A skull processing material matching method, system and equipment

Abstract

The present invention discloses a method, system, and device for matching skull processing materials. The matching method includes: obtaining multiple skull data of a skull to be processed and multiple material data of materials to be matched; converting the skull data and material data into point clouds to obtain skull point cloud data corresponding to the skull to be processed and material point cloud data corresponding to the materials to be matched; performing concave-convex surface segmentation on the material point cloud data to obtain material concave-convex surface data; and determining a target processing material that matches the skull to be processed based on the skull point cloud data and the material concave-convex surface data. Compared with the existing technology, by obtaining point cloud data of the skull to be processed and the materials to be matched, and performing concave-convex surface segmentation on the material point cloud data, automated matching of the target processing material for processing the skull to be processed is achieved, solving the problems of low efficiency, large errors, and excessive material waste in traditional manual matching. While avoiding human errors, it significantly improves matching efficiency and accuracy and reduces material costs.

Description

Technical Field

[0001] The present invention relates to the technical field of skull processing material matching, and in particular to a skull processing material matching method, system and equipment. Background Art

[0002] Currently, skull repair materials primarily include titanium alloy plates, polymethyl methacrylate (PMMA), and autologous bone. Titanium alloys are widely used due to their excellent biocompatibility and high mechanical strength. Polyetheretherketone (PEEK) is experiencing rapid market growth due to its advantages such as avoiding allergic reactions and having similar physical properties to autologous skull bone. During this period of rapid growth in the skull repair material market, most companies still rely on mechanical processing to shape the skull, which inevitably results in swarf from milling the blank. The cost of this unused material is also considerable.

[0003] Therefore, the company will design multiple processing blanks of different specifications, and manually match them one by one to find the minimum specification that can just completely wrap the skull to be processed, further avoiding more blank waste.

[0004] However, the manual matching process often takes more time and may also result in matching larger specifications of processing materials due to manual matching differences, which in turn leads to material waste and increased material costs. Summary of the Invention

[0005] In order to overcome the shortcomings of the existing technology, the purpose of the present invention is to provide a skull processing material matching method, system and equipment, which does not require manual matching, thereby reducing matching time, while reducing manual participation to avoid material waste and increased material costs due to human differences.

[0006] In order to solve the above problems, the present invention is implemented according to the following scheme:

[0007] A skull processing material matching method is provided, comprising:

[0008] Acquire multiple skull data of skulls to be processed and multiple material data of materials to be matched;

[0009] Performing point cloud conversion on the skull data and the material data to obtain skull point cloud data corresponding to the skull to be processed and material point cloud data corresponding to the material to be matched;

[0010] Performing concave-convex surface segmentation on the material point cloud data to obtain material concave-convex surface data;

[0011] A target processing material matching the skull to be processed is determined based on the skull point cloud data and the material concave-convex surface data.

[0012] Compared with the existing technology, the beneficial effects of the skull processing material matching method of the present invention are as follows: by obtaining the point cloud data of the skull to be processed and the material to be matched, and performing concave-convex surface segmentation on the material point cloud data, automatic matching of the target processing material for processing the skull to be processed is achieved, which solves the problems of low efficiency, large errors and high material waste of traditional manual matching, while avoiding human differences, significantly improving matching efficiency and accuracy, and reducing material costs.

[0013] Optionally, the step of converting the skull data and the material to be matched into point cloud data to obtain skull point cloud data corresponding to the skull to be processed and material point cloud data corresponding to multiple materials to be matched includes:

[0014] Separating the skull data and the material data respectively to obtain a plurality of skull surface data included in the skull data and a plurality of material surface data included in the material data;

[0015] Sampling the skull surface data and the material surface data respectively to obtain skull discrete data corresponding to the skull surface data and material discrete data corresponding to the material surface data;

[0016] Obtaining initial skull point cloud data according to the skull discrete data;

[0017] Obtaining the material point cloud data according to the material discrete data;

[0018] The skull point cloud data is determined according to the initial skull point cloud data and the material point cloud data.

