Airplane surface waviness measurement and analysis method and system

Abstract

The invention discloses an aircraft surface waviness measurement and analysis method and system, and the method comprises the steps: scanning the surface of an aircraft according to a planned path through employing a three-coordinate measuring machine and a scanning probe, obtaining scanning data, and obtaining the scanning data for evaluation through employing a segmented scanning and segment-to-segment direct splicing mode for a hole, a pin and other structural parts; fitting based on scanning data to obtain a point deviation data set, calculating corresponding point arc length data based on the point deviation data and the point coordinate data on this basis, and drawing a graph by taking a horizontal axis as the point arc length data and a longitudinal axis as the point deviation data; and determining the surface waviness of the airplane by combining the derivative at the intersection point of the point deviation data curve and the transverse axis and the distance between the wave crest and the wave trough, thereby realizing the efficient and accurate measurement of cavities, welding seams and other obstacle structures on the outer surface of a large-scale airplane part or the whole machine and the accurate calculation of the waviness.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of aircraft measurement, and in particular relates to an aircraft surface waviness measurement and analysis method and system. BACKGROUND

[0002] Traditional aircraft skin surface waviness measurement methods, such as using steel rulers and feeler gauges for measurement, not only require multiple people to collaborate, but also have low precision, single application scenarios, and cannot meet the requirements of modern high-precision detection. With the development of science and technology, three-coordinate measuring machines (CMM) and their supporting scanning probes can provide high-precision three-dimensional point cloud data and are suitable for complex surface measurement tasks. However, how to efficiently and accurately use these devices to measure aircraft surface waviness, especially in the presence of obstacle structures such as holes and welds on path lines, is still a problem to be solved. SUMMARY

[0003] The purpose of the present application is to provide an aircraft surface waviness measurement and analysis method and system, which uses a three-coordinate measuring machine and a scanning probe to collect aircraft surface point cloud data according to a planned path, then splices the point cloud data according to the application scenario, calculates the waviness by crossing line segmentation combined with peak and valley distance discrimination, thereby achieving efficient and accurate measurement of large aircraft components or the outer surface of the whole machine in the presence of obstacle structures such as holes and welds, and accurate calculation of waviness.

[0004] To solve the above technical problems, the present application uses the following technical solutions: An aircraft surface waviness measurement and analysis method is proposed, comprising: S1: obtaining scanning data according to a planned scanning path; S2: fitting to obtain a point deviation data set based on the scanning data; S3: calculating corresponding point arc length data based on the point deviation data and point coordinate data; S4: drawing a graph with the horizontal axis as the point arc length data and the vertical axis as the point deviation data, and determining the aircraft surface waviness based on the drawn graph, comprising: Finding waviness elements: determining the starting point and ending point of the waviness elements in combination with the curve derivative at the intersection of the curve and the horizontal axis and the peak value of the adjacent peak / valley; Calculating waviness: within the evaluation length, taking to calculate the waviness, and taking the maximum value as the final waviness; wherein, represents the height of the th waviness element; represents the spacing of the th waviness element, is the number of waviness elements.

[0005] In some embodiments of the present application, before step S2, the method further comprises: Directly splicing the segment-to-segment segmental scans to obtain continuous scan data.

[0006] In some embodiments of the present application, before step S2, the method further comprises: Noise reduction processing and removing of abnormal points are performed on the point deviation data. Filtering operation is performed on the point deviation data to filter out low and high frequency components and retain waviness components.

[0007] In some embodiments of the present application, in step S2, when there is no data model, S2 comprises: Defining a set of scan data points , wherein represents the spatial coordinates of the i-th point; Dividing the entire segmental scan curve into a plurality of segments according to the y length; Performing linear fitting on each segment by using the least square method to obtain a set of fitting data points; For each scan point , finding the segment to which it belongs, then projecting it onto the corresponding fitting straight line to obtain a theoretical point , and constructing a set of theoretical points corresponding to the scan points one by one; Defining a set of point deviations for each segment along the direction of the point vector , wherein represents the Euclidean distance between the i-th point and its corresponding theoretical point; represents the index of the starting point of the i-th segment, represents the index of the ending point of the i-th segment; Collecting the point deviation data of all segments and arranging them in the original scan point order to obtain a set of point deviation data , wherein is the total number of points on the entire curve, is the number of segments. In some embodiments of the present application, in step S2, when there is a data model and the measurement data can be aligned with the data model, S2 comprises: Defining a set of theoretical points:

