Digital base model design method based on jaw position relationship

Abstract

The present invention relates to the technical field of three-dimensional point cloud data processing, and particularly to a method for designing a digital base model based on jaw position relationship. After obtaining initial three-dimensional point clouds of a patient at different mouth opening amplitudes, a soft tissue interference area where soft tissue movement may occur and a hard tissue boundary false displacement area corresponding to point cloud overlap caused by a scanning angle are obtained based on comparison of the initial three-dimensional point clouds at different mouth opening amplitudes. The movement trend of each point in the hard tissue boundary false displacement area is corrected in combination with the movement trend of adjacent areas in the oral cavity, so that the corrected three-dimensional point cloud obtained after correction is more accurate, the accuracy of recording based on the corrected three-dimensional point cloud is higher, and the accuracy of the generated digital base is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional point cloud data processing, and in particular to a digital base model design method based on jaw position relationship. Background Art

[0002] Fields such as oral restoration and implantology often use digital bases and jaw relationship records to design patients' virtual restoration models. Compared with traditional methods such as wax rims and jaw supports, they can provide more accurate and repeatable jaw position data.

[0003] Existing technology for constructing digital dental bases and jaw position records typically requires using an ICP algorithm to register the 3D dental and gingival data obtained from intraoral scanning with the 3D dental and gingival data from cone-beam computed tomography scans. This generates 3D point cloud data of the oral cavity. This 3D point cloud data is then recorded and used to design the digital base. Denture fabrication is then carried out using technologies such as CAD / CAM and 3D printing. Specifically, reverse and forward engineering are combined with research into digital modeling techniques based on patient oral data to achieve personalized base design. A database of parameters such as base shape, thickness, and edge closure is generated. Research is also underway to identify base materials suitable for digital fabrication, ensuring they possess excellent mechanical properties and aesthetics while also being compatible with digital fabrication processes such as 3D printing or CNC turning. The effects of cleaning and curing processes on the material's surface and stability are also studied, with the goal of determining optimal technical specifications. Dedicated 3D printing processes (such as stereolithography and selective laser sintering) and CNC machining path optimization are also being developed for base fabrication. For the printing process, research is conducted on support structures that can be automatically generated and easily removed to reduce post-processing time.

[0004] However, during actual intraoral scanning, the three-dimensional data obtained from the intraoral scan is locally distorted due to soft tissue movement (such as the patient's swallowing action), thereby affecting the accuracy of the ICP algorithm when performing point cloud registration; that is, the accuracy of the three-dimensional point cloud data obtained by the existing technology using the three-dimensional data of the intraoral scan and the three-dimensional data obtained after cone-beam computed tomography to perform ICP algorithm registration is low, resulting in low accuracy of the recorded three-dimensional point cloud data and inaccurate generated digital base. Summary of the Invention

[0005] In order to solve the technical problem of low accuracy of 3D point cloud data obtained by performing ICP algorithm registration on 3D data obtained from intraoral scanning and 3D data obtained from cone-beam computed tomography in the existing technology, the purpose of this application is to provide a digital base model design method based on jaw position relationship. The technical solution adopted is as follows:

[0006] The first aspect of the present application provides a method for designing a digital base model based on jaw position relationship, comprising:

[0007] Collecting the initial three-dimensional point cloud of the oral cavity of the patient at each mouth opening amplitude; obtaining each oral structure area in the initial three-dimensional point cloud and its corresponding matching structure area at the next mouth opening amplitude;

[0008] Calculating a data point matching degree between each point cloud data point in the oral structure region and each point cloud data point in the corresponding matching structure region based on curvature similarity and position projection proximity; determining a matching data point corresponding to each point cloud data point according to the data point matching degree;

[0009] Determine the corresponding matching displacement vector based on the relative position between each point cloud data point and the corresponding matching data point; determine the corresponding displacement anomaly probability based on the degree of data point matching and the deviation of the matching displacement vector of each point cloud data point compared to other point cloud data points in the oral structure area; and screen out soft tissue interference areas and hard tissue boundary false displacement areas based on the overall distribution of displacement anomaly probabilities of each point cloud data point in each oral structure area;

[0010] Based on the initial three-dimensional point cloud, all point cloud data points in the soft tissue interference area are deleted and the point cloud data points in the hard tissue boundary false displacement area are corrected to obtain a corrected three-dimensional point cloud; the corrected three-dimensional point cloud is recorded to generate a digital base.

