Levy flight until the surface shape error of all cutting regions of interest is calculated, and the minimum surface shape error value is output, comprising:

[0025] Constructing an optimization target equation;

[0026] Initializing the population size and the maximum number of iterations;

[0027] Based on the contour equation and the optimization target equation, a surface shape error calculation function of the cutting region of interest is constructed, the fitness of each individual is calculated by using the surface shape error calculation function as the fitness function, and the fitness is used as the initial memory value;

[0028] Updating the random number coefficient, and updating the position of all individuals by using Levy flight;

[0029] The fitness value of the individual after position updating is calculated by using the fitness function;

[0030] If the current fitness value is greater than the initial memory value, the individual position is updated, otherwise the initial position is kept;

[0031] The individual position is repeatedly updated until the current iteration number is not less than the preset iteration number, and the global optimal solution after iteration is output, and the optimal surface shape error value of the current cutting region of interest is obtained;

[0032] The optimal surface shape error of all cutting regions of interest is calculated, and the minimum surface shape error value and the corresponding cutting region of interest are output as the best cutting region of the single capillary tube to be cut.

[0033] In one embodiment of the present application, the optimization target equation is expressed as an Mx3 matrix, and the expression is:

[0034]

[0035] Wherein, all coordinates satisfy the standard equation of ellipse x P , x P+1 …x Q represent the horizontal coordinates of the two long axis boundary points and M points; y UpperFit_P , y UpperFit_P+1 …y UpperFit_Q represent the standard longitudinal coordinates of the two long axis boundary points and M points on the curve in Y axis; y LowerFit_P , y LowerFit_P+1 …y LowerFit_QYi represents the standard longitudinal coordinate of the two long axis boundary points and M points on the lower curve of the Y axis.

[0036] In one embodiment of the present application, the surface error calculation function expression is:

[0037]

[0038] wherein y Lower_i Yi represents the longitudinal coordinate of the i th individual on the lower curve of the Y axis, y LowerFit_i Yi represents the standard longitudinal coordinate of the i th individual on the lower curve of the Y axis, Yi represents the longitudinal coordinate of the i th individual on the upper curve of the Y axis, y UpperFit_i Yi represents the standard longitudinal coordinate of the i th individual on the upper curve of the Y axis.

[0039] In one embodiment of the present application, the position of each individual is updated by using Levy flight, and the expression is represented as:

[0040]

[0041] wherein, Yi represents the position of the i th individual when the iteration number is t; P i t Yi represents the optimal position of the i th individual when the iteration number is t; r1, r2, r3 and r4 are random numbers, wherein r1 represents the direction of update, r2 represents the distance of update; r3 is a random weight, when r3>1, the influence of the target on determining the distance is randomly strengthened, when r3<1, the influence of the target on determining the distance is randomly weakened; r4 represents the selection of sine or cosine motion; Yi represents the flight distance of the i th individual under the t th iteration, when Yi performs local search, when Yi performs global search; Yi represents the food hiding point of the i th individual in the t th iteration process, that is, the optimal position; represents element-wise multiplication; α represents a step control quantity, which is initialized as 1; Levy (s, β) defines a Levy random search path, and the expression is:

[0042]

[0043] wherein, u and v satisfy the normal distribution, β is initialized as 1.5, and Γ is a gamma function.

[0044] The embodiment of the present application also provides an ellipsoidal single capillary lens cutting region selection device, which comprises:

[0045] A profile data acquisition module is configured to acquire profile data of the single capillary tube to be cut.

[0046] A profile equation construction module is configured to select a plurality of lens interest cutting regions according to the profile data and preset cutting data, and construct a profile equation for each interest cutting region.

[0047] A surface error calculation module is configured to solve the surface error of each interest cutting region based on the profile equation by using the improved sine-cosine crow search algorithm, and update the individual position according to Levy flight during the calculation until the surface error of all interest cutting regions is calculated.

