A method for measuring the leading and trailing edge parameters of a high-precision curved surface thin sheet

Abstract

The present invention discloses a method for measuring the leading and trailing edge parameters of a high-precision curved surface thin sheet, and the method steps are as follows: camera calibration; point cloud slicing; principal component analysis; blade contour extraction; leading and trailing edge parameter calculation. The beneficial effects of the present invention are as follows: The present invention uses a high-precision calibration block to calibrate the camera. By comparing the size of the point cloud of the camera calibration block with the size of the actual calibration block, the system accuracy of the camera is guaranteed to be 10 μm. On the premise of ensuring the measurement accuracy of aviation blades, the measurement efficiency of aviation blades can be greatly improved; The method of iterative search is used to search for the leading and trailing edge radii and the center of the circle of the blade, and the parameter calculation accuracy is extremely high in the algorithm; There is no need for manual intervention, and the hardware platform and the algorithm platform are well integrated to realize the "production line" detection of aviation blades.

Description

Technical Field

[0001] The invention relates to the technical field of engine blade measurement, in particular to a method for measuring leading and trailing edge parameters of a high-precision curved thin sheet. Background Art

[0002] Aircraft engines are one of the important deployments of the current national manufacturing power strategy. With its high standards and high positioning requirements, aviation machinery products have played a role in lifting the weight in all previous aerospace launches and space flights with the advantages of high precision and high reliability. In recent years, with the increasing difficulty of space missions and the continuous improvement of flight indicators, the structures of various mechanical components have become more and more complex, among which some irregular curved surface contour parts are particularly prominent. How to measure irregular curved surface contours and obtain accurate results will become an important support for improving product reliability. Taking aviation blades as an example, the present invention elaborates on a method for measuring the leading and trailing edge parameters of irregular curved surface sheets.

[0003] At present, the manufacturing of aircraft engine blades usually cannot achieve zero-residue processing in one step. It is necessary to carry out precision measurement after the previous processing, and the measurement results will further guide the subsequent processing. During the maintenance of engine blades, it is also necessary to measure the blades to extract the surface parameters such as the amount of blade wear, so as to ensure the repair accuracy. In addition, as a core component of an aircraft engine, the aircraft engine blade is also a typical high-precision free-form surface part. Its precise physical size parameters directly affect the performance of the aircraft engine. Inside the aircraft engine, there is absolutely no slight assembly size error allowed, and the internal structure of the aircraft engine is very complex. Moreover, the blade is one of the parts with the worst working environment inside the engine. Because the blade has to work continuously for a long time in a harsh and complex environment, if there is an error in the design size of the blade, it is very easy to cause the blade to be subjected to uneven cyclic stress during normal operation of the engine, resulting in the risk of fracture and failure. All these put forward higher requirements for the detection of aircraft engine blades. At present, there are a series of difficulties in the detection of aircraft blades, including: 1) High measurement accuracy requirements. The blade profile measurement accuracy directly affects the blade manufacturing accuracy. Usually, the measurement accuracy is required to reach 0.01mm, or even 0.005mm, that is, it is required to be within 10μm. 2) High measurement efficiency is required. Since blades are mass-produced parts with tens of thousands of production quantities, improving measurement efficiency is a very important task. 3) High measurement reliability is required. The blade measurement data processing results must accurately reflect the actual state of the blade, so as to ensure that the manufacturing quality of the blade meets the requirements.

[0004] Currently, to meet the high-precision measurement requirements of aviation blades, the traditional measurement method for aviation blades uses a coordinate measuring machine to perform surface measurement on aviation blades. The coordinate measuring machine measurement method is a high-precision three-dimensional space detection method. It mainly obtains the cross-section parameters and shape errors of the blade by measuring the coordinate values of each measurement point on the blade contour and then using some modeling and data analysis software. As a contact measurement method, the coordinate measuring machine is currently one of the most accurate means for blade detection. However, when the coordinate measuring machine is measuring, it needs to contact the surface of the object to be measured, which will inevitably cause certain damage to the blade. Moreover, the coordinate measuring machine has a small measurement range, a large volume and is not easy to disassemble, is greatly limited by the size of the workpiece to be measured, and the speed of generating blade point clouds is slow. All these pose challenges to the rapid and accurate measurement of blade parameters. Traditional measurement of aviation blades usually uses a coordinate measuring machine, which has a complex process, a long operation time, and is relatively dependent on the proficiency of instrument operators. All these pose challenges to the efficient and high-precision measurement of aviation blades.