[0019] Optionally, determining the skull point cloud data according to the initial skull point cloud data and the material point cloud data includes:

[0020] Select one point cloud data from multiple material point cloud data as target material point cloud data;

[0021] determining a skull moment of inertia for representing the moment of inertia of the initial skull point cloud data and a material moment of inertia for representing the target material point cloud data;

[0022] determining a rotation matrix based on the skull moment of inertia and the material moment of inertia;

[0023] The initial skull point cloud data is transformed according to the rotation matrix to obtain the skull point cloud data.

[0024] Optionally, the material concave-convex surface data includes material convex surface data and material concave surface data;

[0025] Performing concave-convex surface segmentation on the material point cloud data to obtain material concave-convex surface data, including:

[0026] Determining the highest point of the convex surface, the highest point of the concave surface, and the segmentation surface in the material point cloud data;

[0027] Determining the material convex surface data according to the highest point of the convex surface and the segmentation surface;

[0028] The concave surface data of the material is determined according to the highest point of the concave surface and the dividing surface.

[0029] Optionally, the material point cloud data includes point cloud data on a first axis and point cloud data on a second axis perpendicular to the first axis;

[0030] Determining the highest point of the convex surface, the highest point of the concave surface, and the split surface in the material point cloud data includes:

[0031] Determine the point cloud data of the maximum value on the first axis as the highest point of the convex surface;

[0032] Determine the highest point of the concave surface according to the highest point of the convex surface;

[0033] Determine the surface where the point cloud data with the minimum value on the second axis is located as the target surface;

[0034] Determine the point cloud data with the maximum value on the target surface and the first axis as a segmentation point;

[0035] A dividing surface for dividing the convex surface and the concave surface is determined according to the dividing point.

[0036] Optionally, performing concave-convex surface segmentation on the material point cloud data to obtain material concave-convex surface data further includes:

[0037] Determine the horizontal and vertical extents of the mis-segmented area;

[0038] Constructing a correction equation with the highest point of the concave surface as the origin according to the transverse range and the longitudinal range;

[0039] determining mis-segmented data in the convex surface data of the material according to the correction equation;

[0040] The mis-segmented data is determined as the material concave surface data.

[0041] Optionally, determining a target processing material that matches the skull to be processed based on the skull point cloud data and the material concave-convex surface data includes:

[0042] respectively determining the vertices of the skull point cloud data and the material concave-convex surface data on the same axis;

[0043] Determining initial positioning parameters and initial Euler angles according to the vertices;

[0044] Determining the amount of interference point cloud data of the skull point cloud data and the material concave-convex surface data under the initial positioning parameters and the initial Euler angles;

[0045] Adjusting the initial positioning parameters and the initial Euler angles according to the amount of interference point cloud data, and re-determining the adjusted amount of interference point cloud data;

[0046] When the number of adjustments to the initial positioning parameters and the initial Euler angles is equal to the preset number, if the amount of interference point cloud data does not meet the preset conditions, the material concave and convex surface data does not match the skull point cloud data; if the amount of interference point cloud data meets the preset conditions, the material concave and convex surface data matches the skull point cloud data, and the material to be matched corresponding to the material concave and convex surface data is determined as the target processing material.

[0047] Optionally, adjusting the initial positioning parameters and the initial Euler angles according to the amount of interference point cloud data, and re-determining the adjusted amount of interference point cloud data, includes:

[0048] When the amount of interference point cloud data is less than the preset number of point clouds, the initial positioning parameters are used as secondary positioning parameters, and the initial Euler angles are adjusted to obtain secondary Euler angles;

[0049] When the amount of interference point cloud data is greater than or equal to the preset point cloud quantity, the initial positioning parameters and the initial Euler angles are adjusted to obtain secondary positioning parameters and secondary Euler angles;

[0050] Determine the amount of interference point cloud data of the skull point cloud data and the material concave-convex surface data under the secondary positioning parameters and the secondary Euler angles.

[0051] A skull processing material matching system is also provided, which is applied to the skull processing material matching method, comprising:

[0052] Point cloud module, used for:

[0053] Acquire multiple skull data of skulls to be processed and multiple material data of materials to be matched;

[0054] Performing point cloud conversion on the skull data and the material data to obtain skull point cloud data corresponding to the skull to be processed and material point cloud data corresponding to the material to be matched;

[0055] A concave-convex surface segmentation module is used to perform concave-convex surface segmentation on the material point cloud data to obtain material concave-convex surface data;

[0056] The matching module is used to determine a target processing material that matches the skull to be processed based on the skull point cloud data and the material concave-convex surface data.