[0011] Furthermore, the process of obtaining the matching structure region includes:

[0012] The PFFH algorithm is used to determine the matching structural area of ​​each oral structure area under each mouth opening amplitude and the matching structural area under the next mouth opening amplitude.

[0013] Furthermore, the process of obtaining the matching degree of the data points includes:

[0014] The vector corresponding to the centroid point in each oral structure area to each point cloud data point is used as the reference direction vector of each point cloud data point; each point cloud data point in each oral structure area is used as the target data point in turn; each point cloud data point in the matching structure area corresponding to the target data point is used as the comparison data point in turn;

[0015] Normalizing the cosine value of the angle between the reference direction vector of the target data point and the reference direction vector of the comparison data point to determine the corresponding direction similarity;

[0016] Determining a corresponding curvature deviation value according to a difference between the maximum principal curvature of the target data point and the maximum principal curvature of the comparison data point;

[0017] Determining a corresponding center displacement deviation value according to a difference between the modulus of the reference direction vector of the target data point and the modulus of the reference direction vector of the comparison data point;

[0018] A negative correlation mapping value of the product of the center displacement deviation value and the curvature deviation value is multiplied by the direction similarity to determine a data point matching degree between the target data point and the comparison data point.

[0019] Furthermore, the process of obtaining the matching data points includes:

[0020] In the matching structure region corresponding to the oral structure region of the target data point, the comparison data point with the greatest matching degree of the corresponding data point is used as the matching data point of the target data point in the corresponding matching structure region.

[0021] Furthermore, the process of obtaining the matching displacement vector includes:

[0022] The reference direction vector of the matching data point corresponding to each point cloud data point is used as the matching vector; the matching vector is vector-subtracted from the reference direction vector of each point cloud data point to obtain the matching displacement vector of each point cloud data point.

[0023] Furthermore, the process of obtaining the possibility of displacement anomaly includes:

[0024] The other point cloud data points with matching data points in the oral structure area where each point cloud data point is located are used as corresponding reference data points;

[0025] The cosine value of the angle between the matching displacement vector of each point cloud data point and the matching displacement vector of the corresponding reference data point is negatively correlated to determine the displacement direction deviation value of each reference data point; the degree of directional anomaly of each point cloud data point is determined based on the mean of the displacement direction deviation values ​​of all reference data points corresponding to each point cloud data point;

[0026] Determine the displacement amplitude deviation value of each reference data point based on the difference between the modulus of the matching displacement vector of each point cloud data point and the modulus of the matching displacement vector of the corresponding reference data point; determine the amplitude anomaly degree of each point cloud data point based on the average of the displacement amplitude deviation values ​​of all reference data points corresponding to each point cloud data point;

[0027] Negatively correlate the degree of matching between each point cloud data point and the corresponding matching data point to determine the matching anomaly weight of each point cloud data point;

[0028] The product of the matching anomaly weight, the direction anomaly degree, and the amplitude anomaly degree is normalized to determine the displacement anomaly possibility of each point cloud data point.