[0005] To ensure the high quality and high performance requirements of aeroengine blades, it is necessary to quickly and accurately measure the size parameters of aeroengines.

[0006] Therefore, in view of the above problems, a method for measuring the leading and trailing edge parameters of a high-precision curved thin sheet is proposed. Summary of the Invention

[0007] Aiming at the deficiencies existing in the above-mentioned prior art, the purpose of the present invention is to provide a method for measuring the leading and trailing edge parameters of a high-precision curved thin sheet, which can improve the measurement efficiency of aviation blades while ensuring the measurement accuracy.

[0008] A method for measuring the leading and trailing edge parameters of a high-precision curved thin sheet, the method steps are as follows:

[0009] Step 1: Camera calibration;

[0010] Step 2: Point cloud slicing;

[0011] Step 3: Principal component analysis;

[0012] Step 4: Blade contour extraction;

[0013] Step 5: Leading and trailing edge parameter calculation.

[0014] Among them, the camera calibration method in step 1 is as follows: a. Use a high-precision calibration block to calibrate two structured light cameras placed opposite to each other. Place the calibration block in the middle of the two structured light cameras to ensure that the calibration block is within the measurement range of the two structured light cameras; b. Use the structured light cameras to collect the point cloud data of the calibration block; c. Then use the Iteration Closest Point (ICP) algorithm to match the two frames of point clouds of the calibration block to the same coordinate frame. The obtained rotation matrix and translation matrix are the calibration parameters of the two structured light cameras.

[0015] Among them, the point cloud slicing in step 2 slices the blade point cloud along the axial direction, processes the sliced point cloud, and the leading and trailing edges of the sliced point cloud are the leading and trailing edges of the blade at the slicing position.

[0016] Among them, the Principle Component Analysis (PCA) in step 3 processes the cross-sectional point cloud, reduces the dimension of the point cloud in the three-dimensional space to the two-dimensional space, and reduces the complexity of data processing.

[0017] Among them, the blade contour extraction in step 4 converts the unordered point cloud into an ordered point cloud, and its steps are as follows:

[0018] S1. Two-dimensional convex hull construction: Use the Graham line scanning method to construct the two-dimensional convex hull of the blade cross-sectional point cloud, and extract the convex hull points. The remaining points are the concave hull points (candidate points);

[0019] S2. For each candidate point P C , traverse all adjacent vertices V i and V i+1 on the convex hull polygon, and calculate the distance d from P C to the line segment V i V i+1 according to the following formula;

[0020] S3. According to the principle of minimizing the distance from the point to the line segment, insert P C between the two adjacent vertices that make d the smallest, and update the polygon;

[0021] S4. Repeat the above steps until all candidate points are inserted into the polygon;

[0022]

[0023] In the formula, the magnitude of the ρ value represents the relative position distribution of P C and V i V i+1 , and i represents the point cloud index: if ρ ∈ (-∞, 0], it indicates that V i P C is on V i Vi+1 The projection on it falls on V i V i+1 on the left extension line of; if ρ ∈ (0, 1), it indicates that the projection falls on V i V i+1 on; if ρ ∈ [1, +∞), it indicates that the projection falls on V i V i+1 on the right extension line of; the calculation formula of the ρ value is:

[0024]

[0025] Among them, for the calculation of the leading and trailing edge parameters in step 5, before measuring the leading and trailing edge radii of the blade, it is necessary to fit the leading and trailing edge arcs of the blade. The steps for fitting the leading and trailing edge arcs of the blade are as follows:

[0026] (1) Determine a measurement point on the leading and trailing edge arcs as the seed point. After principal component analysis, the coordinate limit points must be located on the leading and trailing edge arcs. Search for the maximum and minimum points of the X coordinate or Y coordinate in the cross-sectional point cloud after contour extraction, which are the points on the leading edge arc and the trailing edge arc of the blade;

[0027] (2) Search for two measurement points in the front and rear directions of the limit point, and fit the arc according to the least squares method based on the data of these 5 measurement points;