[0048] An optimal cutting region acquisition module is configured to output the minimum surface error value and the corresponding interest cutting region as the optimal cutting region of the single capillary tube to be cut.

[0049] The above technical solutions of the present application have the following advantages compared with the prior art:

[0050] The single capillary tube lens cutting region selection method provided by the present application acquires the profile data of the single capillary tube, selects the lens interest cutting region, and constructs the profile equation; based on the profile equation, the improved sine-cosine crow search algorithm is used to constantly update the individual position and calculate the surface error of the interest cutting region. When updating the individual position, Levy flight is used to replace the fixed step length, which reduces the blindness of the sine-cosine crow search algorithm, solves the problems of easy falling into local optimum and slow convergence speed of traditional capillary cutting, improves the search efficiency, makes the calculation of the surface error of the single capillary tube more efficient and stable, effectively reduces the surface error of the single capillary tube lens, greatly improves the optical quality of the single capillary tube lens, and has good application prospect. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to make the content of the present application more easily understood, the present application will be further described in detail below according to specific embodiments of the present application and in conjunction with the drawings, in which

[0052] Figure 1 is a step flow chart of the single capillary tube lens cutting region selection method provided by the present application;

[0053] Figure 2 is a schematic diagram of the lens interest cutting region selection provided by the present application;

[0054] Figure 3 is a flow chart of the improved sine-cosine crow search algorithm provided by the present application;

[0055] Figure 4is a comparison diagram of the improved cosine crow search algorithm provided by the present application and the existing algorithm in the convergence time of surface error calculation. DETAILED DESCRIPTION

[0056] The present application will be further described below in conjunction with the drawings and specific embodiments, so that those skilled in the art can better understand the present application and implement it. The embodiments are not limiting to the present application.

[0057] Referring to Figure 1 The ellipsoidal single capillary lens cutting area selection method of the present application has the following specific steps:

[0058] S1: Collect the profile data of the ellipsoidal single capillary to be cut;

[0059] S11: Clean the surface of the ellipsoidal single capillary to be cut with a three-dimensional optical profiler;

[0060] S12: Place the ellipsoidal single capillary to be cut on the measurement platform of the three-dimensional optical profiler;

[0061] S13: Adjust the position of the ellipsoidal single capillary to be cut until the three-dimensional optical profiler can measure the profile data of the entire ellipsoidal single capillary to be cut, start measuring, and acquire and store the profile data of the ellipsoidal single capillary to be cut.

[0062] S2: Select a plurality of lens interest cutting areas according to the profile data and the preset cutting data, and construct a profile equation for each interest cutting area;

[0063] Referring to Figure 2 As shown in the figure, the lens interest cutting area selection diagram provided by the present application is shown in the figure;

[0064] S21: Map the profile data to a two-dimensional coordinate, with the X-axis corresponding to the long axis direction of the ellipsoidal single capillary to be cut, and the Y-axis corresponding to the short axis direction of the ellipsoidal single capillary to be cut;

[0065] S22: According to the profile data and the preset cutting data, select a plurality of pairs of long axis boundary points in the long axis direction of the ellipsoidal single capillary;

[0066] S23: Each pair of long axis boundary points corresponds to four short axis boundary points on the short axis, and the area surrounded by the six boundary points constitutes a lens interest cutting area;

[0067] Wherein, the difference between the horizontal coordinate distance of each pair of long axis boundary points on the X-axis and the horizontal coordinate distance in the preset cutting data is not greater than a preset threshold value;

[0068] S24: In the interest cutting area, the two long axis boundary points xP With x Q M points are collected between the x-coordinates to construct the contour equation, which is represented as an M×3 contour matrix, and its expression is:

[0069]

[0070] Where, x P x P+1 …x Q Represents the x-coordinates of the two major axis boundary points and M points; y Upper_P y Upper_P+1 …y Upper_Q This represents the ordinate of the two major axis boundary points and M points on the curve along the Y-axis; y Lower_P y Lower_P+1 …y Lower_Q This represents the ordinate of the two major axis boundary points and M points on the curve below the Y-axis.

