A scanning planning method for aircraft shape based on multi-objective optimization

Abstract

The present invention relates to the field of aircraft shape detection technology, and solves the technical problems of the prior art in scanning aircraft shapes, such as scanning blind spots, uneven path coverage, and high repetitive scanning rates. In particular, it relates to a method for scanning planning of the entire aircraft shape based on multi-objective optimization, which first imports aircraft digital model information, scanning and automation equipment parameter information, and measurement field and control field information; performs local curvature adaptive discretization on the input aircraft digital model; then establishes multiple constraints; constructs a scanning path planning task based on a multi-objective optimization function, and solves to obtain a globally optimal parameter set; performs path redundancy detection and simplification; and finally realizes scanning planning of large-size aircraft shapes based on multi-objective optimization. The present invention adopts a variety of information as input, comprehensively considers factors such as efficiency, coverage, and accuracy, and combines a multi-objective optimization method to realize the planning of scanning the entire aircraft shape, thereby improving the efficiency and data quality of scanning the aircraft shape.

Description

Technical Field

[0001] The invention relates to the technical field of aircraft shape detection, and in particular to a whole aircraft shape scanning planning method based on multi-objective optimization. Background Art

[0002] Aircraft assembly is the core link in the aircraft production process. The assembly quality directly affects the aircraft's aerodynamic performance and structural stability, and aircraft shape detection is a key step in the aircraft assembly process. The current main inspection method relies on manual use of 3D scanning equipment to obtain point cloud data for analysis, but this detection method has problems such as low automation, slow speed, and incompleteness. The current aircraft shape detection method can no longer meet the modern aviation industry's requirements for high-precision, high-efficiency, and full coverage of aircraft detection.

[0003] In order to solve the above shortcomings and improve scanning efficiency and the quality of scanned data, it is necessary to plan the scanning task before scanning the aircraft shape. However, due to the large size of the aircraft target, complex surface curvature, and significant differences in reflectivity and detail requirements in different areas, scanning path planning faces a series of technical challenges, such as scanning blind areas, uneven path coverage, and high repetitive scanning rates. Therefore, achieving efficient, comprehensive, and reasonable scanning path planning becomes the key to ensuring data quality. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides an aircraft shape scanning planning method based on multi-objective optimization, which solves the technical problems of the prior art in scanning aircraft shape, such as scanning blind spots, uneven path coverage and high repeated scanning rate.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for scanning and planning the whole aircraft shape based on multi-objective optimization, the method comprising the following steps:

[0006] S1. Obtain aircraft digital model information , scanning and automation equipment parameter information , measurement field and control field information ;

[0007] S2. Aircraft digital model information Perform local curvature adaptive discretization to obtain the digital model discrete point set ;

[0008] S3, based on digital model discrete point set and scanning and automation equipment parameter information , measurement field and control field information , establish multiple constraints for the task of 3D scanner to scan the whole aircraft shape;

[0009] S4. Under multiple constraints, construct a comprehensive mathematical optimization model based on multi-objective optimization scanning path planning tasks, and solve to obtain the global optimal scanning position and scan path combination ;

[0010] S5. Globally optimal scanning position and scan path combination Perform path redundancy detection and simplification to obtain the final set of scanning stations and scan path collection ;

[0011] S6. According to the scanning station collection and scan path collection Realize scanning planning and visualization of large-scale aircraft shapes.

