WO2024011779A1

WO2024011779A1 - Swing angle planning method for group-hole measurement, and readable medium and device

Info

Publication number: WO2024011779A1
Application number: PCT/CN2022/125794
Authority: WO
Inventors: 张桂; 高鑫; 沈昕; 姜振喜; 毛一砚; 王斌利; 胡立; 游莉萍
Original assignee: 成都飞机工业（集团）有限责任公司
Priority date: 2022-07-15
Filing date: 2022-10-18
Publication date: 2024-01-18
Also published as: CN114897110B; CN114897110A

Abstract

A swing angle planning method for group-hole measurement, and a readable medium and a device. The method comprises: acquiring feature parameters of a hole to be subjected to measurement and feature parameters of a measurement probe; calculating a measurement included-angle threshold, and determining an initial condition for iteration and a termination condition for same; executing clustering, updating a cluster set and a measurement normal vector set, and calculating candidate seeds; updating a seed set, and performing iterative optimization; determining whether the number of iterations exceeds a limit; adding the candidate seeds to the seed set; performing iterative optimization until a cluster is converged; and converting the measurement normal vector set into a measurement swing angle set. The time for calibrating a measurement probe can be reduced, thereby improving the on-machine inspection efficiency.

Description

Group hole measurement swing angle planning method, readable media and equipment

Technical field

The present invention relates to the technical field of on-machine detection. Specifically, it relates to a group hole measurement swing angle planning method, readable media and equipment, which are used to plan the measurement swing angle reasonably and efficiently during on-machine detection.

Background technique

On-machine inspection can complete the measurement of part dimensions without disassembling the workpiece, thereby avoiding problems such as installation errors caused by secondary clamping, covering part deformation, loss of measurement efficiency, etc., to improve measurement quality and measurement efficiency, and in aviation manufacturing It has been increasingly widely used in the industry. However, when measuring hole characteristics, the stylus needs to enter the inside of the hole. Due to the poor openness in the hole, the angle between the stylus swing angle and the hole axis needs to be strictly controlled. If the angle is too large, measurement interference is likely to occur; if the angle is too small, , the measured swing angle will increase sharply and the measurement efficiency will decrease. Therefore, how to determine the measurement swing angle for on-machine inspection of hole features is a key technology for on-machine inspection.

At present, when determining the hole position and measuring the swing angle, there are two main ways: one is to directly measure the hole position in the French direction. This method is easy to determine the swing angle, but there are many measurement angles and the stylus calibration efficiency is low. It is difficult for complex structures and For parts with a large number of holes, the calibration time even far exceeds the processing time and measurement time; second, for holes with similar axes, roughly determine the feasible measurement swing angle, use simulation software to perform interference simulation, and finally determine the feasible swing angle; this method can It can effectively reduce the measurement angle, but it requires repeated attempts and corrections, and the efficiency is low. For complex parts, it is impossible to complete manually.

For example, in the existing technology, such as the Chinese patent "A Group Hole Measurement Pendulum Angle Planning Method Based on Three-Coordinate Stylus" published by the Chinese patent No. CN112033331A, the pendulum angle planning method is a brute force solution, that is, all possible measurement pendulum angles are listed in a row. out, and then optimize the swing angle according to the principle of "the most hole features that can be measured" until the swing angle planning of all hole features is completed. Although this method can realize swing angle planning, its sequential calculation in an enumerated manner is still inefficient, and there is a possibility of omission or repeated calculation during the actual operation.

Contents of the invention

The object of the present invention is to solve the deficiencies in the existing technology and provide a method, readable medium and equipment for group hole measurement swing angle planning of hole characteristics, so as to solve the inconvenience of on-machine hole feature measurement swing angle calculation and group hole measurement swing angle calculation. The problem of low on-machine detection efficiency caused by too many numbers; by obtaining the hole feature normal vector set, stylus parameters and other parameters, calculate the angle threshold between the stylus axis and the hole axis when the hole and stylus do not interfere , and set the initial conditions and termination conditions of the clustering iteration; then use the hole feature normal vector set as the data set to perform clustering iteration, and obtain the measurement normal vector set. Stop the clustering iteration until the clustering converges, and convert the measurement normal vector set. In order to measure the swing angle set, the planning process of measuring the swing angle is completed at this point; through the above method, the automatic planning of the swing angle measurement is realized, simplifying the operation steps and avoiding omissions or duplications in the planning process.