[0071] S3: Utilizing an improved sine and cosine crow search algorithm, the surface shape error of each region of interest is calculated based on the contour equation. During calculation, the individual positions are updated according to Levy flight patterns. This process is repeated until the surface shape errors of all regions of interest are calculated, and the minimum surface shape error value is output.

[0072] Reference Figure 3 The diagram shown illustrates the steps of the improved sine and cosine crow search algorithm provided by this invention.

[0073] S31: Establish the optimization objective equation based on the standard elliptic curve;

[0074] The optimization objective equation is represented as an M×3 matrix, and its expression is:

[0075]

[0076] All coordinates satisfy the standard equation of an ellipse. x P x P+1 …x Q Represents the x-coordinates of the two major axis boundary points and M points; y UpperFit_P y UpperFit_P+1 …y UpperFit_Q This represents the standard ordinate of the curve on the Y-axis of the two major axis boundary points and M points; y LowerFit_P y LowerFit_P+1 …y LowerFit_Q This represents the standard ordinate of the two major axis boundary points and M points on the curve below the Y-axis;

[0077] S32: Preset initial parameters, including population size and maximum number of iterations;

[0078] S33: randomly generate the initial position of the crow, and calculate the initial fitness value;

[0079] S34: update the coefficient, update the position of each individual by using Levy flight, and calculate the fitness value of the individual after position update by using the target equation, and compare the initial fitness value;

[0080] The position of each individual is updated by using Levy flight, and the expression is represented as:

[0081]

[0082] Wherein, Pi(t) represents the position of individual i at the iteration number t; P i t Pi(t) represents the optimal position of individual i at the iteration number t; r1, r2, r3 and r4 are random numbers, in the optimization process, r1 represents the direction of update, r2 represents the distance of update; r3 is a random weight, when r3>1, the influence of the target on determining the distance is randomly strengthened, when r3<1, the influence of the target on determining the distance is randomly weakened; r4 represents the selection of sine or cosine motion; Pi(t) represents the flight distance of individual i at the iteration number t, when Pi(t) represents the flight distance of individual i at the iteration number t, when Pi(t) represents the flight distance of individual i at the iteration number t, when Pi(t) represents the food hiding point of individual i in the tth iteration process, that is, the optimal position; Pi(t) represents the food hiding point of individual i in the tth iteration process, that is, the optimal position;

[0083]

[0084] S35: if the current fitness value is greater than the initial fitness value, update the individual position, otherwise stay in the initial position;

[0085] S36: repeat the update of the individual position until the current iteration number is not less than the preset iteration number, output the global optimal solution after iteration, and obtain the minimum surface shape error value;

[0086] S4: obtain the cutting region of interest corresponding to the minimum surface shape error value as the best cutting region of the single capillary tube to be cut.

[0087] The ellipsoidal single capillary lens cutting region selection method provided by the application collects profile data of the ellipsoidal single capillary, selects a lens cutting region of interest, and constructs a profile equation; based on the profile equation, the improved sine cosine crow search algorithm is used to constantly update the individual position and calculate the surface shape error of the cutting region of interest. When updating the individual position, the Levy flight is used to replace the fixed step length, the blindness of the sine cosine crow search algorithm is reduced, the problems of the traditional capillary cutting, such as being prone to local optimization and slow convergence speed, are solved, the search efficiency is improved, and the calculation of the surface shape error of the single capillary is more efficient and stable.

[0088] Specifically, in one embodiment of the application, the ellipsoidal single capillary measured by the three-dimensional profilometer is about 30 mm long, and the number of sampling points is 23265, which represents the sampling accuracy of the profilometer. The measured single capillary inlet diameter is about 0.5230 mm, and the outlet diameter is about 0.4672 mm. The preset cutting data of the single capillary has a horizontal coordinate distance length of 20 mm, and the distance between the two long axis boundary points is greater than 15 mm when selecting the long axis boundary points of the region of interest. In this example, the initial population size pop_size is set to 40, and the maximum iteration number max_iter is set to 1000.

