TWI417752B - System and method for comparing two-dimensional color gradation of curve - Google Patents

Description

Two-dimensional curve gradation comparison system and method

The invention relates to a two-dimensional curve gradation comparison method.

The three-coordinate measuring machine is a measuring device widely used in industrial and scientific research to measure products. The general measuring method is to place the measured object in the three-coordinate measuring space, and the contact probe along the three-coordinate measuring machine is measured along the edge. The surface of the object captures the data point by point through a programmed path, and analyzes the standard curve quality of the measured object based on the captured data. However, as the product standard curve modeling becomes more and more complex, such as mobile phones, MP3, MP4, the measurement data obtained by the traditional measurement method is difficult to reflect the true condition of the standard curve, and the measurement speed is slow and the efficiency is low.

In view of the above, it is necessary to provide a two-dimensional curve gradation comparison system, which can detect complex standard curves and improve the efficiency and accuracy of standard curve detection.

In view of the above, it is also necessary to provide a two-dimensional curve gradation comparison method, which can detect complex standard curves and improve the efficiency and accuracy of standard curve detection.

A two-dimensional curve gradation comparison system is installed in a computer, the computer is connected to a database, and the database stores the object to be detected The two-dimensional standard curve data of the piece and the scanned point cloud data, wherein the two-dimensional curve level comparison system comprises: a receiving module, configured to receive standard curve data and point cloud data imported by the user from the database; The alignment module is configured to align the point cloud with the standard curve according to the received standard curve data and the point cloud data; the receiving module is further configured to receive tolerance setting information and color setting information; and the computing module is configured to be aligned according to the alignment The following standard curve data and point cloud data calculate the closest distance from each point in the point cloud to the standard curve, and obtain the closest point from the point in the point cloud to the standard curve; the numbering module is used to Pointing in the order of the standard curve, serializing the points in the point cloud so that each point in the point cloud has a number; generating a module for using each point in the point cloud according to the number of the point The line segments are connected to generate an actual curve of the object; the color identification module is configured to identify the color of the actual curve according to the tolerance segment segment to which the closest distance from each point in the point cloud to the standard curve belongs.

A two-dimensional curve gradation comparison method, the method comprising the steps of: (a) receiving standard curve data and point cloud data imported by a user from a database; (b) selecting a point cloud according to the standard curve data and point cloud data Align with the standard curve; (c) receive tolerance setting information and color setting information; (d) calculate the closest distance from each point in the point cloud to the standard curve according to the aligned standard curve data and point cloud data, and obtain a point Each point in the cloud reaches the nearest point on the standard curve; (e) serializes each point in the point cloud according to the order of the nearest point on the standard curve, so that each point in the point cloud has a number; (f) connecting the points in the point cloud by line segments according to the number of the points to generate an actual curve of the object; (g) identifying the above-mentioned tolerance segment segments according to the closest distance from each point in the point cloud to the standard curve The color of the actual curve.

Compared with the traditional measurement method of the three-seat scalar measuring machine, the two-dimensional curve gradation comparison method provided by the present invention can be different according to the closest distance of the point cloud point of the scanned object to the standard curve of the CAD model of the object. The tolerance segmentation section marks the standard curve of the CAD model with different colors, and processes the standard curve of the CAD model to generate an intuitive test report, which improves the accuracy and speed of detecting the complex standard curve.

10‧‧‧Database

20‧‧‧ computer

30‧‧‧Display equipment

200‧‧‧Two-dimensional curve gradation comparison system

210‧‧‧ receiving module

211‧‧‧Alignment module

212‧‧‧Computation Module

213‧‧‧Number module

214‧‧‧Generation module

215‧‧‧Color identification module

216‧‧‧ group module

217‧‧‧Comparative Module

S101‧‧‧ Receive standard curve data and point cloud data imported by users from the database

S102‧‧‧ Align the point cloud with the standard curve based on the received standard curve data and point cloud data