[0012] Furthermore, in step S2, the specific process includes the following steps:

[0013] S21, aircraft digital model information The grid file is converted into a point set and each point is calculated The covariance matrix and normal vector of , and through the quadratic polynomial Perform local surface fitting, the expression is:

[0014] ;

[0015] In the formula, Indicates that the variable is The function of For point The coordinates of are quadratic polynomials that form a plane The polynomial coefficients of ;

[0016] S22. Establish an error square sum objective function, and solve the polynomial coefficients by minimizing the error square sum. The expression of the error square sum objective function is:

[0017] ;

[0018] In the formula, is the weight factor; is the gradient of the fitted surface, used for the normal vector To constrain the directionality of For point points in the neighborhood of ; Respectively indicate points Neighborhood of; Indicate point The coordinates of

[0019] S23, sampling decisions are made for each point in the point set, if the point exceeds the threshold, it is deleted, otherwise it is retained and a representative point set of the shape is generated;

[0020] S24. Smoothly optimize the representative point set of the shape and finally obtain the optimized point cloud, which is the digital model discrete point set , the expression of smooth optimization is:

[0021] ;

[0022] In the formula, and is the smoothing factor; is the center of the smoothed neighborhood point, and the smoothed point set is ; is the point where the shape representative points are concentrated; For point Points in the neighborhood; is the smoothed point.

[0023] Furthermore, in step S3, the multiple constraints are:

[0024] Points on the surface of the object to be measured To raster 3D scanner Projection lens connection and the unit normal vector of the point The angle should be smaller than the constraint angle , the constraints are:

[0025] ;

[0026] The field of view is limited to:

[0027] ;

[0028] Depth of field is the range of data acquisition, with an upper limit of , the lower limit is , then the constraints are:

[0029] ;

[0030] in, is the initial field of view; is the depth of field; Indicates the point on the surface of the object to be measured The unit normal vector of Represents raster 3D scanner The bisector of the outermost fringes of the projected grating.

[0031] Furthermore, in step S4, the specific process includes the following steps:

[0032] S41. Model the scanning accuracy relationship to obtain the accuracy loss function , and the accuracy loss function Minimize the accuracy loss to determine the scanning accuracy optimization function ,in:

[0033] Accuracy loss function The expression is:

[0034] ;

[0035] In the formula, is the initial distance error; is the distance-related error coefficient; is the distance from the laser emission point to the target; is the surface reflection characteristic error coefficient; is the horizontal angle error and distance relationship; is the angular error and distance in the vertical direction relationship; is the angle between the incident angle and the normal of the surface to be measured; It is a very small parameter to avoid the singularity problem caused by the denominator being zero in the formula;

[0036] S42. Model the scanning path problem to obtain the scanning path length at the current position , and according to the scanning path length Determine the scanning path optimization function by maximizing scanning efficiency ,in:

[0037] Scan path length The expression is:

[0038] ;

[0039] In the formula, and is the distance between adjacent points; , is the scanning order, the path must cover all the points covered by the current station; Indicates the number of points in the scan path;

[0040] S43. Constructing a single-station scanning coverage evaluation function , and based on the single-station scanning coverage evaluation function Maximize coverage and determine the scan coverage optimization function ,in:

[0041] Evaluation function of single-station scanning coverage The expression is:

[0042] ;

[0043] In the formula, represents the intersection of all scan coverage areas; A set of path points that need to be passed for scanning;

[0044] S44. Optimize function according to scanning accuracy , Scan path optimization function and scan coverage optimization function A comprehensive mathematical optimization model is established, and the expression is:

[0045] ;

[0046] In the formula, Indicates a negative value;

[0047] S45, using the weighted summation method to transform the multi-objective optimization scanning path planning task into a single objective Solve and finally obtain the global optimal scanning position and scan path combination .

[0048] Furthermore, the scanning accuracy optimization function The expression is:

[0049] ;

[0050] In the formula, is the number of all points scanned;

[0051] Scan path optimization function The expression is:

[0052] ;

[0053] In the formula, is the number of stations; and The weights of the moving energy consumption and scanning energy consumption of the device; The time taken to scan the current station; Indicates the total length of the scan path;

[0054] Scan coverage optimization function The expression is:

[0055] ;

[0056] In the formula, The area to be scanned is the entire shape of the aircraft.

[0057] Further, in step S45, a single target The expression is:

[0058] ;

[0059] In the formula, is the weight of the respective goal; The total number of point clouds scanned at the current position.