[0029] Furthermore, the process of screening out the soft tissue interference area and the hard tissue boundary false displacement area includes:

[0030] Point cloud data points with a displacement abnormality probability greater than a preset abnormality threshold are considered as displacement abnormal points; point cloud data points with a displacement abnormality probability less than or equal to the preset abnormality threshold are considered as displacement normal points; cluster analysis is performed on the displacement abnormal points in each oral structure area to obtain at least two abnormal point clusters;

[0031] The displacement normal point with the smallest Euclidean distance to each displacement abnormal point is used as the reference normal point of each displacement abnormal point;

[0032] According to the chaotic distribution of displacement directions of the displacement abnormal points in each abnormal point cluster and the concentrated distribution of displacement directions of the corresponding reference normal points, the overall displacement chaos of each abnormal point cluster is determined;

[0033] The maximum value of the maximum principal curvature of all displacement abnormal points in each abnormal point cluster is used as the curvature abnormality degree; the hard tissue boundary possibility of each abnormal point cluster is determined according to the normalized value of the product between the body displacement disorder and the curvature abnormality degree;

[0034] The area composed of point cloud data points of the abnormal point clusters whose hard tissue boundary probability is greater than the preset interference threshold is taken as the hard tissue boundary false displacement area; the area composed of point cloud data points of the abnormal point clusters whose hard tissue boundary probability is less than or equal to the preset interference threshold is taken as the soft tissue interference area.

[0035] Furthermore, the process of obtaining the overall displacement disorder includes:

[0036] The corresponding displacement direction chaos is determined according to the variance of the displacement direction deviation values ​​of all displacement anomalies in each anomaly cluster; the displacement normal point with the smallest Euclidean distance to each displacement anomaly point is used as the reference normal point for each displacement anomaly point; the corresponding normal reference weight is determined according to the variance of the displacement direction deviation values ​​of all reference normal points corresponding to all displacement anomalies in each anomaly cluster; the overall displacement chaos of each anomaly cluster is determined according to the ratio between the displacement direction chaos and the normal reference weight.

[0037] Furthermore, the process of obtaining the corrected three-dimensional point cloud includes:

[0038] For each displacement anomaly point in the hard tissue boundary false displacement region:

[0039] The cosine value of the angle between the matched displacement vector of the displacement abnormal point and the matched displacement vector of each normal displacement point is used as the reference cosine value of each normal displacement point; the normal displacement point whose Euclidean distance to the displacement abnormal point is less than the preset distance threshold and whose reference cosine value is greater than the preset cosine threshold is used as the effective comparison point of the displacement abnormal point;

[0040] Determine the corresponding reference displacement amount based on the mean value of the modulus of the matching displacement vectors of all valid comparison points corresponding to the displacement abnormal point; perform positive correlation mapping on the difference between the reference displacement amount and the modulus of the matching displacement vector of the displacement abnormal point to determine the displacement amplitude adjustment weight;

[0041] determining a final displacement amplitude of the displacement abnormal point according to the product of the modulus of the matched displacement vector of the displacement abnormal point and the displacement amplitude adjustment weight;

[0042] Normalizing the sum of the matching displacement vectors of all valid comparison points as a valid unit vector; determining an adjusted displacement vector of the displacement abnormal point according to the product of the final displacement amplitude and the valid unit vector;

[0043] Based on the initial three-dimensional point cloud, all point cloud data points in the soft tissue interference area are deleted, and each displacement abnormal point in the hard tissue boundary false displacement area is corrected according to the corresponding adjustment displacement vector to obtain a corrected three-dimensional point cloud.

[0044] Furthermore, the process of generating a digital base after recording the corrected three-dimensional point cloud includes:

[0045] The corrected 3D point cloud is transferred to the recording device and then input into the 3Shape Dental System to generate the corresponding digital base.

[0046] In a second aspect, the present application provides a digital base and jaw relationship recording system, the system comprising:

[0047] The data acquisition and preprocessing module is used to collect the initial three-dimensional point cloud of the oral cavity of the patient at each mouth opening amplitude; obtain each oral structure area in the initial three-dimensional point cloud and its corresponding matching structure area at the next mouth opening amplitude;

[0048] A data point matching module is configured to calculate a degree of data point matching between each point cloud data point in the oral structure region and each point cloud data point in the corresponding matching structure region based on curvature similarity and position projection proximity; and determine a matching data point corresponding to each point cloud data point based on the data point matching degree;