S103‧‧‧Receive tolerance setting information and color setting information

S104‧‧‧ Calculate the nearest distance from each point in the point cloud to the standard curve based on the aligned standard curve data and point cloud data, and obtain the nearest point from the point cloud to the standard curve

S105‧‧‧ Serialize the points in the point cloud according to the order of the nearest point in the standard curve, so that each point in the point cloud has a number

S106‧‧‧Connect the points in the point cloud by line segments according to the number of the points to generate the actual curve of the object

S107‧‧‧Identifies the color of the above actual curve according to the tolerance segment to which the nearest distance from each point in the point cloud to the standard curve belongs

S108‧‧‧ grouping numbered points in the point cloud

S109‧‧‧ Compare the closest distances of each point in each group to the standard curve to obtain the maximum and minimum values of the closest distance in each group

S110‧‧‧ mark the maximum and minimum values of the closest distance from the midpoint to the standard curve in each set in the actual curve, and then output together with the standard curve to generate a test report.

1 is an application environment diagram of a preferred embodiment of a two-dimensional curve gradation comparison system of the present invention.

2 is a functional block diagram of the two-dimensional curve gradation comparison system of FIG.

3 is a main flow chart of a preferred embodiment of the two-dimensional curve gradation method of the present invention.

FIG. 4 is a detailed flowchart of step S104 in FIG.

FIG. 5 is a graph of a curve detection report obtained in step S110.

As shown in FIG. 1, it is an application environment diagram of a preferred embodiment of the two-dimensional curve gradation system of the present invention. The two-dimensional curve scale comparison system 200 is installed in a computer 20, and the computer 20 is connected to a database 10 to which a display device 30 is also connected.

The database 10 is used for storing the standard curve data of the standard CAD model of the object (hereinafter referred to as the standard curve data), the point cloud data obtained by scanning the object, and the data generated during the standard curve detection process. In the preferred embodiment, the standard curve data and the point cloud data are generated in a two-dimensional plane, and the coordinates of each point in the point cloud are two-dimensional.

The two-dimensional curve gradation comparison system 200 receives the standard curve data and the point cloud data, receives the user's tolerance setting and color setting information, according to the received standard curve data and points. The cloud data aligns the point cloud with the standard curve, and calculates the closest distance from each point in the point cloud to the standard curve according to the aligned standard curve data and the point cloud data, and displays the color in different colors according to the tolerance interval in which each nearest distance falls. standard curve line. In addition, the two-dimensional curve gradation comparison system 200 is further configured to generate a detection report according to the processed standard curve data.

The display device 30 provides a display interface for displaying a standard curve, a point cloud obtained by scanning the object, a standard curve and a test report of different color indications obtained by using the system to detect the standard curve.

As shown in FIG. 2, it is a functional module diagram of the two-dimensional curve gradation comparison system in FIG. The two-dimensional curve gradation comparison system 200 includes a receiving module 210, an alignment module 211, a computing module 212, a numbering module 213, a generating module 214, a color identification module 215, a grouping module 216, and a comparison module. Group 217. The module referred to in the present invention is a computer program segment for performing a specific function, and is more suitable for describing the execution process of the software in the computer than the program. Therefore, the following description of the software is described in the module.

The receiving module 210 is configured to receive standard curve data and point cloud data imported by the user from the database 10.

The alignment module 211 is configured to align the point cloud with the standard curve according to the received standard curve data and the point cloud data. When the user draws the standard curve data, there is a corresponding coordinate system. The point cloud obtained by scanning the contour of the object is also in a set coordinate system. In this embodiment, the two coordinate systems are aligned by coordinate alignment or iterative method. Specifically, the coordinate alignment method: 1. On the standard curve graph, three elements (such as: face, line, origin) are fitted by the least square method to construct the reference plane, the reference axis, and the reference origin is used to create the coordinate system to obtain the coordinate transformation. 4*4 (that is, the matrix has 4 rows and 4 columns) unit matrix mat1; 2. Fit three elements (such as: face, line, origin) at the same position of the scanned point cloud to construct the reference plane, the reference axis The reference origin creates a coordinate system to obtain a coordinate transformation 4*4 unit matrix mat2; 3. The point cloud object transforms into a transformation coordinate matrix in the reference coordinate system matTrans=mat1 * mat2; 4. multiplies all point coordinates of the point cloud by matTrans to obtain alignment The coordinates after. Iterative method: Iterate the function f(x) according to the quasi-Newton formula to get the minimum function f(x): (X1, Y1, Z1) and (X2, Y2, Z2) are the coordinates of the point cloud after alignment and the coordinates of the point to the nearest point on the standard curve, respectively, where n is the number of point clouds.