[0060] Furthermore, in step S5, the specific process includes the following steps:

[0061] S51, detect the mean of each path point and the path points within the K neighborhood And set it as the threshold, when any path point is less than the mean The redundant points are put into the set Otherwise, no processing is done;

[0062] S52, for the redundant point set Check if the number of path points that can be replaced by other path points at the current scanning station is higher than the threshold , then the current scanning station is a redundant station;

[0063] S53, repeat steps S51-S52 to obtain the final scanning station set and scan path collection .

[0064] By means of the above technical solution, the present invention provides an aircraft shape scanning planning method based on multi-objective optimization, which has at least the following beneficial effects:

[0065] 1. The present invention uses a variety of information as input, comprehensively considers factors such as efficiency, coverage, accuracy, etc., and combines a multi-objective optimization method to realize the planning of the aircraft shape scanning, improve the efficiency and data quality of the aircraft shape scanning, thereby realizing efficient, comprehensive and reasonable scanning path planning.

[0066] 2. The present invention can ensure the quality and reliability of scanned data by establishing multiple constraints, thereby constraining the scanning posture to obtain reliable high-quality data.

[0067] 3. The present invention can optimize based on the generated scanning positions and scanning paths, delete redundant path points and redundant positions, reduce the number of movements and time during the scanning process, thereby improving the overall scanning efficiency, and reduce the error accumulation during the scanning process, ensuring that the scanning results are more accurate and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0069] Figure 1 It is a schematic diagram of the digital model information of the aircraft in the present invention;

[0070] Figure 2 It is a schematic diagram of the aircraft digital model information after being discretized in the present invention;

[0071] Figure 3 A schematic diagram of establishing multiple constraints in the present invention;

[0072] Figure 4 A visualized schematic diagram of the station position in the aircraft shape scanning planning result in the present invention;

[0073] Figure 5 It is a visualization diagram of the path points in the aircraft shape scanning planning result in the present invention. DETAILED DESCRIPTION

[0074] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific implementation methods, so that the implementation process of how the present application uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly.

[0075] Please refer to Figure 1-Figure 5 This embodiment proposes a method for planning the scanning of the entire aircraft shape based on multi-objective optimization. By taking various information as input, comprehensively considering factors such as efficiency, coverage, and accuracy, and combining the multi-objective optimization method, the planning of the scanning of the entire aircraft shape is realized, thereby improving the efficiency and data quality of the scanning of the aircraft shape. The method includes the following steps:

[0076] S1. Obtain aircraft digital model information , scanning and automation equipment parameter information , measurement field and control field information , the aircraft digital model information obtained in this embodiment like Figure 1 In this embodiment, the digital model file can be directly obtained from the aircraft manufacturer, which usually includes the CAD model and related technical parameters, or access to professional databases in the aviation field, which may contain detailed three-dimensional models and parameters of the aircraft. You can contact the manufacturer of the scanning equipment and automation equipment directly to obtain detailed technical specifications and parameters of the equipment, or consult the technical manual or user guide of the equipment, which usually contains detailed parameters and instructions for use of the equipment. You can refer to the planning documents of the measurement and control fields, which usually contain information such as the layout, dimensions, equipment configuration, etc. of the site, or conduct on-site inspections of the measurement and control fields to obtain more intuitive and detailed information.