[0049] A region screening module is configured to determine a corresponding matching displacement vector based on the relative position between each point cloud data point and the corresponding matching data point; determine the corresponding displacement anomaly probability based on the degree of data point matching and the deviation of the matching displacement vector of each point cloud data point compared to other point cloud data points in the oral structure region; and screen out soft tissue interference regions and hard tissue boundary false displacement regions based on the overall distribution of displacement anomaly probabilities of each point cloud data point in each oral structure region;

[0050] The 3D point cloud correction module is used to delete all point cloud data points in the soft tissue interference area and correct the point cloud data points in the hard tissue boundary false displacement area on the basis of the initial 3D point cloud to obtain a corrected 3D point cloud; and to generate a digital base after recording the corrected 3D point cloud.

[0051] In a third aspect, the present application provides a computer device comprising a memory and a processor. The memory is configured to store computer program code, and the processor is configured to call and execute the computer program code from the memory to perform the method according to the first aspect or any embodiment of the first aspect of the present application.

[0052] In a fourth aspect, the present application provides a computer program product, comprising a computer program code. When the computer program code is executed, the method of the first aspect or any embodiment of the first aspect of the present application is performed.

[0053] In a fifth aspect, the present application provides a computer-readable storage medium, which stores a computer program code. When the computer program code is executed, it performs the method of the first aspect of the present application or any embodiment of the first aspect.

[0054] This application has the following beneficial effects:

[0055] After obtaining the initial three-dimensional point cloud of the patient at different mouth opening amplitudes, the present application obtains the soft tissue interference area where soft tissue movement may exist and the hard tissue boundary false displacement area corresponding to the point cloud overlap caused by the scanning angle based on the comparison of the initial three-dimensional point cloud at different mouth opening amplitudes, and corrects the movement trend of each point in the hard tissue boundary false displacement area in combination with the movement trend of the adjacent area in the oral cavity, so that the corrected three-dimensional point cloud obtained after correction is more accurate, the accuracy of recording based on the corrected three-dimensional point cloud is higher, and the accuracy of the generated digital base is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0057] Figure 1 A flowchart of a method for designing a digital base model based on jaw position relationship provided by one embodiment of the present invention;

[0058] Figure 2 A structural diagram of a digital base and its jaw relationship recording system provided by one embodiment of the present invention;

[0059] Figure 3 The present invention provides a schematic diagram of a computer device structure according to an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the following, in combination with the accompanying drawings and preferred embodiments, describes in detail a digital base model design method based on jaw relationship proposed by the present invention, its specific implementation method, structure, features and its effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment, and the specific features, structures or characteristics in one or more embodiments may be combined in any suitable form. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features.

[0061] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0062] The following describes in detail a specific solution of a digital base model design method based on jaw position relationship provided by the present invention with reference to the accompanying drawings.

[0063] The present invention provides a method for designing a digital base model based on jaw position relationship. Figure 1 , which shows a flow chart of a method for designing a digital base model based on jaw position relationship provided by one embodiment of the present invention, the method comprising:

[0064] Step S101: collecting an initial three-dimensional point cloud of the oral cavity of an oral patient at each mouth opening amplitude; obtaining each oral structure region in the initial three-dimensional point cloud and its corresponding matching structure region at the next mouth opening amplitude.

[0065] In a specific implementation of an embodiment of the present invention, an initial three-dimensional point cloud of the interior of the oral cavity of an oral patient at each mouth opening amplitude is collected using a 3Shape TRIOS intraoral scanner, and the maximum principal curvature value of each point cloud data point is determined; wherein the mouth opening amplitude is set to 1 cm, 2 cm, 3 cm, and 4 cm; the mouth opening amplitude setting and the method of collecting the initial three-dimensional point cloud can be adjusted according to the specific implementation environment.

