Oral cavity three-dimensional model construction and tooth occlusion characteristic analysis method

Abstract

The invention relates to the technical field of computer image processing, in particular to an oral cavity three-dimensional model construction and tooth occlusion feature analysis method. The method comprises the following steps: carrying out imaging scanning processing and three-dimensional model construction on an oral cavity structure of a patient to generate an oral cavity internal structure three-dimensional space model; performing tooth segmentation and tooth feature analysis on the oral cavity internal structure three-dimensional space model, and performing inter-tooth contact evaluation analysis to obtain oral cavity inter-tooth arrangement contact condition data; performing tooth occlusal point positioning analysis and tooth occlusal surface connection division on the oral cavity internal structure three-dimensional space model to generate an oral cavity tooth occlusal connection surface; and performing tooth occlusion characteristic analysis on the oral tooth occlusion connection surface to obtain a tooth occlusion surface parallelism and a tooth occlusion surface contour shape, and performing tooth occlusion adjustment processing to generate an oral tooth occlusion surface adjustment scheme. According to the invention, rapid reconstruction of an oral cavity structure and accurate analysis of tooth occlusion characteristics can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of computer image processing, and in particular to a method for constructing an oral three-dimensional model and analyzing tooth occlusion characteristics. Background Art

[0002] In recent years, with the development of computer vision and digital technology, some new methods for constructing oral three-dimensional models and analyzing tooth occlusion features have begun to attract attention. These methods are mainly based on image processing and machine learning technology, which can realize the automatic analysis and three-dimensional reconstruction of oral image data, thereby realizing the precise quantitative analysis of tooth occlusion features. However, traditional oral image acquisition methods mainly include X-ray photography, CT scanning and magnetic resonance imaging. Although these methods can provide a certain degree of oral structure information, they often cannot obtain accurate three-dimensional model data and it is difficult to accurately reflect tooth occlusion characteristics and occlusion relationships. In addition, traditional methods for analyzing tooth occlusion features mainly rely on the doctor's experience and observation, and have the problem of low accuracy, which limits their wide application in the field of stomatology. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method for constructing an oral three-dimensional model and analyzing tooth occlusion characteristics to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for constructing an oral three-dimensional model and analyzing tooth occlusion characteristics comprises the following steps:

[0005] Step S1: performing imaging scanning on the patient's oral structure through an oral scanner to obtain an image set of the internal structure of the oral cavity; constructing a three-dimensional model of the image set of the internal structure of the oral cavity to generate a three-dimensional spatial model of the internal structure of the oral cavity;

[0006] Step S2: performing tooth segmentation and tooth feature analysis on the three-dimensional spatial model of the internal structure of the oral cavity to obtain three-dimensional geometric shape feature data of the oral teeth and three-dimensional spatial position feature data of the oral teeth; performing tooth contact evaluation analysis on the three-dimensional spatial model of the internal structure of the oral cavity based on the three-dimensional geometric shape feature data of the oral teeth and the three-dimensional spatial position feature data of the oral teeth to obtain data on the arrangement contact status of the oral teeth;

[0007] Step S3: performing tooth occlusal point positioning analysis on the three-dimensional space model of the internal structure of the oral cavity to obtain the position data of the occlusal points of the oral cavity teeth; performing tooth occlusal surface connection division on the three-dimensional space model of the internal structure of the oral cavity based on the arrangement contact status data between the oral teeth and the position data of the occlusal points of the oral teeth to generate the occlusal connection surface of the oral cavity teeth;

[0008] Step S4: Analyze the occlusal characteristics of the oral tooth occlusal connection surface to obtain the parallelism of the tooth occlusal surface and the contour shape of the tooth occlusal surface; perform tooth occlusal adjustment processing on the three-dimensional space model of the oral internal structure based on the parallelism of the tooth occlusal surface and the contour shape of the tooth occlusal surface to generate an oral tooth occlusal surface adjustment plan.

[0009] The present invention firstly uses an oral scanner to perform imaging scanning on the patient's oral structure, thereby scanning and obtaining an oral internal structure image set. These image data are a true reflection of the patient's oral internal structure, and provide a necessary basis for subsequent processing and analysis. At the same time, by constructing a three-dimensional model of the oral internal structure image set, a three-dimensional space model of the oral internal structure can be accurately constructed. This model can not only intuitively display the structural characteristics of the patient's oral cavity, but also provide an important reference for the subsequent tooth occlusion feature analysis process, and provide important support for further research and practice in the field of oral medicine. Secondly, by performing tooth segmentation processing on the three-dimensional space model of the oral internal structure, the tooth part in the complex oral structure can be extracted separately, thereby obtaining a three-dimensional space model of the oral tooth structure area. The key to this step is that it can provide a basis for subsequent tooth-related analysis, making it possible to perform a refined analysis of the morphology, position and other features of the teeth. By performing tooth feature analysis on the segmented three-dimensional space model of the oral teeth, the geometric shape characteristics of the teeth, such as the shape and curvature of the teeth, can be revealed, and the specific position information of the teeth in the oral cavity can be obtained, thereby providing a basis for evaluating problems such as tooth arrangement and occlusion. The contact between teeth is also evaluated and analyzed based on the three-dimensional geometric shape feature data of the oral teeth and the three-dimensional spatial position feature data of the oral teeth. The contact between teeth can be evaluated, including the number of contact points and the contact area. The key to this step is to help evaluate the arrangement and occlusal function of the teeth, and provide data support for the subsequent occlusal feature analysis process, thereby promoting the precision and personalization of oral health management. Then, the occlusal point positioning analysis of the three-dimensional spatial model of the oral internal structure is performed to determine the occlusal point of the teeth inside the oral cavity, and the specific position data of the occlusal point of the oral teeth is obtained by positioning and analyzing. These data provide data support for the subsequent processing process. The occlusal surface connection division of the three-dimensional spatial model of the oral internal structure is performed based on the arrangement contact status data between the oral teeth and the position data of the occlusal point of the oral teeth. The purpose of this step is to generate the occlusal connection surface of the oral teeth, and provide more detailed and accurate information for further analysis of the oral structure. By dividing the occlusal connection surface, the overall morphology and functional characteristics of the oral structure can be better understood, and more scientific and effective support can be provided for the subsequent occlusal feature analysis, so as to accurately reflect the occlusal characteristics and occlusal relationship of the teeth. Finally, by analyzing the occlusal characteristics of the oral teeth, we aim to evaluate and analyze the parallelism of the occlusal surfaces and the contour shape of the occlusal surfaces, so as to understand the morphological characteristics of the occlusal surfaces. This analysis helps to reveal the occlusal relationship and morphological characteristics between teeth, and provide more comprehensive information for the evaluation of oral structure, thereby improving the accuracy and precision of the occlusal feature analysis.In addition, the tooth occlusion adjustment is performed on the three-dimensional spatial model of the internal structure of the oral cavity based on the parallelism of the tooth occlusal surface and the contour shape of the tooth occlusal surface. This step aims to formulate a targeted tooth occlusion adjustment plan based on the above analysis results, and provide guidance for the adjustment and repair of the oral structure occlusion process. By adjusting the tooth occlusal surface, malocclusion can be corrected and the bite function can be improved, thereby improving oral health and quality of life. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0011] Figure 1 A schematic diagram of the steps of the method for constructing a three-dimensional oral cavity model and analyzing tooth occlusion characteristics of the present invention;

[0012] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0013] Figure 3 for Figure 2 Detailed step flow chart of step S15 in FIG. DETAILED DESCRIPTION

[0014] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0015] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different network and / or processor methods and / or microcontroller methods.

[0016] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0017] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for constructing an oral three-dimensional model and analyzing tooth occlusion characteristics, the method comprising the following steps:

[0018] Step S1: performing imaging scanning on the patient's oral structure through an oral scanner to obtain an image set of the internal structure of the oral cavity; constructing a three-dimensional model of the image set of the internal structure of the oral cavity to generate a three-dimensional spatial model of the internal structure of the oral cavity;

[0019] Step S2: performing tooth segmentation and tooth feature analysis on the three-dimensional spatial model of the internal structure of the oral cavity to obtain three-dimensional geometric shape feature data of the oral teeth and three-dimensional spatial position feature data of the oral teeth; performing tooth contact evaluation analysis on the three-dimensional spatial model of the internal structure of the oral cavity based on the three-dimensional geometric shape feature data of the oral teeth and the three-dimensional spatial position feature data of the oral teeth to obtain data on the arrangement contact status of the oral teeth;

[0020] Step S3: performing tooth occlusal point positioning analysis on the three-dimensional space model of the internal structure of the oral cavity to obtain the position data of the occlusal points of the oral cavity teeth; performing tooth occlusal surface connection division on the three-dimensional space model of the internal structure of the oral cavity based on the arrangement contact status data between the oral teeth and the position data of the occlusal points of the oral teeth to generate the occlusal connection surface of the oral cavity teeth;

[0021] Step S4: Analyze the occlusal characteristics of the oral tooth occlusal connection surface to obtain the parallelism of the tooth occlusal surface and the contour shape of the tooth occlusal surface; perform tooth occlusal adjustment processing on the three-dimensional space model of the oral internal structure based on the parallelism of the tooth occlusal surface and the contour shape of the tooth occlusal surface to generate an oral tooth occlusal surface adjustment plan.