The receiving module 210 is further configured to receive tolerance setting information and color setting information. In the preferred embodiment, the tolerance refers to the range of fluctuations in the value of the closest distance from the point in the point cloud to the standard curve after the point cloud is aligned with the standard curve. The tolerance setting information includes: the maximum upper tolerance value allowed (such as 0.350), the maximum allowable lower tolerance value (such as -0.350), and the segmentation interval of the tolerance, such as the maximum upper tolerance value allowed and the allowed The interval [0.350, -0.350] determined by the maximum lower tolerance value is divided into several tolerance intervals [-0.350, -0.300], [-0.300, -0.250], ..., [0.250, 0.300], [0.300, 0.350]. The color setting information includes colors of different tolerance intervals, generally gradually changing from cool color (blue) to warm color (red), such as the tolerance interval [-0.350, -0.300], the color is dark blue, and the tolerance interval [-0.300, -0.250 The color of the sky is sky blue, the color of the tolerance interval [0.250, 0.300] is pink, the tolerance interval The color of [0.300, 0.350] is dark red, the interval larger than the maximum allowable upper tolerance value [0.300, +] is purple, and the color of the interval [-, -0.350] smaller than the maximum allowable lower tolerance value is brown. .

The calculation module 212 is configured to calculate the closest distance from each point in the point cloud to the standard curve according to the aligned standard curve data and the point cloud data, and obtain the closest point of each point in the point cloud to the standard curve. The distance between the point in the point cloud and the nearest point is the closest distance.

The numbering module 213 is configured to serialize each point in the point cloud according to the order of the nearest point in the standard curve, so that each point in the point cloud has a number. Specifically, when the user draws a standard curve, there is a starting point, which is clockwise or counterclockwise, and each nearest point is sequentially sequenced on the standard curve from small to Large number, the number of each nearest point is the number of the point corresponding to the actual point cloud.

The generating module 214 is configured to connect points in the point cloud by straight lines according to the number of the points to generate an actual curve of the object. Specifically, it is assumed that there are four points in the point cloud, and the number of each point is 1, 2, 3, and 4 respectively. If the standard curve is a closed curve, the above four points will be connected in the order of numbering. Then the wired segments 1-2, 2-3, 3-4, 4-1 respectively form a closed curve. If the standard curve is not a closed curve, the starting point and the ending point are not connected, respectively, the wired segment 1 -2, 2-3, 3-4, forming an unclosed curve, the curve connected by all the points in the point cloud is the actual curve composed of the point cloud.

The color identification module 215 is configured to approximate each point in the point cloud to a standard curve. The tolerance segmentation interval to which the distance belongs identifies the color of the actual curve described above. Specifically, since the actual curve is composed of the connected line segments between the respective points, the color of the actual curve can be clearly displayed after the color of each line segment is identified, and the color of the line segment connected between the points in the actual curve is specifically displayed. It is determined by the color of the point. For example, there are two adjacent points, which are 1, 2 respectively. If the closest distance from point 1 to the standard curve falls in the red tolerance section, the color of the point 1 is red, and point 2 is The closest distance of the standard curve falls in the tolerance section of the brown color, then the color of the point 2 is brown, and the color of the line segment 1-2 is half of the red color and half of the brown color; assuming that the colors of the point 1 and the point 2 are both red, the line segment 1 The color of -2 is red.