[0089] In order to verify the effectiveness of the ellipsoidal single capillary lens cutting region selection method provided by the application on different data sets, four X-ray single capillary focusing lenses with different parameters are measured and experimented. Due to the unpredictability of meta-heuristic algorithms, the results of a single test may not be accurate. The average (AVG) and standard deviation (STD) represent the statistical results obtained by the improved SCA-CSA algorithm and other algorithms. The AVG value represents the average result obtained by executing the algorithm 30 times independently, and the STD value represents the dispersion degree of the results of running these algorithms 30 times relative to their mean value, and thus serves as an indicator of the stability of the results. Therefore, each algorithm is executed 30 times, and the statistical results (average and standard deviation) are shown in Tables 1 and 2.

[0090] Referring to Table 1, the following is a comparison of the surface shape errors calculated by using the particle swarm optimization algorithm (PSO), the sine cosine algorithm (SCA), the crow search algorithm (CSA), the sine cosine-crow search algorithm (SCA-CSA), and the improved SCA-CSA algorithm:

[0091] Table 1: Comparison of surface shape errors calculated by improved SCA-CSA and other algorithms (μm)

[0092]

[0093]

[0094] According to the results in Table 1, the improved SCA-CSA algorithm is superior to other algorithms in most indicators. First, the average value of the surface shape error E s obtained by the improved SCA-CSA algorithm remains stable; the sum of the average E s values is 0.728 μm, which is the smallest value among all results, proving that the improved SCA-CSA is superior to other algorithms in the iterative calculation of the surface shape error E s . Second, as shown in Table 1, the improved SCA-CSA algorithm is superior to most of the adopted algorithms (PSO, SCA, SCA-CSA) in terms of the standard deviation of E s , which is only 1.347 x 10-5 μm, significantly lower than other algorithms.

[0095] Referring to Table 2, the comparison results of the running time for calculating the surface shape error by using the particle swarm optimization algorithm (PSO), the sine cosine algorithm (SCA), the crow search algorithm (CSA), the sine cosine-crow search algorithm (SCA-CSA), and the improved SCA-CSA algorithm are shown:

[0096] Table 2: Comparison of running time of improved SCA-CSA and other algorithms (s)

[0097]

[0098] The results of Table 2 show the running time and stability of the five algorithms. Under the same 1000 iterations, the running time of the improved SCA-CSA algorithm provided by the present application is significantly shorter than that of other algorithms, and the sum of the average running time is only 8.682 s; in addition, the time stability of the improved SCA-CSA is also more superior; the improved SCA-CSA algorithm has the smallest time standard deviation value, which reflects the competitiveness of the algorithm. Therefore, the ellipsoidal single capillary lens cutting region selection method provided by the present application has good performance in efficiency and stability.

[0099] Referring to Figure 4 , the convergence speed of the calculated surface shape error with the increase of the number of iterations by using the improved SCA-CSA algorithm provided by the present application, the particle swarm optimization algorithm (PSO), the sine cosine algorithm (SCA), the crow search algorithm (CSA), and the sine cosine-crow search algorithm (SCA-CSA) is shown. Figure 4 It can be seen that the improved SCA-CSA algorithm provided by the present application enters the convergence stage earliest, which reflects the high efficiency of the improved SCA-CSA algorithm provided by the present application.

[0100] Based on the above embodiment, the ellipsoidal single capillary lens cutting region selection device provided by the present application comprises:

[0101] The profile data acquisition module 100 is configured to acquire profile data of the single capillary tube to be cut.

[0102] The profile equation construction module 200 is configured to select a plurality of lens cutting regions of interest according to the profile data and preset cutting data, and construct a profile equation for each cutting region of interest.

