Automatic asymptotic calculation method for airplane profile waviness in MATLAB environment

Abstract

The invention discloses an aircraft profile waviness automatic asymptotic calculation method in an MATLAB environment, and the method comprises the steps: 1, carrying out the preprocessing of measured aircraft profile point cloud data, building a plane section model of the aircraft profile waviness, and outputting the section point location data of each group of sections; step 2, aircraft profile waviness automatic asymptotic calculation under the MATLAB environment, including circularly performing aircraft profile waviness asymptotic calculation on the section point location data of each group of sections based on the section point location data of each group of sections; wherein the mode of performing aircraft profile waviness asymptotic calculation on the section point location data of each group of sections is as follows: establishing a measurement reference line based on the section point location data of each group of sections output in the step 1, calculating the distance from each point location to the measurement reference line, performing initial calculation on the serial numbers of wave crest and wave trough points on the point location data, and calculating the waviness asymptotic calculation of the aircraft profile waviness. Carrying out the asymptotic calculation of the serial numbers of wave crest and wave trough points, carrying out the endpoint data processing of the point location data, carrying out the airplane appearance waviness calculation, and outputting the calculation result.

Description

Technical Field

[0001] This invention belongs to the field of digital aircraft manufacturing and relates to an automatic asymptotic calculation method for aircraft shape waviness in a MATLAB environment. Background Technology

[0002] Aircraft shape waviness refers to unexpected undulations near the theoretical shape of an aircraft's surface. It is an important parameter of aircraft shape, usually expressed as the ratio of amplitude to wavelength. During the development of new aircraft, aircraft shape waviness is of significant research importance for optimizing aerodynamic performance, wing lift, reducing air resistance, shortening takeoff distance, ensuring smoother airflow over the aircraft surface, reducing airflow separation and turbulence, and improving fuel efficiency. Furthermore, for new aircraft with stealth requirements, precise control of waviness results in a smoother, more streamlined surface, effectively reducing radar cross-section, electromagnetic leakage and interference, improving stealth performance, and lowering the probability of being detected by enemy radar.

[0003] Existing methods for detecting aircraft surface waviness mainly include visual inspection, template inspection, dial indicator inspection, and reverse engineering. Visual inspection and template inspection are simple and easy to perform, quickly identifying obvious waviness problems, but their measurement accuracy is low, making it difficult to accurately inspect fine waviness. Dial indicator inspection involves fixing a dial indicator to a bracket, with the indicator head in contact with the aircraft for measurement; it offers high accuracy but low efficiency. Reverse engineering utilizes equipment such as lasers, photographs, and radar waves to collect data from the aircraft surface. Based on the point cloud data collected in reverse engineering, data processing is performed, comparing it with theoretical data iteratively to extract the waviness curve and calculate relevant parameters. It offers high calculation accuracy, but the data processing is complex, typically handled in 3D CAD software. This is limited by the complex interactive logic and algorithm execution efficiency of 3D CAD software. Furthermore, reverse engineering is constrained by different point cloud sampling densities and the large amount of noise in the reverse engineering process, directly affecting the accuracy and precision of waviness calculations in aircraft surface waviness measurements. Summary of the Invention

[0004] The purpose of this invention is to solve the above-mentioned problems by proposing an automatic asymptotic calculation method for aircraft shape waviness in the MATLAB environment, so as to solve the problem that various existing detection methods for aircraft shape waviness are difficult to achieve high measurement accuracy, high measurement efficiency and measurement quality at the same time.

[0005] The technical solution of the present invention is as follows: Firstly, the present invention provides an automatic asymptotic calculation method for aircraft shape waviness in a MATLAB environment, comprising: Step 1: By preprocessing the measured point cloud data of the aircraft shape, a planar cross-sectional model of the aircraft shape waviness is established, and the cross-sectional point data of each set of cross-sections are output. Step 2 involves automatic asymptotic calculation of aircraft shape waviness in the MATLAB environment. This includes: based on the cross-sectional point data of each set of cross-sections output in Step 1, iteratively calculating the aircraft shape waviness for each set of cross-sections. The method for calculating the aircraft shape waviness asymptotically for each set of cross-sections is as follows: establishing a measurement baseline based on the cross-sectional point data of each set of cross-sections output in Step 1, calculating the distance from each point to the measurement baseline, initially calculating the index of the peak and trough points of the point data, asymptotically calculating the index of the peak and trough points, processing the endpoint data of the point data, calculating the aircraft shape waviness, and outputting the above calculation results.

[0006] Optionally, the automatic asymptotic calculation method for aircraft shape waviness in a MATLAB environment as described above is characterized in that step 1 includes: Step 1-1: Open the aircraft shape point cloud data obtained from reverse measurement in 3D CAD software; Steps 1-2: In 3D CAD software, a set of planes is created for analyzing the waviness of the aircraft shape. These planes intersect with the point cloud data of the aircraft shape to form a set of cross sections, thereby creating a planar cross section model of the aircraft shape waviness. Steps 1-3: In the 3D CAD software, based on the established plane cross-sectional model of the aircraft's external waviness, output the cross-sectional point data.

[0007] Optionally, in the automatic asymptotic calculation of aircraft shape waviness in the MATLAB environment as described above, step 2 includes: Step 21: Read the cross-sectional point data of each set of cross-sections output in Steps 1-3; Step 22: Establish a cross-section loop variable i, where i takes values ​​in the range of (1, sn), i is initially 1, the step size is 1, and sn is the number of cross-sections in the plane cross-section model of the aircraft's shape waviness. Step 23: Take the i-th set of cross-sectional point data SCi from the planar cross-sectional model of the aircraft's external waviness; Step 24: Establish the measurement baseline ML based on the i-th set of cross-sectional point data SCI; Step 25: Calculate the distance array Dpt from each point in the i-th set of cross-sectional point data SCi to the measurement baseline ML; Step 26: Calculate the initial wave crest number array Ptfr in the i-th group of cross-sectional point data SCi; Step 27: Calculate the initial wave trough point index array Ptvr in the i-th group of cross-sectional point data SCi; Step 28: Using the initial calculation of the peak point index array Ptfr in the i-th group of cross-sectional point data SCi, the peak point index array Ptf is asymptotically calculated; Step 29: Calculate the initial valley point index array Ptvr using the i-th set of cross-sectional point data SCi, and asymptotically calculate the valley point index array Ptv. Step 30: Based on the peak point index array Ptf and trough point index array Ptv obtained by progressive calculation of the i-th cross-sectional point data SCi, the peak and trough point calculation array ptfv is obtained by processing the endpoint data through classification. Step 31: Sort the peak and trough point calculation array ptfv obtained based on the i-th group of cross-sectional point data SCi to obtain the sorted peak and trough point calculation array ptfvs; Step 32: Calculate the aircraft's surface waviness based on the sorted peak and valley point calculation array ptfvs obtained from the i-th group of cross-sectional point data SCi. Step 33: Repeat steps 23 to 32 to calculate the aircraft shape waviness of each set of cross-sectional point data, and output the results in the form of a report.