[0022] In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a flow chart of the steps of the method for constructing a three-dimensional oral cavity model and analyzing the characteristics of tooth occlusion according to the present invention. In this example, the method for constructing a three-dimensional oral cavity model and analyzing the characteristics of tooth occlusion comprises the following steps:

[0023] Step S1: performing imaging scanning on the patient's oral structure through an oral scanner to obtain an image set of the internal structure of the oral cavity; constructing a three-dimensional model of the image set of the internal structure of the oral cavity to generate a three-dimensional spatial model of the internal structure of the oral cavity;

[0024] In the embodiment of the present invention, an oral scanner is used to apply the oral scanning adjustment parameters determined in real time to perform imaging scanning processing on the oral structure of the patient, so as to place the oral scanner in an appropriate position, and obtain the image data of the internal structure of the oral cavity through the light source and sensor of the oral scanner, thereby obtaining an image set of the internal structure of the oral cavity. Then, the image set of the internal structure of the oral cavity is analyzed by using a spatial feature analysis method to extract key features of the oral structure space from the two-dimensional image set, such as the position, shape, size of the teeth, the contour of the gums, and the structural features of other oral tissues, and project the two-dimensional structure feature points into the three-dimensional space, while removing the abnormal points or noise points therein to retain the valid feature point data, and at the same time, by using point cloud registration, surface reconstruction and other methods, the oral structure space feature point cloud data obtained after the outlier removal process is used to reconstruct the three-dimensional model of the oral structure, so as to fuse the point cloud data obtained from different perspectives or scans into a complete model, and map the surface detail information and texture information in the two-dimensional image to the initially constructed space model for optimization, so as to enhance the realism and visualization effect of the model, and finally generate a three-dimensional space model of the internal structure of the oral cavity.

[0025] Step S2: performing tooth segmentation and tooth feature analysis on the three-dimensional spatial model of the internal structure of the oral cavity to obtain three-dimensional geometric shape feature data of the oral teeth and three-dimensional spatial position feature data of the oral teeth; performing tooth contact evaluation analysis on the three-dimensional spatial model of the internal structure of the oral cavity based on the three-dimensional geometric shape feature data of the oral teeth and the three-dimensional spatial position feature data of the oral teeth to obtain data on the arrangement contact status of the oral teeth;

[0026] The embodiment of the present invention uses a computer tomography processing method to segment the tooth part in the three-dimensional space model of the internal structure of the oral cavity, so as to extract the tooth part in the complex oral structure separately, and uses a measuring tool or software to measure the size of each tooth structure in the segmented oral tooth structure space model, so as to measure the length, width, height, etc. of the tooth structure, and analyze the geometric shape, contour and other features of the tooth, so as to obtain the three-dimensional geometric shape feature data of the oral teeth. At the same time, the corresponding tooth structure in the three-dimensional space model of the internal structure of the oral cavity is positionally identified and analyzed by using a spatial position analysis method, so as to analyze the position and relative position of the tooth structure on the three-dimensional space model of the internal structure of the oral cavity, and obtain the specific position information of the tooth in the oral cavity, so as to obtain the three-dimensional spatial position feature data of the oral teeth. Then, by combining the three-dimensional geometric shape feature data of oral teeth and the three-dimensional spatial position feature data of oral teeth obtained by analysis, the contact conditions of the corresponding tooth structures in the three-dimensional spatial model of the internal structure of the oral cavity during the occlusion process are evaluated and analyzed using contact mechanics theory and simulation algorithms, so as to evaluate and analyze the contact conditions between the teeth, the stability of the contact points, etc., and thereby evaluate and analyze the arrangement contact conditions between the teeth inside the oral cavity during the occlusion process, and finally obtain the arrangement contact condition data between the oral teeth.

[0027] Step S3: performing tooth occlusal point positioning analysis on the three-dimensional space model of the internal structure of the oral cavity to obtain the position data of the occlusal points of the oral cavity teeth; performing tooth occlusal surface connection division on the three-dimensional space model of the internal structure of the oral cavity based on the arrangement contact status data between the oral teeth and the position data of the occlusal points of the oral teeth to generate the occlusal connection surface of the oral cavity teeth;

[0028] The embodiment of the present invention uses an occlusal relationship analysis method to analyze the interaction and relationship between teeth on the three-dimensional space model of the internal structure of the oral cavity, so as to check the contact points, occlusal mode, etc. between the teeth in the model to evaluate and analyze the malocclusion relationship and occlusal relationship between the teeth, and uses computer-aided analysis software or algorithms to identify and mark the occlusal points between the teeth on the three-dimensional space model of the internal structure of the oral cavity by combining the occlusal relationship of the internal teeth of the oral cavity obtained by the analysis, so as to obtain the occlusal points of contact between the teeth, and determine the specific position of the three-dimensional coordinates of each occlusal point, so as to obtain the position data of the occlusal points of the oral teeth. Then, by combining the contact status data of the arrangement of the oral teeth obtained by the analysis, the occlusal connection relationship between the teeth on the three-dimensional space model of the internal structure of the oral cavity is mined and analyzed by using a connection relationship mining algorithm, so as to analyze and determine which teeth have contact or occlusal relationship, and the corresponding teeth in the three-dimensional space model of the internal structure of the oral cavity are connected to the occlusal surface of the teeth in combination with the occlusal point position data of the oral teeth, so as to determine which teeth jointly constitute an occlusal surface, and mark and connect them, and finally generate the occlusal connection surface of the oral teeth.

[0029] Step S4: Analyze the occlusal characteristics of the oral tooth occlusal connection surface to obtain the parallelism of the tooth occlusal surface and the contour shape of the tooth occlusal surface; perform tooth occlusal adjustment processing on the three-dimensional space model of the oral internal structure based on the parallelism of the tooth occlusal surface and the contour shape of the tooth occlusal surface to generate an oral tooth occlusal surface adjustment plan.

[0030] The embodiment of the present invention uses an angle measuring instrument or a computer-aided measurement tool to measure the inclination angle of the oral tooth occlusal connection surface on the three-dimensional space model to accurately measure the inclination angle of each occlusal connection surface, and uses a statistical method to perform statistical calculations on the inclination curvature of the corresponding oral tooth occlusal connection surface to quantitatively calculate the curvature of the occlusal connection surface. At the same time, the occlusal parallelism of the oral tooth occlusal connection surface is calculated by combining the measured inclination angle and inclination curvature to quantitatively calculate the parallelism of the tooth occlusal surface, thereby obtaining the parallelism of the tooth occlusal surface. The contour shape of the connected oral tooth occlusal connection surface is also identified and analyzed by using a computer vision algorithm to identify and obtain the contour shape of the tooth occlusal surface to understand the morphological characteristics of the tooth occlusal surface, such as the degree of concavity and convexity and irregularity, thereby obtaining the contour shape of the tooth occlusal surface. Then, by combining the tooth occlusal features such as the parallelism of the tooth occlusal surfaces and the contour shape of the tooth occlusal surfaces obtained in the previous analysis, the three-dimensional spatial model of the internal structure of the oral cavity is adjusted using computer-aided design tools or three-dimensional simulation methods. The internal structure of the oral cavity is adjusted according to its parallelism and contour shape information, so that the tooth occlusal surfaces are parallel and their contour shapes conform to the oral anatomical structure and functional requirements, thereby formulating a targeted tooth occlusal adjustment plan to correct tooth malocclusion, improve occlusal function, etc., and finally generate an oral tooth occlusal surface adjustment plan.

[0031] The present invention firstly uses an oral scanner to perform imaging scanning on the patient's oral structure, thereby scanning and obtaining an oral internal structure image set. These image data are a true reflection of the patient's oral internal structure, and provide a necessary basis for subsequent processing and analysis. At the same time, by constructing a three-dimensional model of the oral internal structure image set, a three-dimensional space model of the oral internal structure can be accurately constructed. This model can not only intuitively display the structural characteristics of the patient's oral cavity, but also provide an important reference for the subsequent tooth occlusion feature analysis process, and provide important support for further research and practice in the field of oral medicine. Secondly, by performing tooth segmentation processing on the three-dimensional space model of the oral internal structure, the tooth part in the complex oral structure can be extracted separately, thereby obtaining a three-dimensional space model of the oral tooth structure area. The key to this step is that it can provide a basis for subsequent tooth-related analysis, making it possible to perform a refined analysis of the morphology, position and other features of the teeth. By performing tooth feature analysis on the segmented three-dimensional space model of the oral teeth, the geometric shape characteristics of the teeth, such as the shape and curvature of the teeth, can be revealed, and the specific position information of the teeth in the oral cavity can be obtained, thereby providing a basis for evaluating problems such as tooth arrangement and occlusion. The contact between teeth is also evaluated and analyzed based on the three-dimensional geometric shape feature data of the oral teeth and the three-dimensional spatial position feature data of the oral teeth. The contact between teeth can be evaluated, including the number of contact points and the contact area. The key to this step is to help evaluate the arrangement and occlusal function of the teeth, and provide data support for the subsequent occlusal feature analysis process, thereby promoting the precision and personalization of oral health management. Then, the occlusal point positioning analysis of the three-dimensional spatial model of the oral internal structure is performed to determine the occlusal point of the teeth inside the oral cavity, and the specific position data of the occlusal point of the oral teeth is obtained by positioning and analyzing. These data provide data support for the subsequent processing process. The occlusal surface connection division of the three-dimensional spatial model of the oral internal structure is performed based on the arrangement contact status data between the oral teeth and the position data of the occlusal point of the oral teeth. The purpose of this step is to generate the occlusal connection surface of the oral teeth, and provide more detailed and accurate information for further analysis of the oral structure. By dividing the occlusal connection surface, the overall morphology and functional characteristics of the oral structure can be better understood, and more scientific and effective support can be provided for the subsequent occlusal feature analysis, so as to accurately reflect the occlusal characteristics and occlusal relationship of the teeth. Finally, by analyzing the occlusal characteristics of the oral teeth, we aim to evaluate and analyze the parallelism of the occlusal surfaces and the contour shape of the occlusal surfaces, so as to understand the morphological characteristics of the occlusal surfaces. This analysis helps to reveal the occlusal relationship and morphological characteristics between teeth, and provide more comprehensive information for the evaluation of oral structure, thereby improving the accuracy and precision of the occlusal feature analysis.In addition, the tooth occlusion adjustment is performed on the three-dimensional spatial model of the internal structure of the oral cavity based on the parallelism of the tooth occlusal surface and the contour shape of the tooth occlusal surface. This step aims to formulate a targeted tooth occlusion adjustment plan based on the above analysis results, and provide guidance for the adjustment and repair of the oral structure occlusion process. By adjusting the tooth occlusal surface, malocclusion can be corrected and the bite function can be improved, thereby improving oral health and quality of life.