[0103] The surface error calculation module 300 is configured to solve the surface error of each cutting region of interest based on the profile equation by using the improved cosine crow search algorithm, and update the individual position according to the Levy flight during the calculation until the surface error of all cutting regions of interest is calculated.

[0104] The optimal cutting region acquisition module 400 is configured to output the minimum surface error value and the corresponding cutting region of interest as the optimal cutting region of the single capillary tube to be cut.

[0105] The single capillary tube lens cutting region selection device provided in the embodiment is used to implement the single capillary tube lens cutting region selection method, and thus the specific embodiments of the single capillary tube lens cutting region selection device can be seen from the foregoing embodiment part of the single capillary tube lens cutting region selection method, for example, the profile data acquisition module 100, the profile equation construction module 200, the surface error calculation module 300, and the optimal cutting region acquisition module 400 are respectively used to implement steps S1, S2, S3, and S4 in the foregoing single capillary tube lens cutting region selection method. Therefore, the specific embodiments can be referred to the description of the respective embodiment parts, and will not be described here.

[0106] Based on the foregoing embodiment, the embodiment of the present application provides a single capillary tube lens cutting region selection device, which comprises:

[0107] The three-dimensional optical profiler is configured to acquire profile data of the single capillary tube to be cut.

[0108] The host computer is in communication connection with the three-dimensional optical profiler, and is configured to execute a computer program to implement the steps of the single capillary tube lens cutting region selection method, and output the optimal cutting region of the single capillary tube to be cut.

[0109] The ellipsoid single capillary lens cutting region selection method provided by the application collects profile data of the ellipsoid single capillary, selects a lens cutting region of interest, and constructs a profile equation; based on the profile equation, the improved cosine crow search algorithm is used to constantly update the individual position and calculate the surface shape error of the cutting region of interest. When updating the individual position, the Levy flight is used to replace the fixed step length, the blindness of the cosine crow search algorithm is reduced, the problems of the traditional capillary cutting, such as being prone to local optimization and slow convergence speed, are solved, the search efficiency is improved, the calculation of the surface shape error of the single capillary is more efficient and stable, the surface shape error of the single capillary lens is effectively reduced, the optical quality of the single capillary lens is greatly improved, and the single capillary lens has good application prospects.

[0110] Those skilled in the art will appreciate that embodiments of the application can be provided as methods, systems, or computer program products. Accordingly, the application can be embodied in the form of complete hardware embodiments, complete software embodiments, or embodiments combining software and hardware aspects. Moreover, the application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) having computer-usable program code embodied thereon.

[0111] The application is described with reference to flowcharts and / or block diagrams that illustrate the methods, apparatus (systems), and computer program products according to embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions, which are executed via the processor of the computer or other programmable data processing apparatus, generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks. Figure 1 The functions specified in a flow or multiple flows and / or blocks.

[0112] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction means, which implement the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks. Figure 1 The functions specified in a flow or multiple flows and / or blocks.

[0113] These computer program instructions can also be loaded into a computer or other programmable data processing devices, so that a series of operational steps are performed on the computer or other programmable data processing devices to generate a computer implemented process, so that the instructions executed on the computer or other programmable data processing devices provide steps for implementing the function specified in the flowchart Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0114] Obviously, the above-mentioned embodiments are only examples for clearly illustrating the present application, and are not intended to limit the embodiments. Based on the above description, other different forms of changes or variations can be made by those skilled in the art. Here, all the embodiments are not required to be exhausted, and the obvious changes or variations derived therefrom are still within the protection scope of the present application.