[0057] A computer device is also provided, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement the skull processing material matching method. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a flow chart of the matching method of the present invention;

[0059] Figure 2 Schematic diagram of the concave-convex surface data of the material of the present invention. DETAILED DESCRIPTION

[0060] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0061] When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims. In the description of the present application, it should be understood that the terms "first", "second", "third", etc. are only used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0062] See also Figure 1 As shown, a skull processing material matching method of the present invention includes:

[0063] S1: Acquire multiple skull data of a skull to be processed and material data of multiple materials to be matched; wherein, the skull to be processed is a skull with a defective area that needs to be repaired, and the skull data mostly comes from medical imaging equipment such as CT and MRI. The skull to be processed (skull defect area) is scanned with high precision by these medical imaging equipment to obtain the skull data corresponding to the skull to be processed; in order to obtain three-dimensional data of the skull to be processed, when the skull to be processed is scanned by the medical imaging equipment, it is scanned from different angles, so one skull to be processed includes multiple skull data.

[0064] The materials used for skull repair include but are not limited to titanium alloy plates, polymethyl methacrylate (PMMA), and autologous bones. When selecting a processing material that matches the skull to be processed, it is necessary to consider what material matches the defective area in the skull to be processed. Therefore, when the defective areas are different, it is impossible to fix one repair processing material, and it is necessary to select the processing material that best matches the defective area from multiple materials.

[0065] S2: Convert the skull data and material data into point cloud data to obtain skull point cloud data corresponding to the skull to be processed and material point cloud data corresponding to the material to be matched, including:

[0066] First, the skull data and material data are separated respectively to obtain multiple skull surface data included in the skull data and multiple material surface data included in the material data. That is, one skull to be processed corresponds to multiple skull data, and each skull data includes multiple skull surface data. One material to be matched corresponds to one material data, and each material data includes multiple material surface data.

[0067] Each skull data and each material data is usually composed of multiple independent facets (such as triangular facets in a three-dimensional mesh), each of which may represent a different area or material structure. By separating the skull data and material data to obtain corresponding facet data, the geometric details of the skull to be processed and the material to be matched (the structure of the skull or the structure of the material) can be retained, thereby improving the accuracy of matching the skull to be processed.

[0068] The patch data is a continuous surface and needs to be converted into discrete data through sampling. The discrete data is specifically the coordinate points distributed in the three-dimensional space, and the geometric characteristics of the patch data are represented by the coordinate points. Therefore, after obtaining the skull patch data and the material patch data, the skull patch data and the material patch data are sampled respectively to obtain the skull discrete data corresponding to the skull patch data and the material discrete data corresponding to the material patch data.

[0069] Based on the skull discrete data, the initial skull point cloud data is obtained; the skull discrete data is a three-dimensional point set obtained by sampling the skull surface data, and the three-dimensional point set includes the geometric information of the skull surface to be processed, and multiple skull discrete data constitute the initial skull point cloud data, that is, the initial skull point cloud data is specifically a collection of discrete points (skull discrete data).

[0070] Material point cloud data is obtained based on the material discrete data; the material discrete data is a three-dimensional point set obtained by sampling the material surface data, and the three-dimensional point set includes the geometric information of the material structure to be matched, and multiple material discrete data constitute the material point cloud data, that is, the material point cloud data is specifically a collection of discrete points (material discrete data).

[0071] Different point cloud data may have differences in spatial directions, and the moment of inertia reflects the direction of the main axis of the object. By aligning the moments of inertia of the two, the rotation matrix can be calculated. Because a skull to be processed corresponds to skull data obtained by shooting at different angles, the skull data is used to obtain skull facial data, and then the skull discrete data is obtained from the skull facial data, and finally the initial skull point cloud data is obtained from the skull discrete data. Different initial skull point cloud data may have differences in angle, that is, there are differences in the main axis direction of different initial skull point cloud data; and the main axis directions of the skull to be processed and the material to be matched will also be different. Therefore, after aligning the moments of inertia of the initial skull point cloud data and the material point cloud data, the skull point cloud can be rotated to be consistent with the main axis direction of the material point cloud, ensuring that the coordinate systems of the skull and the material are aligned during matching or processing, which can reduce the shape error of the skull and the material.