[0008] Optionally, in the automatic asymptotic calculation of aircraft shape waviness in the MATLAB environment as described above, step 24 includes: Step 24-1: Calculate the midpoint Ptm of the i-th set of cross-sectional point data SCi; Ptmn=floor(length(SCi) / 2); Ptm=SCi(Ptmn,1:3); Where Ptmn is the midpoint number of the i-th cross-sectional point data SCi; Step 24-2: Set the initial measurement baseline MLr, and calculate the foot of the perpendicular Ptf from the midpoint Ptm of the i-th set of cross-sectional point data SCi to the initial measurement baseline MLr, where the foot of the perpendicular Ptf is the projection point of the midpoint Ptm onto the initial measurement baseline MLr. Pst = SCi(1,1:3); Pet = SCi(end, 1:3); v = Pet–Pst; numerator = dot(v, Ptm - Pst); denominator = dot(v, v); t = numerator / denominator; Ptf = Pst + t * v; The starting endpoint Pst of the initial measurement baseline MLr is the first point in the i-th set of cross-sectional point data SCi, and the ending endpoint Pet is the last point in the i-th set of cross-sectional point data SCi. Where v is the direction vector of the initial measurement baseline MLr, numerator is the dot product of vector v and the direction vector from the starting point to the intermediate target point (Ptm-Pst), denominator is the dot product of vector v and itself, and t is the ratio of the projected distance; Step 24-3: The measurement baseline ML is obtained by translating the initial measurement baseline MLr.

[0009] Optionally, as described above, an automatic asymptotic calculation of aircraft shape waviness in a MATLAB environment is characterized in that step 24-3 includes: Step 24-3-1: Establish the measurement baseline offset variable offset for the i-th set of cross-sectional point data SCi, where offset is a positive integer greater than 0; Step 24-3-2, based on the midpoint Ptm and perpendicular foot Ptf of the i-th cross-sectional point data SCi, calculate the offset direction vector dv of the initial measurement baseline MLr: vn = Ptf - Ptm; dv = vn / norm(vn); Where norm(vn) represents the magnitude of vector vn; Step 24-3-3: Calculate the starting coordinate Post and ending coordinate Poet of the measurement baseline ML after the initial measurement baseline MLr offset variable offset, thus obtaining the measurement baseline ML; Post = Pst + dv * offset; Poet = Pet + dv * offset.

[0010] Optionally, in the automatic asymptotic calculation of aircraft shape waviness in the MATLAB environment as described above, step 25 includes: Step 25-1: Establish a loop variable j for the midpoint of the i-th cross section, where the value range of j is (1, length(SCi)), the initial value of j is 1, and the step size is 1; Step 25-2: Take the j-th point Ptj from the i-th set of cross-sectional point data SCi; Step 25-3: Calculate the distance Dptj from the j-th point Ptj in the i-th cross-sectional point data SCi to the measurement baseline ML, and add Dptj to the array Dpt; AP = Ptj - Post; AB = Poet - Post; crossAPAB = cross(AP, AB); Dptj=norm(crossAPAB) / norm(AB)- offset; where crossAPAB is the cross product between vector AP and vector AB; norm(crossAPAB) is the norm of vector crossAPAB; norm(AB) is the norm of vector AB; Step 25-4, loop through Steps 25-1 to 25-3 to calculate the distances from all points of the i-th group of cross-section point data SCi to the measurement reference line ML, forming an array Dpt; Optionally, in the automatic asymptotic calculation of aircraft shape waviness in the MATLAB environment as described above, Step 26 includes: Step 26-1, establish the sparse parameter fkp of the peak points in the i-th group of cross-section point data SCi, where fkp is a positive integer and the value range is 1 < fkp < length(SCi); Step 26-2, establish the sparse peak loop variable u, where the value range of u is (fkp + 1, length(SCi) - fkp), the initial value of u is fkp + 1, and the step size is fkp; Step 26-3, take the (u - fkp)-th group of data in the array Dpt of the distances from the points of the i-th group of cross-section point data SCi to the measurement reference line ML as the first peak point determination data Dpt(u - fkp); Step 26-4, take the (u + fkp)-th group of data in the array Dpt of the distances from the points of the i-th group of cross-section point data SCi to the measurement reference line ML as the second peak point determination data Dpt(u + fkp); Step 26-5, take the u-th group of data in the array Dpt of the distances from the points of the i-th group of cross-section point data SCi to the measurement reference line ML as the peak point determination data Dpt(u); Step 26-6, when Dpt(u - fkp) < Dpt(u) and Dpt(u + fkp) < Dpt(u), include the variable u in the preliminary calculation peak point sequence number array Ptfr; Step 26-7, loop through Steps 26-2 to 26-6 to complete the calculation of the preliminary calculation peak point sequence number array Ptfr in the i-th group of cross-section point data SCi.

[0011] Optionally, in the automatic asymptotic calculation of aircraft shape waviness in the MATLAB environment as described above, Step 27 includes: Step 27-1: Establish the sparse parameter fkv of the valley points in the i-th cross-sectional point data SCi, where fkv is a positive integer and its value range is (1: length(SCi)). Step 27-2: Establish a sparse valley cyclic variable v, where the value range of v is (fkv+1, length(SCi)-fkv), the initial value of v is fkv+1, and the step size is fkv; Step 27-3: Take the v-fkv group of data from the distance array Dpt from the i-th cross-sectional point data SCi to the measurement baseline ML as the first determination data Dpt(v-fkv) of the trough point; Step 27-4: Take the v+fkv group of data from the distance array Dpt from the i-th cross-sectional point data SCi to the measurement baseline ML as the second determination data Dpt(v+fkv) of the trough point; Step 27-5: Take the v-th data from the distance array Dpt from the i-th cross-sectional point data SCi to the measurement baseline ML as the trough point determination data Dpt(v); Step 27-6: When Dpt(v-fkv)>Dpt(v) and Dpt(v+fkv)>Dpt(v), add the variable v to the initial calculation trough point index array Ptvr; Step 27-7: Repeat steps 27-2 to 27-6 to complete the initial calculation of the valley point index array Ptvr in the i-th group of cross-sectional point data SCi.