[0032] Preferably, step S1 comprises the following steps:

[0033] Step S11: Analyze the oral condition and scanning requirements of the patient's oral structure to obtain the patient's oral structure condition data and the patient's oral scanning requirement data;

[0034] Step S12: performing scanning parameter adaptive adjustment processing on the oral scanner according to the patient's oral structure data and the patient's oral scanning requirement data to generate an oral scanning adaptive adjustment parameter set;

[0035] Step S13: performing imaging scanning processing on the patient's oral structure through an oral scanner based on the oral scanning adaptive adjustment parameter set to obtain an oral internal structure image set;

[0036] Step S14: performing image denoising processing on the oral internal structure image set to obtain an oral structure denoised image set; performing standardization processing on the oral structure denoised image set to obtain an oral structure standard image set;

[0037] Step S15: construct a three-dimensional model of the standard image set of the oral structure to generate a three-dimensional spatial model of the internal structure of the oral cavity.

[0038] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0039] Step S11: Analyze the oral condition and scanning requirements of the patient's oral structure to obtain the patient's oral structure condition data and the patient's oral scanning requirement data;

[0040] The embodiment of the present invention obtains the patient's oral structure data by performing a detailed analysis of the patient's oral structure, including checking the arrangement, size, shape, health of the gums, and any existing oral diseases or abnormalities. At the same time, the patient's oral structure is analyzed using a demand analysis method according to the patient's specific situation to analyze and determine the scope, accuracy, and parameters of the oral scan, and finally obtain the patient's oral scan demand data.

[0041] Step S12: performing scanning parameter adaptive adjustment processing on the oral scanner according to the patient's oral structure data and the patient's oral scanning requirement data to generate an oral scanning adaptive adjustment parameter set;

[0042] The embodiment of the present invention adaptively adjusts the parameters of the oral scanner by combining the patient's oral structure condition data and the patient's oral scanning requirement data obtained by analysis, so as to adjust the scanner's resolution, scanning speed, exposure time, focal length, scanning angle, scanning mode and other parameters according to the patient's oral condition, and ensure that the detailed information of the oral structure is captured comprehensively and accurately, and finally generate an oral scanning adaptive adjustment parameter set.

[0043] Step S13: performing imaging scanning processing on the patient's oral structure through an oral scanner based on the oral scanning adaptive adjustment parameter set to obtain an oral internal structure image set;

[0044] The embodiment of the present invention uses an oral scanner to apply an oral scanning adaptive adjustment parameter set previously determined in real time to perform imaging scanning processing on the patient's oral structure, so as to place the oral scanner in an appropriate position, and obtain image data of the internal structure of the oral cavity through the light source and sensor of the oral scanner, and finally obtain an image set of the internal structure of the oral cavity.

[0045] Step S14: performing image denoising processing on the oral internal structure image set to obtain an oral structure denoised image set; performing standardization processing on the oral structure denoised image set to obtain an oral structure standard image set;

[0046] The embodiment of the present invention uses various image processing algorithms (such as median filtering, Gaussian filtering, etc.) to denoise the oral internal structure image set to reduce noise and interference in the image and improve the quality and clarity of the image, thereby obtaining an oral structure denoised image set. At the same time, the oral structure denoised image set is processed by using an image standardization method to adjust the image parameters such as brightness, contrast, and color balance, so that the oral structure image has a unified visual effect and ensures consistency and comparability between different images, and finally obtains an oral structure standard image set.

[0047] Step S15: construct a three-dimensional model of the standard image set of the oral structure to generate a three-dimensional spatial model of the internal structure of the oral cavity.

[0048] The embodiment of the present invention analyzes the standard image set of oral structure by using a spatial feature analysis method to extract key features of the oral structure space from the two-dimensional image set, such as the position, shape, size of teeth, the contour of gums, and the structural features of other oral tissues, and projects the two-dimensional structure feature points into three-dimensional space, while removing abnormal points or noise points to retain valid feature point data. At the same time, the three-dimensional model of the oral structure is reconstructed by using point cloud alignment, surface reconstruction and other methods to obtain the oral structure space feature point cloud data after outlier removal, so as to fuse the point cloud data obtained from different perspectives or scans into a complete model, and map the surface detail information and texture information in the two-dimensional image to the initially constructed spatial model for optimization, so as to enhance the realism and visualization effect of the model, and finally generate a three-dimensional spatial model of the internal structure of the oral cavity.

[0049] The present invention first analyzes the oral condition and scanning requirements of the patient's oral structure. This step is a key step in the entire oral scanning process. Through careful analysis, the patient's oral structure can be fully understood, including the conditions of various parts such as teeth, gums, and tongue. At the same time, the patient's scanning requirement data, such as scanning range, accuracy requirements, etc., can also be obtained. Such an analysis process helps to determine the specific plan for subsequent scanning and processing, laying a solid foundation for the oral scanning process. Secondly, by adaptively adjusting the scanning parameters of the oral scanner according to the patient's oral structure data and the patient's oral scanning requirement data, the personalized customization of the scanning process parameters can be achieved. This step can also maximize the accuracy and efficiency of the scan by adjusting the parameters of the scanner according to the patient's specific situation, thereby ensuring that high-quality oral internal structure image data is obtained. Then, the patient's oral structure is imaged and scanned by the oral scanner based on the oral scanning adaptive adjustment parameter set, so that the oral internal structure image set is scanned. These image data are a true reflection of the patient's oral internal structure, providing a necessary basis for subsequent processing and analysis. Next, by performing image denoising and standardization on the oral internal structure image set, the image data can be further optimized to remove noise and interference in the image, and the denoised image can be standardized, which can make subsequent processing more accurate and reliable, thereby improving the quality and availability of oral structure images. Finally, by constructing a three-dimensional model of the oral structure standard image set, a three-dimensional spatial model of the oral internal structure was constructed. This model can not only intuitively display the structural characteristics of the patient's oral cavity, but also provide an important reference for the subsequent tooth occlusion feature analysis process, providing important support for further research and practice in the field of oral medicine.

[0050] Preferably, step S15 comprises the following steps:

[0051] Step S151: performing oral structure spatial feature analysis on the oral structure standard image set to obtain oral structure spatial feature data;

[0052] Step S152: performing feature point cloud conversion processing on the oral structure spatial feature data to obtain three-dimensional spatial feature point cloud data of the oral structure;

[0053] Step S153: performing outlier elimination processing on the three-dimensional spatial feature point cloud data of the oral structure to obtain the oral structure spatial feature outlier elimination point cloud data;

[0054] Step S154: reconstructing a three-dimensional model of the oral structure on the oral structure spatial feature outlier elimination point cloud data to obtain an initial spatial model of the oral structure three-dimensional reconstruction;

[0055] Step S155: performing texture mapping rendering processing on the three-dimensional reconstructed initial spatial model of the oral structure based on the standard image set of the oral structure to generate a three-dimensional spatial model of the internal structure of the oral cavity.

[0056] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 Detailed step flow diagram of step S15 in FIG. 1 , in this embodiment, step S15 includes the following steps:

[0057] Step S151: performing oral structure spatial feature analysis on the oral structure standard image set to obtain oral structure spatial feature data;

[0058] The embodiment of the present invention analyzes the standard image set of oral structure by using a spatial feature analysis method to extract key features of the oral structure space from the two-dimensional image set, such as the position, shape, size of teeth, the contour of gums, and the structural spatial features of other oral tissues, and finally obtains oral structure spatial feature data.

[0059] Step S152: performing feature point cloud conversion processing on the oral structure spatial feature data to obtain three-dimensional spatial feature point cloud data of the oral structure;

[0060] The embodiments of the present invention convert the oral structure spatial feature data into a feature point cloud by using stereo matching, structured light scanning and other methods to project the two-dimensional feature points into three-dimensional space, perform coordinate transformation according to camera parameters, and determine the spatial position of each spatial feature point according to depth information, thereby finally obtaining the three-dimensional spatial feature point cloud data of the oral structure.

[0061] Step S153: performing outlier elimination processing on the three-dimensional spatial feature point cloud data of the oral structure to obtain the oral structure spatial feature outlier elimination point cloud data;

[0062] The embodiment of the present invention eliminates outliers in the three-dimensional spatial feature point cloud data of the oral structure by using a density-based clustering method and setting appropriate thresholds or parameters to remove abnormal points or noise points in the point cloud data and retain valid feature point data, ultimately obtaining the oral structure spatial feature outlier elimination point cloud data.

[0063] Step S154: reconstructing a three-dimensional model of the oral structure on the oral structure spatial feature outlier elimination point cloud data to obtain an initial spatial model of the oral structure three-dimensional reconstruction;

[0064] The embodiment of the present invention reconstructs a three-dimensional model of the oral structure by using point cloud registration, surface reconstruction and other methods to obtain the oral structure spatial feature outlier elimination point cloud data after outlier elimination processing, so as to fuse the point cloud data obtained from different perspectives or scans into a complete model, and then uses a surface reconstruction algorithm, such as a grid-based method or a voxelization method, to generate an initial three-dimensional spatial model of the oral structure, and finally obtain the initial spatial model of the three-dimensional reconstruction of the oral structure.

[0065] Step S155: performing texture mapping rendering processing on the three-dimensional reconstructed initial spatial model of the oral structure based on the standard image set of the oral structure to generate a three-dimensional spatial model of the internal structure of the oral cavity.

[0066] The embodiment of the present invention analyzes the surface detail information and texture information in the two-dimensional image from the standard image set of the oral structure and maps it to the initial spatial model of the three-dimensional reconstruction of the oral structure for optimization, so as to enhance the realism and visualization effect of the model, and renders the lighting, shadow, material and other aspects of the model, and finally generates a three-dimensional spatial model of the internal structure of the oral cavity.

