A method and system for measuring and analyzing the surface waviness of an aircraft.

Abstract

This invention discloses a method and system for measuring and analyzing the surface waviness of aircraft. It employs a coordinate measuring machine and a scanning probe to scan the aircraft surface along a planned path to obtain scan data. For structural components such as holes and pins, segmented scanning and direct segment splicing are used to obtain scan data for evaluation. A set of point deviation data is obtained by fitting the scan data. Based on this, the corresponding point arc length data is calculated using the point deviation data and point coordinate data. A graph is plotted with the horizontal axis representing the point arc length data and the vertical axis representing the point deviation data. The surface waviness of the aircraft is determined by combining the derivative of the point deviation data curve at the intersection with the horizontal axis and the distance between the peaks and troughs. This achieves efficient and accurate measurement and calculation of waviness for large aircraft components or the outer surface of the entire aircraft where there are obstacles such as holes and welds.

Description

Technical Field

[0001] This invention belongs to the field of aircraft measurement technology, specifically, it relates to a method and system for measuring and analyzing the surface waviness of an aircraft. Background Technology

[0002] Traditional methods for measuring aircraft skin surface waviness, such as using steel rulers and feeler gauges, require multiple people to work together, have low accuracy, and are limited to specific applications, failing to meet the requirements of modern high-precision inspection. With technological advancements, coordinate measuring machines (CMMs) and their accompanying scanning probes can provide high-precision 3D point cloud data, suitable for measuring complex curved surfaces. However, how to efficiently and accurately utilize these devices for measuring aircraft surface waviness, especially for waviness analysis along paths with obstacles such as holes and welds, remains a pressing problem to be solved. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for measuring and analyzing the waviness of aircraft surfaces. The method uses a coordinate measuring machine and a scanning probe to collect point cloud data of the aircraft surface according to a planned path. Then, the point cloud data is stitched together according to the application scenario. The waviness is calculated by segmenting across lines and judging the distance between peaks and valleys. This enables efficient and accurate measurement of obstacles such as voids and welds on the outer surface of large aircraft components or the entire aircraft, as well as accurate calculation of waviness.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0005] A method for measuring and analyzing the surface waviness of aircraft is proposed, including:

[0006] S1: Acquire scan data according to the planned scan path;

[0007] S2: A set of point deviation data is obtained by fitting the scan data;

[0008] S3: Calculate the corresponding point arc length data based on point deviation data and point coordinate data;

[0009] S4: Plot a graph with the horizontal axis representing the arc length data and the vertical axis representing the deviation data. Determine the surface waviness of the aircraft based on the plotted graph, including:

[0010] Finding the waviness element: Determine the start and end points of the waviness element by combining the curve derivative at the intersection of the curve and the horizontal axis, as well as the peak values ​​of adjacent peaks / troughs;

[0011] Calculate waviness: within the evaluation length, using Calculate the waviness and take the maximum value as the final waviness; where, Indicates the first The height of each waviness element; Indicates the first The spacing of each waviness element The number of waviness elements.

[0012] In some embodiments of the present invention, before step S2, the method further includes:

[0013] Continuous scan data is obtained by directly splicing the segments of the segmented scan.

[0014] In some embodiments of the present invention, before step S2, the method further includes:

[0015] Denoising the point deviation data and removing outliers;

[0016] The point deviation data is filtered to remove low and high frequency components while retaining the waviness component.

[0017] In some embodiments of the present invention, when a data model does not exist, step S2 includes:

[0018] Define the set of scan data points ,in, Represents the spatial coordinates of the i-th point;

[0019] Divide the entire scan curve into several segments according to the y-length;

[0020] The least squares method is used to fit a straight line to each segment to obtain the set of fitted data points;

[0021] For each scan point Find the segment to which it belongs. Then, the theoretical point is obtained by projecting it onto the corresponding fitted line. Construct a set of theoretical points Each scan point corresponds to one of the scan points;

[0022] The set of deviations from each defined point along the distance of the point vector direction. ,in, Indicates the first The Euclidean distance between a point and its corresponding theoretical point; Indicates the first Segment start point index, Indicates the first Segment end point index;

[0023] Collect all segment point deviation data and arrange them in the original scan point order to obtain the point deviation data set. ,in It is the total number of points on the entire curve. The number of segments.

[0024] In some embodiments of the present invention, in step S2, when a data model exists and the measurement data can be aligned with the data model, S2 includes:

[0025] Define the set of theoretical points: ;

[0026] Obtain the set of scanned data points ;

[0027] Find an optimal rigid body transformation that makes the scanned data point set With theoretical data point set Best match;

[0028] 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 According to 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.