[0028] (3) Calculate the distances from each measurement point to the fitted arc respectively. If all the distances are less than the given threshold, then add adjacent points in the front and rear directions in the measurement points to form a new arc fitting point set, and fit the arc according to the least squares method with the new point set; if the distance between the measurement point and the fitted arc is greater than the limit value, it is determined that the point does not belong to the leading and trailing edge arcs. If the point number belongs to the maximum (or minimum) index point in the arc fitting point set, then only adjacent points with the minimum (or maximum) index point can be added when adding measurement points later; if all the added measurement points exceed the limit value, then remove the newly added points and keep the original arc fitting point set;

[0029] (4) Repeat step (3). When the distances from the points with the maximum and minimum indices in the arc fitting point set to the fitted arc are both greater than the given threshold, the iterative process of step (3) ends at this time;

[0030] (5) Fit the arc according to the least squares method. The fitting result is the leading and trailing edge arcs of the blade, and the leading and trailing edge radii and the arc center coordinates of the blade can be obtained according to the fitting result.

[0031] Compared with the prior art, the beneficial effects of the present invention:

[0032] 1. The present invention calibrates the camera using a high-precision calibration block. By measuring the size of the point cloud of the camera calibration block and comparing it with the size of the actual calibration block, the system accuracy of the camera adopted by the present invention is guaranteed to be 10 μm. Therefore, on the premise of ensuring the measurement accuracy of the aviation blade, the measurement efficiency of the aviation blade can be greatly improved;

[0033] 2. In the present invention, an iterative search method is used to search for the leading and trailing edge radii and the center of the blade, and the algorithm has extremely high parameter calculation accuracy;

[0034] 3. The present invention does not require manual intervention, and preferably integrates the hardware platform and the algorithm platform to realize the "production line" detection of aviation blades. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a schematic diagram of a method for measuring the leading and trailing edge parameters of a high-precision aeroengine blade according to the present invention;

[0036] Figures 2 to 4 is a schematic diagram of the distance from a candidate point to a line segment according to the present invention;

[0037] Figure 5 is a schematic diagram of the three-dimensional point cloud of an aviation blade according to the present invention;

[0038] Figure 6 is a schematic diagram of the cross-section of an aviation blade according to the present invention;

[0039] Figure 7 is a detailed view of the leading edge circle of the cross-section according to the present invention;

[0040] Figure 8 is a detailed view of the trailing edge circle of the cross-section according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0042] As Figure 1 and in combination with Figures 2 to 8 shown, a method for measuring the leading and trailing edge parameters of a high-precision aeroengine blade, the method steps are as follows:

[0043] Step 1, camera calibration

[0044] Two structured light cameras are used to measure the two surfaces of the aviation blade respectively, and the three-dimensional point clouds of both sides of the blade can be obtained. However, the three-dimensional point clouds of the two blades are both in the three-dimensional rectangular coordinate system with the optical center of their respective cameras as the origin. The two frames of point clouds must be registered to the same coordinate frame. Calibrate two oppositely placed structured light cameras with a high-precision calibration block. Place the calibration block in the middle of the two structured light cameras to ensure that the calibration block is within the measurement range of the two structured light cameras. Use the structured light cameras to collect the point cloud data of the calibration block. Use the Iterative Closest Point (ICP) algorithm to match the two frames of point clouds of the calibration block to the same coordinate frame. The obtained rotation matrix and translation matrix are the calibration parameters of the two structured light cameras. Hereinafter, the calibration parameters of the structured light cameras are referred to as the external camera parameters. The size information of the high-precision calibration block can be calculated from the point cloud collected by the structured light camera. By comparing the size information with the actual manufacturing size of the high-precision calibration block, the accuracy of the entire data acquisition system can be obtained as 10 μm.

[0045] Step 2: Point cloud slicing

[0046] To simplify the calculation process of the parameters of the blade leading and trailing edges, slice the blade point cloud along the axial direction, process the sliced point cloud, and the leading and trailing edges of the obtained sliced point cloud are the leading and trailing edges of the blade at the slicing position. Common point cloud slicing algorithms often slice the point cloud in the coordinate axis direction. However, during the measurement of the aviation blade, the axial direction of the blade may not coincide precisely with the coordinate axis direction. Slice the point cloud according to the normal vector direction, and set the axial direction of the blade point cloud as the normal vector direction to obtain a more accurate blade cross-section.