[0067] The present invention firstly analyzes the spatial characteristics of the oral structure on the standard image set of the oral structure, and can deeply understand the complexity and characteristic distribution of the internal structure of the oral cavity. This analysis involves a detailed study of various characteristics, forms and spatial distributions in the image, such as the position, shape, size, etc. of the teeth. Through this step, the spatial characteristic data of the oral structure can be obtained, thereby providing an important basis for the subsequent processing process. Secondly, by performing feature point cloud conversion processing on the spatial characteristic data of the oral structure, the feature point information in the spatial structural characteristics of the image can be converted into point cloud data in three-dimensional space. This processing process can convert the two-dimensional information in the spatial structural characteristics of the image into more specific and more intuitive three-dimensional data, so that the spatial characteristics of the oral structure can be presented more clearly. This conversion process can provide basic data guarantee for subsequent three-dimensional reconstruction. Then, by performing outlier elimination processing on the three-dimensional spatial characteristic point cloud data of the oral structure, it is to remove abnormal points or interference points existing in the conversion process to improve the accuracy and reliability of the point cloud data. This step can ensure that the subsequent three-dimensional modeling process is not disturbed by identifying and eliminating outliers, thereby ensuring the quality of the final three-dimensional model. Next, the 3D model of the oral structure is reconstructed by removing outliers from the point cloud data of the oral structure's spatial features. This work involves using point cloud data for surface reconstruction, voxelization and other technologies to generate a precise and accurate initial spatial model of the 3D reconstruction of the oral structure, thus providing a reliable basis for subsequent model processing and analysis. Finally, by performing texture mapping and rendering processing on the initial spatial model of the 3D reconstruction of the oral structure based on the standard image set of the oral structure, the texture information of the oral structure can be mapped to the 3D model and rendered, making the generated 3D model more realistic and with real appearance features. This processing makes the 3D spatial model of the internal structure of the oral cavity more visual and expressive, thus providing important support for further research and practice in the subsequent analysis of tooth occlusion characteristics.

[0068] Preferably, step S153 includes the following steps:

[0069] Performing local density estimation on the three-dimensional spatial feature point cloud data of the oral structure to obtain a local density estimation value of each three-dimensional spatial feature point of the oral structure;

[0070] The embodiment of the present invention uses kernel density estimation technology to select an appropriate window size or neighborhood range at each point in the three-dimensional spatial feature point cloud data of the oral structure to perform density estimation calculation, so as to calculate the number of points of each feature point cloud in the neighborhood, and determine the point density around each feature point cloud by dividing the number of points by the volume or area of ​​the neighborhood, and finally obtain the local density estimation value of each three-dimensional spatial feature point of the oral structure.

[0071] Preferably, based on the local density estimation value of each oral structure three-dimensional spatial feature point, the three-dimensional spatial feature point cloud data of the oral structure is subjected to density center point clustering processing to obtain the three-dimensional spatial feature density center point of the oral structure;

[0072] The embodiment of the present invention uses a density-based clustering algorithm to perform cluster analysis on the corresponding three-dimensional spatial feature point cloud data of the oral structure by combining the local density estimation value of each three-dimensional spatial feature point of the oral structure obtained by quantitative calculation, so as to set appropriate density thresholds and neighborhood parameters to divide the feature point cloud into different clustering clusters, and determine the center point of each cluster as the density center point, that is, the core point with high density in the point cloud data, and finally obtain the three-dimensional spatial feature density center point of the oral structure.

[0073] Preferably, based on the oral structure three-dimensional spatial characteristic density center point, the density porosity calculation formula is used to perform density porosity statistics calculation on other three-dimensional spatial characteristic points in the oral structure three-dimensional spatial characteristic point cloud data, so as to obtain the point cloud density porosity value between the oral structure three-dimensional spatial characteristic point and the density center point;

[0074] The embodiment of the present invention forms a suitable point cloud density porosity calculation formula by combining the number parameters of three-dimensional spatial feature points, the spatial Euclidean distance value, the spatial position parameter, the local density estimation value, the local density adjustment coefficient, the local density gradient adjustment coefficient and related parameters to perform density porosity statistics calculation on other three-dimensional spatial feature points in the three-dimensional spatial feature point cloud data of the oral structure, so as to measure the density distribution difference between each feature point and the density center point of the cluster to which it belongs, and finally obtain the point cloud density porosity value between the three-dimensional spatial feature point of the oral structure and the density center point.

[0075] Preferably, outlier points are marked on the three-dimensional space feature point cloud data of the oral structure according to the point cloud density gap value between the three-dimensional space feature point of the oral structure and the density center point, so as to obtain the three-dimensional space feature density outlier points of the oral structure;

[0076] The embodiment of the present invention compares and judges the point cloud density porosity values ​​between the three-dimensional spatial feature points of the oral structure obtained by quantitative calculation and the density center point according to a preset threshold value, so as to mark the feature points whose point cloud density porosity values ​​exceed a certain range threshold as outliers, so as to represent abnormal points or noise points in the point cloud data, and finally obtain the three-dimensional spatial feature density outliers of the oral structure.

[0077] Preferably, the oral structure three-dimensional spatial feature density outliers are eliminated from the oral structure three-dimensional spatial feature point cloud data to obtain oral structure spatial feature outlier elimination point cloud data.

[0078] The embodiment of the present invention removes outliers of the three-dimensional spatial feature density of the oral structure from the three-dimensional spatial feature point cloud data of the oral structure to eliminate noise points or abnormal points in the point cloud data, thereby obtaining a cleaner and more accurate point cloud data set, and finally obtaining oral structure spatial feature outlier removal point cloud data.

[0079] The present invention firstly estimates the local density of the three-dimensional spatial feature point cloud data of the oral structure. This step is to determine the data density around each point in the point cloud data, which is crucial for subsequent clustering and outlier detection. By calculating the number of neighboring points around each point, an estimated value of the point density can be obtained, thereby revealing the characteristics of the density distribution in the oral structure. The key to this step is to evaluate its local density by calculating the number of points in the neighborhood around each point, which can help identify the dense and sparse areas in the oral structure, thereby providing an important reference for subsequent data processing and analysis, and helping to more accurately identify the key areas and feature points in the oral structure. Secondly, the three-dimensional spatial feature point cloud data of the oral structure is clustered by density center points based on the local density estimation value of each three-dimensional spatial feature point of the oral structure. The purpose of this step is to divide the oral structure point cloud data into areas with similar density characteristics and find the center point of each area. The key to this step is that the point cloud data can be divided into different areas and the center point of each area can be determined, thereby better understanding the distribution characteristics of the oral structure, and at the same time providing a basis for subsequent outlier detection and data analysis. Then, based on the density center point of the three-dimensional space feature of the oral structure, the density gap calculation formula is used to perform density gap statistics on other three-dimensional space feature points in the three-dimensional space feature point cloud data of the oral structure, in order to measure the relative density difference between the three-dimensional space feature points of the oral structure and the density center point. The key to this step is that by quantifying the density difference between the point clouds, it can help quantify the density gap between each point and the center of the area to which it belongs, so that it can more accurately determine which points are outliers or points with abnormal density characteristics. Next, the three-dimensional space feature point cloud data of the oral structure is marked as outliers according to the point cloud density gap value between the three-dimensional space feature point of the oral structure and the density center point, in order to identify the density abnormal points in the oral structure, that is, the points with significantly different density from the surrounding points. The key to this step is to mark the potential abnormal points, so as to help further analyze the abnormal situation or data errors in the oral structure. Finally, the purpose of removing the density outliers from the point cloud data is to remove the abnormal points in the oral structure point cloud data, so as to obtain a cleaner and more accurate point cloud data set. The key to this step is to improve the quality and reliability of the oral structure data, thereby ensuring the quality of the final three-dimensional model.

[0080] Preferably, the point cloud density porosity calculation formula is specifically:

[0081]

[0082] Where D i is the point cloud density gap value between the i-th oral structure three-dimensional spatial feature point and the density center point, N is the total number of other three-dimensional spatial feature points in the oral structure three-dimensional spatial feature point cloud data, i is the feature point item index parameter, d i is the spatial Euclidean distance between the ith oral structure three-dimensional spatial feature point and the center point of the oral structure three-dimensional spatial feature density, x i is the spatial position parameter of the ith oral structure three-dimensional spatial feature point, ρ(x i ) is the local density estimation value of the ith oral structure three-dimensional spatial feature point, α is the local density adjustment coefficient, β is the local density gradient adjustment coefficient, and η is the correction coefficient of the point cloud density porosity value.

[0083] The present invention obtains a point cloud density gap calculation formula by using a specific mathematical model and after verification, which is used to perform density gap statistics calculation on other three-dimensional spatial feature points in the three-dimensional spatial feature point cloud data of the oral structure. The goal of the point cloud density gap calculation formula is to quantitatively describe the density gap between the three-dimensional spatial feature points of the oral structure and the density center point. Through this measurement method, outliers in the oral structure can be identified, that is, those points with abnormally high or abnormally low density compared with other points. Secondly, the formula first calculates the spatial distance from each point to the center point, and then adjusts and corrects the density of the point through the local density estimation value and the corresponding adjustment parameter. This comprehensive consideration of the position of the point and the local density information can more accurately describe the density gap of the point. At the same time, the formula also uses the Euclidean distance and the local density information for comprehensive calculation, thereby obtaining the point cloud density gap value, which is used to quantitatively describe the point cloud density gap between the point and the center point. This distance value can well reflect the density of the point, and then be used to identify outliers. The calculated density porosity value can effectively identify points with abnormal density, and can accurately clean and process the characteristic points of the oral structure, thereby improving the accuracy and reliability of oral structure analysis. In summary, this formula fully considers the point cloud density porosity value D between the i-th oral structure three-dimensional space characteristic point and the density center point. i , the total number of other three-dimensional spatial feature points N in the three-dimensional spatial feature point cloud data of the oral structure, the feature point item index parameter i, the spatial Euclidean distance value d between the i-th three-dimensional spatial feature point of the oral structure and the center point of the three-dimensional spatial feature density of the oral structure i , the spatial position parameter x of the i-th oral structure three-dimensional spatial feature point i , the local density estimation value ρ(xi ), local density adjustment coefficient α, local density gradient adjustment coefficient β, point cloud density gap value correction coefficient η, according to the point cloud density gap value D between the i-th oral structure three-dimensional space feature point and the density center point i The correlation between the above parameters constitutes a functional relationship The formula can realize the statistical calculation process of the density porosity of other three-dimensional spatial feature points in the three-dimensional spatial feature point cloud data of the oral structure. At the same time, by introducing the correction coefficient η of the point cloud density porosity value, it can be adjusted according to the error occurring in the calculation process, thereby improving the accuracy and applicability of the point cloud density porosity calculation formula.