The present invention is realized through the following technical solutions:

First, the present invention provides a group hole measurement swing angle planning method for on-machine hole feature detection, which includes the following steps:

Step S1: Obtain the hole feature normal vector set, stylus parameters, measurement depth and tolerance information;

Step S2: Based on the stylus parameters, measurement depth and tolerance information, calculate the angle threshold between the stylus axis and the hole axis when the hole and stylus do not interfere; set the iterative clustering initial conditions, initialize the seed set, and set Iterative clustering termination conditions;

Step S3: Perform clustering: use the hole feature normal vector set as the data set, calculate the clustering set and the measurement normal vector set, and calculate candidate seeds;

Step S4: Determine the clustering convergence: if the clustering converges, execute step S6; if the clustering does not converge, execute step S5;

Step S5: Update the seed set first; then, if the current iteration number is less than or equal to the maximum iteration number, increase the current iteration number by one, and re-execute steps S3 and S4 in sequence; if the current iteration number is greater than the maximum iteration number, the current clustering Increase the scale by one, that is, add candidate seeds to the current seed set to form a new seed set, and re-execute steps S3 and S4;

Step S6: Convert the set of measured normal vectors into a set of measured pendulum angles, output the set of measured pendulum angles and merge to end the program.

In the present invention, the hole feature normal vector set is expressed as

The tolerance information is expressed as tol, the measurement depth is expressed as h, and the angle threshold is expressed as T _Agl ; the cluster set is expressed as M _c , the cluster scale is expressed as T, and the number of cluster subsets in the cluster set M _c is expressed by the cluster Determined by the scale T, the cluster subset is represented as M _ci , and the index i of the cluster subset M _ci represents the sequence number in its cluster set M _c ; the seed set is represented as M _s , and the elements in the seed set M _s are seeds. , and express the seed as

The subscript j represents the seed

The sequence number in the seed set M _s , the seed in the seed set M _s

The number is equal to the clustering scale T. The seeds to be added to the seed set M _s are candidate seeds and are represented by S _new ; the current number of iterations is represented by n, and the maximum number of iterations is represented by T _n ; the measurement normal vector set is represented by M _n , and the measurement pendulum The set of angles is denoted M _A .

In order to better improve the present invention, further optimally, the stylus parameters include the diameter of the probe and the diameter of the probe. The diameter of the side rod is represented by d and the diameter of the probe is represented by D.

In order to better perfect the present invention, further optimally, the value of the tolerance information is greater than or equal to 0.5°. In order to better perfect the present invention, further optimally, according to the tolerance information tol, side rod diameter d, probe diameter D. Measure the depth h and calculate the angle threshold T _Agl through the following formula:

In order to better improve the present invention, further optimally set the initial conditions of the iterative clustering as follows: the initial clustering scale is 1, the current iteration number is 1; the initialization seed set is: the hole feature normal vector is centered on the first element As the only seed of the seed set; set the iterative clustering termination condition: determine the maximum number of iterations.

In order to better improve the present invention, in further optimization, in step S3, the cluster set and the measurement normal vector set are calculated through the following specific steps. The specific steps are as follows:

Step S31: Add the angles formed by the i-th element in the hole feature normal vector set and various sub-elements in the seed set as elements into the i-th included angle set;

Step S32: If the j-th seed in the seed set forms an angle with the i-th element in the hole feature normal vector set, and is the element with the smallest value in the i-th angle set; then add the i-th element in the hole feature normal vector set to the cluster. The jth cluster subset in the class set corresponds to adding the jth seed in the seed set to the measurement normal vector set; where, {i∈Z|1≤i≤N}, N is the number of elements in the hole feature normal vector set;

Step S33: Traverse the cluster set, and add the angle formed by each element of the j-th cluster subset in the cluster set and the j-th seed in the seed set to the candidate set as an element. The corresponding cluster when the element in the candidate set reaches the maximum value is The specific elements of the jth cluster subset in the class set are used as candidate seeds; where {j∈Z|1≤j≤M}, M is the number of seeds in the seed set.

In order to better improve the present invention, further optimally, the cluster subsets in the cluster set correspond to the seeds in the seed set in sequence.

In order to better improve the present invention, it is further optimized that when the maximum value obtained by the elements in the candidate set is greater than the angle threshold, it is determined that the clustering does not converge, otherwise it is determined that the clustering has converged.