[0077] In this embodiment, the aircraft digital model information provides an accurate three-dimensional model of the aircraft design, which can be used as a benchmark for the scanning results, and includes various parameters and specifications of the aircraft design, which helps to ensure the accuracy of the scanning results during the scanning process. The parameter information of the scanning equipment determines its scanning accuracy, speed, range and other performances. The ability to select the appropriate scanning equipment according to the size and shape of the aircraft is the key to ensuring the scanning quality. It also determines the various settings in the scanning process, such as scanning speed, resolution, light source intensity, etc., which can directly affect the accuracy and clarity of the scanning results. The layout, size and equipment configuration of the measurement field and the control field are crucial for the smooth progress of the scanning process. Reasonable site planning can ensure that the scanning equipment has enough space and conditions to work. The information of the control field includes the control requirements of environmental factors such as temperature, humidity, and light. These factors directly affect the stability and accuracy of the scanning results and need to be strictly controlled during the scanning process. Therefore, the aircraft digital model information, scanning and automation equipment parameter information, measurement field and control field information play a vital role in the scanning of the aircraft shape. They together constitute the basis and guarantee of the scanning process and ensure the accuracy and reliability of the scanning results.

[0078] S2. Aircraft digital model information Perform local curvature adaptive discretization and obtain Figure 2 The digital analog discrete point set shown The specific process includes the following steps:

[0079] S21, aircraft digital model information The grid file is converted into a point set and each point is calculated The covariance matrix and normal vector of , and through the quadratic polynomial Perform local surface fitting, the expression is:

[0080] ;

[0081] In the formula, Indicates that the variable is The function of For point The coordinates of are quadratic polynomials that form a plane The polynomial coefficients of ;

[0082] Among them, due to the aircraft digital model information is a grid in STL format. A grid is a patch composed of points. Therefore, the existing method is used to discretize the patch into a point set. , within its radius neighborhood, the local geometric distribution of the point is described by calculating the covariance matrix of the neighborhood points. The covariance matrix is ​​defined as:

[0083] ;

[0084] In the formula, are the coordinates of the neighborhood points; is the centroid of the neighborhood points; N is the number of points in the neighborhood; by performing eigenvalue decomposition on the covariance matrix, the eigenvector corresponding to the minimum eigenvalue is the normal vector of the current point .

[0085] S22. Establish an error square sum objective function, and solve the polynomial coefficients by minimizing the error square sum. The expression of the error square sum objective function is:

[0086] ;

[0087] In the formula, is the weight factor; is the gradient of the fitted surface, used for the normal vector To constrain the directionality of For point points in the neighborhood of ; Respectively indicate points Neighborhood of; Indicate point The coordinates of .

[0088] S23. Sampling decisions are made for each point in the point set. If the number exceeds the threshold, it is deleted. Otherwise, it is retained and a representative point set of the shape is generated. Specifically, if the number of point clouds within the radius neighborhood of the point exceeds the average density per unit surface of the entire point cloud, then this point is removed. In this embodiment, the threshold is the average density, which needs to be set according to actual parameters and is not limited here.

[0089] S24. Smoothly optimize the representative point set of the shape and finally obtain the optimized point cloud, which is the digital model discrete point set , the expression of smooth optimization is:

[0090] ;

[0091] In the formula, and is the smoothing factor; is the center of the smoothed neighborhood point, and the smoothed point set is ; is the point where the shape representative points are concentrated; For point Points in the neighborhood; is the smoothed point.

[0092] S3, based on digital model discrete point set and scanning and automation equipment parameter information , measurement field and control field information , establish multiple constraints for the task of 3D scanner scanning the entire aircraft shape. Figure 3 As shown, and Respectively represent the point on the surface of the object to be measured and the unit normal vector of the point, Represents a raster 3D scanner, Represents the bisector of the outermost stripes of the projected grating. The following constraints need to be met when the 3D scanner works:

[0093] Through constraint analysis, raster 3D scanner The most ideal state for topography measurement is that the beam projected by the scanner coincides with the unit normal vector of a point on the object to be measured, but this ideal state is difficult to achieve. In order to obtain a better scanning effect, there is generally an angle between the two. , and this angle cannot exceed the limit value. To raster 3D scanner Projection lens connection and the unit normal vector of the point The angle should be smaller than the constraint angle , the constraints are: The field of view can be limited to , the depth of field is the range of data acquisition, and the upper limit is , the lower limit is ,but .