[0072] Therefore, after obtaining the initial skull point cloud data and material point cloud data, it is necessary to align the initial skull point cloud data and the material point cloud data with the moment of inertia to rotate the skull point cloud to be consistent with the main axis direction of the material point cloud. Specifically, the skull point cloud data is determined based on the initial skull point cloud data and the material point cloud data, including:

[0073] First, one point cloud data from multiple material point cloud data is selected as the target material point cloud data; then, the skull moment of inertia used to represent the inertia moment of the initial skull point cloud data and the material moment of inertia used to represent the target material point cloud data are determined; finally, the rotation matrix is ​​determined based on the skull moment of inertia and the material moment of inertia; based on the rotation matrix, the initial skull point cloud data is transformed to obtain skull point cloud data that is consistent with the main axis direction of the material point cloud data.

[0074] S3: performing concave-convex surface segmentation on the material point cloud data to obtain material concave-convex surface data; wherein the material concave-convex surface data includes material convex surface data and material concave surface data, and the material point cloud data includes point cloud data on a first axis and point cloud data on a second axis perpendicular to the first axis; obtaining the material concave-convex surface data by performing concave-convex surface segmentation on the material point cloud data includes:

[0075] First, the highest point of the convex surface, the highest point of the concave surface and the splitting surface in the material point cloud data are determined, including: determining the point cloud data with the maximum value on the first axis as the highest point of the convex surface; determining the highest point of the concave surface based on the highest point of the convex surface; determining the surface where the point cloud data with the minimum value on the second axis is located as the target surface; determining the point cloud data with the maximum value on the first axis on the target surface as the splitting point; and determining the splitting surface for splitting the convex surface and the concave surface based on the splitting point.

[0076] Among them, the first axis is specifically the Z axis, and the second axis is specifically the X axis, that is, determining the highest point of the convex surface, the highest point of the concave surface and the splitting surface in the material point cloud data, specifically, first determine the point cloud data with the maximum value of the Z axis in the material point cloud data as the highest point of the convex surface; and because the highest point of the convex surface (the maximum value of the Z axis) is usually located at the top of the material point cloud data, and the highest point of the concave surface is the corresponding point of the highest point of the convex surface in the concave area, after finding the highest point of the convex surface, the local highest point of the concave area can be found downward along the Z axis, and this point is the highest point of the concave surface, so after determining the highest point of the convex surface, the highest point of the concave surface can be simultaneously determined through the highest point of the convex surface.

[0077] Next, determine the surface where the point cloud data with the minimum value on the X-axis of the material point cloud data is located, determine this surface as the target surface, and determine the point cloud data on the target surface with the maximum value on the Z-axis as the segmentation point, that is, determine the Z value of all point cloud data on the target surface in the Z-axis direction, and use the point cloud data with the largest Z value as the segmentation point.

[0078] Finally, the surface where the Z value of the split point is located is used as the split surface, that is, the split surface is a surface perpendicular to the Z axis. The split surface is in the Z axis direction, and the material point cloud data of the material to be matched is divided into two parts, upper and lower. Figure 2 As shown, the upper part of the figure is the convex surface data of the material, and the lower part of the figure is the concave surface data of the material.

[0079] Therefore, after determining the highest point of the convex surface, the highest point of the concave surface and the dividing surface, the material point cloud data can be divided into material convex surface data and material concave surface data. Specifically, the material convex surface data is determined according to the highest point of the convex surface and the dividing surface; the material concave surface data is determined according to the highest point of the concave surface and the dividing surface.

[0080] When performing concave-convex surface segmentation, due to the complex geometric shape of the material point cloud data, the segmentation surface may not be able to completely and accurately separate the convex and concave surfaces, resulting in some point cloud data being mis-segmented; for example, some point clouds that should belong to the concave surface may be mistakenly divided into convex surface data. In order to ensure the accuracy of the material point cloud data segmentation, the mis-segmented areas need to be corrected to reclassify the mis-segmented point cloud data into the correct point cloud data.