The grouping module 216 is configured to group the numbered points in the point cloud. In the preferred embodiment, the manner of grouping is grouped according to the change in curvature of the actual curve. In other embodiments, the user may also group according to other methods, for example, taking a fixed number of groups, assuming that numbers 1 to 100 are a group, and 101 to 200 are the next group. Through the grouping, the actual curve can be divided into many line segments, and the segmentation trend of the actual curve is obtained.

The comparison module 217 is further configured to compare the closest distances of each point in each group to the standard curve to obtain the maximum value and the minimum value of the closest distance in each group.

The generating module 214 is configured to mark the maximum value and the minimum value of the closest distance from the midpoint to the standard curve in each set in the actual curve, and then output together with the standard curve to generate a detection report. As shown in FIG. 5, a detection report of an object is obtained according to the two-dimensional curve gradation comparison method.

As shown in FIG. 3, it is the mainstream of the preferred embodiment of the two-dimensional curve gradation method of the present invention. Cheng Tu.

First, in step S101, the receiving module 210 receives the standard curve data and the point cloud data imported by the user from the database 10.

In step S102, the alignment module 211 aligns the point cloud with the standard curve according to the received standard curve data and the point cloud data. When the user draws the standard curve data, there is a corresponding coordinate system. The point cloud obtained by scanning the contour of the object is also in a set coordinate system. In this embodiment, the two coordinate systems are aligned by coordinate alignment or iterative method. Specifically, the coordinate alignment method: 1. On the standard curve, the least squares method is used to fit three elements (such as: face, line, origin) to construct the datum plane, the datum axis, and the datum origin to create the coordinate system to obtain the coordinate transformation 4 *4 (that is, the matrix has 4 rows and 4 columns) the unit matrix mat1; 2. Fit three elements (such as: face, line, origin) at the same position of the scanned point cloud to construct the reference plane, the reference axis, The coordinate origin creation coordinate system obtains the coordinate transformation 4*4 unit matrix mat2; 3. The point cloud object transforms into the transformation coordinate matrix in the reference coordinate system matTrans=mat1 * mat2; 4. Multiplies all point coordinates of the point cloud by matTrans to obtain alignment The coordinates of the coordinates. Iterative method: Iterate the function f(x) according to the quasi-Newton formula to get the minimum function f(x): (X1, Y1, Z1) and (X2, Y2, Z2) are the coordinates of the point cloud after alignment and the coordinates of the point to the nearest point on the standard curve, respectively, where n is the number of point clouds.

In step S103, the receiving module 210 receives the tolerance setting information and the color setting information. in In the preferred embodiment, the tolerance refers to the fluctuation range of the value of the closest distance from the point in the point cloud to the standard curve after the point cloud is aligned with the standard curve. The tolerance setting information includes: the maximum upper tolerance value allowed (such as 0.350), the maximum allowable lower tolerance value (such as -0.350), and the segmentation interval of the tolerance, such as the maximum upper tolerance value allowed and the allowed The interval [0.350, -0.350] determined by the maximum lower tolerance value is divided into several tolerance intervals [-0.350, -0.300], [-0.300, -0.250], ..., [0.250, 0.300], [0.300, 0.350]. The color setting information includes colors of different tolerance intervals, generally gradually changing from cool color (blue) to warm color (red), such as the tolerance interval [-0.350, -0.300], the color is dark blue, and the tolerance interval [-0.300, -0.250 The color of the sky is blue, the color of the tolerance interval [0.250, 0.300] is pink, the color of the tolerance interval [0.300, 0.350] is dark red, and the color of the interval [0.300, +] larger than the maximum allowable upper tolerance value is purple. The color of the interval [-, -0.350] smaller than the maximum allowable lower tolerance value is brown.

Step S104, the calculation module 212 calculates the closest distance from each point in the aligned point cloud to the standard curve according to the aligned standard curve data and the point cloud data (the specific steps will be described in detail in FIG. 4), and obtain the point cloud. The closest point of each point to the standard curve. The distance between the point in the point cloud and the nearest point is the closest distance.