[0029] Calculate the deviation Obtain the point deviation data set ,in It represents the total number of points on the entire scan curve, with all point deviation values ​​arranged in the order of the original scan points.

[0030] In some embodiments of the present invention, an optimal rigid body transformation is found that results in a set of measured data points. With theoretical data point set Best matches include:

[0031] Define rigid body transformation as ,in, It is a rotation matrix that satisfies , It is a translation vector;

[0032] Search , making the cost function Minimum: Among them In the theoretical point set Midpoint The closest point.

[0033] In some embodiments of the present invention, S3 specifically includes:

[0034] For continuously scanned point cloud data Assume the scan segment has n points, and the point spacing is:

[0035] ; ,and hour ;

[0036] According to the point number Find the arc length from each point to the starting point. , It is the first The point and the first The distances between points form a point arc length data set. .

[0037] In some embodiments of the present invention, S4 includes performing waviness analysis from the start point to the end point of the curve:

[0038] The intersection of the search curve and the horizontal axis is used to determine the starting point of a waviness element. When the derivative of the curve at the intersection point is positive and the height of the next peak is greater than the set percentage of the maximum amplitude tolerance, the waviness element is determined to be the starting point of the waviness element. Under the current waviness element, when the derivative of the curve at the next intersection point is positive and the depth of the trough point before this intersection point is greater than the set percentage of the maximum amplitude tolerance, the waviness element is determined to be the ending point of the current waviness element. The maximum amplitude is the amplitude between the highest and lowest points within the evaluation range.

[0039] In some embodiments of the present invention, S4 includes performing a waviness analysis from the end point of the curve to the start point:

[0040] The intersection of the search curve and the horizontal axis is used to determine the starting point of a waviness element. When the derivative of the curve at the intersection point is negative and the height of the next peak is greater than the set percentage of the maximum amplitude tolerance, the waviness element is determined to be the starting point of the waviness element. Under the current waviness element, when the derivative of the curve at the next intersection point is negative and the depth of the trough point before this intersection point is greater than the set percentage of the maximum amplitude tolerance, the waviness element is determined to be the ending point of the current waviness element.

[0041] A system for measuring and analyzing the surface waviness of an aircraft is proposed, comprising:

[0042] A coordinate scanning measuring machine and scanning probe are used to acquire scanning data according to a planned scanning path;

[0043] A waviness analysis device is used to analyze the waviness of an aircraft surface according to the following steps:

[0044] A set of point deviation data was obtained by fitting the scanned data;

[0045] Calculate the corresponding arc length data based on the point coordinate data;

[0046] A graph is plotted with the horizontal axis representing the arc length data and the vertical axis representing the deviation data. Based on the plotted graph, the surface waviness of the aircraft is determined, including:

[0047] Finding the waviness element: Determine the start and end points of the waviness element by combining the curve derivative at the intersection of the curve and the horizontal axis, as well as the peak values ​​of adjacent peaks / troughs;

[0048] Calculate waviness: within the evaluation length, using Calculate the waviness and take the maximum value as the final waviness; where, Indicates the first The height of each waviness element; Indicates the first The spacing of each waviness element The number of waviness elements.

[0049] Compared with the prior art, the advantages and positive effects of the present invention are:

[0050] 1. For structural components such as holes and pins on the aircraft surface, scanning data is obtained by segmented scanning, and the overall waviness data is obtained by directly splicing the segments together. This creates conditions for accurate waviness evaluation of aircraft components or the entire aircraft surface with obstacles such as voids and welds.

[0051] 2. For measurement situations where there is no data model, the least squares algorithm combined with piecewise linear fitting is used to fit the point deviation data set, which simplifies the measurement difficulty.

[0052] 3. The surface waviness of the aircraft is determined by combining the derivative of the deviation data curve at the intersection with the horizontal axis and the distance between the peak and trough. Furthermore, the waviness elements are searched in two directions: from the start to the end of the evaluation length and from the end to the start of the evaluation length. This improves the ability and accuracy of searching for waviness elements.

[0053] 4. The coordinate measuring machine and its matching scanning probe provide high-precision three-dimensional point cloud data, which significantly improves measurement accuracy.

[0054] 5. High degree of automation reduces manual operation and improves measurement and analysis efficiency.

[0055] 6. Applicable to measuring the waviness of aircraft parts or the outer surface of the whole aircraft of different sizes and shapes.