[0084] Preferably, step S155 includes the following steps:

[0085] Perform oral surface and texture feature analysis on the oral structure standard image set to obtain oral structure surface feature data and oral structure texture feature data;

[0086] The embodiment of the present invention uses image feature processing technology to perform feature analysis on a standard image set of oral structures to extract surface features and texture features of the oral structures, wherein the surface features involve information on the shape, contour, etc. of the internal structure of the oral cavity, while the texture features refer to the texture details of the surface of the oral structure, such as wrinkles, bumps, etc., and ultimately obtain surface feature data of the oral structure and texture feature data of the oral structure.

[0087] Preferably, the surface details of the initial spatial model of the three-dimensional reconstruction of the oral structure are optimized based on the surface feature data of the oral structure to obtain a three-dimensional spatial surface optimization model of the oral structure;

[0088] The embodiment of the present invention optimizes the surface details of the reconstructed three-dimensional reconstructed initial spatial model of the oral structure by combining the oral structure surface feature data obtained by analysis, using techniques such as mesh editing and surface fitting, so as to effectively improve the geometric shape of the model and make it more consistent with the actual morphology of the oral structure surface, and finally obtain an optimized three-dimensional spatial surface model of the oral structure.

[0089] Preferably, the three-dimensional surface optimization model of the oral structure is optimized by texture mapping matching based on the oral structure texture feature data to obtain the three-dimensional texture mapping optimization model of the oral structure;

[0090] The embodiment of the present invention optimizes the texture features of the three-dimensional surface optimization model of the oral structure obtained after surface optimization by combining the analysis of the texture feature data of the oral structure with a texture mapping algorithm, texture fusion technology, etc., so as to map the texture features to the three-dimensional model surface to maintain the continuity and authenticity of the texture, and ensure that the final three-dimensional model has a realistic texture effect, and finally obtains a three-dimensional texture mapping optimization model of the oral structure.

[0091] Preferably, the three-dimensional space texture mapping optimization model of the oral structure is subjected to model rendering processing to generate a three-dimensional space model of the internal structure of the oral cavity.

[0092] The embodiment of the present invention renders the three-dimensional texture mapping optimization model of the oral structure by using rendering methods in terms of lighting, shadow, material, etc., so as to increase the realism and visual effect of the model, and presents the oral structure model as a realistic three-dimensional space model, and finally generates a three-dimensional space model of the internal structure of the oral cavity.

[0093] The present invention first analyzes the oral surface and texture features of the standard image set of the oral structure, and can deeply understand the surface morphology and texture features of the oral structure. The acquisition of surface feature data can provide geometric shape information of the oral structure, and the texture feature data reflects the subtle changes and texture features of the oral surface. The key to this step is that it can provide basic data for subsequent three-dimensional reconstruction and optimization, which helps to retain the real details and features of the oral structure. Secondly, by optimizing the surface details of the initial spatial model of the three-dimensional reconstruction of the oral structure based on the surface feature data of the oral structure, the geometry of the model can be effectively improved, and its sense of reality and fidelity can be enhanced. By optimizing the surface details, the reconstructed oral structure model can be closer to the real anatomical structure, which is helpful for the subsequent model application and analysis. The key to this step is to improve the accuracy and visualization effect of the oral structure model, and provide a reliable basis for further analysis and processing. Then, by performing texture mapping matching optimization on the three-dimensional space surface optimization model of the oral structure based on the texture feature data of the oral structure, the texture features of the real oral structure can be mapped to the model surface, which enhances the sense of reality and fidelity of the model. Through texture mapping matching optimization, the model surface can have texture features similar to the real oral structure, so as to better simulate the visual effect of the interior of the oral cavity. The key to this step is to improve the visual effect and realism of the spatial model, so that the model is more consistent with the appearance characteristics of the real oral structure, thus providing basic data guarantee for the subsequent processing process. Finally, by rendering the optimized three-dimensional spatial texture mapping model of the oral structure, a highly realistic three-dimensional spatial model of the internal structure of the oral cavity can be generated. Through model rendering, the oral structure model can be presented as a realistic image or video, showing its internal structure and texture features, and providing visualization tools and references for subsequent processing. The key to this step is to provide high-quality visualization results of the oral structure model, thus providing important support for further research and practice of subsequent tooth occlusion feature analysis.

[0094] Preferably, step S2 comprises the following steps:

[0095] Step S21: performing tooth segmentation processing on the three-dimensional spatial model of the internal structure of the oral cavity to obtain a three-dimensional spatial model of the oral tooth structure area;

[0096] The embodiment of the present invention uses a computer tomography processing method to segment the tooth part in the three-dimensional spatial model of the internal structure of the oral cavity, so as to extract the tooth part in the complex oral structure separately, and finally obtain a three-dimensional spatial model of the oral tooth structure area.

[0097] Step S22: performing tooth structure size measurement processing on the three-dimensional space model of the oral tooth structure area to obtain the size range of the three-dimensional space model of the oral tooth structure;

[0098] The embodiment of the present invention uses a measuring tool or software to measure the size of each tooth structure within the segmented three-dimensional spatial model of the oral tooth structure area, so as to measure the length, width, height, etc. of the tooth structure, thereby helping to evaluate the size range of each tooth structure and ultimately obtaining the size range of the three-dimensional spatial model of the oral tooth structure.

[0099] Step S23: performing tooth shape feature analysis on the three-dimensional space model of the oral tooth structure region based on the size range of the three-dimensional space model of the oral tooth structure to obtain three-dimensional geometric shape feature data of the oral tooth;

[0100] The embodiment of the present invention performs shape feature analysis on the corresponding tooth structure in the three-dimensional space model of the oral tooth structure area by combining the size range of the three-dimensional space model of the oral tooth structure obtained by analysis, so as to analyze the geometric shape, contour and other features of the teeth, and finally obtain the three-dimensional geometric shape feature data of the oral teeth.

[0101] Step S24: performing a tooth spatial position feature analysis on the three-dimensional spatial model of the oral internal structure to obtain three-dimensional spatial position feature data of the oral teeth;

[0102] The embodiment of the present invention uses a spatial position analysis method to perform position recognition analysis on the corresponding tooth structure in the three-dimensional spatial model of the internal structure of the oral cavity, so as to analyze the position and relative position of the tooth structure on the three-dimensional spatial model of the internal structure of the oral cavity, and obtain the specific position information of the teeth in the oral cavity, and finally obtain the three-dimensional spatial position feature data of the oral teeth.

[0103] Step S25: Based on the three-dimensional geometric shape feature data of the oral teeth and the three-dimensional spatial position feature data of the oral teeth, an inter-tooth contact evaluation analysis is performed on the three-dimensional spatial model of the oral internal structure to obtain the contact status data of the arrangement of the oral teeth.

[0104] The embodiment of the present invention uses contact mechanics theory and simulation algorithms to evaluate and analyze the contact conditions of corresponding tooth structures in the three-dimensional spatial model of the internal structure of the oral cavity during the occlusion process by combining the three-dimensional geometric shape feature data of the oral teeth and the three-dimensional spatial position feature data of the oral teeth obtained by analysis, so as to evaluate and analyze the contact conditions between the teeth, the stability of the contact points, etc., and thereby evaluate and analyze the arrangement and contact conditions between the teeth inside the oral cavity during the occlusion process, and finally obtain the arrangement and contact condition data between the oral teeth.

[0105] The present invention firstly performs tooth segmentation processing on the three-dimensional spatial model of the internal structure of the oral cavity, and can extract the tooth part in the complex oral structure separately, so as to obtain the three-dimensional spatial model of the oral tooth structure area. The key of this step is that it can provide a basis for subsequent tooth-related analysis, making it possible to perform refined analysis on the morphology, position and other characteristics of the teeth. Secondly, by measuring the tooth structure size of the three-dimensional spatial model of the oral tooth structure area, the size range of the teeth can be obtained. The key of this step is that it can provide a size reference for subsequent analysis, help evaluate the size variation range of the teeth, and thus provide data support for the personalized analysis of the oral structure. Then, by analyzing the tooth shape characteristics of the three-dimensional spatial model of the oral tooth structure area based on the size range of the three-dimensional spatial model of the oral tooth structure, the geometric shape characteristics of the teeth, such as the shape and curvature of the teeth, can be revealed. The key of this step is that it can help doctors or researchers understand the morphological characteristics of oral teeth, thereby providing data support for the subsequent processing process. Next, by analyzing the tooth space position characteristics of the three-dimensional spatial model of the internal structure of the oral cavity, the specific position information of the teeth in the oral cavity can be obtained. The key of this step is that the relative position relationship between the teeth can be understood, thereby providing a basis for evaluating problems such as tooth arrangement and occlusion. Finally, by performing inter-tooth contact evaluation and analysis on the three-dimensional spatial model of the internal structure of the oral cavity based on the three-dimensional geometric shape feature data of the oral teeth and the three-dimensional spatial position feature data of the oral teeth, the contact conditions between the teeth can be evaluated, including the number of contact points, contact area, etc. The key to this step is to help evaluate the arrangement of the teeth and the occlusal function, and provide data support for the subsequent occlusal feature analysis process, thereby promoting the precision and personalization of oral health management.

[0106] Preferably, step S25 comprises the following steps:

[0107] Step S251: performing a tooth occlusion simulation analysis on the three-dimensional spatial model of the internal structure of the oral cavity to obtain a tooth occlusion simulation process inside the oral cavity;

[0108] The embodiment of the present invention imports the three-dimensional spatial model of the internal structure of the oral cavity into the simulation software, and starts the corresponding occlusion process simulation parameters of the device, such as material properties, loading conditions, etc., and at the same time starts the simulation software to simulate and analyze the occlusion process of the teeth inside the oral cavity, so as to simulate the contact and occlusion process between the teeth and observe the movement and stress distribution between the teeth, and finally obtain the occlusion simulation process of the teeth inside the oral cavity.