In order to better improve the present invention, further optimally, the method for updating the seed set in step S5 is: converting each cluster subset in the cluster set into various sub-sets in the new seed set one by one; specifically, using each cluster subset The average value of the elements in the class subset is sequentially used as various subsets in the new seed set, which can be expressed by the following formula:

In this formula, the seed S _j is expressed in the form of S _j (x _j , y _j , z _j ). The subscript j represents the sequence number of the seed in the seed set. (x _j , y _j , z _j ) defines the seed S The proportional value of _j in the X, Y, and Z directions of the space rectangular coordinate system; in the cluster subset M _ci , the index i represents the serial number of the cluster subset in the cluster set. The index j in the above formula is the same as The index i is equal.

In order to better improve the present invention, in a further optimized manner, the measurement normal vector set is M _n , and the elements of the measurement normal vector set M _n are

Each element (A _v , C _v ) in the measured swing angle set M _A is obtained by the following formula:

Among them, in the formula

represents the measurement normal vector, i represents the coordinate value of the measurement normal vector on the X-axis, j represents the coordinate value of the measurement normal vector on the Y-axis, k represents the coordinate value of the measurement normal vector on the Z-axis, (i, j, k) defines the measurement normal vector

Proportional values in the X, Y, and Z directions; (A _v , C _v ) represents the measurement swing angle, A _v represents the working angle of the A-axis during measurement, C _v represents the working angle of the C-axis during measurement; V is the labeled measurement method vector There is a one-to-one correspondence with the corner mark of the measured pendulum angle serial number, the measured normal vector and the measured pendulum angle in sequence.

Secondly, the present invention provides a computer-readable medium on which a computer program is stored. When the computer program is executed, the group hole measurement swing angle planning method described in any one of the above is implemented.

An electronic device including:

processor;

memory for storing executable instructions for the processor;

Wherein, the processor is configured to execute any one of the above group hole measurement swing angle planning methods by executing the executable instructions.

Beneficial effects: The present invention optimizes the planning of hole feature measurement swing angles and clusters similar swing angles, thereby reducing measurement angles, saving probe calibration time, and improving on-machine detection efficiency.

Description of drawings

The technical solution will be clearly and completely described below in conjunction with the accompanying drawings. It is obvious that the described embodiments are some, not all, of the embodiments of the present invention.

Figure 1 is a schematic flow chart of the group hole measurement swing angle planning method provided by the present invention;

Figure 2 is a schematic diagram of the state when the probe is in the hole to be measured in the present invention.

Detailed ways

The above content created by the present invention will be further described in detail below in conjunction with the specific implementation of the embodiments. However, this should not be understood to mean that the scope of the above subject matter of the present invention is limited to the following examples. Without departing from the above technical ideas of the present invention, various substitutions or changes made based on common technical knowledge and conventional means in the art should be included in the scope of the present invention.

The following examples demonstrate the method of the present invention by comparing it with methods disclosed in the prior art.

Example 1:

This embodiment provides a group hole measurement swing angle planning method for on-machine hole feature detection, which specifically includes the following steps:

Step S5: Update the seed set first; then, if the current iteration number is less than or equal to the maximum iteration number, increase the current iteration number by one, and re-execute steps S3 and S4 in sequence; if the current iteration number is greater than the maximum iteration number, the current clustering The scale is increased by one, that is, candidate seeds are added to the current seed set to form a new seed set, and then the current number of iterations returns to the initial value, and steps S3 and S4 are re-executed;

In the present invention, the hole feature normal vector set is expressed as

Referring to Figure 2, the tolerance information is expressed as tol and the tolerance information is the allowed angular deviation range of the stylus axis, the measurement depth is expressed as h, and the angle threshold is expressed as T _Agl ; the clustering set is expressed as M c, and the clustering set is expressed as M _c . The class scale is expressed as T. The number of cluster subsets in the cluster set M _c is determined by the cluster scale T. The cluster subset is expressed as M _ci . The index i of the cluster subset M _ci is expressed in its cluster set M The sequence number in _c ; the seed set is expressed as M _s , the elements in the seed set M _s are seeds, and the seeds are expressed as

The subscript j represents the seed

The sequence number in the seed set M _s , the seed in the seed set M _s

To further improve this embodiment: the stylus parameters include the diameter of the probe and the diameter of the probe rod. The diameter of the side rod is represented by d and the diameter of the probe is represented by D.

To further improve this embodiment: the value of the tolerance information is greater than or equal to 0.5°, that is, tol≥05°.

