Method and system for detecting internal thread parameters based on laser point cloud analysis

Abstract

This invention discloses a method and system for detecting internal thread parameters based on laser point cloud analysis. Addressing the shortcomings of incomplete results and slow speed of traditional internal thread measurement methods, this device can quickly acquire point cloud information of the internal thread to be measured. Within the system, outlier removal, cylindrical fitting, and sawtooth wave fitting operations are performed on this point cloud information to determine the major diameter, minor diameter, pitch diameter, pitch, and thread angle parameters of the internal thread.

Description

Technical Field

[0001] This invention relates to the field of measuring internal thread parameters based on laser point clouds, and particularly to innovative algorithms for point cloud information outlier removal (removal of outliers), three-dimensional point cloud cylindrical surface fitting, and sawtooth wave nonlinear fitting. Background Technology

[0002] Currently, most thread inspection instruments are contact-based. Due to the limitation of the probe diameter, contact-based instruments cannot completely detect the bottom of the thread; they can only select a suitable probe based on the pitch to detect the pitch diameter and pitch. This is a contact-based measurement method. While these traditional methods are convenient and fast, they also have significant drawbacks: slow measurement speed, the ability to measure only a portion of thread parameters, and the inherent estimation errors that occur during human measurement. Therefore, in the current context, it is essential to develop a suitable non-contact method for detecting internal thread parameters. This lays the groundwork for the feasibility of the "Invention Content of this Patent" described below. Summary of the Invention

[0003] In order to solve the problems existing in the background art, the present invention aims to provide a method and system for detecting internal thread parameters based on laser point cloud analysis, so as to solve the above-mentioned situation.

[0004] A method for detecting internal thread parameters based on laser point cloud analysis, the method comprising:

[0005] Using a line-scan laser profilometer, after rotating at a constant speed for more than 360°, the line-scan laser profilometer reads the profilometer data while rotating. Using a registration algorithm, the effective data of 360° is automatically extracted to generate polar coordinate point cloud data, i.e., the original point cloud information data.

[0006] Outlier removal is performed on the acquired raw point cloud data to obtain the first dataset;

[0007] The first dataset is split to obtain the second dataset, which includes: the split tooth crest data, the split tooth floor data, and the split slope data.

[0008] Cylinder fitting was performed on the tooth crest data and tooth floor data respectively to obtain minor diameter data and major diameter data.

[0009] Furthermore, the fitting process for the inclined plane data specifically includes:

[0010] The inclined plane data is fitted with a sawtooth wave to obtain tooth angle data and tooth spacing data;

[0011] Using the sawtooth wave fitting results, the profile points on the thread midline are calculated to obtain the thread pitch diameter point cloud data;

[0012] Perform cylindrical fitting on the pitch diameter point cloud data to obtain the pitch diameter data.

[0013] Furthermore, the outlier rejection of the acquired original point cloud information data specifically includes:

[0014] Encode and decode the data using the result of PCA dimensionality reduction of the normal data set;

[0015] Compare the decoded data with the original point cloud information data, determine the values exceeding the threshold as outliers, and save the data after outlier rejection to generate a new point cloud data set, namely the first data set.

[0016] Furthermore, the data separation process for the first data set specifically includes:

[0017] Separate the crest data:

[0018] For the first data set, find the maximum z value z_max and the minimum z value z_min corresponding to each contour respectively,

[0019] max_z = max(xz(m).z);

[0020] min_z = min(xz(m).z);

[0021] where: xz represents the point cloud information of the internal thread after outlier rejection, m represents the contour number, and z represents the distance from the sensor to the thread; take the points with z value less than min_z + 0.25 as the crest data, and use the linear regression method to delete the points with large errors from the crest data to obtain the crest point cloud information, namely the separated crest data;

[0022] Separate the inclined plane data:

[0023] Take min_z + 0.05 < z < min_z + 0.7 as the inclined plane data, namely the separated inclined plane data;

[0024] Separate the root data:

[0025] Take z > max_z - 0.4 as the root data, and use the linear regression method to delete the points with large errors from the root data to obtain the crest point cloud information, namely the separated crest data.