In step S105, the numbering module 213 serializes each point in the point cloud according to the order of the nearest point in the standard curve, so that each point in the point cloud has a number. Specifically, when the user draws a standard curve, there is a starting point, which is clockwise or counterclockwise, and each nearest point is sequentially sequenced on the standard curve from small to Large number, the number of each nearest point is the number of the point corresponding to the actual point cloud.

In step S106, the generating module 214 connects the points in the point cloud by line segments according to the number of the points, and generates an actual curve of the object. Specifically, it is assumed that there are four points in the point cloud, and the number of each point is 1, 2, 3, and 4 respectively. If the standard curve is a closed curve, the above four points will be connected in the order of numbering. Then the wired segments 1-2, 2-3, 3-4, 4-1 respectively form a closed curve. If the standard curve is not a closed curve, the starting point and the ending point are not connected, respectively, the wired segment 1 -2, 2-3, 3-4, forming an unclosed curve, the curve connected by all the points in the point cloud is the actual curve composed of the point cloud.

In step S107, the color identification module 215 identifies the color of the actual curve according to the tolerance segment segment to which the closest distance from each point in the point cloud to the standard curve belongs. Specifically, since the actual curve is composed of the connected line segments between the respective points, the color of the actual curve can be clearly displayed after the color of each line segment is identified, and the color of the line segment connected between the points in the actual curve is specifically displayed. It is determined by the color of the point. For example, there are two adjacent points, which are 1, 2 respectively. If the closest distance from point 1 to the standard curve falls in the red tolerance section, the color of the point 1 is red, and point 2 is The closest distance of the standard curve falls in the tolerance section of the brown color, then the color of the point 2 is brown, and the color of the line segment 1-2 is half of the red color and half of the brown color; assuming that the colors of the point 1 and the point 2 are both red, the line segment 1 The color of -2 is red.

In step S108, the grouping module 216 groups the numbered points in the point cloud. In the preferred embodiment, the manner of grouping is grouped according to changes in the curvature of the curve. In other embodiments, the user may also group according to other methods, for example, taking a fixed number of groups, assuming that numbers 1 to 100 are a group, and 101 to 200 are the next group. Through the grouping, the actual curve can be divided into many line segments to get the actual curve. Segmentation trends.

In step S109, the comparison module 217 compares the closest distances of the respective points in each group to the standard curve to obtain the maximum value and the minimum value of the closest distance in each group.

In step S110, the maximum value and the minimum value of the closest distance from the midpoint to the standard curve of each group are marked in the actual curve, and then output together with the standard curve, and the generation module 217 generates a detection report. As shown in FIG. 5, a detection report of an object is obtained according to the two-dimensional curve gradation comparison method.

As shown in FIG. 4, it is a refinement flowchart for calculating the closest distance from each point in the point cloud to the standard curve in step S104 in FIG. 3 (in this embodiment, taking a certain point in the point cloud as an example) . First, in step S201, according to the coordinates of all points in the point cloud data, the coordinates of the smallest area point pt1Min in the point cloud of the object (pt1Min[x], pt1Min[y]) and the coordinates of the largest area point pt1Max (pt1Max) are obtained. [x], pt1Max[y]), which in turn can be obtained by (pt1Min[x], pt1Min[y]), (pt1Min[x], pt1Max[y]), (pt1Min[x], pt1Max[y]), The point cloud of the object consisting of (pt1Max[x], pt1Max[y]) surrounds the box boxR. In general, the bounding box of the point cloud is square or rectangular.

In step S202, the bounding box is divided into a plurality of rectangles or squares (in the embodiment, a rectangle is taken as an example). The standard curve drawn by the computer is composed of one primitive point. The nearest distance from the point in the point cloud to the point on the standard curve is calculated through the coordinates, and then a minimum distance is obtained. In order to avoid each point in the point cloud. Both the calculation and the primitive points on the standard curve are calculated, and the bounding box is divided into a plurality of rectangles, and the distance from the point to the pixel point is calculated in the rectangle, which can greatly improve the operation time and effect. rate. The number of divided rectangles is set by the user. Generally, according to the test result, the closest distance from each point in the point cloud to the standard curve can be calculated most quickly, so that each point in the point cloud falls within only one rectangle.