[0047] Let there be a set of scattered points P = {p1, p2,..., p n}, p i = (x i , y i , z i ) ∈ R 3 , where R 3 represents the three-dimensional Euclidean space and i represents the point cloud index; then the coordinate range of the point set P is (x min , y min , z min ) ~ (x max , y max , z max ); the generation of the point cloud slice can be described as dividing the three-dimensional point cloud with a set of parallel planes in a given direction; assume there is a set of planes T with the normal vector pointing to the axial direction of the blade, and let z pitch be the thickness of the point cloud slice. Calculate the distance from each point in the blade point cloud to the plane T i . All points that satisfy the distance less than z pitch form a point cloud slice.

[0048] Step 3, Principal Component Analysis

[0049] The cross-sectional point cloud of the aviation blade is obtained by slicing the point cloud. Since the point cloud is in a three-dimensional rectangular coordinate system, considering that the cross-sectional point cloud has large variance components only in two directions, and in the other direction, due to the small slice thickness z pitch which is small and the variance component is close to zero, the principal component analysis (PCA) is used to process the cross-sectional point cloud, reducing the dimension of the point cloud in three-dimensional space to two-dimensional space and reducing the complexity of data processing.

[0050] Step 4, Blade Profile Extraction

[0051] For the scattered sliced point cloud after dimension reduction, since the scattered point cloud does not have an ordered topological structure, it brings extremely high complexity to subsequent algorithm processing. Therefore, a point cloud sorting algorithm based on the convex hull is adopted, as Figures 2 - 4 shown, to convert the unordered point cloud into an ordered point cloud; the specific steps are as follows:

[0052] (1) Two-dimensional convex hull construction: Use the Graham line scanning method to construct the two-dimensional convex hull of the blade cross-sectional point cloud, and extract the convex hull points. The remaining points are the concave hull points (candidate points);

[0053] (2) For each candidate point P C , traverse all adjacent vertices V i and V i+1 on the convex hull polygon, and calculate the distance d from P C to the line segment V i V i+1 according to the following formula;

[0054] (3) According to the principle of minimizing the distance from the point to the line segment, insert P C between the two adjacent vertices that make d the smallest, and update the polygon.

[0055] (4) Repeat the above steps until all candidate points are inserted into the polygon;

[0056]

[0057] In the formula, the magnitude of the ρ value represents the relative position distribution of P C and V i V i+1 , and i represents the point cloud index: if ρ ∈ (-∞, 0], it indicates that the projection of V i P C on V i V i+1 falls on V i Vi+1 On the left extension line of, as Figure 2 shown, the distance from the candidate point ρ ∈ (-∞, 0] to the line segment; if ρ ∈ (0, 1), it indicates that the projection falls on V i V i+1 above, as Figure 3 shown, the distance from the candidate point ρ ∈ (0, 1) to the line segment; if ρ ∈ [1, +∞), it indicates that the projection falls on V i V i+1 on the right extension line of, as Figure 4 shown, the distance from the candidate point ρ ∈ [1, +∞) to the line segment; the calculation formula of the ρ value is:

[0058]

[0059] Step Five, Leading and Trailing Edge Parameter Calculation

[0060] Before measuring the leading and trailing edge radii of the blade, it is necessary to fit the leading and trailing edge arcs of the blade; it is necessary to distinguish the measurement points belonging to the leading and trailing edge arcs in the blade section point cloud; after determining the measurement points of the leading and trailing edge arcs, the least squares circle fitting algorithm can be used to obtain the leading and trailing edge radii and the center coordinates of the arcs; the steps of fitting the leading and trailing edge arcs of the blade are as follows:

[0061] (1) Determine a measurement point located on the leading and trailing edge arcs as the seed point; after principal component analysis, the coordinate limit points must be located on the arcs of the leading edge and the trailing edge. Search for the maximum and minimum points of the X coordinate or Y coordinate in the section point cloud after contour extraction, which are the points on the leading edge arc and the trailing edge arc of the blade;

[0062] (2) Search for two measurement points in the front and rear directions of the limit point, and fit the arc based on the data of these 5 measurement points according to the least squares method;

[0063] (3) Calculate the distance from each measurement point to the fitted arc respectively. If all the distances are less than the given threshold, then add adjacent points in the front and rear directions in the measurement points to form a new arc fitting point set, and fit the arc according to the least squares method with the new point set; if the distance between the measurement point and the fitted arc is greater than the limit value, it is determined that the point does not belong to the leading and trailing edge arcs. If the point number belongs to the maximum (or minimum) index point in the arc fitting point set, then only the adjacent point with the minimum (or maximum) index point can be added when adding measurement points subsequently; if all the added measurement points exceed the limit value, then remove the newly added points and keep the original arc fitting point set;