[0026] Furthermore, the cylindrical fitting process for the crest data specifically includes:

[0027] Convert the point cloud coordinates from the cylindrical coordinate system to the Cartesian coordinate system to generate the Cartesian coordinate point cloud format:

[0028] theta = theta + deltaTheta;

[0029] xyz(m).x=xz(m).z.*cos(theta);

[0030] xyz(m).y=xz(m).z.*sin(theta);

[0031] xyz(m).z = xz(m).x;

[0032] Where deltaTheta represents the angle between adjacent contours, and theta represents the angle of the current contour;

[0033] Multiple nonlinear regression is performed on the tooth crest data to determine the parameters of the cylindrical fitting. The necessary parameters of the cylindrical fitting are x0, y0, z0, a, b, c, r.

[0034] Where (x0, y0, z0) represents a point on the axis, all initialized to 0, and (a, b, c) represents the axis vector, where a 2 +b 2 +c 2 =1, a and b are both initially set to 0, r is the radius, and the initial value of r is set to the average value of the tooth crest profile. The fitted nonlinear regression model function is:

[0035]

[0036] The parameter values ​​returned after fitting are stored in array p, which contains data including x0, y0, z0, a, b, c, and r. Parameter r is the minor diameter of the thread.

[0037] Furthermore, the sawtooth wave fitting process for the inclined plane data specifically includes:

[0038] The necessary parameters for fitting the sawtooth wave are a0, a1, b, w, x0;

[0039] The model type for fitting is determined as follows:

[0040] y=a0+a1*sawtooth(w*(x-x0),0.5)+b*x;

[0041] Where a0 is the DC component; a1 is the amplitude; w is the frequency; x0 is the initial phase; b is the slope; and sawtooth is the sawtooth wave function. A nonlinear least squares method is used to store the fitted parameters in the fitresult array; where fitresult is the variable result of the function fitting, and the data in the array includes the fitted a0, a1, b, w, and x0.

[0042] The tooth profile angle and tooth pitch for each profile can be calculated using the following formulas:

[0043] Tooth angle: angle = abs(atan(T / 4 / a1)*2*180 / pi);

[0044] Tooth pitch: T = 2 * pi / w;

[0045] Where angle is the thread profile angle; abs is the absolute value function; atan is the arctangent function; pi is 3.14159; T is the thread pitch. The average of the thread profile angle and thread pitch obtained above is used to obtain the thread profile angle data and thread pitch data of the internal thread.

[0046] Furthermore, the contour points located on the thread midline are calculated for the fitted a0, a1, b, w, x0 to obtain the thread midline point cloud data. Then, cylindrical fitting is performed on the thread midline point cloud data to obtain the internal thread midline diameter data.

[0047] An internal thread parameter detection system based on laser point cloud analysis, the system comprising:

[0048] The data removal module is used to remove outliers from the acquired raw point cloud information data to obtain the first dataset;

[0049] The data separation module is used to separate the first dataset to obtain the second dataset, which includes: separated tooth crest data, separated tooth floor data, and separated bevel data.

[0050] The first fitting processing module is used to perform cylindrical fitting processing on the tooth crest data and tooth floor data respectively to obtain the minor diameter data and major diameter data.

[0051] The second fitting processing module is used to perform sawtooth wave fitting and cylindrical surface fitting processing on the separated inclined plane data to obtain tooth pitch data, tooth angle data and median diameter data.

[0052] Beneficial effects:

[0053] The advantages of the above technical solution are: unlike traditional contact algorithms, it effectively protects the thread under test while acquiring data quickly and improving measurement accuracy. Attached image description:

[0054] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0055] Figure 1 This is a schematic diagram of the internal thread parameter detection method based on laser point cloud analysis in an embodiment of the present invention.

[0056] Figure 2This is a flowchart of the internal thread parameter detection method based on laser point cloud analysis in the implementation of this invention.

[0057] Figure 3 3D point cloud data of the thread;

[0058] Figure 4 Point cloud of thread minor diameter data;

[0059] Figure 5 Fitting the point cloud of thread minor diameter data to a cylindrical surface;

[0060] Figure 6 Point cloud of thread major diameter data;

[0061] Figure 7 Fitting the point cloud of thread major diameter data to a cylindrical surface;

[0062] Figure 8 Sawtooth wave fitting results for single contour slope data;

[0063] Figure 9 Point cloud of thread pitch diameter data;

[0064] Figure 10 Fitting the point cloud of thread pitch diameter data to a cylindrical surface.