[0012] Optionally, in the automatic asymptotic calculation of aircraft shape waviness in the MATLAB environment as described above, step 28 includes: Step 28-1: Establish the asymptotic peak loop variable k for the array Ptf of asymptotic peak point index, where the value range of k is (1, length(Ptfr)), the initial value of k is 1, and the step size is 1. Step 28-2: Take the kth index Ptfr(k) from the initial calculation wave crest index array Ptfr in the i-th group of cross-sectional point data SCi; Step 28-3: Use the sparse parameter of the peak point to establish the starting point for asymptotic calculation of the peak point sequence stnf= Ptfr(k)- fkp; Step 28-4: Use the sparse parameter of the peak point to establish the asymptotic calculation of the peak point sequence termination point ednf = Ptfr(k) + fkp; Step 28-5: Use the max function to calculate the index Ptf(k) of the maximum value in the distance array Dpt from each point in the i-th group of cross-sectional point data SCi corresponding to the asymptotically calculated peak point index starting point stnf to the asymptotically calculated peak point index ending point ednf; [maxval,indexa]=max(Dpt (stnf:ednf)); Ptf(k) = indexa + stnf-1; Where max(Dpt (stnf:ednf)) is the maximum value of the distance array Dpt, maxval is the maximum value in the array, and indexa is the index of the maximum value in the array; Step 28-6: Repeat steps 28-2 to 28-5 to complete the asymptotic calculation of the peak point index array Ptfr in the i-th group of cross-sectional point data SCi. Optionally, in the automatic asymptotic calculation of aircraft shape waviness in the MATLAB environment as described above, step 29 includes: Step 29-1: Establish the asymptotic valley loop variable d for the array Ptfr of asymptotic valley point indices, where the value range of d is (1, length(Ptvr)), the initial value of d is 1, and the step size is 1. Step 29-2: Take the d-th index Ptvr(d) from the initial calculation valley point index array Ptvr in the i-th group of cross-sectional point data SCi; Step 29-3: Use the sparse parameters of the valley points to establish the starting point for asymptotic calculation of the valley point sequence stnv= Ptvr(d)- fkv; Step 29-4: Use the sparse parameters of the valley points to establish the asymptotic calculation of the valley point sequence termination point ednv = Ptvr(d) + fkv; Step 29-5: Use the min function to calculate the index Ptv(d) of the minimum distance from each point in the i-th set of cross-sectional point data SCi corresponding to the asymptotically calculated trough point index starting point stnv to the asymptotically calculated trough point index ending point ednv to the measurement baseline ML in the distance array Dpt. [minval,indexi]=min(Dpt (stnv:ednv)); Ptv(d) = indexi + stnv-1; Where min (Dpt (stnf:ednf)) is the minimum value of the distance array Dpt, minval is the minimum value xi in the array, and indexi is the index of the minimum value in the array; Step 29-6: Repeat steps 29-2 to 29-5 to complete the asymptotic calculation of the valley point index array Ptvr in the i-th group of cross-sectional point data SCi. Optionally, in the automatic asymptotic calculation of aircraft shape waviness in the MATLAB environment as described above, step 30 includes: Step 30-1, calculate the length w of the asymptotic calculation peak point sequence number array Ptf; where, w = length(Ptf); Step 30-2, calculate the length q of the asymptotic calculation valley point sequence number array Ptv; where, q = length(Ptv); Step 30-3, process the endpoints by classification according to the length w of the peak point sequence number array Ptf and the length q of the valley point sequence number array Ptv, and establish the peak-valley point calculation array ptfv; Among them, the said Step 30-3 includes: Step 30-3-1, when w - q = 1, the peak-valley point calculation array ptfv = [ptf ptv]; Step 30-3-2, when w - q = -1, the peak-valley point calculation array ptfv = [1 ptf ptv]; Step 30-3-3, when abs(w - q) ≠ 1, process the endpoints by classification and establish the peak-valley point calculation array ptfv, where abs(w - q) is the absolute value of the difference of w - q; The said Step 30-3-3 includes: Step 30-3-3-1, when w < q, the peak-valley point calculation array ptfv = [1 ptf ptv]; Step 30-3-3-2, when w ≥ q, the peak-valley point calculation array ptfv = [ptf ptv length(SCi)].

[0013] Optionally, in the automatic asymptotic calculation of the aircraft shape waviness in the MATLAB environment as described above, the said Step 31 includes: Step 31-1, establish the waviness loop variable x of the peak-valley point calculation array ptfv, where the value range of x is (2, length(ptfvs)), the initial value of x is 2, and the step size is 2; Step 31-2, respectively take the (x - 1)-th point, the x-th point, and the (x + 1)-th point in the sorted peak-valley point calculation array ptfvs and denote them as ptfvsdx, ptfvsx, ptfvsux; ptfvsdx = SCi(ptfvs(x - 1), 1:3 ) ptfvsx = SCi(ptfvs(x), 1:3 ); ptfvsux = SCi(ptfvs(x + 1), 1:3 ); Step 31-3, calculate the distance ptfvsdl between the point ptfvsdx and the point ptfvsux as the wavelength of the aircraft shape waviness; ptfvsdl=norm(ptfvsdx-ptfvsux); Where norm(ptfvsdx-ptfvsux) is a modulo operation on two points to calculate the length of the two points; Step 31-4: Calculate the distance ptfvsdr from point ptfvsx to the straight line formed by points ptfvsdx and ptfvsux, and use it as the amplitude ptfvsdr of the aircraft's shape waviness. ap=ptfvsx-ptfvsdx; ab=ptfvsux- ptfvsdx; crossp = cross(ap, ab); ptfvsdr =norm(crossp) / norm(ab); Where crossp calculates the cross product of vectors ap and ab, norm(crossp) calculates the magnitude of the cross product vector, and norm(ab) calculates the magnitude of vector ab. Steps 31-5: Based on the wavelength (ptfvsdl) and amplitude (ptfvsdr) of the aircraft shape waviness, calculate the ptfvw of the x-th group of aircraft shape waviness in the sorted peak and trough point calculation array (ptfvs). ptfvw=ptfvsdr / ptfvsdl; Step 31-6: Repeat steps 31-2 to 31-5 to complete the calculation of aircraft shape waviness based on the i-th set of cross-sectional point data SCi.

[0014] The beneficial effects of this invention are as follows: Based on the inverse point cloud data of an aircraft shape, planar cross-sectional point data is established. This invention proposes an automatic asymptotic calculation method for aircraft shape waviness in a MATLAB environment. This automatic asymptotic calculation method for aircraft shape waviness, based on the inverse point cloud data of the aircraft shape, uses MATLAB programming to program an automatic asymptotic calculation program for aircraft shape waviness. It realizes the establishment of measurement baselines for point data, calculation of distances from point to measurement baselines, initial calculation of peak and valley point numbers, asymptotic calculation of peak and valley point numbers, endpoint data processing, and aircraft shape waviness calculation method. Finally, through automatic generation of program forms, it realizes high-precision, fast, efficient, automatic batch calculation of aircraft shape waviness and standardized report output. That is, it realizes fast and efficient processing of aircraft shape waviness data through pure mathematical logic calculation method.

[0015] The automatic asymptotic calculation method for aircraft shape waviness in the MATLAB environment provided by this invention fully leverages the unique advantages of MATLAB in matrix calculation, vector operation, and mathematical modeling. It uses an asymptotic approach to solve the influence of aircraft shape point cloud density and measurement noise on the calculation of aircraft shape waviness. It achieves the capture of the main features of aircraft shape waviness through preliminary calculation, and realizes automatic filtering and efficient and high-precision calculation of aircraft shape waviness by using an asymptotic search method based on the main feature points.

[0016] The automatic asymptotic calculation method for aircraft shape waviness in the MATLAB environment provided by this invention has a clear approach, simple and straightforward logic, and strong applicability. It provides a new solution for aircraft shape inspection, aircraft assembly quality control, rapid product quality inspection, and rapid acquisition and processing of assembly process data. Attached Figure Description

[0017] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of the present invention and do not constitute a limitation on the technical solutions of the present invention.