Claims

1. A method for selecting the cutting region of an ellipsoidal single capillary lens, characterized in that, include: Collect the contour data of the ellipsoidal single capillary to be cut; Based on the contour data and preset cutting data, select multiple lens cutting regions of interest, and construct a contour equation for each cutting region of interest; An improved sine and cosine crow search algorithm is used to solve the surface shape error of each region of interest based on the contour equation. During calculation, the individual positions are updated according to Levy flight patterns. This process continues until the surface shape error of all regions of interest is calculated. The minimum surface shape error value and its corresponding region of interest are output as the optimal cutting region for the ellipsoidal single capillary to be cut, including: Construct the optimization objective equation, represented as an M×3 matrix, whose expression is: All coordinates satisfy the standard equation of an ellipse. ; , … This represents the x-coordinates of the two major axis boundary points and M points; , … This represents the standard ordinate of the curve on the Y-axis of the two major axis boundary points and M points; , … This represents the standard ordinate of the two major axis boundary points and M points on the curve below the Y-axis; Initialize the population size and maximum number of iterations; A surface error calculation function for the region of interest is constructed based on the contour equation and the optimization objective equation. The surface error calculation function is used as the fitness function to calculate the fitness of each individual and use the fitness as the initial memory value. Update the random number coefficients and use Levy flight to update the positions of all individuals, as follows: ,in, This represents the position of individual i when the number of iterations is t; This represents the optimal position of individual i when the number of iterations is t; , , and The numbers are random, and during the optimization process, Indicates the direction of the update. Indicates the updated distance; For random weights, when Then, the effect of the target on a given distance is randomly amplified, when Then the influence of the target on a given distance will be randomly reduced; This indicates the choice between sine or cosine motion; Let i represent the flight distance of individual i in the t-th iteration, when At that time, the individual performs a local search, when At that time, the individual performs a global search; This represents the food hiding point of individual i during the t-th iteration, i.e., the optimal location; This represents element-wise multiplication; This represents the step size control variable, initialized to 1; The Levy random search path is defined by the expression: ;in, , and It follows a normal distribution. Initialized to 1.

5. It is a gamma function; The fitness value of an individual after location update is calculated using a fitness function; If the current fitness value is greater than the initial memory value, update the individual's position; otherwise, remain at the initial position. The individual positions are updated repeatedly until the current iteration number is not less than the preset iteration number. The global optimal solution after the iteration is completed is output, and the optimal surface error value of the current cutting region of interest is obtained. Calculate the optimal surface shape error for all regions of interest, output the minimum surface shape error value and its corresponding region of interest, and use it as the optimal cutting region for the ellipsoidal single capillary to be cut.

2. The method for selecting the cutting region of an ellipsoidal single capillary lens according to claim 1, characterized in that, The acquisition of the contour data of the ellipsoidal single capillary to be cut includes: The ellipsoidal capillary tube to be cut and the surface of the three-dimensional optical profilometer are cleaned. The ellipsoidal capillary tube to be cut is placed on the measurement platform of the three-dimensional optical profilometer; Adjust the position of the ellipsoidal single capillary to be cut until the three-dimensional optical profilometer can measure the contour data of the entire ellipsoidal single capillary to be cut, start the measurement, and acquire and store the contour data of the ellipsoidal single capillary to be cut.

3. The method for selecting the cutting region of an ellipsoidal single capillary lens according to claim 1, characterized in that, The step of selecting multiple lens cutting regions of interest based on the contour data and preset cutting data includes: The contour data is mapped to two-dimensional coordinates, with the X-axis corresponding to the major axis of the ellipsoidal single capillary to be cut and the Y-axis corresponding to the minor axis of the ellipsoidal single capillary to be cut. Based on the contour data and the preset cutting data, multiple pairs of long axis boundary points are selected along the long axis direction of the ellipsoidal single capillary. Each pair of major axis boundary points corresponds to four minor axis boundary points, and the area enclosed by the six boundary points constitutes a lens region of interest.

4. The method for selecting the cutting region of an ellipsoidal single capillary lens according to claim 3, characterized in that, The difference between the x-coordinate distance of each pair of major axis boundary points on the X-axis and the x-coordinate distance in the preset cutting data is no greater than a preset threshold.