[0065] Figure label:

[0066] In the diagram: 1. Servo motor, 2. Line scan profilometer, 3. Reflector, 4. Test piece, 5. Clamp, 6. Linear module, 7. Support, 8. Servo motor. Detailed Implementation

[0067] The specific implementation of the present invention is described below with reference to embodiments:

[0068] It should be noted that the structures, proportions, sizes, etc. shown in this specification are only used to complement the content disclosed in the specification for those skilled in the art to understand and read, and are not intended to limit the conditions under which the present invention can be implemented. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0069] Furthermore, the terms such as "upper," "lower," "left," "right," "middle," and "one" used in this specification are merely for clarity of description and are not intended to limit the scope of the invention. Any changes or adjustments to their relative relationships, without substantially altering the technical content, should also be considered within the scope of the invention.

[0070] Example 1:

[0071] A method for detecting internal thread parameters based on laser point cloud analysis includes:

[0072] (1) By using the device transmission mechanism in conjunction with the line scanning laser profiler, the original point cloud information of the internal thread can be collected quickly;

[0073] (2) Perform outlier removal on the original point cloud data;

[0074] (3) Perform data separation operation based on the data after the outlier removal operation is completed;

[0075] (4) Perform cylindrical fitting based on the tooth crest data after data separation to determine the minor diameter;

[0076] (5) Based on the inclined plane data after data separation, the sawtooth wave is fitted to obtain the tooth angle and tooth distance.

[0077] (6) Extract the contour points located on the median line from the fitted sawtooth wave to form median line point cloud data, and perform cylindrical fitting based on the median line point cloud data to calculate the median diameter.

[0078] (7) Perform cylindrical fitting based on the tooth base data after data separation to determine the major diameter.

[0079] As one embodiment of the present invention: information on thread point clouds is collected using a device transmission mechanism in conjunction with a line scanning profiler, including:

[0080] A uniformly rotating line scanning profilometer can scan more than 360°. While the profilometer is rotating, it reads the profile data and uses a registration algorithm to automatically extract the effective data from 360° to generate polar coordinate point cloud data.

[0081] As one embodiment of the present invention: performing outlier removal on the original data includes:

[0082] Outlier removal involves decoding the encoded data using the results of PCA dimensionality reduction on a normal dataset. The decoded data is compared with the original data, and outliers exceeding a threshold are considered outliers. The outlier-removed data is then saved, and new point cloud data is generated.

[0083] As one embodiment of the present invention: a data separation operation is performed on the data after the outlier removal operation, including:

[0084] Separation of tooth crest data:

[0085] Find the maximum z-value z_max and minimum z-value z_min for each contour in the dataset.

[0086] max_z = max(xz(m).z);

[0087] min_z = min(xz(m).z);

[0088] Where: xz represents the point cloud information of the internal thread after wild value rejection, m represents the contour number, and z represents the distance from the sensor to the thread. Points with z values less than min_z + 0.25 are taken as the tooth crest data. Linear regression method is used to delete points with large errors from the tooth crest data to obtain the tooth crest point cloud information.

[0089] Separate the inclined plane data:

[0090] Take min_z + 0.05 < z < min_z + 0.7 as the inclined plane data;

[0091] Separate the tooth bottom data:

[0092] Take z > max_z - 0.4 as the tooth bottom data. Linear regression method is used to delete points with large errors from the tooth bottom data to obtain the tooth crest point cloud information.