[0094] In the formula, is the initial field of view; For depth of field, it is a raster 3D scanner The range of data that can be acquired, expressed as a raster 3D scanner The range to the farthest available data minus the range to the nearest available data.

[0095] In this embodiment, the establishment of multiple constraint conditions can ensure the quality of the scanned data and the reliability of the data, thereby constraining the scanning posture to obtain reliable high-quality data.

[0096] S4. Under multiple constraints, construct a comprehensive mathematical optimization model based on multi-objective optimization scanning path planning tasks, and solve to obtain the global optimal scanning position and scan path combination The specific process includes the following steps:

[0097] S41. Model the scanning accuracy relationship to obtain the accuracy loss function , and the accuracy loss function Minimize the accuracy loss to determine the scanning accuracy optimization function ,in:

[0098] Accuracy loss function The expression is:

[0099] ;

[0100] In the formula, is the initial distance error; is the distance-related error coefficient; is the distance from the laser emission point to the target; is the surface reflection characteristic error coefficient; is the horizontal angle error and distance relationship; is the angular error and distance in the vertical direction relationship; is the angle between the incident angle and the normal of the surface to be measured; It is a very small parameter to avoid the singularity problem caused by the denominator being zero in the formula;

[0101] Scanning accuracy optimization function The expression is:

[0102] ;

[0103] In the formula, is the total number of points scanned.

[0104] S42. Model the scanning path problem to obtain the scanning path length at the current position , and according to the scanning path length Determine the scanning path optimization function by maximizing scanning efficiency ,in:

[0105] Scan path length The expression is:

[0106] ;

[0107] In the formula, and is the distance between adjacent points; , is the scanning order, the path must cover all the points covered by the current station; Indicates the number of points in the scan path;

[0108] Scan path optimization function The expression is:

[0109] ;

[0110] In the formula, is the number of stations; and The weights of the moving energy consumption and scanning energy consumption of the device; The scanning time for the current station; Indicates the total length of the scan path.

[0111] S43. To accurately define the scan coverage, a single-station scan coverage evaluation function is constructed. , and based on the single-station scanning coverage evaluation function Maximize coverage and determine the scan coverage optimization function ,in:

[0112] Evaluation function of single-station scanning coverage The expression is:

[0113] ;

[0114] In the formula, represents the intersection of all scan coverage areas; is a set of path points that need to be scanned; in this embodiment, It refers to the range of the point cloud covered by the current station scan, which is composed of several points, that is, the path points that need to be passed by the scan. That is, all the path points that need to be scanned;

[0115] Scan coverage optimization function The expression is:

[0116] ;

[0117] In the formula, The area to be scanned is the entire shape of the aircraft.

[0118] S44. Optimize function according to scanning accuracy , Scan path optimization function and scan coverage optimization function A comprehensive mathematical optimization model is established, and the expression is:

[0119] ;

[0120] In the formula, Indicates a negative value;

[0121] S45, using the weighted summation method to transform the multi-objective optimization scanning path planning task into a single objective Solve and finally obtain the global optimal scanning position and scan path combination , single target The expression is:

[0122] ;

[0123] In the formula, is the weight of the respective goal; The total number of point clouds scanned at the current position.

[0124] In this embodiment, firstly, the scanning accuracy optimization function is constructed respectively , Scan path optimization function and scan coverage optimization function Then, multiple objective optimization functions are transformed into a single objective optimization function by weighted summation, and the global optimal scanning position is obtained by solving the single objective optimization function. and scan path combination .