In step S203, a minimum rectangle including a point in the point cloud is selected to determine whether there is a primitive point of the standard curve.

Step S204, if there is no primitive point in the minimum rectangle, expand the range of the primitive point of the standard curve, and take all the smallest rectangles adjacent to the rectangle to form a larger rectangle until the larger rectangle There are primitive points in the standard curve.

Step S205, in the rectangle in which the standard curve element point exists, calculate the distance from the point to the standard curve element point found in the rectangle according to the coordinates of the point in the point cloud. Specifically, suppose that the point in a rectangular point cloud is A, and there are two standard curve elements in the rectangle as B and C, then calculate the distance from A to B, the distance from A to C, and then The two distances are compared to obtain a minimum distance, and the resulting distance is the closest distance from point A to the standard curve.

Step S206, comparing the distance of the point to the point of the standard curve element, and obtaining a minimum distance, that is, the closest distance from the point to the standard curve.

In step S203, if there is a primitive point of the standard curve in the minimum rectangle, the process proceeds directly to step S205.

It should be noted that the above embodiments are only intended to illustrate the technical solutions of the present invention and are not intended to be limiting, and the present invention will be described in detail with reference to the preferred embodiments thereof Technical solution Modifications or equivalents are made without departing from the spirit and scope of the invention.

S101‧‧‧ Receive standard curve data and point cloud data imported by users from the database

S102‧‧‧ Align the point cloud with the standard curve based on the received standard curve data and point cloud data

S103‧‧‧Receive tolerance setting information and color setting information

S104‧‧‧ Calculate the nearest distance from each point in the point cloud to the standard curve based on the aligned standard curve data and point cloud data, and obtain the nearest point from the point cloud to the standard curve

S105‧‧‧ Serialize the points in the point cloud according to the order of the nearest point in the standard curve, so that each point in the point cloud has a number

S106‧‧‧Connect the points in the point cloud by line segments according to the number of the points to generate the actual curve of the object

S107‧‧‧Identifies the color of the above actual curve according to the tolerance segment to which the nearest distance from each point in the point cloud to the standard curve belongs

S108‧‧‧ grouping numbered points in the point cloud

S109‧‧‧ Compare the closest distances of each point in each group to the standard curve to obtain the maximum and minimum values of the closest distance in each group

S110‧‧‧ mark the maximum and minimum values of the closest distance from the midpoint to the standard curve in each set in the actual curve, and then output together with the standard curve to generate a test report.

Claims (10)