[0064] (4) Repeat step (3). When the distances from the maximum and minimum index points in the arc fitting point set to the fitted arc are both greater than the given threshold, the iterative process of step (3) ends at this time;

[0065] (5) The least squares method is used to fit the circular arcs. The fitting results are the front and rear edge circular arcs of the blade. According to the fitting results, the front and rear edge radii of the blade and the coordinates of the circular arc center can be obtained.

[0066] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0067] 1. The present invention uses a high-precision calibration block to calibrate the camera. By comparing the measured size of the camera calibration block point cloud with the actual size of the calibration block, it is obtained that the system accuracy of the camera adopted by the present invention is guaranteed to be 10 μm. On the premise of ensuring the measurement accuracy of aviation blades, the measurement efficiency of aviation blades can be greatly improved.

[0068] 2. In the present invention, the iterative search method is used to search for the front and rear edge radii and the center of the circle of the blade, and the algorithm has extremely high parameter calculation accuracy.

[0069] 3. The present invention does not require manual intervention, and preferably integrates the hardware platform and the algorithm platform to realize the "production line" detection of aviation blades.

[0070] The present invention designs a structured light three-dimensional scanning measurement system according to the measurement model. As shown in the overall structural framework diagram of the system, it is composed of a hardware system and an algorithm system; this system is based on a surface structured light camera for measurement; as the measurement device base, two surface structured light cameras placed opposite to each other are installed on the base, so that the two surface structured light cameras can irradiate both sides of the aviation blade to the greatest extent and communicate with the computer; the aviation blade is placed in the feeding box, and the robot is used to grab and send the aviation blade to the specified position for measuring the point cloud on the blade surface. The computer records the point cloud data of the blade surface returned by the structured light camera and processes it, and finally obtains the front and rear edge parameter information of any position of the aviation blade.

[0071] In the present invention, by arranging two line-scan cameras and adopting the opposed photography method to collect the three-dimensional point cloud data of the aviation blade surface, at the same time, each camera is connected to the same data processing unit; the pose transformation parameters between the two cameras are obtained through the precise calibration of the structured light camera, so as to register the point cloud data obtained by the two cameras under the same coordinate frame, and then the point cloud is sliced according to its normal vector direction by using the algorithm to obtain the cross-sectional point cloud data of the aviation blade. By sorting the cross-sectional point cloud, the surface contour of the aviation blade is extracted, and finally the front and rear edge parameters of the aviation blade contour are calculated iteratively through the algorithm in this patent.

[0072] After the present invention uses the structured light camera to collect the point cloud data of the aviation blade surface, through the pre-calibrated external parameters of the camera, the point cloud data collected by the cameras at two different workstations are registered under the same coordinate frame to obtain the point cloud of the aviation blade surface as Figure 5As shown; in order to obtain the leading and trailing edge parameters of the aviation blade, the obtained blade surface point cloud data is sliced along the axial direction of the blade (the cross-section normal vector direction), as Figure 6 Shown is a schematic diagram of the blade point cloud slice. In the figure, the blade cross-section IDs are numbered along the cross-section normal vector direction to obtain the aviation blade cross-section point cloud data; then, for each frame of the blade cross-section point cloud data, using the principal component analysis algorithm and the point cloud contour extraction algorithm, the cross-section point cloud data is reduced to two-dimensional ordered point cloud data. Based on the ordered point cloud, the leading and trailing edge parameters of the cross-section point cloud are extracted to obtain the detailed drawing of the leading edge circle of the blade as Figure 7 Shown, and the detailed drawing of the trailing edge circle is as Figure 8 Shown.

[0073] The above is only the preferred embodiment of the invention, and does not limit the patent scope of the invention. Any equivalent structure or equivalent process transformation made by using the content of the invention specification and drawings, or directly or indirectly applied in other related technical fields, shall be included in the patent protection scope of the invention by the same token.