[0125] S5. Globally optimal scanning position based on graph simplification algorithm and scan path combination Perform path redundancy detection and simplification to obtain the final set of scanning stations and scan path collection Graph simplification algorithm is an important field in graph theory, which aims to more efficiently represent and analyze graph structures by removing redundant information or reducing the complexity of graphs. Graphs are simplified using specific algorithmic rules or strategies to reduce the number of nodes, edges or complexity of graphs while retaining the key information and structural features of graphs as much as possible. Its purpose usually includes improving the readability of graphs, reducing computational complexity, optimizing storage and transmission efficiency, etc. The specific process includes the following steps:

[0126] S51, detect the mean of each path point and the path points within the K neighborhood And set it as the threshold, when any path point is less than the mean The redundant points are put into the set Otherwise, no processing is done;

[0127] Among them, the station in the scanning station set is a position, and the path point is subordinate to the station, so the scanning path Corresponding scanning station The path point of . It is to calculate all the path points, search for the number of all points within a radius r from each point as the center, add them up and then find the average.

[0128] S52, for the redundant point set The detection is performed by setting up redundant judgment conditions. The multiple constraints in the redundant judgment condition step S3 are redundant points. If the number of path points that can be replaced by path points under other scanning stations at the current scanning station is higher than the threshold , then the current scanning station is a redundant station; where the threshold 95% of the total number of path points before redundancy optimization for this station.

[0129] S53, repeat steps S51-S52 to obtain the final scanning station set and scan path collection .

[0130] This embodiment can optimize based on the generated scanning positions and scanning paths, delete redundant path points and redundant positions, and reduce the number of movements and time during the scanning process, thereby improving the overall scanning efficiency, which is particularly important for application scenarios that require a large number of scanning tasks to be completed quickly. Since each movement of the scanning device may introduce a certain error, it is possible to reduce the accumulation of errors during the scanning process, ensure that the scanning results are more accurate and reliable, and reduce the steps that the operator needs to monitor and manage, which helps to reduce the complexity of the operation and improve the operator's work efficiency.

[0131] S6. According to the scanning station collection and scan path collection Realize scanning planning and visualization of large-scale aircraft shapes. Figure 4 and Figure 5 As shown in the figure, by visualizing the discretized point cloud and path planning information, the relevant planning effects can be intuitively understood, so that corresponding adjustments can be made according to the situation.

[0132] Those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, so the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0133] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the above embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0134] The above implementation methods have been described in detail. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for scanning and planning the whole aircraft shape based on multi-objective optimization, characterized in that: The method comprises the following steps: S1. Obtain aircraft digital model information , scanning and automation equipment parameter information , measurement field and control field information ; S2. Aircraft digital model information Perform local curvature adaptive discretization to obtain the digital model discrete point set ; S3, based on digital model discrete point set and scanning and automation equipment parameter information , measurement field and control field information , establish multiple constraints for the task of 3D scanner to scan the whole aircraft shape; S4. Under multiple constraints, construct a comprehensive mathematical optimization model based on multi-objective optimization scanning path planning tasks, and solve to obtain the global optimal scanning position and scan path combination ; S5. Globally optimal scanning position and scan path combination Perform path redundancy detection and simplification to obtain the final set of scanning stations and scan path collection ; S6. According to the scanning station collection and scan path collection Realize scanning planning and visualization of large-scale aircraft shapes.

2. The method for scanning and planning the entire aircraft shape according to claim 1, characterized in that: In step S2, the specific process includes the following steps: S21, aircraft digital model information The grid file is converted into a point set and each point is calculated The covariance matrix and normal vector of , and through the quadratic polynomial Perform local surface fitting, the expression is: ； In the formula, Indicates that the variable is The function of For point The coordinates of are quadratic polynomials that form the plane The polynomial coefficients of ; S22. Establish an error square sum objective function, and solve the polynomial coefficients by minimizing the error square sum. The expression of the error square sum objective function is: ； In the formula, is the weight factor; is the gradient of the fitted surface, used for the normal vector To constrain the directionality of For point points in the neighborhood of ; Respectively indicate points Neighborhood of; Indicate point The coordinates of S23, sampling decisions are made for each point in the point set, if the point exceeds the threshold, it is deleted, otherwise it is retained and a representative point set of the shape is generated; S24. Smoothly optimize the representative point set of the shape and finally obtain the optimized point cloud, which is the digital model discrete point set , the expression of smooth optimization is: ； In the formula, and is the smoothing factor; is the center of the smoothed neighborhood point, and the smoothed point set is ; is the point where the shape representative points are concentrated; For point Points in the neighborhood; is the smoothed point.