[0109] Step S252: performing occlusal contact area recognition and analysis on the internal teeth occlusal simulation process based on the three-dimensional geometric shape feature data of the oral teeth and the three-dimensional spatial position feature data of the oral teeth to obtain the internal teeth occlusal contact area;

[0110] The embodiment of the present invention performs identification and analysis of the occlusal contact area of ​​the internal teeth in the oral cavity occlusion simulation process by combining the analyzed three-dimensional geometric shape feature data of the oral teeth and the three-dimensional spatial position feature data of the oral teeth, so as to identify and determine the contact area of ​​the internal teeth in the oral cavity during the occlusion process, and finally obtain the internal teeth occlusal contact area.

[0111] Step S253: performing contact mechanical response simulation analysis on the internal tooth occlusion simulation process according to the internal tooth occlusion contact area of ​​the oral cavity, and obtaining contact pressure distribution data between the internal teeth of the oral cavity;

[0112] The embodiment of the present invention uses contact mechanics theory and simulation algorithm in combination with the identified occlusal contact area of ​​the teeth inside the oral cavity to perform response simulation analysis on the contact mechanics behavior of the corresponding occlusal simulation process of the teeth inside the oral cavity in the occlusal contact area, so as to simulate the mechanical response of the teeth inside the oral cavity during the occlusion process, including pressure distribution, etc., and reveal the contact pressure distribution between the teeth, and finally obtain the contact pressure distribution data between the teeth inside the oral cavity.

[0113] Step S254: performing real-time tracking and analysis of contact points during the oral cavity tooth occlusion simulation process to obtain movement status data of occlusal contact points between teeth in the oral cavity;

[0114] The embodiment of the present invention performs real-time tracking and analysis of contact points during the tooth occlusion simulation process inside the oral cavity by setting a contact point tracking algorithm in the simulation software, so as to track the contact points between the teeth in the occlusal contact area in real time, and record the movement of the occlusal contact points between the teeth inside the oral cavity, and at the same time capture the dynamic changes of the contact points during the tooth occlusion process inside the oral cavity, and finally obtain the movement status data of the occlusal contact points between the teeth inside the oral cavity.

[0115] Step S255: Based on the contact pressure distribution data between the teeth in the oral cavity and the movement status data of the occlusal contact points between the teeth in the oral cavity, an inter-tooth contact evaluation analysis is performed on the intra-oral tooth occlusion simulation process to obtain the contact status data of the arrangement of the teeth in the oral cavity.

[0116] The embodiment of the present invention uses a contact degree evaluation algorithm to evaluate and analyze the arrangement and contact conditions between teeth during the tooth occlusion simulation process in the oral cavity by combining the contact pressure distribution data between teeth in the oral cavity and the movement status data of the occlusal contact points between teeth in the oral cavity obtained by analysis, so as to evaluate and analyze the contact conditions between teeth, the stability of the contact points, etc., and thereby evaluate and analyze the arrangement and contact conditions between teeth in the oral cavity, and finally obtain the arrangement and contact status data between teeth in the oral cavity.

[0117] The present invention firstly simulates the occlusion process of the teeth in the oral cavity by performing a tooth occlusion simulation analysis on the three-dimensional spatial model of the internal structure of the oral cavity, so as to understand the dynamic behavior of the teeth during occlusion. The key to this step is to simulate the occlusion process in the oral cavity through simulation analysis, which provides an important reference for studying the tooth occlusion function, oral health management, etc. Secondly, by performing an occlusion contact area identification analysis on the occlusion simulation process of the teeth in the oral cavity based on the three-dimensional geometric shape feature data of the oral teeth and the three-dimensional spatial position feature data of the oral teeth, the contact area of ​​the teeth in the oral cavity during the occlusion process can be determined. The key to this step is to accurately identify the contact area between the teeth, thereby providing a basis for subsequent contact mechanical response simulation and analysis. Then, by performing a contact mechanical response simulation analysis on the occlusion simulation process of the teeth in the oral cavity according to the occlusion contact area of ​​the teeth in the oral cavity, the mechanical response of the teeth in the oral cavity during the occlusion process can be simulated, including pressure distribution, etc. The key to this step is to reveal the distribution of contact pressure between teeth, thereby helping to understand the mechanical characteristics of the teeth in the oral cavity during the occlusion process. Next, by performing real-time tracking and analysis of contact points during the oral occlusion simulation process, the movement of the occlusal contact points between the teeth in the mouth can be tracked and recorded in real time, so that the dynamic changes of the contact points during the oral occlusion process can be captured, thereby providing key data for further occlusion analysis. Finally, by performing tooth contact evaluation and analysis on the oral occlusion simulation process based on the contact pressure distribution data between the teeth in the mouth and the movement status data of the occlusal contact points between the teeth in the mouth, the arrangement contact status between the teeth in the mouth can be comprehensively evaluated, which can provide a comprehensive evaluation of the occlusal status of the teeth in the mouth, thereby providing data support for the subsequent occlusal feature analysis process.

[0118] Preferably, step S3 comprises the following steps:

[0119] Step S31: performing tooth occlusal relationship analysis on the three-dimensional spatial model of the internal structure of the oral cavity to obtain the tooth occlusal relationship data of the internal structure of the oral cavity;

[0120] The embodiment of the present invention uses an occlusal relationship analysis method to analyze the interactions and relationships between teeth on a three-dimensional spatial model of the internal structure of the oral cavity, so as to examine the contact points, occlusal methods, etc. between the teeth in the model to evaluate and analyze the malocclusion relationship and occlusal relationship between the teeth, and finally obtain the occlusal relationship data of the teeth inside the oral cavity.

[0121] Step S32: performing bite point recognition analysis on the three-dimensional space model of the internal structure of the oral cavity based on the occlusal relationship data of the teeth in the oral cavity to obtain the occlusal points of the teeth in the oral cavity;

[0122] The embodiment of the present invention uses computer-aided analysis software or algorithms to identify and mark the bite points between teeth on the three-dimensional spatial model of the internal structure of the oral cavity by combining the occlusal relationship data of the teeth inside the oral cavity obtained by analysis, so as to obtain the points or areas where the teeth are in contact with each other, and mark them as the bite points between the teeth, and finally obtain the bite points of the teeth inside the oral cavity.

[0123] Step S33: performing bite point location analysis on the bite points of the teeth inside the oral cavity to obtain the bite point position data of the teeth inside the oral cavity;

[0124] The embodiment of the present invention uses the coordinate system of the three-dimensional space model to perform positioning analysis on the marked occlusal points of the teeth inside the oral cavity to determine the specific position of the three-dimensional coordinates of each occlusal point, and finally obtains the position data of the occlusal points of the teeth in the oral cavity.

[0125] Step S34: performing tooth occlusal connection relationship mining and analysis on the three-dimensional spatial model of the internal structure of the oral cavity based on the contact status data of the arrangement of the oral teeth, so as to obtain the connection relationship between the teeth in the oral occlusal process;

[0126] The embodiment of the present invention uses a connection relationship mining algorithm to mine and analyze the occlusal connection relationship between teeth on a three-dimensional spatial model of the internal structure of the oral cavity by combining the data on the arrangement and contact status of the oral teeth obtained by analysis, so as to analyze and determine which teeth have contact or occlusal relationships, as well as the nature and characteristics of these relationships, and thereby understand the interaction between teeth during the oral occlusion process, and finally obtain the connection relationship between teeth during the oral occlusion process.

[0127] Step S35: dividing the three-dimensional space model of the internal structure of the oral cavity into tooth occlusal surface connections according to the connection relationship between teeth during the oral occlusion process and the position data of the oral tooth occlusal points, so as to generate the oral tooth occlusal connection surface.

[0128] The embodiment of the present invention performs connection processing on the occlusal surfaces of the corresponding teeth in the three-dimensional spatial model of the internal structure of the oral cavity by combining the connection relationship between teeth in the oral occlusion process and the position data of the oral tooth occlusion points obtained by previous analysis, so as to determine which teeth together constitute an occlusal surface, identify and connect them, and finally generate the oral tooth occlusal connection surface.

[0129] The present invention first analyzes the occlusal relationship of the teeth on the three-dimensional spatial model of the internal structure of the oral cavity, which can deeply understand the interaction and relationship between the teeth in the oral cavity. This analysis can not only help evaluate the malocclusion relationship and occlusion problems between the teeth, but also provide important references for the subsequent processing process. By obtaining the occlusal relationship data of the teeth in the oral cavity, the position, angle and contact of each tooth with the surrounding teeth can be systematically recorded, providing accurate basic information for the subsequent bite point identification process. Secondly, based on the occlusal relationship data of the teeth in the oral cavity, the bite point recognition analysis is performed on the three-dimensional spatial model of the internal structure of the oral cavity to determine the bite point of the teeth in the oral cavity. The purpose of this step is to accurately locate the key bite points in the oral cavity and provide an accurate basis for further analysis of the oral structure. By identifying the bite point, the anatomical characteristics and functional mechanisms of the oral structure can be better understood. Then, after positioning and analyzing the bite points of the teeth in the oral cavity, the specific position data of the bite points of the teeth in the oral cavity can be obtained, and these data provide data support for the subsequent processing process. Next, the occlusal connection relationship of the three-dimensional model of the internal structure of the oral cavity is mined and analyzed based on the data of the contact status of the arrangement of the teeth in the oral cavity to understand the connection mode and interaction between the teeth in the oral cavity. This analysis helps to reveal the functional connection between the teeth and provide a more comprehensive perspective for the subsequent occlusal surface connection division. Finally, the occlusal surface connection division of the three-dimensional model of the internal structure of the oral cavity is carried out according to the connection relationship between the teeth during the oral occlusion process and the position data of the occlusal points of the oral teeth. The purpose of this step is to generate the occlusal connection surface of the oral teeth, which provides more detailed and accurate information for further analysis of the oral structure. By dividing the occlusal connection surface, the overall morphology and functional characteristics of the oral structure can be better understood, providing more scientific and effective support for the subsequent occlusal feature analysis.