[0008] ; Obtaining a set of scan data points ; Finding an optimal rigid transformation to make the set of scan data points best match the set of theoretical data points . ​​​Map the scan data points into the coordinate system of the theoretical data points: set the maximum number of iterations and a convergence threshold , initialize the transformation parameters: , set the current transformation to ; for each iteration , transform the scan points by and for each transformed scan point , find the nearest neighbor in the theoretical point set : , construct the set of point pairs , using all point pairs , compute the new rotation and translation using the SVD method: , update the total transformation with , if the change between the new and old transformation is less than the threshold , stop the iteration; compute the deviation get the set of point deviation data , where is the total number of points along the scan curve, and all point deviation values are arranged in the original scan point order.

[0009] In some embodiments of the present application, an optimal rigid body transformation is found that best matches the measured data point set to the theoretical data point set , including: define the rigid body transformation as , where is the rotation matrix, satisfying , is the translation vector; find , such that the cost function is minimized: where denotes the point in the theoretical point set closest to the point .

[0010] In some embodiments of the present application, S3 specifically includes: for the point cloud data of a continuous scan : assuming that the scan segment has a total of n points, the point spacing is: ; , and when ; according to the point order number , the arc length of each point to the starting point is obtained , is the th point and the th pointThe distance between the points forms a point arc length data set .

[0011] In some embodiments of the present application, S4 comprises performing waviness analysis from the starting point to the ending point of the curve: The intersection of the curve and the horizontal axis is searched, and when the derivative of the curve at the intersection is positive and the height value of the next peak point is greater than the set percentage of the maximum amplitude tolerance, the starting point of a waviness element is determined. In the current waviness element, when the derivative of the curve at the next intersection is positive and the depth value of the valley point before this intersection is greater than the set percentage of the maximum amplitude tolerance, the ending point of the current waviness element is determined. The maximum amplitude is the amplitude of the highest point minus the lowest point in the evaluation range.

[0012] In some embodiments of the present application, S4 comprises performing waviness analysis from the ending point to the starting point of the curve: The intersection of the curve and the horizontal axis is searched, and when the derivative of the curve at the intersection is negative and the height value of the next peak point is greater than the set percentage of the maximum amplitude tolerance, the starting point of a waviness element is determined. In the current waviness element, when the derivative of the curve at the next intersection is negative and the depth value of the valley point before this intersection is greater than the set percentage of the maximum amplitude tolerance, the ending point of the current waviness element is determined.

[0013] An aircraft surface waviness measurement and analysis system is proposed, comprising: A three-coordinate scanning measuring machine and a scanning probe are used to obtain scanning data according to a planned scanning path; A waviness analysis device is used to analyze the waviness of the aircraft surface according to the following steps: A point deviation data set is fitted based on the scanning data; Point arc length data is calculated based on the point coordinate data; A graph is drawn with the horizontal axis as the point arc length data and the vertical axis as the point deviation data, and the aircraft surface waviness is determined based on the drawn graph, including: Finding a waviness element: the starting point and ending point of the waviness element are determined in combination with the derivative of the curve at the intersection of the curve and the horizontal axis and the peak value of the adjacent peak / valley; Calculating the waviness: in the evaluation length, the maximum value of the absolute value of the derivative of the curve is taken as the waviness value of the evaluation length. The waviness is calculated, and the maximum value is taken as the final waviness; wherein, represents the height of the th waviness element; represents the spacing of the th waviness element, is the number of waviness elements.

[0014] Compared with the prior art, the present application has the following advantages and positive effects: 1. For the structure parts of the holes on the surface of the airplane, pin, etc., the scanning data is obtained in the way of segmented scanning, and the data of the overall evaluation corrugation is obtained in the way of direct splicing of segments, which creates conditions for realizing the accurate corrugation evaluation of the airplane parts or the surface of the whole machine with obstacles such as holes and welds.

[0015] 2. For the measurement without data model, the least square algorithm + segmented straight line fitting is used to fit the point deviation data set, which simplifies the measurement difficulty.

[0016] 3. The derivative of the intersection of the point deviation data curve and the horizontal axis and the distance between the wave crest and the wave trough are combined to determine the corrugation of the surface of the airplane, and the corrugation elements are further searched from the start point to the end point of the evaluation length and from the end point to the start point of the evaluation length, which improves the ability and accuracy of searching the corrugation elements.