5. The method for selecting the cutting region of an ellipsoidal single capillary lens according to claim 3, characterized in that, The process of constructing the contour equation for each region of interest includes: At the two major axis boundary points of the region of interest and M points are collected between the x-coordinates to construct the contour equation, which is represented as an M×3 contour matrix, and its expression is: ， in, , … This represents the x-coordinates of the two major axis boundary points and M points; , … This represents the ordinate of the two major axis boundary points and M points on the curve along the Y-axis; , … This represents the ordinate of the two major axis boundary points and M points on the curve below the Y-axis.

6. The method for selecting the cutting region of an ellipsoidal single capillary lens according to claim 1, characterized in that, The expression for the surface shape error calculation function is as follows: ， in, This represents the ordinate of the i-th individual on the curve below the y-axis. This represents the standard ordinate of the i-th individual on the curve below the y-axis. This represents the y-coordinate of the i-th individual on the curve along the y-axis. This represents the standard ordinate of the i-th individual on the curve along the y-axis.

7. A device for selecting the cutting area of ​​an ellipsoidal single capillary lens, characterized in that, include: The contour data acquisition module is used to acquire the contour data of the ellipsoidal single capillary tube to be cut. The contour equation construction module is used to select multiple lens cutting regions of interest based on the contour data and preset cutting data, and construct a contour equation for each cutting region of interest. The surface shape error calculation module is used to solve the surface shape error of each region of interest based on the contour equation using an improved sine and cosine crow search algorithm. During the calculation, the individual position is updated according to Levy flight until the surface shape error of all regions of interest is calculated. The optimal cutting region acquisition module is used to output the minimum surface shape error value and its corresponding cutting region of interest, which serves as the optimal cutting region for the ellipsoidal single capillary to be cut. The surface shape error calculation module and the optimal cutting region acquisition module include: Construct the optimization objective equation, represented as an M×3 matrix, whose expression is: All coordinates satisfy the standard equation of an ellipse. ; , … This represents the x-coordinates of the two major axis boundary points and M points; , … This represents the standard ordinate of the curve on the Y-axis of the two major axis boundary points and M points; , … This represents the standard ordinate of the two major axis boundary points and M points on the curve below the Y-axis; Initialize the population size and maximum number of iterations; A surface error calculation function for the region of interest is constructed based on the contour equation and the optimization objective equation. The surface error calculation function is used as the fitness function to calculate the fitness of each individual and use the fitness as the initial memory value. Update the random number coefficients and use Levy flight to update the positions of all individuals, as follows: ,in, This represents the position of individual i when the number of iterations is t; This represents the optimal position of individual i when the number of iterations is t; , , and The numbers are random, and during the optimization process, Indicates the direction of the update. Indicates the updated distance; For random weights, when Then, the effect of the target on a given distance is randomly amplified, when Then the influence of the target on a given distance will be randomly reduced; This indicates the choice between sine or cosine motion; Let i represent the flight distance of individual i in the t-th iteration, when At that time, the individual performs a local search, when At that time, the individual performs a global search; This represents the food hiding point of individual i during the t-th iteration, i.e., the optimal location; This represents element-wise multiplication; This represents the step size control variable, initialized to 1; The Levy random search path is defined by the expression: ;in, , and It follows a normal distribution. Initialized to 1.

5. It is a gamma function; The fitness value of an individual after location update is calculated using a fitness function; If the current fitness value is greater than the initial memory value, update the individual's position; otherwise, remain at the initial position. The individual positions are updated repeatedly until the current iteration number is not less than the preset iteration number. The global optimal solution after the iteration is completed is output, and the optimal surface error value of the current cutting region of interest is obtained. Calculate the optimal surface shape error for all regions of interest, output the minimum surface shape error value and its corresponding region of interest, and use it as the optimal cutting region for the ellipsoidal single capillary to be cut.

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