[0018] Figure 1 This is a schematic diagram of the measurement data preprocessing model in the automatic asymptotic calculation method for aircraft shape waviness in the MATLAB environment provided by the present invention. Figure 2 This is a schematic diagram of the planar section model in the automatic asymptotic calculation method for aircraft shape waviness in the MATLAB environment provided by the present invention. Figure 3 This is a schematic diagram of the measurement baseline model in the automatic asymptotic calculation method for aircraft shape waviness in the MATLAB environment provided by the present invention. Figure 4 This is a schematic diagram of the waviness model in the automatic asymptotic calculation method for aircraft shape waviness in the MATLAB environment provided by the present invention. Explanation of reference numerals in the attached figures: 1-Point cloud data, 2-A set of planes, 3-Plane cross-section model, 4-Starting endpoint, 5-Ending endpoint, 6-Midpoint, 7-Perpendicular foot, 8-Initial measurement baseline, 9-Measurement baseline, 10-Crest point, 11-Trough point, 12-Wavelength, 13-Amplitude. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

[0020] The present invention provides the following specific embodiments, which can be combined with each other. For the same or similar concepts or processes, they may not be described again in some embodiments.

[0021] according to Figures 1 to 4 As shown, the automatic asymptotic calculation method for aircraft shape waviness in the MATLAB environment provided by this invention includes the following steps: Step 1: By preprocessing the measured aircraft shape point cloud data 1, a planar cross-sectional model 3 of the aircraft shape waviness is established, and the cross-sectional point data of each set of cross-sections is output. Step 2 involves automatic asymptotic calculation of aircraft shape waviness in the MATLAB environment. This includes: based on the cross-sectional point data of each set of cross-sections output in Step 1, iteratively calculating the aircraft shape waviness for each set of cross-sections. The method for calculating the aircraft shape waviness asymptotically for each set of cross-sections is as follows: establishing a measurement baseline based on the cross-sectional point data of each set of cross-sections output in Step 1, calculating the distance from each point to the measurement baseline, initially calculating the index of the peak and trough points of the point data, asymptotically calculating the index of the peak and trough points, processing the endpoint data of the point data, calculating the aircraft shape waviness, and outputting the above calculation results.

[0022] In one implementation of this invention, step 1 includes: Step 1-1: Open the aircraft shape point cloud data 1 obtained by reverse measurement in the 3D CAD software; Steps 1-2: In 3D CAD software, a set of planes 2 are created for analyzing the waviness of the aircraft shape. These planes intersect with the aircraft shape point cloud data 1 to form a set of cross sections, thereby creating a planar cross section model 3 of the aircraft shape waviness. Steps 1-3: In the 3D CAD software, based on the established plane section model 3 of the aircraft's external waviness, output the section point data, as shown in Table 1: Table 1 Cross-sectional point data

[0023] In one implementation of this invention, step 2 includes: Step 21: Read the cross-sectional point data of each group of cross-sections output in Table 1 from Steps 1-3; Step 22: Establish a cross-section loop variable i, where the value range of i is (1, sn) = (1, 2), the initial value of i is 1, the step size is 1, and sn = 2 is the number of cross sections in the plane cross-section model 3 of the aircraft shape waviness. Step 23: Take the i-th set of cross-sectional point data SCi from the plane cross-sectional model 3 of the aircraft shape waviness; Step 24: Establish measurement baseline 9 based on the i=1th group of cross-sectional point data SCi, denoted by measurement baseline ML; Step 24 is implemented through the following steps: Step 24-1, calculate the midpoint 6 of the i=1th group of cross-sectional point data SCi. The midpoint 6 (Ptm) is represented as: Ptmn=floor(length(SCi) / 2) =8; Ptm=SCi(Ptmn,1:3) =[56.978010.062039.9650]; Wherein, Ptmn is the index of the midpoint 6 of the i=1th group of cross-sectional point data SCi; Step 24-2: Set the initial measurement baseline 8, denoted by the initial measurement baseline MLr, and calculate the perpendicular foot 7 from the midpoint 6 of the i=1 group of cross-sectional point data SCI to the initial measurement baseline MLr. The perpendicular foot 7 is the projection point from the midpoint 6 to the initial measurement baseline MLr, and the perpendicular foot 7 is denoted by the perpendicular foot Ptf. Pst= SCi(1,1:3) =[81.1550-18.230038.4190]; Pet= SCi(end,1:3)= [18.507050.370046.1900]; v= Pet–Pst=[-62.648068.60007.7710]; numerator = dot(v, Ptm - Pst) = 3467.4858; denominator = dot(v, v) = 8691.1203; t = numerator / denominator =0.3990; Ptf = Pst + t * v=[ 56.16049.139341.5194]; The starting endpoint 4 of the initial measurement baseline MLr is represented by the starting endpoint Pst being the first point in the i=1th group of cross-sectional point data SCi, and the ending endpoint 5 is represented by the ending endpoint Pet being the last point in the i=1th group of cross-sectional point data SCi. Where v is the direction vector of the initial measurement baseline MLr, numerator is the dot product of vector v and the direction vector from the starting point to the intermediate target point (Ptm-Pst), denominator is the dot product of vector v and itself, and t is the ratio of the projected distance; Step 24-3: The measurement baseline ML is obtained by translating the initial measurement baseline MLr.