[0017] 4. The three-coordinate measuring machine and its matching scanning probe provide high-precision three-dimensional point cloud data, which significantly improves the measurement accuracy.

[0018] 5. The degree of automation is high, the manual operation is reduced, and the measurement and analysis efficiency is improved.

[0019] 6. It is suitable for the corrugation measurement of different sizes and shapes of airplane parts or the surface of the whole machine.

[0020] Other features and advantages of the present application will become more apparent after reading the detailed description of the embodiments of the present application in combination with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 The execution steps of the airplane surface corrugation measurement and analysis method proposed in the present application; Figure 2 The airplane surface corrugation measurement and analysis method shown in the embodiment of the present application; Figure 3 The corrugation calculation method in the embodiment of the present application; Figure 4 The corrugation calculation method in the embodiment of the present application. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0023] In the description of the present application, it needs to be understood that the terms "center", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0024] In the description of the present application, it needs to be understood that unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connecting" should be understood in a broad sense, for example, it can be fixed connection, or detachable connection, or integrally connected. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances. In the description of the above embodiments, the specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0025] The terms "first", "second" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more features. In the description of the present application, unless otherwise specified, the meaning of "multiple" is two or more.

[0026] The aircraft surface waviness measurement and analysis method provided by the present application, as shown in Figure 1 includes the following steps: Measurement preparation: prepare a three-coordinate measuring machine (CMM) and a corresponding scanning probe (equipped with a multi-directional measuring needle). Ensure that the three-coordinate measuring machine has sufficient travel range and accuracy to cover the entire surface of the aircraft part to be measured. Prepare the aircraft part to be measured and clean its surface to ensure that there is no dust, oil stains and other impurities.

[0027] S1: Obtain scanning data according to the planned scanning path.

[0028] In the embodiment of the present application, the scanning mode includes continuous scanning and segmented scanning.

[0029] Continuous scanning: refers to the absence of structures such as holes, pins and welds in the scanning process, and the scanning data can be obtained at one time.

[0030] Segmented scanning: refers to the presence of structures such as holes, pins and welds in the scanning process, which cannot be scanned at one time to obtain scanning data, and the structure needs to be skipped to scan multiple segments of data. Ultimately, continuous scanning data is obtained through splicing to evaluate the waviness.

[0031] S2: A set of point deviation data is obtained by fitting the scan data.

[0032] In actual measurements, two situations exist: 1. No data model exists, or the scanned data is difficult to align with the data model; 2. A data model exists, and the scanned data can be aligned with the data model. In this embodiment of the invention, different fitting methods are used for the two different situations.

[0033] For segmented scan data, this invention solves the problem of data discontinuity in scenarios with structural components such as holes and pins by using segmented scanning and overall evaluation. Therefore, for segmented scan point cloud data, before fitting, the segments are directly spliced ​​together, and then fitting is performed according to different cases.

[0034] like Figure 2 As shown, for cases where a data model does not exist, or where the scanned data is difficult to align with the data model, the least squares algorithm combined with piecewise linear fitting is used to obtain the fitted data point set and the point deviation data set: (1) Point definition: Let the set of scanned data points be: ,in, This represents the spatial coordinates of the i-th point.

[0035] (2) Segmentation: The entire scanning curve is divided into several segments according to the length y, with Y representing the total length, which is divided into several parts. part, Round up, let the first... The set of points corresponding to the segment is: ; in, Indicates the first Segment start point index, Indicates the first Segment end point index.

[0036] (3) Use the least squares method to fit a straight line to obtain the set of fitted data points for each segment: For each segment Use the least squares method to fit a straight line, and let the parameters of the line be: ; in, It is a parameter variable on the straight line, used to describe the position of any point on the straight line; Let it be a point on the straight line; , The fitted direction vector; This represents the length of the parameter range for this segment. The fitting objective is to minimize the sum of the squared distances from all points to the line. .

[0037] (4) Construct the theoretical point set: For each scan point Find the segment to which it belongs. Then, the corresponding fitted line Theoretical points are obtained by projection. ,Right now: ; Construct the theoretical point set: ; Corresponds one-to-one with the original scan points.

[0038] (5) Calculate the point deviation: the distance along the point vector direction for each segment Define the set of point deviations as: ; in, Indicates the first The Euclidean distance between a point and its corresponding theoretical point.

[0039] The final set of point deviation data is obtained: ; in, It represents the total number of points on the entire curve, with all point deviation values ​​arranged in the order of the original scan points.