[0056] Other features and advantages of the present invention will become clearer after reading the detailed description of the embodiments of the present invention in conjunction with the accompanying drawings. Attached Figure Description

[0057] Figure 1 These are the execution steps of the aircraft surface waviness measurement and analysis method proposed in this invention;

[0058] Figure 2 This is an example of an aircraft surface waviness measurement and analysis method shown in this embodiment of the invention;

[0059] Figure 3 This is a schematic diagram of the waviness calculation method in an embodiment of the present invention;

[0060] Figure 4 This is a schematic diagram of the waviness calculation method in an embodiment of the present invention. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] In the description of this invention, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0063] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0064] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0065] The method for measuring and analyzing aircraft surface waviness proposed in this invention, such as... Figure 1 As shown, it includes the following steps:

[0066] Measurement Preparation: Prepare a coordinate measuring machine (CMM) and a corresponding scanning probe (equipped with multi-directional styluses). Ensure the CMM has sufficient travel range and accuracy to cover the entire surface of the aircraft component under test. Prepare the aircraft component under test and clean its surface, ensuring it is free of dust, oil, and other impurities.

[0067] S1: Obtain scan data according to the planned scan path.

[0068] In this embodiment of the invention, the scanning methods include continuous scanning and segmented scanning.

[0069] Continuous scanning: refers to scanning data that can be obtained in one go when there are no voids, pins, welds, or other structures present during the scanning process.

[0070] Segmented scanning refers to scanning in situations where there are voids, pins, welds, or other structures that cannot be scanned in one go. In such cases, it is necessary to skip these structures, scan multiple segments of data, and finally stitch them together to obtain continuous scan data to evaluate the waviness.

[0071] S2: A set of point deviation data is obtained by fitting the scan data.

[0072] 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.

[0073] 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.

[0074] 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:

[0075] (1) Point definition: Let the set of scanned data points be: ,in, This represents the spatial coordinates of the i-th point.

[0076] (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:

[0077] ;

[0078] in, Indicates the first Segment start point index, Indicates the first Segment end point index.

[0079] (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:

[0080] ;

[0081] 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.

[0082] .

[0083] (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:

[0084] ;

[0085] Construct the set of theoretical points: ; Corresponds one-to-one with the original scan points.

[0086] (5) Calculate the point deviation: the distance along the point vector direction for each segment Define the set of point deviations as:

[0087] ;

[0088] in, Indicates the first The Euclidean distance between a point and its corresponding theoretical point.

[0089] The final set of point deviation data is obtained:

[0090] ;

[0091] 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.

[0092] 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:

[0093] (1) Obtain the set of scan data points.

[0094] Obtain the set of scanned data points ( (Point set): Number of scan points This may not equal the theoretical number of points. Furthermore, the distribution is not necessarily one-to-one.

[0095] (2) Find an optimal rigid body transformation (rotation and translation) that makes the scanned data point set With theoretical data point set Best match.

[0096] Define rigid body transformation as: ;in, It is a rotation matrix (satisfying) ), It is a translation vector.

[0097] Search This minimizes the following cost function:

[0098] ;in In the theoretical point set Midpoint The nearest point (i.e., nearest neighbor search).

[0099] This optimization problem is usually solved using the ICP (Iterative Closest Point) algorithm, or the SVD (Singular Value Decomposition) method can be used to solve the initial transformation.

[0100] (3) Iterate the nearest point.

[0101] 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 According to 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.

[0102] (4) Point deviation calculation.

[0103] 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.:

[0104] 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:

[0105] ;

[0106] The final set of point deviation data is obtained:

[0107] ;

[0108] 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.

[0109] S3: Calculate the corresponding arc length data based on the point coordinate data.

[0110] Before calculating the arc length data, the point deviation data is first denoised and outliers are removed: given a set of point deviation data Calculate the population mean and standard deviation of the data:

[0111] ;

[0112] .

[0113] Define normal data range For each observation Data points that meet any of the following conditions will be marked as outliers (data points that are "dirty" due to environmental factors such as oil stains and dust):

[0114] ;

[0115] In other words, any data point that is more than three times the sample standard deviation away from the data mean is considered an outlier.

[0116] Next, the point deviation data is filtered to remove low and high frequency components, retaining only the ripple component. The filtering operation uses a Gaussian weighting function:

[0117] ;

[0118] in, It is the distance from the center (maximum value) of the Gaussian weighted function. It depends on Gaussian weighted function, It is the cutoff wavelength. It is a constant of 0.4697, used at the cutoff wavelength. It provides 50% of the transmission characteristics.

[0119] After filtering the point deviation data D and the point coordinate data of each point cloud data P, the point spacing is first calculated. Then according to the point number Find the arc length from each point to the starting point. Finally, based on the point number The point deviation data and the point arc length data are matched one-to-one to form data pairs.