[0081] Therefore, after the material point cloud data is segmented by the highest point of the convex surface, the highest point of the concave surface, and the segmentation surface, it is necessary to correct the convex surface data and the concave surface data of the material, that is, to perform concave-convex surface segmentation on the material point cloud data to obtain concave-convex surface data of the material, which also includes: determining the horizontal range and vertical range of the mis-segmentation area, the horizontal range is specifically the mis-segmentation range on the X axis, and the vertical range is specifically the mis-segmentation range on the Z axis; according to the horizontal range and the vertical range, constructing a correction equation with the highest point of the concave surface as the origin, the correction equation is specifically an ellipse equation, and the expression of the ellipse equation is as follows:

[0082]

[0083] Among them, X is the value on the X-axis, Z is the value on the Z-axis, a and b are the parameters of the ellipse equation, which are the semi-axis lengths of the ellipse in the horizontal (X-axis) and vertical (Z-axis) directions, respectively. According to the horizontal and vertical ranges of the mis-segmented area, the parameters a and b of the ellipse equation are adjusted so that the area represented by the ellipse equation is the same as the area represented by the actual convex surface point cloud data of the material to be matched; when there are point cloud data outside the area of ​​the ellipse equation (corrected equation) in the material point cloud data, these point cloud data are concave surface data that are mistakenly segmented into the convex surface. Therefore, according to the corrected equation, the mis-segmented data in the convex surface data of the material is determined; the mis-segmented data is determined as the concave surface data of the material, that is, the mis-segmented data is divided into the concave surface area.

[0084] S4: Determine the target processing material that matches the skull to be processed based on the skull point cloud data and the material concave-convex surface data, including:

[0085] First, determine the vertices of the skull point cloud data and the material concave-convex surface data on the same axis, specifically, determine the vertices of the skull point cloud data in the Z-axis direction and the vertices of the material concave-convex surface data in the Z-axis direction. The vertices of the material concave-convex surface data are the highest points of the convex surface and the highest points of the concave surface.

[0086] Next, according to the vertices, the initial positioning parameters and initial Euler angles are determined, and the skull point cloud data and the material concave and convex surface data are determined. The amount of interference point cloud data under the initial positioning parameters and initial Euler angles is the amount of point cloud data where there is interference between the skull point cloud data and the material concave and convex surface data.

[0087] Then, according to the amount of interference point cloud data, the initial positioning parameters and initial Euler angles are adjusted, and the adjusted amount of interference point cloud data is determined again; when the number of adjustments to the initial positioning parameters and initial Euler angles is equal to the preset number, if the amount of interference point cloud data does not meet the preset conditions, the material concave and convex surface data does not match the skull point cloud data; if the amount of interference point cloud data meets the preset conditions, the material concave and convex surface data matches the skull point cloud data, and the material to be matched corresponding to the material concave and convex surface data is determined as the target processing material.

[0088] According to the amount of interference point cloud data, the initial positioning parameters and initial Euler angles are adjusted, and the adjusted amount of interference point cloud data is determined again, including:

[0089] When the amount of interference point cloud data is less than the preset number of point clouds, the initial positioning parameters are used as secondary positioning parameters, and the initial Euler angles are adjusted to obtain secondary Euler angles; when the amount of interference point cloud data is greater than or equal to the preset number of point clouds, the initial positioning parameters and initial Euler angles are adjusted to obtain secondary positioning parameters and secondary Euler angles; finally, the amount of interference point cloud data of the skull point cloud data and the material concave and convex surface data under the secondary positioning parameters and secondary Euler angles is determined.

[0090] The method for determining whether the skull to be processed matches the material to be matched is to calculate whether there is point cloud data within a specified radius between the skull point cloud data and the material concave and convex surface data. In one embodiment of the present invention, the material concave and convex surface data (point cloud) of the material to be matched is KDTree initialized, and the radius range is set to 0.5mm. The established KDTree is searched to determine whether there is a point cloud (skull point cloud data) of the skull to be processed within the set radius, thereby determining whether the skull to be processed and the material to be matched are successfully matched.