[0040] like Figure 2 As shown, for cases where a data model exists and the scanned data can be aligned with the data model, theoretical point data is obtained from the data model, and the fitted scanned point data set and point deviation data set are obtained through the following fitting method: (1) Obtain the set of scan data points.

[0041] Obtain the set of scanned data points ( (Point set): Number of scan points This may not be equal to the theoretical number of points. Furthermore, the distribution is not necessarily one-to-one.

[0042] (2) Find an optimal rigid body transformation (rotation and translation) that makes the scanned data point set With theoretical data point set Best match.

[0043] Define rigid body transformation as: ;in, It is a rotation matrix (satisfying) ), It is a translation vector.

[0044] Search This minimizes the following cost function: ;in In the theoretical point set Midpoint The nearest point (i.e., nearest neighbor search).

[0045] This optimization problem is usually solved using the ICP (Iterative Closest Point) algorithm, or the initial transformation can be solved using the SVD (Singular Value Decomposition) method.

[0046] (3) Iterate the nearest point.

[0047] Set the maximum number of iterations and convergence threshold Initialize the transformation parameters: Set the current transformation to For each iteration ,pass Transform the scan points, and for each transformed scan point In the theoretical point set Find the nearest neighbor: Construct a set of point pairs Based on all point pairs The new rotation and translation are calculated using the SVD method: ,use Update the total transformation if the change between the old and new transformations is less than a threshold. If the iteration stops, then stop.

[0048] (4) Point deviation calculation.

[0049] After the scanned data point set P and the theoretical point set Q are optimally matched, the scanned data point set and the theoretical point set are aligned. In step (3), the optimal rotation matrix R and translation vector t are obtained, such that the scanned data point set P is mapped to the coordinate system of the theoretical point set Q, i.e.: For each scan point ,use Perform the transformation; project the scan points onto the theoretical curve using the following steps: to transform each transformed scan point Vertical projection onto the theoretical curve The above is the goal, through Find the curve with The point with the shortest distance, once the projection point is found. Then the deviation can be calculated: ; The final set of point deviation data is obtained: ; in is the total number of points on the whole curve, and all point deviation values are arranged in the original scanning point order.

[0050] S3: Calculate the corresponding point arc length data based on the point coordinate data.

[0051] Before calculating the point arc length data, the point deviation data is first denoised and abnormal points are removed: given a set of point deviation data , calculate the overall mean and standard deviation of the data: ; .

[0052] Define the normal data range , for each observation value , if any of the following conditions are met, it is marked as an abnormal point (‘dirty’ point data caused by environmental factors such as oil stains and dust): ; That is, any data point whose distance from the data mean is more than three times the sample standard deviation is considered an abnormal point.

[0053] Next, the point deviation data is filtered to filter out low and high frequency components and retain the waviness component. The filtering operation uses a Gaussian weighting function: ; where is the distance of the Gaussian weighting function center (maximum value), is the Gaussian weighting function dependent on , is the cutoff wavelength, is a constant 0.4697, used to provide a 50% transmission characteristic at the cutoff wavelength .

[0054] After filtering the point deviation data D and the point coordinate data of each point cloud P, the point distance is calculated, then the arc length of each point to the starting point is obtained according to the point sequence number , and finally the point deviation data and the point arc length data are one-to-one corresponding to form a data pair according to the point sequence number .

[0055] Specifically, for continuous scanning point cloud data (including continuous scanning data spliced in sections), assuming that the scanning section has n points, the point distance is: ; , and . . ​

[0056] According to the point sequence number The arc length of each point to the starting point is obtained , is the distance between the first point and the first point, forming a point arc length data set .

[0057] The foregoing point deviation data set is obtained , two sets of data pairs are formed with the point deviation and the arc length, and a graph is drawn with the horizontal axis as the arc length and the vertical axis as the point deviation, which is similar to the graph shown in Figure 3 , Figure 4 .

[0058] S4: Draw a graph with the horizontal axis as the point arc length data and the vertical axis as the point deviation data, and determine the surface waviness of the aircraft based on the drawn graph.

[0059] The vertical axis is the point deviation data, and the horizontal axis is the corresponding point arc length data, as shown in Figure 3 and Figure 4 .

[0060] A complete peak and valley curve profile is called a waviness element, and the derivative at the intersection of the point deviation data curve and the horizontal axis line and the peak and valley distance determination method are used in the present application to determine the waviness element, and the waviness is calculated based on the waviness element.