Further improve this embodiment: Please refer to Figure 2. According to the tolerance information tol, side rod diameter d, probe diameter D, and measurement depth h, calculate the angle threshold T _Agl through the following formula:

Further improve this embodiment: set the initial conditions of the iterative clustering as follows: the initial value of the clustering scale is 1, and the initial value of the number of iterations is 1; the initialization seed set is: use the first element in the hole feature normal vector as the seed The unique seed of the set; determines the maximum number of iterations followed by each cluster size, where the maximum number of iterations is 5.

To further improve this embodiment: in step S3, the cluster set and the measurement normal vector set are calculated through the following specific steps. The specific steps are as follows:

To further improve this embodiment: the cluster subsets in the cluster set and the seeds in the seed set correspond one to one in sequence.

To further improve this embodiment: when the maximum value obtained by the elements in the candidate set is greater than the angle threshold, it is determined that the clustering does not converge, otherwise it is determined that the clustering has converged.

To further improve this embodiment: the method for updating the seed set in step S5 is: converting each cluster subset in the cluster set into various subsets in the new seed set one by one; specifically, using the average value of the elements in each cluster subset As various children in the new seed set in turn, they can be expressed by the following formula:

In the above formula, the seed S _j is expressed in the form of S _j (x _j , y _j , z _j ). The subscript j represents the serial number of the seed in the seed set. (x _j , y _j , z _j ) defines the seed. The proportional value of S _j in the X, Y, and Z directions of the space rectangular coordinate system; in the cluster subset M _ci , the index i represents the serial number of the cluster subset in the cluster set, and the index j in the above formula It is equal to the index i.

Further improve this embodiment: where the measurement normal vector set is M _n , and the elements of the measurement normal vector set M _n are

Among them, in the above formula

Example 2:

This embodiment uses the group hole measurement swing angle planning method provided in Embodiment 1, and implements it under the following implementation conditions:

① Hole feature normal vector set

②Measurement depth h=3mm, tolerance information tol=0.5°, rod diameter d=1.5mm, probe diameter D=2mm;

③The machine tool structure is an AC swing angle five-axis machine tool, and the forming range is: A: -90° to 90°, C: -180° to 180°.

This embodiment provides a group hole measurement swing angle planning method, as shown in the flow chart of Figure 1, in which the specific implementation steps based on the implementation conditions provided by this embodiment are as follows:

Step S1: Obtain the hole feature normal vector set

Obtain the measurement depth h=3mm, the tolerance information tol=0.5°, and obtain the stylus parameters. The stylus parameters include the diameter of the measuring rod d=1.5mm and the diameter of the measuring head D=2mm;

Step S2: Set the initial conditions for iterative clustering: initial clustering scale T = 1, current iteration number n = 1; initialize seed set M _s : change the hole feature normal vector

The first element in is used as the only seed of the seed set M _s

It can be expressed as

Set the iterative clustering termination condition: the maximum number of iterations T _n =5 that is common to each cluster scale; calculate the angle threshold T _Agl between the stylus axis and the hole axis under the condition of non-interference measurement, and obtain T _Agl =3.9272°.

Step S3: Execute clustering: use the hole feature normal vector set as the data set and the measured swing angle set as the target set, calculate the clustering set M _c and the measured normal vector set M _n , and calculate the candidate seed S _new .

Step S31: Calculate the hole feature normal vector set

elements in

With various seeds in the seed set M _s

angle between

is the first included angle;

At this time, there is a unique seed in the seed set M _s

Set with hole feature normal vectors

The angle between each element in is obtained as the element with the smallest value in the i-th angle set, which is expressed as:

then will

The first cluster subset M _c1 added to the cluster set M _c will

Add to the measurement method vector set M _n ;

Step S33: Traverse the cluster set M _c and select the cluster subset

Each element to the corresponding seed

The angle between is added to the candidate set as an element, and the elements of the candidate set

Obtain the maximum value T _max =45.0021°, and obtain the element with the maximum value T _max from

middle

Take out S _new as the candidate seed,

Step S4: Determine the clustering convergence: If the maximum value T _max of the elements in the candidate set is greater than the angle threshold

Then the clustering does not converge, otherwise it converges; at this time, T _max >, the clustering does not converge;

Step S5: If the clustering does not converge, update the seed set: the only seed in the updated seed set M _s

If the current number of iterations n=1≤5= _Tn , then add 1 to the number of iterations, record the number of iterations n=n+1=2, and start to repeat steps S3 to S4 with the clustering scale T=1 unchanged. , until n=6:

T _max =34.269°; S _new =(0.3536, -0.6124, 0.7071)

The clustering still does not converge, and n>T _n at this time. At this time, increase the clustering scale by one, record the new clustering scale T=T+1=2, and add candidate seeds S _new to the original seed set M _s , then the new seed set M _s =M _s +S _new :

Re-execute step S3 to step S4,

T _max =14.577°>T _Agl

Clustering does not converge, n=n+1=2, update the seed set:

Step S10: Repeat steps S3 to S6 again,

T _max =3.885°＜T _Agl

The clustering converges, the loop exits, and M _n is output.