[0091] Among them, the matching process is specifically as follows: the skull point cloud data of the skull to be processed is roughly aligned with the material point cloud data of the material to be matched, so that the vertices of the skull to be processed and the material to be matched are in the same direction of the Z axis, and the vertices of the skull to be processed are lower than the vertices of the material to be matched; within the range of ±1mm of the Z value in the Z-axis direction of the current position, a value is randomly obtained for the first matching Z value (initial positioning parameter). At the same time, the value of the Euler angle (initial Euler angle) is also randomly obtained based on the range of ±2 of the current position.

[0092] If the amount of interference point cloud data between the skull to be processed and the material to be matched is less than 100, the Z value (positioning parameter) is considered to be the better option, and the value of the Z axis will not be changed, that is, the value will still be used as the positioning parameter in the second matching, and only the value of the Euler angle will be changed; that is, the preset number of point clouds is 100. When the amount of interference point cloud data is less than 100, the initial positioning parameters are used as secondary positioning parameters, and the initial Euler angles are adjusted to obtain secondary Euler angles. When the amount of interference point cloud data is greater than or equal to 100, the initial positioning parameters and initial Euler angles are adjusted to obtain secondary positioning parameters and secondary Euler angles.

[0093] In one embodiment of the present invention, the number of iterations is set to 200, that is, the skull to be processed and each material to be matched are matched a maximum of 200 times. If the amount of interference point cloud data is 0 in 200 iterations, it means that the matching is successful; if the amount of interference point cloud data is not 0 after 200 iterations, it means that the matching fails; that is, when the amount of interference point cloud data is 0, the amount of interference point cloud data meets the preset conditions, and when the amount of interference point cloud data is not 0, the amount of interference point cloud data does not meet the preset conditions.

[0094] The present invention also provides a skull processing material matching system, which is applied to a skull processing material matching method, comprising:

[0095] Point cloud module, used for:

[0096] Acquire multiple skull data of skulls to be processed and multiple material data of materials to be matched;

[0097] Convert the skull data and material data into point cloud data to obtain skull point cloud data corresponding to the skull to be processed and material point cloud data corresponding to the material to be matched;

[0098] Concave-convex surface segmentation module, used to perform concave-convex surface segmentation on material point cloud data to obtain material concave-convex surface data;

[0099] The matching module is used to determine the target processing material that matches the skull to be processed based on the skull point cloud data and the material concave and convex surface data.

[0100] It also includes a processing material library for storing material data of multiple materials to be matched, which is managed through json files to record the name, size and current path information of the materials to be matched.

[0101] The present invention also provides a computer device comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement the above-mentioned matching method.

[0102] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0103] The memory can be used to store the computer program or module, and the processor implements the various functions of the matching method by running or executing the computer program or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created based on the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0104] The above are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A skull processing material matching method, characterized in that: include: Acquire multiple skull data of skulls to be processed and multiple material data of materials to be matched; Performing point cloud conversion on the skull data and the material data to obtain skull point cloud data corresponding to the skull to be processed and material point cloud data corresponding to the material to be matched; Performing concave-convex surface segmentation on the material point cloud data to obtain material concave-convex surface data; Determining a target processing material that matches the skull to be processed based on the skull point cloud data and the material concave-convex surface data; Point clouding is performed on the skull data and the material to be matched to obtain skull point cloud data corresponding to the skull to be processed and material point cloud data corresponding to multiple materials to be matched, including: Separating the skull data and the material data respectively to obtain a plurality of skull surface data included in the skull data and a plurality of material surface data included in the material data; Sampling the skull surface data and the material surface data respectively to obtain skull discrete data corresponding to the skull surface data and material discrete data corresponding to the material surface data; Obtaining initial skull point cloud data according to the skull discrete data; Obtaining the material point cloud data according to the material discrete data; Determining the skull point cloud data according to the initial skull point cloud data and the material point cloud data; Determining a target processing material that matches the skull to be processed based on the skull point cloud data and the material concave-convex surface data includes: respectively determining the vertices of the skull point cloud data and the material concave-convex surface data on the same axis; Determining initial positioning parameters and initial Euler angles according to the vertices; Determining the amount of interference point cloud data of the skull point cloud data and the material concave-convex surface data under the initial positioning parameters and the initial Euler angles; Adjusting the initial positioning parameters and the initial Euler angles according to the amount of interference point cloud data, and re-determining the adjusted amount of interference point cloud data; When the number of adjustments to the initial positioning parameters and the initial Euler angles is equal to the preset number, if the amount of interference point cloud data does not meet the preset conditions, the material concave and convex surface data does not match the skull point cloud data; if the amount of interference point cloud data meets the preset conditions, the material concave and convex surface data matches the skull point cloud data, and the material to be matched corresponding to the material concave and convex surface data is determined as the target processing material.