[0066] In one specific implementation of an embodiment of the present invention, an initial three-dimensional point cloud is divided into various oral structure regions based on a semantic segmentation method. These oral structure regions include the areas corresponding to the teeth and gums. Specifically, a large amount of oral scan point cloud data is collected, and the teeth and gum regions are manually annotated as a training set. After a semantic segmentation model is trained using a point cloud deep learning framework, the initial three-dimensional point cloud is input into the semantic segmentation model to output the various oral structure regions. It should be noted that semantic segmentation is a well-known technical method used by those skilled in the art and will not be further defined or elaborated upon herein.

[0067] Because a single intraoral scan of a patient is susceptible to soft tissue interference, the initial 3D point cloud obtained can inaccurately represent the oral structure. This is especially true when the mouth opening range changes, as soft tissue can shift or deform with the change, leading to local point cloud anomalies. Therefore, matching initial 3D point clouds with adjacent mouth opening ranges facilitates subsequent screening of points with displacement anomalies based on the matching results, thereby identifying areas suspected of soft tissue interference.

[0068] Preferably, in some possible implementations of the embodiments of the present invention, the process of acquiring the matching structure region includes:

[0069] The matching structural area of ​​each oral structure area at each mouth opening amplitude is determined by the Fast Point Feature Histograms (PFFH) algorithm. In a specific implementation of an embodiment of the present invention, the order of the mouth opening amplitudes is arranged from small to large, which conforms to the temporal characteristics of the oral patient's mouth gradually opening, and matching the oral structure areas of adjacent mouth opening amplitudes can improve the accuracy of the matching. It should be noted that the PFFH algorithm is a technical means well known to those skilled in the art and will not be further described here.

[0070] Step S102: Calculate the degree of data point matching between each point cloud data point in the oral structure area and each point cloud data point in the corresponding matching structure area based on the curvature similarity and the proximity of the position projection; determine the matching data point corresponding to each point cloud data point according to the data point matching degree.

[0071] Since the morphology of teeth and bones is relatively stable under different mouth opening amplitudes, point cloud matching is performed based on the stability of the morphology of each region. Under normal circumstances, the displacement of hard tissue areas has overall characteristics, that is, each point in the same hard tissue area has similar movement displacement. Therefore, based on this characteristic, the matching degree of the point cloud data points between the oral structure area and the corresponding matching structure area is calculated.

[0072] Preferably, in some possible implementations of the embodiments of the present invention, the process of obtaining the degree of matching of data points includes:

[0073] The vector corresponding to the centroid point in each oral structure area to each point cloud data point is used as the reference direction vector of each point cloud data point; each point cloud data point in each oral structure area is used as the target data point in turn; each point cloud data point in the matching structure area corresponding to the oral structure area of ​​the target data point is used as the comparison data point in turn; the cosine value of the angle between the reference direction vector of the target data point and the reference direction vector of the comparison data point is normalized to determine the corresponding direction similarity; the corresponding curvature deviation value is determined according to the difference between the maximum principal curvature of the target data point and the maximum principal curvature of the comparison data point; the corresponding center displacement deviation value is determined according to the difference between the modulus of the reference direction vector of the target data point and the modulus of the reference direction vector of the comparison data point; the negative correlation mapping value of the product between the center displacement deviation value and the curvature deviation value is multiplied by the direction similarity to determine the degree of data point matching between the target data point and the comparison data point.

[0074] First, the more consistent the orientation and length between the reference direction vector of the target data point and the reference direction vector of the comparison data point are, the closer the target data point and the comparison data point are in relative position. In the specific calculation, the larger the cosine value of the angle between the reference direction vector of the target data point and the reference direction vector of the comparison data point, and the smaller the difference between the two modules, the closer the relative positions of the target data point and the comparison data point based on their respective centroids are, and the greater the corresponding matching degree should be. In addition, if the maximum principal curvature between the target data point and the comparison data point is more similar, the more similar the local geometric structures of the two point cloud data points are, the greater the possibility of being in the same anatomical structure, and the greater the corresponding matching degree should be. Therefore, finally, the direction similarity, curvature deviation value and center displacement deviation value are fused according to the correlation relationship to determine a more accurate data point matching degree.