[0024] In practice, step 24-3 includes: Step 24-3-1: Establish the measurement baseline offset variable offset for the i=1th group of cross-sectional point data SCi, where offset is a positive integer greater than 0. In this embodiment, offset=1000. Step 24-3-2, based on the midpoint Ptm and perpendicular foot Ptf of the i=1th group of cross-sectional point data SCi, calculate the offset direction vector dv of the initial measurement baseline MLr: vn = Ptf - Ptm=[ -0.8176-0.92271.5544]; dv = vn / norm(vn) =[-0.4121-0.46510.7835]; Where norm(vn) represents the magnitude of vector vn; Step 24-3-3: Calculate the starting coordinate Post and ending coordinate Poet of the measurement baseline ML after the initial measurement baseline MLr offset variable offset=1000, thus obtaining the measurement baseline ML; Post=Pst+dv*offset=[ -330.9522 -483.3341821.9022]; Poet= Pet +dv*offset=[ -393.6002 -414.7341829.6732]; Step 25: Calculate the distance array Dpt from each point in the i=1th group of cross-sectional point data SCi to the measurement baseline ML; Step 25 is implemented through the following steps: Step 25-1: Establish a loop variable j for the midpoint of the i=1th cross section, where the value range of j is (1, length(SCi)) = (1, 17), the initial value of j is 1, and the step size is 1; Step 25-2, take the j=1 point Ptj=[81.1550-18.230038.4190] from the i=1th group of cross-sectional point data SCi; Step 25-3: Calculate the distance Dptj from the j=1st point Ptj in the cross-section point data SCi of the i=1st group to the measurement reference line ML, and record Dptj in the array Dpt; AP = Ptj - Post = [412.1072 465.1041 -783.4832]; AB = Poet - Post = [-62.6480 68.6000 7.7710]; crossAPAB = cross(AP, AB) = [57361.2701 45881.1698 57408.3904]; Dptj = norm(crossAPAB) / norm(AB) - offset = 0; where crossAPAB is the cross product between vector AP and vector AB; norm(crossAPAB) is the norm of vector crossAPAB; norm(AB) is the norm of vector AB; Step 25-4: Loop through Steps 25-1 to 25-3 to calculate the distances from all points in the cross-section point data SCi of the i=1st group to the measurement reference line ML, and form the array Dpt; Dpt = [0.0000 -6.1250 -10.9685 -9.7707 -5.0099 -0.1134 3.1030 1.9839 -0.8652 -3.2685 -2.9637 0.5008 4.6749 7.6151 5.6286 1.7295 0.0000]; Step 26: Calculate the array Ptfr of the initial calculated peak point 10 sequence numbers in the cross-section point data SCi of the i=1st group; This Step 26 is implemented through the following process: Step 26-1: Establish the sparse parameter fkp of the peak point 10 in the cross-section point data SCi of the i=1st group, where fkp is a positive integer and the value range is 1 < fkp < length(SCi). In this embodiment, fkp = 2 is taken; Step 26-2: Establish the sparse peak loop variable u, where the value range of u is (fkp + 1, length(SCi) - fkp) = (3, 15), the initial value of u is fkp + 1 = 3, and the step size is fkp = 2; Step 26-3: Take the (u - fkp)th data in the array Dpt of the distances from the points in the cross-section point data SCi of the i=1st group to the measurement reference line ML as the first determination data Dpt(u - fkp) = Dpt(3 - 2) = Dpt(1); Step 26-4: Take the data of the (u + fkp)-th group in the array Dpt of the distances from the point positions of the i = 1 group of cross-sectional point position data SCi to the measurement reference line ML as the second determination data of the peak point 10, Dpt(u + fkp) = Dpt(3 + 2) = Dpt(5); Step 26-5: Take the data of the u-th group in the array Dpt of the distances from the point positions of the i = 1 group of cross-sectional point position data SCi to the measurement reference line ML as the determination data of the peak point 10, Dpt(u) = Dpt(3); Step 26-6: When Dpt(u - fkp) < Dpt(u) and Dpt(u + fkp) < Dpt(u), include the variable u in the preliminary calculation peak point 10 sequence number array Ptfr; Step 26-7: Loop steps 26-2 to 26-6 to complete the calculation of the preliminary calculation peak point 10 sequence number array Ptfr in the i = 1 group of cross-sectional point position data SCi; Ptfr = [7 15]; Step 27: Calculate the preliminary calculation trough point 11 sequence number array Ptvr in the i = 1 group of cross-sectional point position data SCi; This step 27 is implemented through the following process: Step 27-1: Establish the trough point 11 sparse parameter fkv in the i = 1 group of cross-sectional point position data SCi, where fkv is a positive integer and the value range is (1:length(SCi)). In this embodiment, fkv = 2 is taken; Step 27-2: Establish the sparse trough loop variable v, where the value range of v is (fkv + 1, length(SCi) - fkv) = (3, 15), the initial value of v is fkv + 1 = 3, and the step size is fkv = 2; Step 27-3: Take the data of the (v - fkv)-th group in the array Dpt of the distances from the point positions of the i = 1 group of cross-sectional point position data SCi to the measurement reference line ML as the first determination data of the trough point 11, Dpt(v - fkv) = Dpt(3 - 2) = Dpt(1); Step 27-4: Take the data of the (v + fkv)-th group in the array Dpt of the distances from the point positions of the i = 1 group of cross-sectional point position data SCi to the measurement reference line ML as the second determination data of the trough point 11, Dpt(v + fkv) = Dpt(3 + 2) = Dpt(5); Step 27-5: Take the data of the v-th group in the array Dpt of the distances from the point positions of the i = 1 group of cross-sectional point position data SCi to the measurement reference line ML as the determination data of the trough point 11, Dpt(v) = Dpt(3); Step 27-6: When Dpt(v-fkv)>Dpt(v) and Dpt(v+fkv)>Dpt(v), add the variable v to the initial calculation trough point 11 sequence array Ptvr; Step 27-7: Repeat steps 27-2 to 27-6 to complete the calculation of the initial wave trough point 11 sequence number array Ptvr in the i=1th group of cross-sectional point data SCi. Ptvr=[3 11]; Step 28: Calculate the initial wave crest point index array Ptfr from the i=1th group of cross-sectional point data SCi, and asymptotically calculate the wave crest point index array Ptf. Step 28 is performed through the following process: Step 28-1: Establish the asymptotic peak loop variable k for the array Ptf of asymptotic peak point 10, where the value range of k is (1, length(Ptfr)) = (1, 2), the initial value of k is 1, and the step size is 1; Step 28-2: Take the k=1th index Ptfr(k) from the initial calculation wave crest point 10 index array Ptfr in the i=1th group of cross-sectional point data SCi; Step 28-3: Use the sparse parameters of point peak 10 to establish the asymptotic calculation of the starting point of peak 10 sequence number stnf=Ptfr(k)- fkp=7-2=5; Step 28-4: Use the sparse parameters of point peak 10 to establish the asymptotic calculation of the termination point of peak 10, ednf=Ptfr(k)+fkp=7+2=9; Step 28-5: Use the max function to calculate the index Ptf(k) of the maximum value in the distance array Dpt from each point in the i=1 group of cross-sectional point data SCi corresponding to the asymptotically calculated peak point 10 (starting point stnf) to the asymptotically calculated peak point 10 (ending point ednf). [maxval,indexa]=max(Dpt (stnf:ednf)) = max(Dpt (5:9)); Ptf(k) = indexa + stnf-1 = 3 + 5 - 1 = 7; Where max(Dpt (stnf:ednf)) is the maximum value of the distance array Dpt, maxval is the maximum value in the array, and indexa is the index of the maximum value in the array; Step 28-6: Repeat steps 28-2 to 28-5 to complete the asymptotic calculation of the peak point 10 sequence number array Ptfr in the i=1th group of cross-sectional point data SCi, and calculate the peak point 10 sequence number array Ptf=

[714] . Step 29: Calculate the initial array Ptvr of the trough point 11 in the i=1th group of cross-sectional point data SCi, and asymptotically calculate the array Ptv of the trough point 11 in the cross-sectional point data SCi. Step 29 is performed through the following process: Step 29-1: Establish the asymptotic valley loop variable d for the array Ptfr of asymptotic calculation valley point 11, where the value range of d is (1, length(Ptvr)) = (1, 2), the initial value of d is 1, and the step size is 1; Step 29-2: Take the d=1st index Ptvr(d) from the initial calculation valley point index array Ptvr in the i=1th group of cross-sectional point data SCi; Step 29-3: Use the sparse parameters of the valley point 11 to establish the asymptotic calculation starting point of the valley point 11 sequence stnv=Ptvr(d)- fkv=3-2=1; Step 29-4: Use the sparse parameters of the valley point 11 to establish the asymptotic calculation of the valley point 11 sequence termination point ednv=Ptvr(d)+fkv=3+2=5; Step 29-5: Use the min function to calculate the index Ptv(d) of the minimum distance from each point in the i=1th group of cross-sectional point data SCi corresponding to the asymptotically calculated trough point 11 (starting point stnv) to the asymptotically calculated trough point 11 (ending point ednv). [minval,indexi]=min(Dpt (stnv:ednv)) = min(Dpt (1:5)); Ptv(d) = indexi + stnv-1 = 3 + 1 - 1 = 3; Where min (Dpt (stnf:ednf)) is the minimum value of the distance array Dpt, minval is the minimum value xi in the array, and indexi is the index of the minimum value in the array; Step 29-6. Repeat steps 29-2 to 29-5 to complete the asymptotic calculation of the valley point 11 sequence number array Ptvr in the i-th cross section point data SCi, and calculate the valley point 11 sequence number array Ptv =