[0130] Preferably, step S4 comprises the following steps:

[0131] Step S41: measuring the occlusal inclination angle of the occlusal connection surface of the oral teeth to obtain the occlusal inclination angle of the oral teeth;

[0132] The embodiment of the present invention measures the inclination angle of the occlusal connection surface of the oral teeth on the three-dimensional space model by using an angle measuring instrument or a computer-aided measurement tool to accurately measure the inclination angle of each occlusal connection surface and record it in digital form, thereby finally obtaining the occlusal inclination angle of the oral teeth.

[0133] Step S42: performing statistical calculation of the inclination curvature of the oral tooth occlusal connection surface based on the oral tooth occlusal inclination angle to obtain the oral tooth occlusal inclination curvature;

[0134] The embodiment of the present invention uses a statistical method to perform statistical calculation of the inclination curvature of the corresponding oral tooth occlusal connection surface in combination with the measured oral tooth occlusal inclination angle, so as to quantitatively calculate the curvature degree of the occlusal connection surface, that is, the inclination degree of the occlusal connection surface, and finally obtain the oral tooth occlusal inclination curvature.

[0135] Step S43: Calculating the occlusal parallelism of the occlusal connection surface of the oral teeth based on the occlusal inclination angle and the occlusal inclination curvature of the oral teeth using an occlusal parallelism calculation formula to obtain the parallelism of the occlusal surface of the teeth;

[0136] The embodiment of the present invention combines tooth length, tooth horizontal position parameters, oral tooth occlusal inclination angle, oral tooth occlusal inclination curvature, distance value between adjacent teeth, tooth occlusal force and related parameters to form a suitable occlusal parallelism calculation formula to calculate the occlusal parallelism of the oral tooth occlusal connection surface, so as to quantitatively calculate the parallelism of the tooth occlusal surface and finally obtain the parallelism of the tooth occlusal surface.

[0137] The calculation formula of occlusal parallelism is as follows:

[0138]

[0139] Wherein, p is the parallelism of the occlusal surface of teeth, L is the length of teeth on the occlusal connection surface of oral teeth, l is the horizontal position parameter of teeth on the occlusal connection surface of oral teeth, θ is the occlusal inclination angle of oral teeth, C is the occlusal inclination curvature of oral teeth, e is the distance value between adjacent teeth on the occlusal connection surface of oral teeth, F(l) is the tooth occlusal force applied on the occlusal connection surface of oral teeth at the horizontal position l, and ε is the correction coefficient of the parallelism of the occlusal surface of teeth;

[0140] The present invention obtains an occlusal parallelism calculation formula by using a specific mathematical model and after verification, which is used to calculate the occlusal parallelism of the occlusal connection surface of the oral teeth. The occlusal parallelism calculation formula is intended to evaluate the parallelism of the occlusal connection surface of the oral teeth, which is crucial for oral health and occlusal function. Through this step, the parallelism of the occlusal surface of the teeth can be quantitatively evaluated, thereby providing guidance for further processing and adjustment. The integral part of the formula covers the tooth conditions at various positions on the occlusal connection surface of the oral teeth, where This section describes the contribution of tooth inclination and curvature to parallelism. This section describes the contribution of the occlusal force to parallelism, which takes into account factors such as the inclination, curvature and bite force of the teeth. In addition, the application of the occlusal parallelism calculation formula allows the parallelism of the occlusal connection surface of the oral teeth to be quantitatively evaluated, thereby determining the quality and adaptability of the occlusal surface. By calculating the occlusal parallelism, the non-parallelism of the occlusal surface of the teeth can be found, thereby providing guidance and basis for subsequent adjustments and treatments, which is of great significance for oral correction, restoration and functional adjustment. In summary, the formula fully considers the parallelism p of the occlusal surface of the teeth, the tooth length L on the occlusal connection surface of the oral teeth, the horizontal position parameter l of the teeth on the occlusal connection surface of the oral teeth, the occlusal inclination angle θ of the oral teeth, the occlusal inclination curvature C of the oral teeth, the distance value e between adjacent teeth on the occlusal connection surface of the oral teeth, the occlusal force F(l) of the teeth at the horizontal position l on the occlusal connection surface of the oral teeth, and the correction coefficient ε of the parallelism of the occlusal surface of the teeth. According to the correlation between the parallelism p of the occlusal surface of the teeth and the above parameters, a functional relationship is formed. The formula can realize the calculation process of the occlusal parallelism of the occlusal connection surfaces of oral teeth. At the same time, by introducing the correction coefficient ε of the parallelism of the occlusal surfaces of teeth, it can be adjusted according to the errors occurring in the calculation process, thereby improving the accuracy and applicability of the occlusal parallelism calculation formula.

[0141] Step S44: performing tooth occlusal contour shape recognition analysis on the oral tooth occlusal connection surface to obtain the tooth occlusal surface contour shape;

[0142] The embodiment of the present invention uses a computer vision algorithm to identify and analyze the contour shape of the connected oral tooth occlusal connection surface, so as to identify and obtain the contour shape of the tooth occlusal surface to understand the morphological characteristics such as the degree of concavity and convexity, irregularity, etc. of the tooth occlusal surface, and thereby reveals the occlusal relationship between the teeth, and finally obtains the contour shape of the tooth occlusal surface.

[0143] Step S45: performing tooth occlusal adjustment processing on the three-dimensional space model of the internal structure of the oral cavity based on the parallelism of the tooth occlusal surface and the contour shape of the tooth occlusal surface to generate an oral tooth occlusal surface adjustment plan.

[0144] The embodiment of the present invention uses a computer-aided design tool or a three-dimensional simulation method to adjust the three-dimensional spatial model of the internal structure of the oral cavity by combining the parallelism of the tooth occlusal surface and the contour shape of the tooth occlusal surface obtained by previous analysis, so as to adjust the internal structure of the oral cavity according to its parallelism and contour shape information, so that the tooth occlusal surfaces are parallel and their contour shape conforms to the oral anatomical structure and functional requirements, thereby formulating a targeted tooth occlusal adjustment plan to correct tooth malocclusion, improve occlusal function, etc., and finally generate an oral tooth occlusal surface adjustment plan.

[0145] The present invention firstly measures the occlusal inclination angle of the occlusal connection surface of the oral teeth, and can accurately measure the inclination angle of the occlusal surface of the teeth to understand the occlusion between the teeth. This measurement process helps to determine the occlusal position and angle of the teeth, and provides important data for further analysis of the oral structure. By obtaining the occlusal inclination angle of the oral teeth, it is possible to evaluate whether the occlusal relationship between the teeth is normal, and provide a basis for the subsequent calculation of the curvature of the occlusal surface. Secondly, by statistically calculating the inclination curvature of the occlusal connection surface of the oral teeth based on the occlusal inclination angle of the oral teeth, the occlusal inclination curvature of the oral teeth is obtained. This calculation process is intended to quantify the inclination degree of the occlusal surface, and provide a quantitative index for the analysis and evaluation of the oral structure. By statistically calculating the inclination curvature, the morphological characteristics and inclination degree of the occlusal surface of the teeth can be more accurately understood, and more objective data support can be provided for the subsequent processing process. Then, by using the occlusal parallelism calculation formula based on the occlusal inclination angle of the oral teeth and the occlusal inclination curvature of the oral teeth, the occlusal parallelism of the occlusal connection surface of the oral teeth is calculated. This calculation process is intended to evaluate the parallelism of the occlusal surface of the teeth, and provide an important reference for the evaluation of the oral structure and the formulation of the occlusal adjustment plan. By calculating the parallelism, the relative position relationship between the occlusal surfaces of the teeth can be determined, thereby providing a quantitative basis for the adjustment process of the tooth occlusion problem. Next, the tooth occlusal contour shape recognition and analysis of the oral tooth occlusal connection surface is performed to identify and analyze the contour shape of the tooth occlusal surface to understand the morphological characteristics of the tooth occlusal surface. This analysis helps to reveal the occlusal relationship and morphological characteristics between the teeth and provide more comprehensive information for the evaluation of the oral structure. By identifying the contour shape of the tooth occlusal surface, the contact situation and morphological characteristics between the teeth can be better understood, thereby providing a more accurate reference for the subsequent tooth occlusal adjustment process. Finally, the tooth occlusion adjustment process is performed on the three-dimensional spatial model of the internal structure of the oral cavity based on the parallelism of the tooth occlusal surface and the contour shape of the tooth occlusal surface. This step aims to formulate a targeted tooth occlusion adjustment plan based on the above analysis results, provide guidance for the adjustment and repair of the oral structure occlusal process, and by adjusting the tooth occlusal surface, tooth malocclusion can be corrected and the occlusal function can be improved, thereby improving the oral health level and quality of life.

[0146] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0147] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for constructing an oral three-dimensional model and analyzing tooth occlusion characteristics, characterized in that: The following steps are involved: Step S1: performing imaging scanning on the patient's oral structure through an oral scanner to obtain an image set of the internal structure of the oral cavity; constructing a three-dimensional model of the image set of the internal structure of the oral cavity to generate a three-dimensional spatial model of the internal structure of the oral cavity; Step S2: performing tooth segmentation and tooth feature analysis on the three-dimensional spatial model of the internal structure of the oral cavity to obtain three-dimensional geometric shape feature data of the oral teeth and three-dimensional spatial position feature data of the oral teeth; Based on the three-dimensional geometric shape feature data of oral teeth and the three-dimensional spatial position feature data of oral teeth, the three-dimensional spatial model of the internal structure of the oral cavity is evaluated and analyzed to obtain the contact status data of the arrangement of the oral teeth; Step S3: performing tooth occlusal point positioning analysis on the three-dimensional space model of the internal structure of the oral cavity to obtain the position data of the occlusal points of the oral cavity teeth; performing tooth occlusal surface connection division on the three-dimensional space model of the internal structure of the oral cavity based on the arrangement contact status data between the oral teeth and the position data of the occlusal points of the oral teeth to generate the occlusal connection surface of the oral cavity teeth; Step S4: performing tooth occlusal feature analysis on the oral tooth occlusal connection surface to obtain the parallelism of the tooth occlusal surface and the contour shape of the tooth occlusal surface; The three-dimensional spatial model of the internal structure of the oral cavity is processed for tooth occlusal adjustment based on the parallelism of the tooth occlusal surface and the contour shape of the tooth occlusal surface to generate an oral tooth occlusal surface adjustment plan.