[0093] As an embodiment of the present invention: Nonlinear fitting is performed based on the data after data separation, including:

[0094] Convert the point cloud coordinates from the cylindrical coordinate system to the rectangular coordinate system to generate the rectangular coordinate point cloud format:

[0095] theta = theta + deltaTheta;

[0096] xyz(m).x = xz(m).z * cos(theta);

[0097] xyz(m).y = xz(m).z * sin(theta);

[0098] xyz(m).z = xz(m).x;

[0099] Where deltaTheta represents the included angle between adjacent contours, and theta represents the angle of the current contour;

[0100] Perform multiple nonlinear regression on the tooth crest data to obtain the parameters of cylindrical fitting. The necessary parameters for cylindrical fitting are x0, y0, z0, a, b, c, r, where (x0, y0, z0) represents a point on the axis, (a, b, c) represents the axis vector, and a 2 + b 2 + c 2 = 1. The initial values of a and b are both set to 0, r is the radius, and the initial value of r is set to the average value of r of the tooth crest contour. The nonlinear regression model function for fitting is:

[0101]

[0102] The return result of the parameter values after fitting is saved in the array p. The data in the array p includes x0, y0, z0, a, b, c, r. The parameter r is the minor diameter of the thread.

[0103] As one embodiment of the present invention: based on the sawtooth wave fitting of the separated inclined plane data, the tooth profile angle and tooth distance are calculated, including:

[0104] Sawtooth wave fitting:

[0105] The necessary parameters for fitting the sawtooth wave are a0, a1, b, w, and x0, and the model type for fitting is:

[0106] y=a0+a1*sawtooth(w*(x-x0),0.5)+b*x;

[0107] Where a0 is the DC component; a1 is the amplitude; w is the frequency; x0 is the initial phase; b is the slope; and sawtooth is the sawtooth wave function. Using a preset nonlinear least squares method, the fitted parameter results are stored in the fitresult array; where the fitresult array contains the variable results of the function fitting, and the data in the array includes the fitted a0, a1, b, w, and x0.

[0108] Calculate the tooth angle and tooth pitch for each profile:

[0109] Tooth angle: angle = abs(atan(T / 4 / a1)*2*180 / pi);

[0110] Tooth pitch: T = 2 * pi / w;

[0111] Where angle is the thread profile angle; abs is the absolute value function; atan is the arctangent function; pi is 3.14159; T is the thread pitch. The average of the thread profile angle and thread pitch obtained above is used to obtain the thread profile angle data and thread pitch data of the internal thread.

[0112] Furthermore, the contour points located on the thread midline are calculated for the fitted a0, a1, b, w, x0 to obtain the thread midline point cloud data. Then, cylindrical fitting is performed on the thread midline point cloud data to obtain the internal thread midline diameter data.

[0113] As one embodiment of the present invention: the feature is based on the separated tooth root data to perform cylindrical fitting, and the fitting algorithm is the same as that for finding the minor diameter, to obtain the major diameter of the internal thread.

[0114] Example 2:

[0115] Figure 1This is a schematic diagram of the internal thread parameter detection method based on laser point cloud analysis in an embodiment of the present invention. In the diagram, the measuring mechanism consists of a servo motor 1, a line scan profilometer 2, and a reflector 3. The measuring mechanism is mounted on a linear module 6. Controlling the rotation of the servo motor 8 allows the linear module to move the measuring mechanism up and down. A clamp 5 is used to fix the component under test. When the reflector is inside the component under test, the servo motor 1 is controlled to rotate at a constant speed exceeding 360°. Simultaneously, the line scan profilometer collects thread point cloud data.

[0116] Figure 2 This is a data processing flowchart of the internal thread parameter detection method based on laser point cloud analysis in an embodiment of the present invention;

[0117] The information of the thread point cloud collected by the line scan profiler is used, and outlier removal is performed on the raw data.

[0118] Based on the data after the outlier removal operation, a data separation operation is performed to separate the data into three parts: tooth crest data, bevel data, and tooth floor data.

[0119] The minor diameter is determined by cylindrical fitting based on the tooth crest data after data separation.

[0120] Based on the inclined plane data after data separation, sawtooth wave fitting is performed to calculate the tooth profile angle and tooth pitch, and the contour point data on the median line is calculated to form thread median line point cloud data.

[0121] Calculate the mean diameter of a cylinder by fitting the point cloud data of the thread median line;

[0122] The major diameter is determined by cylindrical fitting based on the separated tooth base data.