[0120] Specifically, for continuously scanned point cloud data (including continuously scanned data pieced together from segments), assuming a scanned segment has n points and the point spacing is:

[0121] ;

[0122] ,and hour .

[0123] According to the point number Find the arc length from each point to the starting point. , It is the first The point and the first The distances between points form a point arc length data set. .

[0124] The aforementioned point deviation data set was obtained. Two sets of data pairs are formed, one-to-one, based on point deviation and arc length. A graph is plotted with the horizontal axis representing arc length and the vertical axis representing point deviation, similar to... Figure 3 , Figure 4 As shown.

[0125] S4: Plot a graph with the horizontal axis as the arc length data and the vertical axis as the deviation data, and determine the surface waviness of the aircraft based on the plotted graph.

[0126] The vertical axis represents the point deviation data, and the horizontal axis represents the corresponding point arc length data, such as... Figure 3 and Figure 4 As shown.

[0127] A complete peak-valley curve profile is called a waviness element. In this invention, the derivative at the intersection of the point deviation data curve and the horizontal axis and the peak-valley distance discrimination method are used to determine the waviness element, and the waviness is calculated based on the waviness element.

[0128] Specifically, including:

[0129] 1. Evaluate from the starting point to the ending point of the evaluation length, such as... Figure 3 As shown, the X-axis line B passes through the curve from left to right. Figure 3 The direction indicated by D indicates the intersection point. If the derivative of the curve at the intersection point is positive, and the height of the next peak is greater than a set percentage (e.g., 10%) of the maximum amplitude tolerance zone, this intersection point is considered the starting point of a waviness feature. If this condition is not met, continue searching for the next point; if it is, it is taken as the starting point of the waviness feature. When determining the end point of the current waviness feature, continue considering the next intersection point where the derivative of the curve is positive, and the depth of the trough before this intersection point is greater than a set percentage of the maximum amplitude tolerance. If this condition is not met, continue searching for the next intersection point; if it is, it is taken as the end point of the waviness feature.

[0130] By using the derivative at the intersection of this curve and the horizontal axis, and the method of distinguishing between peaks and troughs, it is possible to... Figure 3 Several waviness elements are determined in the direction indicated by D.

[0131] 2. The evaluation should be conducted from the end point of the evaluation length to the starting point, such as... Figure 4 As shown, the X-axis line B crosses the curve from right to left. Figure 4In the direction indicated by D, if the derivative of the curve at the intersection point is negative, and the height of the subsequent peak is greater than the set percentage of the maximum amplitude tolerance zone, this intersection point is determined as the starting point of a waviness element. If this condition is not met, continue searching for the next intersection point; if it is met, it is taken as the starting point of the waviness element. When determining the end point of the current waviness element, continue considering if the derivative of the curve at the next intersection point is negative, and the depth of the trough point before this intersection point is greater than the set percentage of the maximum amplitude tolerance zone. If this condition is not met, continue searching for the next intersection point; if it is met, it is taken as the end point of the waviness element.

[0132] Let N represent the total number of waviness elements found according to steps 1 and 2 above. Indicates the first The height of each waviness element; Indicates the first The spacing between N waviness elements is used to evaluate the waviness of N waviness elements within a given length, calculated using the following formula:

[0133] .

[0134] The maximum waviness within the evaluation length is calculated using the following formula:

[0135] .

[0136] Export the calculated waviness data as tabular data or reports to facilitate subsequent quality control and improvement.

[0137] This invention also proposes an aircraft surface waviness measurement and analysis system, comprising:

[0138] A coordinate measuring machine and scanning probe are used to acquire scanning data according to a planned scanning path.

[0139] The waviness analysis device analyzes the waviness of aircraft components or the entire aircraft surface according to the aircraft surface waviness measurement and analysis methods given above, providing the aviation manufacturing industry with an efficient and reliable aircraft surface waviness detection solution and promoting the advancement of industry technology.

[0140] It should be noted that the above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method of aircraft surface waviness measurement and analysis, characterized in that, The method comprises: S1: obtaining scanning data according to a planned scanning path; wherein, for the hole, pin or weld structure existing on the outer surface of a large aircraft part or whole machine, segmented scanning is performed, and the segmented scanning is directly spliced to obtain continuous scanning data; S2: fitting point deviation data set based on the scanning data; when there is no data model or the scanning data is difficult to align with the data model, a least square algorithm and segmented straight line fitting are used to obtain the fitted point deviation data set; when there is a data model and the scanning data can align with the data model, the fitted point deviation data set is obtained through a fitting method; S3: calculating corresponding point arc length data based on the point deviation data and point coordinate data, comprising: obtaining the arc length of each point to the starting point according to the point serial number, and then forming data pairs by corresponding the point deviation data and the point arc length data according to the point serial number; 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 by combining the curve derivative at the intersection of the curve and the horizontal axis in the drawn graph and the peak value of the adjacent wave peak / trough; a complete wave peak / trough curve contour is a waviness element; The waviness is calculated as the maximum value of the absolute value of the difference between the measured profile and a straight line fitted to the measured profile over the evaluation length. The waviness is calculated as the maximum value of the absolute value of the difference between the measured profile and a straight line fitted to the measured profile over the evaluation length. represents the height of the waviness element; represents the pitch of the waviness element, is 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: Noise reduction processing and removing abnormal points on the point deviation data; Filtering the point deviation data to filter out low and high frequency components and retain the waviness component.