Claims

1. A method for measuring the leading and trailing edge parameters of a high-precision curved surface thin sheet, characterized in that: The method steps are as follows: Step 1: Camera calibration; Step 2: Point cloud slicing; Step 3: Principal component analysis; Step 4: Blade contour extraction; Step 5: Solving the parameters of the leading and trailing edges; Among them, the blade contour extraction in Step 4 converts the unordered point cloud into an ordered point cloud, and its steps are as follows: S1. Construct a two-dimensional convex hull. Use the Graham line scanning method to construct the two-dimensional convex hull of the blade cross-section point cloud, and extract the convex hull points. The remaining points are the concave hull points; S2. For each candidate point P C , traverse all adjacent vertices V i and V i+1 on the convex hull polygon, and calculate the distance d from P C to the line segment V i V i+1 according to the following formula; S3. Insert P between two adjacent vertices that minimize d according to the principle of the distance from the minimization point to the line segment, and update the polygon; C ​ S4. Repeat the above Step S3 until all candidate points are inserted into the polygon; In the formula, the magnitude of the ρ value represents P C and V i V i+1 The relative position distribution of, where i represents the point cloud index: If ρ ∈ (-∞, 0], it indicates that V i P C is projected onto V i V i+1 and the projection falls on the left extension line of V i V i+1 ; If ρ ∈ (0, 1), it indicates that the projection falls on V i V i+1 ; If ρ ∈ [1, +∞), it indicates that the projection falls on the right extension line of V i V i+1 ; The calculation formula for the ρ value is: Among them, for the solution of the leading and trailing edge parameters in Step 5, before measuring the leading and trailing edge radii of the blade, it is necessary to fit the leading and trailing edge arcs of the blade. The steps for fitting the leading and trailing edge arcs of the blade are as follows: (1) Determine a measurement point located on the leading and trailing edge arcs as the seed point; after principal component analysis, the coordinate limit points must be located on the arcs of the leading and trailing edges. Search for the maximum and minimum points of the X coordinate or Y coordinate in the cross-section point cloud after contour extraction, which are the points on the leading edge arc and trailing edge arc of the blade; (2) Search for two measurement points in the front and rear directions of the limit point. Based on the data of these 5 measurement points, fit the arc according to the least squares method; (3) Calculate the distance from each measurement point to the fitted arc respectively. If all the distances are less than the given threshold, then add adjacent points in the front and rear directions among the measurement points to form a new arc fitting point set, and fit the arc according to the least squares method with the new point set; if the distance between the measurement point and the fitted arc is greater than the limit value, it is determined that the point does not belong to the leading and trailing edge arcs. If the serial number of this point in the arc fitting point set belongs to the maximum index point, then only adjacent points with the serial number of the minimum index point can be added when adding measurement points subsequently; if all the added measurement points exceed the limit value, then remove the newly added points and keep the original arc fitting point set; (4) Repeat Step (3). When the distances from the points with the maximum and minimum indices in the arc fitting point set to the fitted arc are both greater than the given threshold, the iterative process of Step (3) ends at this time; (5) Fit the arc using the least squares method. The fitting result is the leading and trailing edge arcs of the blade. According to the fitting result, the leading and trailing edge radii of the blade and the coordinates of the arc center can be obtained.

2. A method for measuring the leading and trailing edge parameters of a high-precision curved thin sheet according to claim 1, characterized in that: The camera calibration method in Step 1 is as follows: a. Calibrate two structured light cameras placed opposite to each other using a high-precision calibration block. Place the calibration block in the middle of the two structured light cameras to ensure that the calibration block is within the measurement range of the two structured light cameras; b. Use the structured light cameras to collect the point cloud data of the calibration block; c. Then use the iterative closest point algorithm to match the two frames of point clouds of the calibration block to the same coordinate frame. The obtained rotation matrix and translation matrix are the calibration parameters of the two structured light cameras.

3. The method for measuring the leading and trailing edge parameters of a high-precision curved sheet according to claim 1, characterized in that: The point cloud slicing in Step 2 slices the blade point cloud along the axial direction, processes the sliced point cloud, and the leading and trailing edges of the sliced point cloud are the leading and trailing edges of the blade at the slicing position.

4. The method for measuring the leading and trailing edge parameters of a high-precision curved sheet as claimed in claim 1, wherein: The principal component analysis in Step 3 processes the cross-section point cloud, reduces the dimension of the point cloud in three-dimensional space to two-dimensional space, and reduces the complexity of data processing.

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