[0123] The principle of the above technical solution is as follows: After collecting point cloud information, the present invention performs outlier removal processing on the point cloud information to remove some outliers and improve measurement accuracy. During the data separation process, the separated tooth crest and tooth floor data are further deleting some outliers using nonlinear fitting, and the separated data is tilt corrected to further improve measurement accuracy; the tooth profile inclined surface data is fitted with sawtooth wave parameters using a nonlinear fitting algorithm to obtain the tooth angle and tooth pitch measurement values. During the internal thread detection process, the sensor rotation center and the internal thread central axis cannot be strictly parallel. Therefore, the separated point cloud data information is transformed from a cylindrical coordinate system to a rectangular coordinate system, and the parameters of the cylindrical surface are obtained through a cylindrical surface fitting algorithm, thereby obtaining the major diameter, minor diameter, and pitch diameter.

[0124] Figure 3 A 3D point cloud image of an internal thread formed by the contour data collected by a line laser scanning profilometer after one rotation;

[0125] Figure 4A 3D point cloud image formed from tooth crest data separated from the 3D point cloud of the internal thread;

[0126] Figure 5 To obtain the thread minor diameter by fitting the cylindrical surface using the point cloud of thread crest data, the thread minor diameter is obtained through the fitting parameters of the cylindrical surface.

[0127] Figure 6 A 3D point cloud image formed from the internal thread root data separated from the 3D point cloud of the internal thread;

[0128] Figure 7 To obtain the major diameter of the thread by fitting the cylindrical surface using the point cloud of the thread root data, the thread major diameter is obtained by fitting the cylindrical surface parameters.

[0129] Figure 8 The points represent the thread bevel portion data separated from a single profile data set, and the lines represent the results of nonlinear sawtooth wave fitting using the bevel data. After nonlinear sawtooth wave fitting of all sawtooth wave profile data, the thread pitch and tooth angle parameters can be obtained using the fitted sawtooth wave parameters.

[0130] Figure 9 After performing nonlinear sawtooth wave fitting on all sawtooth wave profile data, the thread pitch diameter point on each profile is calculated using the fitted sawtooth wave parameters. The pitch diameter point data of all profiles form a pitch diameter data point cloud.

[0131] Figure 10 To obtain the thread pitch diameter by fitting the cylindrical surface using the point cloud of thread pitch diameter data, the thread pitch diameter is obtained through the fitting parameters of the cylindrical surface.

[0132] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0133] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0134] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0135] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention. Many other changes and modifications can be made without departing from the concept and scope of the present invention. It should be understood that the present invention is not limited to the specific embodiments, and the scope of the present invention is defined by the appended claims.

Claims

1. A method for detecting parameters of an internal thread based on laser point cloud analysis, characterized in that, The method comprises: The acquired original point cloud information data is subjected to outlier elimination to obtain a first data set; The first data set is subjected to data separation processing to obtain a second data set, which comprises separated crest data, separated root data and separated flank data; The crest data and the root data are subjected to cylindrical surface fitting processing respectively to obtain minor diameter data and major diameter data; The separated flank data is subjected to sawtooth wave fitting processing and cylindrical surface fitting processing to obtain pitch data, included angle data and mean diameter data; The cylindrical surface fitting processing of the crest data specifically comprises: The point cloud coordinates are converted from a cylindrical coordinate system to a rectangular coordinate system to generate a rectangular coordinate point cloud format: theta = theta + deltaTheta; xyz(m).x = xz(m).z.*cos(theta); xyz(m).y = xz(m).z.*sin(theta); xyz(m).z = xz(m).x; wherein xz represents the point cloud information of the internal thread after outlier elimination, m represents the number of the profile, z represents the distance from the sensor to the thread, deltaTheta represents the angle between adjacent profiles, and theta represents the angle of the current profile; The crest data is subjected to multivariate nonlinear regression to determine the parameters of the cylindrical surface fitting, wherein the necessary parameters of the cylindrical surface fitting are x0, y0, z0, a, b, c and r; wherein (x0, y0, z0) represents a point on the axis, the initial values of which are all 0, (a, b, c) represents an axial vector, and a2+b2+c2=1, the initial values of a and b are both 0, r is the radius, and the initial value of r is the average value of the r of the crest profile, and the nonlinear regression model function of the fitting is: The parameter values after the fitting are returned to the array p, which comprises x0, y0, z0, a, b, c and r, and the cylindrical surface parameter r obtained by the fitting is the minor diameter of the internal thread.