2. A skull processing material matching method according to claim 1, characterized in that: Determining the skull point cloud data according to the initial skull point cloud data and the material point cloud data includes: Select one point cloud data from multiple material point cloud data as target material point cloud data; determining a skull moment of inertia for representing the moment of inertia of the initial skull point cloud data and a material moment of inertia for representing the target material point cloud data; determining a rotation matrix based on the skull moment of inertia and the material moment of inertia; The initial skull point cloud data is transformed according to the rotation matrix to obtain the skull point cloud data.

3. The skull processing material matching method according to claim 1, characterized in that: The material concave-convex surface data includes material convex surface data and material concave surface data; Performing concave-convex surface segmentation on the material point cloud data to obtain material concave-convex surface data, including: Determining the highest point of the convex surface, the highest point of the concave surface, and the segmentation surface in the material point cloud data; Determining the material convex surface data according to the highest point of the convex surface and the segmentation surface; The concave surface data of the material is determined according to the highest point of the concave surface and the dividing surface.

4. The skull processing material matching method according to claim 3, characterized in that: The material point cloud data includes point cloud data on a first axis and point cloud data on a second axis perpendicular to the first axis; Determining the highest point of the convex surface, the highest point of the concave surface, and the split surface in the material point cloud data includes: Determine the point cloud data of the maximum value on the first axis as the highest point of the convex surface; Determine the highest point of the concave surface according to the highest point of the convex surface; Determine the surface where the point cloud data with the minimum value on the second axis is located as the target surface; Determine the point cloud data with the maximum value on the target surface and the first axis as a segmentation point; A dividing surface for dividing the convex surface and the concave surface is determined according to the dividing point.

5. The skull processing material matching method according to claim 4, characterized in that: Performing concave-convex surface segmentation on the material point cloud data to obtain material concave-convex surface data also includes: Determine the horizontal and vertical extents of the mis-segmented area; Constructing a correction equation with the highest point of the concave surface as the origin according to the transverse range and the longitudinal range; determining mis-segmented data in the convex surface data of the material according to the correction equation; The mis-segmented data is determined as the material concave surface data.

6. The skull processing material matching method according to claim 1, characterized in that: Adjusting the initial positioning parameters and the initial Euler angles according to the amount of interference point cloud data, and determining the adjusted amount of interference point cloud data again, including: When the amount of interference point cloud data is less than the preset number of point clouds, the initial positioning parameters are used as secondary positioning parameters, and the initial Euler angles are adjusted to obtain secondary Euler angles; When the amount of interference point cloud data is greater than or equal to the preset point cloud quantity, the initial positioning parameters and the initial Euler angles are adjusted to obtain secondary positioning parameters and secondary Euler angles; Determine the amount of interference point cloud data of the skull point cloud data and the material concave-convex surface data under the secondary positioning parameters and the secondary Euler angles.

7. A skull processing material matching system, using a skull processing material matching method according to any one of claims 1 to 6, characterized in that: include: Point cloud module, used for: Acquire multiple skull data of skulls to be processed and multiple material data of materials to be matched; Performing point cloud conversion on the skull data and the material data to obtain skull point cloud data corresponding to the skull to be processed and material point cloud data corresponding to the material to be matched; A concave-convex surface segmentation module is used to perform concave-convex surface segmentation on the material point cloud data to obtain material concave-convex surface data; The matching module is used to determine a target processing material that matches the skull to be processed based on the skull point cloud data and the material concave-convex surface data.

8. A computer device, characterized in that: The computer device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement the skull processing material matching method as described in any one of claims 1 to 6.

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