Step S11: Convert the measurement normal vector set M _n into the measurement pendulum angle set M _A .

After rounding:

This set is the hole feature normal vector set

The corresponding set of pendulum angles.

Comparative ratio:

What this comparative example provides is a group hole measurement swing angle planning method based on the technical solution disclosed in the Chinese patent "A Group Hole Measurement Swing Angle Planning Method Based on a Three-Coordinate Stylus" with the publication number CN112033331A. Specifically, Proceed as follows:

Step S1: Obtain the hole feature normal vector set

Measuring depth h=3mm, tolerance information tol=0.5°, rod diameter d=1.5mm, probe diameter D=2mm;

Step S12: Under the condition of non-interference in calculation and measurement, set the initial clustering scale T = 1 and initialize the seed set.

Set the maximum number of clustering iterations under each clustering scale T _n =5, the current number of iterations n =1, and the angle threshold T _Agl between the stylus axis and the hole axis:

T _Agl =3.9272°.

Step S3: Discretize the machine tool swing angle range according to 2T _Agl to generate machine tool node A1:

Step S4: For

every element in

Solve the problem that the angle between A1 and A1 is not greater than

Elements:

In A1, the angle between (0.0000, 0.0000, 1.0000) is not greater than 3.9272° (0.32, 1.24)

The angle between (0.0000, 0.0712, 0.9503) is not greater than 3.9272° (0.32, 1.24)

The angle between (0.0000, 0.0508, 0.9802) is not greater than 3.9272° (0.32, 1.24)

There is no element whose angle with (0.3536, -0.6124, 0.7071) is not greater than 3.9272°.

Step S5: A11=(0.32, 1.24) corresponds to 3

The middle element is the node with the most corresponding elements among all nodes in A1.

Step S6: Y1 is not empty, from

Delete Y1;

Step S7: From

Delete Y1 in

If not empty, execute S4;

Step S8; In A1, there is no element whose angle with (0.3536, -0.6124, 0.7071) is not greater than 3.9272°.

Step S9: Any element of A1 corresponds to 0

Medium element, Y1 is the empty set;

Step S10: The remaining nodes cannot measure the remaining holes, so the intervals need to be reduced and divided again to generate denser nodes;

Step S11: Execute step S3 to re-discretize the machine tool swing angle range according to T _Agl to generate machine tool node A2:

Step S12: For

every element in

Solve for the elements in A2 whose angle is no greater than T _Agl :

In A1, the angles with (0.0000, 0.0000, 1.0000) are not greater than 3.9272°: (-0.25, 1.24) (3.33, 1.24), (-0.25, 5.34), (3.33, 5.34);

The angles with (0.0000, 0.0712, 0.9503) are not greater than 3.9272°: (-0.25, 1.24), (3.33, 1.24), (-0.25, 5.34), (-3.43, 5.34);

The angles with (0.0000, 0.0508, 0.9802) are not greater than 3.9272°: (-0.25, 1.24), (3.33, 1.24), (-0.25, 5.34), (-3.43, 5.34);

The angles with (0.3536, -0.6124, 0.7071) are not greater than 3.9272°: (45.23, 28.22), (45.23, 31.25);

S13: A21=(-0.25, 1.24) corresponds to 3

The middle element is the node with the most corresponding elements among all nodes in A2.

Step S14: Y2 is not empty, from

Delete Y2;

Step S15: From

Delete Y2 in

If not empty, execute S12

Step S16: In A2, the angles with (0.3536, -0.6124, 0.7071) are not greater than 3.9272° including (45.23, 28.22), (45.23, 31.25);

Step S17: A22=(45.23, 28.22) corresponds to 1

Step S18: Y2 is not empty, from

Delete Y2;

Step S19: From

After deleting Y2 in

If it is empty, the measurement and swing angle planning is completed.