3. The aircraft overall shape scanning planning method according to claim 1, characterized in that: In step S3, the multiple constraints are: Points on the surface of the object to be measured To raster 3D scanner Projection lens connection and the unit normal vector of the point The angle should be smaller than the constraint angle , the constraints are: ； The field of view is limited to: ； Depth of field is the range of data acquisition, with an upper limit of , the lower limit is , then the constraints are: ； in, is the initial field of view; is the depth of field; Indicates the point on the surface of the object to be measured The unit normal vector of Represents raster 3D scanner The bisector of the outermost fringes of the projected grating.

4. The method for scanning and planning the entire aircraft shape according to claim 1, characterized in that: In step S4, the specific process includes the following steps: S41. Model the scanning accuracy relationship to obtain the accuracy loss function , and the accuracy loss function Minimize the accuracy loss to determine the scanning accuracy optimization function ,in: Accuracy loss function The expression is: ； In the formula, is the initial distance error; is the distance-related error coefficient; is the distance from the laser emission point to the target; is the surface reflection characteristic error coefficient; is the horizontal angle error and distance relationship; is the angular error and distance in the vertical direction relationship; is the angle between the incident angle and the normal of the surface to be measured; It is a very small parameter to avoid the singularity problem caused by the denominator being zero in the formula; S42. Model the scanning path problem to obtain the scanning path length at the current position , and according to the scanning path length Determine the scanning path optimization function by maximizing scanning efficiency ,in: Scan path length The expression is: ； In the formula, and is the distance between adjacent points; , is the scanning order, the path must cover all the points covered by the current station; Indicates the number of points in the scan path; S43. Constructing a single-station scanning coverage evaluation function , and based on the single-station scanning coverage evaluation function Maximize coverage and determine the scan coverage optimization function ,in: Evaluation function of single-station scanning coverage The expression is: ； In the formula, represents the intersection of all scan coverage areas; A set of path points that need to be passed for scanning; S44. Optimize function according to scanning accuracy , Scan path optimization function and scan coverage optimization function A comprehensive mathematical optimization model is established, and the expression is: ； In the formula, Indicates a negative value; S45, using the weighted summation method to transform the multi-objective optimization scanning path planning task into a single objective Solve and finally obtain the global optimal scanning position and scan path combination .

5. The method for scanning and planning the entire aircraft shape according to claim 4, characterized in that: The scanning accuracy optimization function The expression is: ； In the formula, is the number of all points scanned; Scan path optimization function The expression is: ； In the formula, is the number of stations; and The weights of the moving energy consumption and scanning energy consumption of the device; The scanning time for the current station; Indicates the total length of the scan path; Scan coverage optimization function The expression is: ； In the formula, The area to be scanned is the entire shape of the aircraft.

6. The method for scanning and planning the entire aircraft shape according to claim 4, characterized in that: In step S45, a single target The expression is: ； In the formula, is the weight of the respective goal; It is the total number of point clouds scanned at the current position.

7. The method for scanning and planning the entire aircraft shape according to claim 1, characterized in that: In step S5, the specific process includes the following steps: S51, detect the mean of each path point and the path points within the K neighborhood And set it as the threshold, when any path point is less than the mean The redundant points are put into the set Otherwise, no processing is done; S52, for the redundant point set Check if the number of path points that can be replaced by other path points at the current scanning station is higher than the threshold , then the current scanning station is a redundant station; S53, repeat steps S51-S52 to obtain the final scanning station set and scan path collection .

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