2. The method for constructing an oral three-dimensional model and analyzing tooth occlusion characteristics according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Analyze the oral condition and scanning requirements of the patient's oral structure to obtain the patient's oral structure condition data and the patient's oral scanning requirement data; Step S12: performing scanning parameter adaptive adjustment processing on the oral scanner according to the patient's oral structure data and the patient's oral scanning requirement data to generate an oral scanning adaptive adjustment parameter set; Step S13: performing imaging scanning processing on the patient's oral structure through an oral scanner based on the oral scanning adaptive adjustment parameter set to obtain an oral internal structure image set; Step S14: performing image denoising processing on the oral internal structure image set to obtain an oral structure denoised image set; performing standardization processing on the oral structure denoised image set to obtain an oral structure standard image set; Step S15: construct a three-dimensional model of the standard image set of the oral structure to generate a three-dimensional spatial model of the internal structure of the oral cavity.

3. The method for constructing an oral three-dimensional model and analyzing tooth occlusion characteristics according to claim 2, characterized in that: Step S15 includes the following steps: Step S151: performing oral structure spatial feature analysis on the oral structure standard image set to obtain oral structure spatial feature data; Step S152: performing feature point cloud conversion processing on the oral structure spatial feature data to obtain three-dimensional spatial feature point cloud data of the oral structure; Step S153: performing outlier elimination processing on the three-dimensional spatial feature point cloud data of the oral structure to obtain the oral structure spatial feature outlier elimination point cloud data; Step S154: reconstructing a three-dimensional model of the oral structure on the oral structure spatial feature outlier elimination point cloud data to obtain an initial spatial model of the oral structure three-dimensional reconstruction; Step S155: performing texture mapping rendering processing on the three-dimensional reconstructed initial spatial model of the oral structure based on the standard image set of the oral structure to generate a three-dimensional spatial model of the internal structure of the oral cavity.

4. The method for constructing a three-dimensional oral cavity model and analyzing tooth occlusion characteristics according to claim 3, characterized in that: Step S153 includes the following steps: Performing local density estimation on the three-dimensional spatial feature point cloud data of the oral structure to obtain a local density estimation value of each three-dimensional spatial feature point of the oral structure; Based on the local density estimation value of each three-dimensional spatial feature point of the oral structure, the density center point clustering process is performed on the three-dimensional spatial feature point cloud data of the oral structure to obtain the three-dimensional spatial feature density center point of the oral structure; Based on the three-dimensional spatial characteristic density center point of the oral structure, the density gap calculation formula of the point cloud density is used to perform density gap statistics calculation on other three-dimensional spatial characteristic points in the three-dimensional spatial characteristic point cloud data of the oral structure, so as to obtain the point cloud density gap value between the three-dimensional spatial characteristic point of the oral structure and the density center point; Marking outliers on the three-dimensional space feature point cloud data of the oral structure according to the point cloud density gap value between the three-dimensional space feature point of the oral structure and the density center point, and obtaining the three-dimensional space feature density outliers of the oral structure; The outliers of the three-dimensional spatial feature density of the oral structure are eliminated from the three-dimensional spatial feature point cloud data of the oral structure to obtain the oral structure spatial feature outlier elimination point cloud data.

5. The method for constructing a three-dimensional oral cavity model and analyzing tooth occlusion characteristics according to claim 4, characterized in that: The point cloud density porosity calculation formula is specifically: Where D i is the point cloud density gap value between the i-th oral structure three-dimensional spatial feature point and the density center point, N is the total number of other three-dimensional spatial feature points in the oral structure three-dimensional spatial feature point cloud data, i is the feature point item index parameter, d i is the spatial Euclidean distance between the ith oral structure three-dimensional spatial feature point and the center point of the oral structure three-dimensional spatial feature density, x i is the spatial position parameter of the ith oral structure three-dimensional spatial feature point, ρ(x i ) is the local density estimation value of the ith oral structure three-dimensional spatial feature point, α is the local density adjustment coefficient, β is the local density gradient adjustment coefficient, and η is the correction coefficient of the point cloud density porosity value.

6. The method for constructing an oral three-dimensional model and analyzing tooth occlusion characteristics according to claim 3, characterized in that: Step S155 includes the following steps: Perform oral surface and texture feature analysis on the oral structure standard image set to obtain oral structure surface feature data and oral structure texture feature data; Based on the surface feature data of the oral structure, the surface details of the initial spatial model of the oral structure 3D reconstruction are optimized to obtain the 3D spatial surface optimization model of the oral structure; Based on the oral structure texture feature data, the texture mapping matching optimization of the oral structure three-dimensional surface optimization model is performed to obtain the oral structure three-dimensional texture mapping optimization model; The three-dimensional space texture mapping optimization model of the oral structure is rendered to generate a three-dimensional space model of the internal structure of the oral cavity.

7. The method for constructing an oral three-dimensional model and analyzing tooth occlusion characteristics according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing tooth segmentation processing on the three-dimensional spatial model of the internal structure of the oral cavity to obtain a three-dimensional spatial model of the oral tooth structure area; Step S22: performing tooth structure size measurement processing on the three-dimensional space model of the oral tooth structure area to obtain the size range of the three-dimensional space model of the oral tooth structure; Step S23: performing tooth shape feature analysis on the three-dimensional space model of the oral tooth structure region based on the size range of the three-dimensional space model of the oral tooth structure to obtain three-dimensional geometric shape feature data of the oral tooth; Step S24: performing a tooth spatial position feature analysis on the three-dimensional spatial model of the oral internal structure to obtain three-dimensional spatial position feature data of the oral teeth; Step S25: Based on the three-dimensional geometric shape feature data of the oral teeth and the three-dimensional spatial position feature data of the oral teeth, an inter-tooth contact evaluation analysis is performed on the three-dimensional spatial model of the oral internal structure to obtain the contact status data of the arrangement of the oral teeth.

8. The method for constructing a three-dimensional oral cavity model and analyzing tooth occlusion characteristics according to claim 7, characterized in that: Step S25 includes the following steps: Step S251: performing a tooth occlusion simulation analysis on the three-dimensional spatial model of the internal structure of the oral cavity to obtain a tooth occlusion simulation process inside the oral cavity; Step S252: performing occlusal contact area recognition and analysis on the internal teeth occlusal simulation process based on the three-dimensional geometric shape feature data of the oral teeth and the three-dimensional spatial position feature data of the oral teeth to obtain the internal teeth occlusal contact area; Step S253: performing contact mechanical response simulation analysis on the internal tooth occlusion simulation process according to the internal tooth occlusion contact area of ​​the oral cavity, and obtaining contact pressure distribution data between the internal teeth of the oral cavity; Step S254: performing real-time tracking and analysis of contact points during the oral cavity tooth occlusion simulation process to obtain movement status data of occlusal contact points between teeth in the oral cavity; Step S255: Based on the contact pressure distribution data between the teeth in the oral cavity and the movement status data of the occlusal contact points between the teeth in the oral cavity, an inter-tooth contact evaluation analysis is performed on the intra-oral tooth occlusion simulation process to obtain the contact status data of the arrangement of the teeth in the oral cavity.

9. The method for constructing an oral three-dimensional model and analyzing tooth occlusion characteristics according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing a tooth occlusal relationship analysis on the three-dimensional spatial model of the internal structure of the oral cavity to obtain the tooth occlusal relationship data of the internal structure of the oral cavity; Step S32: performing bite point recognition analysis on the three-dimensional space model of the internal structure of the oral cavity based on the occlusal relationship data of the teeth in the oral cavity to obtain the occlusal points of the teeth in the oral cavity; Step S33: performing bite point location analysis on the bite points of the teeth inside the oral cavity to obtain the bite point position data of the teeth inside the oral cavity; Step S34: performing tooth occlusal connection relationship mining and analysis on the three-dimensional spatial model of the internal structure of the oral cavity based on the contact status data of the arrangement of the oral teeth, so as to obtain the connection relationship between the teeth in the oral occlusal process; Step S35: dividing the three-dimensional space model of the internal structure of the oral cavity into tooth occlusal surface connections according to the connection relationship between teeth during the oral occlusion process and the position data of the oral tooth occlusal points, so as to generate the oral tooth occlusal connection surface.

10. The method for constructing an oral three-dimensional model and analyzing tooth occlusion characteristics according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: measuring the occlusal inclination angle of the occlusal connection surface of the oral teeth to obtain the occlusal inclination angle of the oral teeth; Step S42: performing statistical calculation of the inclination curvature of the oral tooth occlusal connection surface based on the oral tooth occlusal inclination angle to obtain the oral tooth occlusal inclination curvature; Step S43: Calculating the occlusal parallelism of the occlusal connection surface of the oral teeth based on the occlusal inclination angle and the occlusal inclination curvature of the oral teeth using an occlusal parallelism calculation formula to obtain the parallelism of the occlusal surface of the teeth; The calculation formula of occlusal parallelism is as follows: Wherein, p is the parallelism of the occlusal surface of teeth, L is the length of teeth on the occlusal connection surface of oral teeth, l is the horizontal position parameter of teeth on the occlusal connection surface of oral teeth, θ is the occlusal inclination angle of oral teeth, C is the occlusal inclination curvature of oral teeth, e is the distance value between adjacent teeth on the occlusal connection surface of oral teeth, F(l) is the tooth occlusal force applied on the occlusal connection surface of oral teeth at the horizontal position l, and ε is the correction coefficient of the parallelism of the occlusal surface of teeth; Step S44: performing tooth occlusal contour shape recognition analysis on the oral tooth occlusal connection surface to obtain the tooth occlusal surface contour shape; Step S45: performing tooth occlusal adjustment processing on the three-dimensional space model of the internal structure of the oral cavity based on the parallelism of the tooth occlusal surface and the contour shape of the tooth occlusal surface to generate an oral tooth occlusal surface adjustment plan.

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