[310] . Step 30: Based on the i=1 group of cross-sectional point data SCi, the peak point 10 sequence array Ptf and the trough point 11 sequence array Ptv are obtained by progressively calculating the peak point 10 sequence array Ptf and the trough point 11 sequence array Ptv. The endpoint data is processed by classification to obtain the peak and trough point 11 calculation array ptfv. Step 30 is performed through the following process: Step 30-1: Calculate the length w of the array Ptf, which represents the asymptotically calculated peak point 10; where w = length(Ptf) = 2. Step 30-2: Calculate the length q of the array Ptv for the asymptotically calculated trough point 11; where q = length(Ptv) = 2. Step 30-3: Based on the length w of the array Ptf of the peak point 10 and the length q of the array Ptv of the trough point 11, process the endpoints according to their categories and establish the calculation array ptfv of the peak and trough points 11. In one possible case of step 30-3, when abs(wq)≠1, the endpoints are processed by classification, and the peak and valley point 11 calculation array ptfv is established, where abs(wq) is the absolute value of the difference with respect to wq.

[0025] Furthermore, when w≥q, the peak and trough points 11 are calculated using the array ptfv=[ptf ptv length(SCi)] =[ 71431017].

[0026] Step 31: Sort the peak and valley point 11 calculation array ptfv obtained based on the i=1th group of cross-sectional point data SCi to obtain the sorted peak and valley point 11 calculation array ptfvs. ptfvs=SCi(sort(ptfv))= [37101417]; The implementation process of step 31 may include: Step 31-1: Establish the ripple circular variable x of the peak and trough point 11 calculation array ptfv, where the value range of x is (2, length(ptfvs)) = (2,5), the initial value of x is 2, and the step size is 2; Step 31-2: Take the sorted peak and trough points 11 respectively and calculate the (x-1)th point, the xth point, and the (x+1)th point in the array ptfvs, denoted as ptfvsdx, ptfvsx, and ptfvsux; ptfvsdx= SCi(ptfvs(x-1),1:3); ptfvsx = SCi(ptfvs(x), 1:3); ptfvsux= SCi(ptfvs(x+1),1:3 ); Step 31-3: Calculate the distance ptfvsdx and ptfvsux, ptfvsdl, as the wavelength 12 of the aircraft shape waviness. ptfvsdl=norm(ptfvsdx-ptfvsux); Where norm(ptfvsdx-ptfvsux) is a modulo operation on two points to calculate the length of the two points; Step 31-4: Calculate the distance ptfvsdr from point ptfvsx to the straight line formed by point ptfvsdx and point ptfvsux. This distance is used as the amplitude 13 of the aircraft shape waviness, and is represented by the amplitude ptfvsdr. ap=ptfvsx-ptfvsdx; ab=ptfvsux- ptfvsdx; crossp = cross(ap, ab); ptfvsdr =norm(crossp) / norm(ab); Where crossp is used to calculate the cross product of vectors ap and ab, norm(crossp) is used to calculate the magnitude of the cross product vector, and norm(ab) is used to calculate the magnitude of vector ab. Steps 31-5: Based on the wavelength ptfvsdl and amplitude ptfvsdr of the aircraft shape waviness, calculate the ptfvw of the x-th group of aircraft shape waviness in the sorted peak and trough points 11 array ptfvs; ptfvw=ptfvsdr / ptfvsdl=[ 0.22970.2076]; Step 31-6: Repeat steps 31-2 to 31-5 to complete the calculation of aircraft shape waviness based on the i=1th set of cross-sectional point data SCi.

[0027] Step 32: Calculate the aircraft shape waviness using the sorted peak and valley points 11 obtained from the i=1 group of cross-sectional point data SCi. Step 33: Repeat steps 23 to 32 to complete the calculation of aircraft shape waviness for each set of cross-sectional point data, and output the results in the form of a report, as shown in Table 2.

[0028] Table 2 Aircraft Outer Ripple Output Form

[0029] Based on inverse point cloud data of an aircraft shape, this invention proposes an automatic asymptotic calculation method for aircraft shape waviness in a MATLAB environment. This method, based on inverse point cloud data of the aircraft shape, utilizes MATLAB programming to automatically calculate aircraft shape waviness asymptotically. It establishes a measurement baseline for the point data, calculates the distance from the point to the measurement baseline, initially calculates the peak and trough point indices, performs asymptotic calculation of the peak and trough point indices, processes endpoint data, and calculates the aircraft shape waviness. Finally, through automatic generation of program forms, it achieves high-precision, fast, efficient, and automatic batch calculation of aircraft shape waviness and standardized report output. In other words, it achieves fast and efficient processing of aircraft shape waviness data through pure mathematical logic calculation.

[0030] The automatic asymptotic calculation method for aircraft shape waviness in the MATLAB environment provided by this invention fully leverages the unique advantages of MATLAB in matrix calculation, vector operation, and mathematical modeling. It uses an asymptotic approach to solve the influence of aircraft shape point cloud density and measurement noise on the calculation of aircraft shape waviness. It achieves the capture of the main features of aircraft shape waviness through preliminary calculation, and realizes automatic filtering and efficient and high-precision calculation of aircraft shape waviness by using an asymptotic search method based on the main feature points.

[0031] The automatic asymptotic calculation method for aircraft shape waviness in the MATLAB environment provided by this invention has a clear approach, simple and straightforward logic, and strong applicability. It provides a new solution for aircraft shape inspection, aircraft assembly quality control, rapid product quality inspection, and rapid acquisition and processing of assembly process data.

[0032] While the embodiments disclosed in this invention are as described above, they are merely illustrative of the embodiments to facilitate understanding of the invention and are not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and variations in the form and details of the implementation without departing from the spirit and scope disclosed herein; however, the scope of patent protection for this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A method for automatically computing the asymptotic waviness of an aircraft shape in a MATLAB environment, characterized in that, The method comprises the following steps: Step 1, a plane section model of the aircraft contour waviness is established by preprocessing the measured aircraft contour point cloud data, and section point position data of each group of sections is outputted; Step 2, automatic asymptotic calculation of the aircraft contour waviness is carried out in the MATLAB environment, which comprises the following steps: based on the section point position data of each group of sections outputted in step 1, the section point position data of each group of sections is cyclically calculated for the aircraft contour waviness; wherein, the asymptotic calculation of the aircraft contour waviness for the section point position data of each group of sections is carried out in the following manner: a measurement reference line is established based on the section point position data of each group of sections outputted in step 1, the distance of each point position to the measurement reference line is calculated, the initial calculation of the sequence numbers of the peak and valley points of the point position data is carried out, the asymptotic calculation of the sequence numbers of the peak and valley points is carried out, the end point data processing of the point position data is carried out, the aircraft contour waviness is calculated, and the calculation results are outputted.

2. The method for automatic asymptotic calculation of the aircraft shape waviness in the MATLAB environment according to claim 1, characterized in that, The step 1 comprises the following steps: Step 1-1, the aircraft contour point cloud data obtained by reverse measurement is opened in the three-dimensional CAD software; Step 1-2, a group of planes for analyzing the aircraft contour waviness is established in the three-dimensional CAD software, the group of planes intersects with the aircraft contour point cloud data to form a group of sections, thereby establishing the plane section model of the aircraft contour waviness; Step 1-3, the section point position data is outputted based on each group of sections in the established plane section model of the aircraft contour waviness in the three-dimensional CAD software.