A two-dimensional curve gradation comparison system, the two-dimensional curve gradation comparison system is installed in a computer, and the computer is connected to a database, wherein the database stores two-dimensional standard curve data of the object to be detected And the scanned point cloud data, wherein the two-dimensional curve gradation comparison system comprises: a receiving module, configured to receive standard curve data and point cloud data imported by the user from the database; and an alignment module, configured to The received standard curve data and the point cloud data align the point cloud with the standard curve; the receiving module is further configured to receive the tolerance setting information and the color setting information, the tolerance setting information includes the maximum allowable upper tolerance value, and the permission The maximum lower tolerance value and the interval determined by the maximum upper tolerance value allowed and the maximum allowable lower tolerance value are divided into a plurality of tolerance intervals, the color setting information includes colors of different tolerance intervals; and the calculation module is used for Calculate the nearest distance from each point in the point cloud to the standard curve based on the aligned standard curve data and point cloud data, and obtain the nearest point to the standard curve in the point cloud. The nearest point; the numbering module is used to serialize each point in the point cloud according to the order of the nearest point in the standard curve, so that each point in the point cloud has a number; generating a module, using According to the number of the point, the points in the point cloud are connected by line segments to generate an actual curve of the object; and the color identification module is used for the tolerance interval according to the closest distance from each point in the point cloud to the standard curve. Identifies the color of the actual curve above. The two-dimensional curve gradation comparison system according to claim 1, wherein the standard curve data includes coordinates constituting the standard curve element point, and the point cloud data includes coordinates of points on the point cloud. The two-dimensional curve gradation comparison system of claim 1, wherein the curve detection program further comprises: a grouping module, configured to group the numbered points in the point cloud. The two-dimensional curve gradation comparison system according to claim 3, wherein the curve detection program further comprises: a comparison module, and is also used for comparing the closest distances of each point in each group to the standard curve, To get the maximum and minimum values of the closest distance in each group. The two-dimensional curve gradation system as described in claim 4, wherein the curve detecting program further includes: the generating module, and is further configured to mark the middle point of each group in the actual curve The maximum and minimum values of the closest distance of the standard curve are then output together with the standard curve to generate a test report. A two-dimensional curve gradation comparison method, wherein the method comprises the following steps: (a) receiving standard curve data and point cloud data imported by a user from a database; (b) according to the standard curve data and point cloud data The point cloud is aligned with the standard curve; (c) receiving tolerance setting information and color setting information including the maximum allowable upper tolerance value, the maximum allowable lower tolerance value, and the maximum upper tolerance value to be allowed The interval determined by the maximum allowable lower tolerance value is divided into a plurality of tolerance intervals, the color setting information includes colors of different tolerance intervals; (d) calculating points in the aligned point cloud according to the aligned standard curve data and point cloud data The closest distance to the standard curve and get the most points from the point cloud to the standard curve The closest point of the close distance; (e) serialize each point in the point cloud according to the order of the nearest point on the standard curve, so that each point in the point cloud has a number; (f) according to the The point number connects the points in the point cloud by line segments to generate the actual curve of the object; and (g) identifies the color of the actual curve according to the tolerance interval to which the closest distance from each point in the point cloud to the standard curve belongs. The two-dimensional curve gradation method according to the sixth aspect of the patent application, wherein the method of aligning in the step (b) is: superimposing the coordinate system of the standard curve and the coordinate system of the point cloud into a coordinate system. The two-dimensional curve gradation method of claim 6, wherein after step (g), the method further comprises the steps of: (h) grouping the numbered points in the point cloud; (i) comparing each The closest distance from each point in the group to the standard curve to obtain the maximum and minimum values of the closest distance in each group; and (j) the maximum and minimum values of the closest distance from each of the above-mentioned midpoints to the standard curve in the actual curve The value is then output with the standard curve to generate a test report. The method of claim 2, wherein the step (d) comprises the following steps: (d1) determining a minimum of a point cloud of the object according to coordinates of all points in the point cloud data. The coordinates of the region point (pt1Min[x], pt1Min[y]) and the coordinates of the maximum region point pt1Max (pt1Max[x], pt1Max[y]), which can be obtained by the point (pt1Min[x], pt1Min[y]) , (pt1Min[x], pt1Max[y]), (pt1Min[x], pt1Max[y]), (pt1Max[x], pt1Max[y]) a point cloud bounding box of the object; (d2) dividing the bounding box into a plurality of regions; (d3) selecting a minimum region including a point in the point cloud, and determining whether the minimum region has a primitive point of the standard curve; (d4) If there is no primitive point in the minimum area, the range of the primitive point of the standard curve is expanded, and all the minimum areas adjacent to the area are taken to form a larger area until there is a standard in the larger area. (d5) in the region where the standard curve element point exists, calculate the distance from the point to the above-mentioned standard curve element point found in the area according to the coordinates of the point in the point cloud; and D6) Compare the distance from the point to the above-mentioned standard curve element point to obtain a minimum distance, which is the closest distance from the point to the standard curve. The two-dimensional curve gradation method of claim 6, wherein the standard curve data includes coordinates constituting the standard curve element point, and the point cloud data includes coordinates of points on the point cloud.