[0061] Specifically, it includes: 1. Evaluate from the starting point to the end point of the evaluation length, as shown in Figure 3 , that is, the X-axis B line passes through the curve from left to right, Figure 3 in the direction indicated by D, the derivative of the curve at the intersection point is positive, and the height value of the next peak point is greater than the set percentage (for example, 10%) of the maximum amplitude tolerance band, and this intersection point is determined as the starting point of a waviness element, if the condition is not met, the next point is found, and if the condition is met, the starting point of the waviness element is determined. When determining the end point of the current waviness element, continue to consider that the derivative of the curve at the next intersection point is positive, and the depth value of the valley point before this intersection point is greater than the set percentage of the maximum amplitude tolerance, if the condition is not met, the next intersection point is found, and if the condition is met, the end point of the waviness element is determined.

[0062] In this way, the derivative of the curve at the intersection point and the peak and valley determination method can determine several waviness elements in the direction indicated by D in Figure 3 .

[0063] 2. Evaluate from the starting point to the end point of the evaluation length, as shown in Figure 4 , that is, the X-axis B line passes through the curve from right to left, Figure 4The direction indicated by the middle D, the derivative of the curve at the intersection point is negative, and the height value of the next peak point is greater than the set percentage of the maximum amplitude tolerance band. The intersection point is determined as the starting point of the corrugation element. If it does not meet the requirements, the next intersection point is found. If it meets the requirements, it is taken as the starting point of the corrugation element. When determining the end point of the current corrugation element, the derivative of the curve at the next intersection point is continuously considered to be negative. The depth value of the valley point before the intersection point is greater than the set percentage of the maximum amplitude tolerance band. If it does not meet the requirements, the next intersection point is found. If it meets the requirements, it is taken as the end point of the corrugation element.

[0064] N represents the total number of corrugation elements found according to steps 1 and 2 above, represents the height of the Nth corrugation element; represents the pitch of the Nth corrugation element, and the corrugation of the N corrugation elements in the evaluation length is calculated according to the following formula: . .

[0065] The maximum corrugation in the evaluation length is calculated according to the following formula: .

[0066] The calculated corrugation data is exported as table data or a report to facilitate subsequent quality control and improvement.

[0067] The present application also provides a system for measuring and analyzing the corrugation of an aircraft surface, comprising: A three-coordinate scanning measuring machine and a scanning probe are used to obtain scanning data according to a planned scanning path.

[0068] A corrugation analysis device is used to analyze the corrugation of an aircraft component or the entire aircraft surface according to the aircraft surface corrugation measurement and analysis method described above, to provide an efficient and reliable aircraft surface corrugation detection solution for the aviation manufacturing industry and to promote the progress of the industry's technical level.

[0069] It should be noted that the above description is not a limitation of the present application, and the present application is not limited to the above examples. Changes, modifications, additions or substitutions made by ordinary skilled persons within the scope of the present application should also be within the scope of the present application.​

Claims

1. A method of aircraft surface waviness measurement and analysis, characterized in that, Comprising: S1: obtaining scan data according to a planned scan path; S2: fitting to obtain a point deviation data set based on the scan data; S3: calculating corresponding point arc length data based on the point deviation data and point coordinate data; S4: drawing a graph with the horizontal axis as the point arc length data and the vertical axis as the point deviation data, and determining the corrugation of the aircraft surface based on the drawn graph, comprising: Finding corrugation elements: determining the start and end points of the corrugation elements in combination with the curve derivative at the intersection of the curve and the horizontal axis and the peak value of the adjacent wave crest / wave trough; Calculate the waviness: in the evaluation length, take the maximum value as the final waviness; wherein, Calculate the waviness, take the maximum value as the final waviness; wherein, Indicates the height of the first waviness element; Indicates the pitch of the first waviness element, The number of waviness elements.

2. The aircraft surface waviness measurement and analysis method of claim 1, wherein, Before step S2, the method further comprises: Directly splicing the segmented scans to obtain continuous scan data.

3. The aircraft surface waviness measurement and analysis method according to claim 1, wherein, Before step S2, the method further comprises: Noise reduction processing and removing outliers on the point deviation data; Filtering the point deviation data to filter out low and high frequency components and retain the corrugation component.