3. The automatic asymptotic calculation of the aircraft shape waviness in the MATLAB environment according to claim 2, characterized in that, The step 2 comprises the following steps: Step 21, the section point position data of each group of sections outputted in step 1-3 is read; Step 22, a section cycle variable i is established, wherein i takes the value range of (1, sn), the initial value of i is 1, the step length is 1, and sn is the number of sections in the plane section model of the aircraft contour waviness; Step 23, the i-th group of section point position data SCi in the plane section model of the aircraft contour waviness is taken; Step 24, a measurement reference line ML is established according to the i-th group of section point position data SCi; Step 25, a distance array Dpt of each point position in the i-th group of section point position data SCi to the measurement reference line ML is calculated; Step 26, an initial calculation peak point sequence number array Ptfr in the i-th group of section point position data SCi is calculated; Step 27, an initial calculation valley point sequence number array Ptvr in the i-th group of section point position data SCi is calculated; Step 28, the initial calculation peak point sequence number array Ptfr in the i-th group of section point position data SCi is used to asymptotically calculate a peak point sequence number array Ptf; Step 29, the initial calculation valley point sequence number array Ptvr in the i-th group of section point position data SCi is used to asymptotically calculate a valley point sequence number array Ptv; Step 30, the peak point sequence number array Ptf and the valley point sequence number array Ptv obtained by the asymptotic calculation of the i-th group of section point position data SCi are classified to carry out end point data processing to obtain a peak and valley point calculation array ptfv; Step 31, the peak and valley point calculation array ptfv calculated based on the i-th group of section point position data SCi is sorted to obtain a sorted peak and valley point calculation array ptfvs. Step 32, calculating the aircraft shape waviness according to the ordered peak and valley point array ptfvs obtained from the i-th group of section point data SCi; Step 33, performing steps 23 to 32 in a loop to complete the calculation of the aircraft shape waviness of each group of section point data, and outputting the results in the form of a report.

4. The automatic asymptotic calculation of the aircraft shape waviness in the MATLAB environment according to claim 3, characterized in that, The step 24 comprises: Step 24-1, calculating the midpoint Ptm of the i-th group of section point data SCi; Ptmn = floor(length(SCi) / 2); Ptm = SCi(Ptmn, 1:3); Wherein, Ptmn is the sequence number of the midpoint Ptm of the i-th group of section point data SCi; Step 24-2, setting an initial measurement reference line MLr and calculating the foot Ptf of the midpoint Ptm of the i-th group of section point data SCi to the initial measurement reference line MLr, wherein the foot Ptf is the projection point of the midpoint Ptm to the initial measurement reference line MLr; Pst = SCi(1, 1:3); Pet = SCi(end, 1:3); v = Pet - Pst; numerator = dot(v, Ptm - Pst); denominator = dot(v, v); t = numerator / denominator; Ptf = Pst + t * v; The starting point Pst of the initial measurement reference line MLr is the first point of the i-th group of section point data SCi, and the ending point Pet is the last point of the i-th group of section point data SCi; Wherein, v is the direction vector of the initial measurement reference line MLr, numerator is the dot product of the vector v and the direction vector from the starting point to the intermediate target point (Ptm-Pst), denominator is the dot product of the vector v and itself, and t is the proportion of the projection distance; Step 24-3, obtaining the measurement reference line ML by translating the initial measurement reference line MLr.

5. The automatic asymptotic calculation of the aircraft shape waviness in the MATLAB environment according to claim 4, characterized in that, The step 24-3 comprises: Step 24-3-1, establishing the measurement reference line offset variable offset of the i-th group of section point data SCi, wherein offset is a positive integer greater than 0; Step 24-3-2, calculating the initial measurement reference line MLr offset direction vector dv according to the midpoint Ptm and the foot Ptf of the i-th group of section point data SCi: vn = Ptf - Ptm; dv = vn / norm(vn); Wherein, norm(vn) represents the norm of the vector vn; Step 24-3-3, calculating the starting coordinate Post and the ending coordinate Poet of the measurement reference line ML after the initial measurement reference line MLr offset variable offset, i.e. obtaining the measurement reference line ML; Post = Pst + dv * offset; Poet = Pet + dv * offset.

6. The automatic asymptotic calculation of the aircraft shape waviness in the MATLAB environment according to claim 3, characterized in that, The step 25 comprises: Step 25-1, establishing a cycle variable j of the midpoint of the i-th section, wherein j takes a value in the range of (1, length(SCi)), the initial value of j is 1, and the step length is 1; Step 25-2, taking the j-th point Ptj in the i-th section point data SCi; Step 25-3, calculating the distance Dptj of the j-th point Ptj in the i-th section point data SCi to the measurement reference line ML, and adding Dptj to the array Dpt; AP = Ptj - Post; AB = Post - Post; crossAPAB = cross(AP, AB); Dptj = norm(crossAPAB) / norm(AB) - offset; Wherein crossAPAB is the cross product between vector AP and vector AB; norm(crossAPAB) is the module of vector crossAPAB; norm(AB) is the module of vector AB; Step 25-4, repeating steps 25-1 to 25-3 to calculate the distance of all points in the i-th section point data SCi to the measurement reference line ML, and forming the array Dpt.

7. The automatic asymptotic calculation of the aircraft shape waviness in the MATLAB environment according to claim 3, characterized in that, The step 26 comprises: Step 26-1, establishing a sparse parameter fkp of the peak point in the i-th section point data SCi, wherein fkp is a positive integer and takes a value in the range of 1 < fkp < length(SCi); Step 26-2, establishing a sparse peak cycle variable u, wherein u takes a value in the range of (fkp+1, length(SCi)-fkp), the initial value of u is fkp+1, and the step length is fkp; Step 26-3, taking the u-fkp group data in the array Dpt of the distance of the points in the i-th section point data SCi to the measurement reference line ML as the first judgment data Dpt(u-fkp) of the peak point; Step 26-4, taking the u+fkp group data in the array Dpt of the distance of the points in the i-th section point data SCi to the measurement reference line ML as the second judgment data Dpt(u+fkp) of the peak point; Step 26-5, taking the u group data in the array Dpt of the distance of the points in the i-th section point data SCi to the measurement reference line ML as the peak point judgment data Dpt(u); Step 26-6, when Dpt(u-fkp) < Dpt(u) and Dpt(u+fkp) < Dpt(u), adding the variable u to the preliminary calculation peak point sequence number array Ptfr; Step 26-7, repeating steps 26-2 to 26-6 to complete the calculation of the preliminary calculation peak point sequence number array Ptfr in the i-th section point data SCi.