3. The aircraft surface waviness measurement and analysis method according to claim 1, wherein, In step S2, when there is no data model, comprising: Defining a set of scan data points wherein, represents the spatial coordinates of the i-th point; Dividing the whole scanning curve into several segments according to the y length; Using the least square method to fit a straight line for each segment to obtain a fitted data point set; For each scan point , find the segment it belongs to , then project on the corresponding fitted line to get a theoretical point , which corresponds to the scan point , construct the set of theoretical points distance along the point vector direction defines a set of point deviation data 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 along the entire scanning curve, is the number of segments.

4. 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 align with the data model, comprising: 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 points best match;​ Mapping the scan points into the coordinate system of the theoretical points: Set a 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 set of theoretical points : , construct a set of point pairs , from all the point pairs , compute a 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; Computing point deviations Obtaining a set of point deviation data wherein is the total number of points on the entire scan curve, all point deviation values arranged in the original scan point order.

5. The aircraft surface waviness measurement and analysis method according to claim 4, wherein, finding an optimal rigid body transformation that best matches a set of scan data points to a set of theoretical points best match, comprising:​ 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 .

6. 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 .

7. The aircraft surface waviness measurement and analysis method according to claim 1, wherein, S4 comprises waviness analysis from the starting point to the ending point of the curve of the drawn graph: Searching for the intersection of the curve of the drawn graph and the horizontal axis, and determining the starting point of a waviness element when the curve derivative at the intersection is positive and the height value of the next wave peak point is greater than the set percentage of the maximum amplitude tolerance, and determining the ending point of the current waviness element when the curve derivative at the next intersection is positive and the depth value of the trough point before the intersection is greater than the 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.

8. The aircraft surface waviness measurement and analysis method according to claim 1 or 7, characterized in that, S4 comprises waviness analysis from the ending point to the starting point of the curve of the drawn graph: Searching for the intersection of the curve of the drawn graph and the horizontal axis, and determining the starting point of a waviness element when the curve derivative at the intersection is negative and the height value of the next wave peak point is greater than the set percentage of the maximum amplitude tolerance, and determining the ending point of the current waviness element when the curve derivative at the next intersection is negative and the depth value of the trough point before the intersection is greater than the set percentage of the maximum amplitude tolerance.

9. An aircraft surface waviness measurement and analysis system, characterized by, Comprise: A three-coordinate scanning measuring machine and a scanning probe are used to obtain scanning data according to a planned scanning path; wherein, for the holes, pins or weld structures existing on the outer surface of a large aircraft part or whole machine, segmented scanning is performed, and the segmented scanning is directly spliced segment by segment to obtain continuous scanning data; A corrugation analysis device is used to analyze the corrugation of an aircraft surface according to the following steps: A point deviation data set is fitted based on the scanning data; when there is no data model or the scanning data is difficult to align with the data model, a least square algorithm and segmented straight line fitting are used to obtain the fitted point deviation data set; when there is a data model and the scanning data can align with the data model, a fitting method is used to obtain the fitted point deviation data set; Point arc length data corresponding to the point coordinate data is calculated, including: obtaining the arc length of each point to the starting point according to the point serial number, and then forming a data pair of the point deviation data and the point arc length data according to the point serial number; 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 corrugation of the aircraft surface is determined based on the drawn graph, including: Finding corrugation elements: the starting point and the ending point of the corrugation elements are determined by combining the curve derivative at the intersection of the curve in the drawn graph and the horizontal axis and the peak value of the adjacent wave peak / wave trough; a complete wave peak / wave trough curve contour is a corrugation element; The waviness is calculated as the maximum value of the absolute value of the difference between the measured profile and a straight line fitted to the measured profile over the evaluation length. The waviness is calculated as the maximum value of the absolute value of the difference between the measured profile and a straight line fitted to the measured profile over the evaluation length. represents the height of the waviness element; represents the pitch of the waviness element, is the number of waviness elements.

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