2. The method for detecting internal thread parameters based on laser point cloud analysis according to claim 1, characterized in that, The line scanning laser profilometer is uniformly rotated by more than 360°, the profile data is read while the line scanning laser profilometer is rotating, the effective data of 360° is automatically extracted by using a registration algorithm, and polar coordinate point cloud data, i.e. the original point cloud information data, is generated.

3. The method for detecting internal thread parameters based on laser point cloud analysis according to claim 1, characterized in that, The original point cloud information data is subjected to outlier elimination, specifically comprising: The data is encoded and decoded by using the PCA dimension reduction result of the normal data set; The decoded data is compared with the original point cloud information data to determine the abnormal values exceeding the threshold, and the data after the outlier elimination is saved to generate a new point cloud data set, i.e. the first data set.

4. The method for detecting internal thread parameters based on laser point cloud analysis according to claim 1, characterized in that, The first data set is subjected to data separation processing, specifically comprising: Separating the crest data: The maximum z value z_max and the minimum z value z_min corresponding to each profile are calculated from the first data set, max_z = max(xz(m).z); min_z = min(xz(m).z); Wherein: xz represents the point cloud information of the internal thread after removing outliers, m represents the number of the profile, and z represents the distance from the sensor to the thread; points with z values less than min_z+0.25 are taken as the tooth crest data, the tooth crest data is subjected to linear regression to remove points with larger errors, and tooth crest point cloud information, i.e., separated tooth crest data, is obtained; Separate the chamfer data: Take min_z+0.05<z<min_z+0.7 as the chamfer data, i.e., the separated chamfer data; Separate the tooth bottom data: Take z>max_z-0.4 as the tooth bottom data, and the tooth bottom data is subjected to linear regression to remove points with larger errors, and tooth crest point cloud information, i.e., separated tooth crest data, is obtained.

5. The internal thread parameter detection method based on laser point cloud analysis according to claim 1, characterized in that: The sawtooth wave fitting processing of the chamfer data specifically comprises: determining necessary parameters a0, a1, b, w, and x0 of the sawtooth wave fitting, and the initial values are determined according to experience; determining the sawtooth wave model for fitting as: y=a0+a1*sawtooth(w*(x-x0),0.5)+b*x; wherein a0 is a direct current component; a1 is an amplitude; w is a frequency; x0 is an initial phase; b is a slope; Sawtooth is a sawtooth wave function; the parameter results obtained after fitting are saved in the fitresult array by using a nonlinear least squares method; wherein the fitresult array is a variable result of function fitting, and the data in the array includes a0, a1, b, w, and x0 after fitting; The tooth profile angle and the thread pitch of each profile are calculated using the following formula: Tooth profile angle: angle=abs(atan(T / 4 / a1)*2*180 / pi); Thread pitch: T=2*pi / w; Wherein angle is the tooth profile angle; abs is an absolute value function; atan is an inverse tangent function; pi is 3.14159; T is the thread pitch; the average values of the tooth profile angle and the thread pitch obtained above are used to obtain the tooth profile angle data and the thread pitch data of the internal thread. The profile points located on the median line of the thread are calculated based on a0, a1, b, w, and x0 after fitting, the thread median point cloud data is obtained, and then the thread median point cloud data is subjected to cylindrical fitting to obtain the internal thread median data. The system comprises:

6. The method for detecting internal thread parameters based on laser point cloud analysis according to claim 5, characterized in that, a data rejection module configured to perform outlier rejection on the obtained original point cloud information data to obtain a first data set; 7. A system for internal thread parameter detection based on laser point cloud analysis, for implementing the method of any one of claims 1-6, characterized in that, a data separation module configured to perform data separation processing on the first data set to obtain a second data set, wherein the second data set comprises separated tooth crest data, separated tooth bottom data, and separated chamfer data; a first fitting processing module configured to perform cylindrical fitting processing on the tooth crest data and the tooth bottom data respectively to obtain small diameter data and large diameter data; a second fitting processing module configured to perform sawtooth wave fitting processing and cylindrical fitting processing on the separated chamfer data to obtain thread pitch data, tooth profile angle data, and median data. ​ ​

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