4. The aircraft surface waviness measurement and analysis method according to claim 1, wherein, In step S2, when there is no data model, S2 comprises: Defining a set of scan data points wherein, represents the spatial coordinates of the i-th point; Dividing the entire scan curve into several segments according to the y length; Using the least squares method to fit each segment to obtain a fitted data point set; For each scan point , find the segment it belongs to , then project it onto the corresponding fitted line to get a theoretical point , construct the set of theoretical points , which is one-to-one corresponding to the scan points; distance along the point vector direction defines a set of point deviations for each segment wherein, denotes the Euclidean distance between the th point and its corresponding theoretical point; denotes the Euclidean distance between the th segment start point index, denotes the Euclidean distance between the th segment end point index; Collecting the point deviation data of all segments and arranging in the original scanning point order to obtain a point deviation data set wherein is the total number of points on the entire curve, is the number of segments.

5. The aircraft surface waviness measurement and analysis method according to claim 1, wherein, In step S2, when there is a data model and the measurement data can be aligned with the data model, S2 comprises: Define set of theory points: ; Acquiring a set of scan data points ; finding an optimal rigid body transformation that best matches a set of scan data points to a set of theoretical data points best match;​ Map the scanned data points to the coordinate system of the theoretical data points: Set the maximum number of iterations. and convergence threshold Initialize the transformation parameters: Set the current transformation to For each iteration ,pass Transform the scan points, and for each transformed scan point In the theoretical point set Find the nearest neighbor: Construct a set of point pairs Based on all point pairs The new rotation and translation are calculated using the SVD method: ,use Update the total transformation if the change between the old and new transformations is less than a threshold. If the iteration stops, then stop. Computing the bias Obtaining a set of point bias data wherein is the total number of points on the entire scan curve, all point bias values arranged in the original scan point order.

6. The aircraft surface waviness measurement and analysis method according to claim 5, wherein, finding an optimal rigid body transformation that best matches a set of measured data points to a set of theoretical data points includes: A rigid body transformation is defined as where, is a rotation matrix satisfying , is a translation vector; seeking minimizing a cost function wherein denotes the point in the set of theoretical points closest to the point .

7. The aircraft surface waviness measurement and analysis method according to claim 1, wherein, S3 specifically comprises: For continuous scanned point cloud data : Assuming that the scanned segment has n points in total, the point spacing is: ； , and when ; According to the point sequence number The arc length of each point to the starting point is obtained , is the distance between the first point and the first point, forming a point arc length data set .

8. The aircraft surface waviness measurement and analysis method according to claim 1, wherein, S4 includes corrugation analysis from the start point to the end point of the curve: Search for the intersection of the curve and the horizontal axis, and determine the start point of a corrugation element when the curve derivative at the intersection is positive and the height value of the next wave crest point is greater than a set percentage of the maximum amplitude tolerance, and determine the end point of the current corrugation element when the curve derivative at the next intersection is positive and the depth value of the wave trough point before this intersection is greater than a set percentage of the maximum amplitude tolerance; wherein the maximum amplitude is the amplitude of the highest point minus the lowest point in the evaluation range.

9. The aircraft surface waviness measurement and analysis method according to claim 1 or 8, characterized in that, S4 includes corrugation analysis from the end point to the start point of the curve: Search for the intersection of the curve and the horizontal axis, and determine the start point of a corrugation element when the curve derivative at the intersection is negative and the height value of the next wave crest point is greater than a set percentage of the maximum amplitude tolerance, and determine the end point of the current corrugation element when the curve derivative at the next intersection is negative and the depth value of the wave trough point before this intersection is greater than a set percentage of the maximum amplitude tolerance.

10. An aircraft surface waviness measurement and analysis system, characterized by, Comprising: A three-coordinate scanning measuring machine and a scanning probe for obtaining scan data according to a planned scan path; A corrugation analysis device for analyzing the corrugation of the aircraft surface according to the following steps: Fitting to obtain a point deviation data set based on the scan data; Calculating corresponding point arc length data based on the point coordinate data; Drawing a graph with the horizontal axis as the point arc length data and the vertical axis as the point deviation data, and determining the corrugation of the aircraft surface based on the drawn graph, comprising: Finding corrugation elements: determining the start and end points of the corrugation elements in combination with the curve derivative at the intersection of the curve and the horizontal axis and the peak value of the adjacent wave crest / wave trough; Calculate the waviness: in the evaluation length, take the maximum value as the final waviness; wherein, Calculate the waviness, take the maximum value as the final waviness; wherein, represents the height of the first waviness element; represents the pitch of the first waviness element, is the number of waviness elements.

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