Step S20: The final planned measured swing angle is

Compared with the swing angle planning method for group hole measurement provided in this embodiment and the swing angle planning method provided in the comparative example, this embodiment greatly improves the accuracy of measurement angles through swing angle planning, reduces the number of required measured swing angles, and reduces the number of holes. The planning time of measuring the swing angle is greatly reduced, and the hole characteristic measurement swing angle can be quickly obtained, which saves the measurement preparation time and improves the processing and measurement efficiency.

The above are only preferred embodiments of the present invention and do not limit the present invention in any form. Any simple modifications or equivalent changes made to the above embodiments based on the technical essence of the present invention fall within the scope of the present invention. within the scope of protection.

Claims

The group hole measurement swing angle planning method is used to detect hole characteristics on the machine, which is characterized by including the following steps:

Step S1: Obtain the hole feature normal vector set, stylus parameters, measurement depth and tolerance information;

Step S2: Based on the stylus parameters, measurement depth and tolerance information, calculate the angle threshold between the stylus axis and the hole axis when the hole and stylus do not interfere; set the initial value of the clustering scale and the initial number of iterations. value, initialize the seed collection, and determine the maximum number of iterations;

Step S3: Perform clustering: use the hole feature normal vector set as the data set, calculate the clustering set and the measurement normal vector set, and calculate candidate seeds;

Step S4: Determine the clustering convergence: if the clustering converges, execute step S6; if the clustering does not converge, execute step S5;

Step S5: Update the seed set first; then, if the number of iterations is less than or equal to the maximum number of iterations, increase the number of iterations by one, and re-execute steps S3 and S4 in sequence; if the number of iterations is greater than the maximum number of iterations, increase the clustering size by one, Add candidate seeds to the current seed set to form a new seed set, return the current iteration number to the initial value, and re-execute steps S3 and S4;

Step S6: Convert the set of measured normal vectors into a set of measured pendulum angles, output the set of measured pendulum angles and merge to end the program.
The method for planning the swing angle of group hole measurement according to claim 1, wherein the stylus parameters include a probe diameter and a probe rod diameter.
The group hole measurement swing angle planning method according to claim 1, characterized in that: the value of the tolerance information is greater than or equal to 0.5°.
The group hole measurement swing angle planning method according to claim 1, characterized in that: the initial value of the clustering scale is 1, the initial value of the number of iterations is 1; the initialization seed set is: the hole feature normal vector is the first Element serves as the unique seed of the seed collection.
The group hole measurement swing angle planning method according to claim 1, characterized in that: in step S3, the cluster set and the measurement normal vector set are calculated through the following specific steps, and the specific steps are as follows:

Step S31: Add the angles formed by the i-th element in the hole feature normal vector set and various sub-elements in the seed set as elements into the i-th included angle set;

Step S32: If the j-th seed in the seed set forms an angle with the i-th element in the hole feature normal vector set, and is the element with the smallest value in the i-th angle set; then add the i-th element in the hole feature normal vector set to the cluster. The jth cluster subset in the class set corresponds to adding the jth seed in the seed set to the measurement normal vector set; where, {i∈Z|1≤i≤N}, N is the number of elements in the hole feature normal vector set;

Step S33: Traverse the cluster set, and add the angle formed by each element of the j-th cluster subset in the cluster set and the j-th seed in the seed set to the candidate set as an element. The corresponding cluster when the element in the candidate set reaches the maximum value is The specific elements of the jth cluster subset in the class set are used as candidate seeds; where {j∈Z|1≤j≤M}, M is the number of seeds in the seed set.
The group hole measurement swing angle planning method according to claim 5, characterized in that: the cluster subsets in the cluster set correspond to the seeds in the seed set in sequence.
The group hole measurement swing angle planning method according to claim 5, characterized in that when the maximum value obtained by the elements in the candidate set is greater than the angle threshold, it is determined that the clustering does not converge, otherwise it is determined that the clustering has converged.
The group hole measurement swing angle planning method according to claim 1, characterized in that: the method for updating the seed set in step S5 is to convert each cluster subset in the cluster set one by one into various types in the new seed set. Specifically, the average value of the elements in each cluster subset is used as the various children in the new seed set.
A computer-readable medium with a computer program stored thereon, characterized in that when the computer program is executed, the method for planning the group hole measurement swing angle described in any one of claims 1 to 8 is implemented.
An electronic device, characterized by including:

processor;

memory for storing executable instructions for the processor;

Wherein, the processor is configured to execute the group hole measurement swing angle planning method according to any one of claims 1 to 8 by executing the executable instructions.