8. The automatic asymptotic calculation of the aircraft shape waviness in the MATLAB environment according to claim 3, characterized in that, The step 27 comprises: Step 27-1, establishing a sparse parameter fkv of the valley point in the i-th section point data SCi, wherein fkv is a positive integer and takes a value in the range of (1: length(SCi)); Step 27-2, establishing a sparse valley cycle variable v, wherein v takes a value in the range of (fkv+1, length(SCi)-fkv), the initial value of v is fkv+1, and the step length is fkv; Step 27-3, take the point position of the i-th cross-section point position data SCi to the v-fkv-th group data in the distance array Dpt of the measuring reference line ML as the first determination data Dpt(v-fkv) of the wave trough point; Step 27-4, take the point position of the i-th cross-section point position data SCi to the v+fkv-th group data in the distance array Dpt of the measuring reference line ML as the second determination data Dpt(v+fkv) of the wave trough point; Step 27-5, take the point position of the i-th cross-section point position data SCi to the v-th group data in the distance array Dpt of the measuring reference line ML as the determination data Dpt(v) of the wave trough point; Step 27-6, when Dpt(v-fkv)> Dpt(v) and Dpt(v+fkv) > Dpt(v), count the variable v into the preliminary calculation wave trough point serial number array Ptvr; Step 27-7, loop steps 27-2 to 27-6 to complete the calculation of the preliminary calculation wave trough point serial number array Ptvr in the i-th cross-section point position data SCi.

9. The automatic asymptotic calculation of the aircraft shape waviness in the MATLAB environment according to claim 3, characterized in that, The step 28 comprises: Step 28-1, establish the asymptotic wave peak loop variable k of the asymptotic calculation wave peak serial number array Ptf, wherein k takes the value range of (1, length(Ptfr)), the initial value of k is 1, and the step length is 1; Step 28-2, take the k-th serial number Ptfr(k) in the preliminary calculation wave peak serial number array Ptfr in the i-th cross-section point position data SCi; Step 28-3, establish the asymptotic calculation wave peak serial number starting point stnf= Ptfr(k)-fkp by using the point position wave peak sparse parameter; Step 28-4, establish the asymptotic calculation wave peak serial number ending point ednf= Ptfr(k)+fkp by using the point position wave peak sparse parameter; Step 28-5, calculate the serial number Ptf(k) of the maximum value in the distance array Dpt of each point position of the i-th cross-section point position data SCi corresponding to the asymptotic calculation wave peak serial number starting point stnf to the asymptotic calculation wave peak serial number ending point ednf by using the max function; [maxval,indexa]=max(Dpt (stnf:ednf)); Ptf(k)=indexa+stnf-1; Wherein max(Dpt (stnf:ednf)) is the maximum value of the distance array Dpt, maxval is the maximum value in the array, and indexa is the serial number of the maximum value in the array; Step 28-6, loop steps 28-2 to 28-5 to complete the asymptotic calculation wave peak serial number array Ptf in the preliminary calculation wave peak serial number array Ptfr in the i-th cross-section point position data SCi; The automatic asymptotic calculation of the MATLB environment of the aircraft shape waviness according to claim 3, wherein the step 29 comprises: Step 29-1, establish the asymptotic wave trough loop variable d of the asymptotic calculation wave trough serial number array Ptfr, wherein d takes the value range of (1, length(Ptvr)), the initial value of d is 1, and the step length is 1; Step 29-2, take the dth index in the initial calculated wave trough point index array Ptvr in the ith group of cross-section point data SCi, Ptvr(d); Step 29-3, establish the asymptotic calculated wave trough point index starting point stnv= Ptvr(d)-fkv using the point wave trough point sparse parameter; Step 29-4, establish the asymptotic calculated wave trough point index ending point ednv= Ptvr(d)+fkv using the point wave trough point sparse parameter; Step 29-5, calculate the minimum value of the distance of each point in the ith group of cross-section point data SCi to the measurement reference line ML in the distance array Dpt corresponding to the index Ptv(d) from the asymptotic calculated wave trough point index starting point stnv to the asymptotic calculated wave trough point index ending point ednv using the min function; [minval,indexi]=min(Dpt (stnv:ednv)); Ptv(d)=indexi+stnv-1; Where min (Dpt (stnf:ednf)) is the minimum value of the distance array Dpt, minval is the minimum value in the array, and indexi is the index of the minimum value in the array; Step 29-6, loop steps 29-2 to 29-5 to complete the asymptotic calculation of the initial wave trough point index array Ptvr in the ith group of cross-section point data SCi.

10. The automatic asymptotic calculation of the aircraft shape waviness in the MATLAB environment according to claim 3, characterized in that, The step 30 includes: Step 30-1, calculate the length w of the asymptotic calculated wave peak point index array Ptf; where w=length(Ptf); Step 30-2, calculate the length q of the asymptotic calculated wave trough point index array Ptv; where q=length(Ptv); Step 30-3, classify and process the end points according to the length w of the wave peak point index array Ptf and the length q of the wave trough point index array Ptv, and establish the wave peak and wave trough point calculation array ptfv; Wherein, the step 30-3 includes: Step 30-3-1, when w-q=1, the wave peak and wave trough point calculation array ptfv=[ptf ptv]; Step 30-3-2, when w-q=-1, the wave peak and wave trough point calculation array ptfv=[1 ptf ptv]; Step 30-3-3, when abs(w-q)≠1, classify and process the end points, and establish the wave peak and wave trough point calculation array ptfv, where abs(w-q) is the absolute value of w-q; The step 30-3-3 includes: Step 30-3-3-1, when w Step 30-3-3-2, when w≥q, the wave peak and wave trough point calculation array ptfv=[ptf ptv length(SCi)].

11. The automatic asymptotic calculation of the aircraft shape waviness in the MATLAB environment according to claim 3, characterized in that, The step 31 includes: Step 31-1, establish the waviness loop variable x of the wave peak and wave trough point calculation array ptfv, where x takes the value range (2, length(ptfvs)), the initial value of x is 2, and the step is 2; Step 31-2, take the x-1th point, the xth point and the x+1th point in the sorted peak-valley point calculation array ptfvs as ptfvsdx, ptfvsx and ptfvsux respectively; ptfvsdx= SCi(ptfvs(x-1),1:3 ) ptfvsx= SCi(ptfvs(x),1:3 ); ptfvsux= SCi(ptfvs(x+1),1:3 ); Step 31-3, calculate the distance ptfvsdl between the point ptfvsdx and the point ptfvsux as the wavelength of the aircraft shape waviness; ptfvsdl=norm(ptfvsdx-ptfvsux); Where norm(ptfvsdx-ptfvsux) is the modulus operation on two points, calculating the length of the two points; Step 31-4, calculate the distance ptfvsdr from the point ptfvsx to the straight line formed by the point ptfvsdx and the point ptfvsux as the amplitude ptfvsdr of the aircraft shape waviness; ap=ptfvsx-ptfvsdx; ab=ptfvsux- ptfvsdx; crossp=cross(ap,ab); ptfvsdr =norm(crossp) / norm(ab); Where crossp is the cross product of vectors ap and ab, norm(crossp) calculates the modulus of the cross product vector, and norm(ab) is the modulus of vector ab; Step 31-5, complete the xth set of aircraft shape waviness calculation ptfvw in the sorted peak-valley point calculation array ptfvs according to the wavelength ptfvsdl and the amplitude ptfvsdr of the aircraft shape waviness; ptfvw=ptfvsdr / ptfvsdl; Step 31-6, loop steps 31-2 to 31-5 to complete the calculation of the aircraft shape waviness based on the i-th set of cross-sectional point data SCi.

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