[0075] In a specific implementation of the embodiment of the present invention, the process of obtaining the degree of data point matching is expressed as follows: ;in, The target data point The corresponding The degree of data point matching between the comparison data points; The target data point The maximum principal curvature of The target data point The corresponding The maximum principal curvature of the comparison data points; The target data point The magnitude of the reference direction vector; The target data point The corresponding The magnitude of the reference direction vector of the comparison data point; The target data point The reference direction vector and the corresponding The cosine value of the angle between the reference direction vectors of the comparison data points; is a linear normalization function; is the absolute value symbol; The target data point The corresponding The curvature deviation value between the comparison data points; The target data point The corresponding The center displacement deviation value between the comparison data points; is an exponential function with a natural constant as its base.

[0076] Since the higher the degree of data point matching, the more likely it is that it belongs to point cloud data reflecting the same oral detail feature, the matching data point of the target data point is further determined based on the degree of data point matching; in a specific implementation method of an embodiment of the present invention, the process of obtaining the matching data point includes: in the matching structure area corresponding to the oral structure area of ​​the target data point, the comparison data point with the largest degree of matching of the corresponding data point is used as the matching data point of the target data point in the corresponding matching structure area; finally, the target data point is changed to determine the matching data point corresponding to each point cloud data point.

[0077] Step S103: Determine the corresponding matching displacement vector based on the relative position between each point cloud data point and the corresponding matching data point; determine the corresponding displacement anomaly possibility based on the degree of data point matching and the deviation of the matching displacement vector of each point cloud data point compared with other point cloud data points in the oral structure area; and screen out soft tissue interference areas and hard tissue boundary false displacement areas based on the overall distribution of the displacement anomaly possibilities of each point cloud data point in each oral structure area.

[0078] Due to the complexity of the anatomical structure, as the mouth opening amplitude changes, soft tissues such as the gums and buccal mucosa may exhibit abnormal displacement in the local point cloud due to deformation jitter. Furthermore, areas of false displacement at the boundaries of hard tissues, such as the contact edge between the gums and teeth, may distort the point cloud at the edge due to changes in the scanning angle at different mouth opening amplitudes, resulting in false displacement and being detected as abnormal movement areas. Therefore, point cloud data points in both soft tissue interference areas and hard tissue boundary false displacement areas often exhibit abnormal displacement. Therefore, before screening soft tissue interference areas and hard tissue boundary false displacement areas, it is necessary to first analyze the abnormal displacement.

[0079] To analyze displacement anomalies, it is first necessary to determine the characteristics of the displacement of the point cloud data points. Preferably, in some possible implementations of the embodiments of the present invention, the process of obtaining the matching displacement vector includes:

[0080] The reference direction vector of the matching data point corresponding to each point cloud data point is used as the matching vector. The matching vector is subtracted from the reference direction vector of each point cloud data point to obtain the matching displacement vector for each point cloud data point. Based on the definition of vector subtraction, the matching displacement vector represents the displacement process of the corresponding point cloud data point to the matching data point.

[0081] Further, the displacement anomaly analysis is performed based on the displacement of the point cloud data points represented by the matching displacement vector. Preferably, in some possible implementations of the embodiments of the present invention, the process of obtaining the possibility of displacement anomaly includes:

[0082] The other point cloud data points with matching data points in the oral structure area where each point cloud data point is located are used as corresponding reference data points; the cosine value of the angle between the matching displacement vector of each point cloud data point and the matching displacement vector of the corresponding reference data point is negatively correlated to determine the displacement direction deviation value of each reference data point; the direction abnormality degree of each point cloud data point is determined according to the mean of the displacement direction deviation values ​​of all reference data points corresponding to each point cloud data point; the displacement amplitude deviation value of each reference data point is determined according to the difference between the modulus of the matching displacement vector of each point cloud data point and the modulus of the matching displacement vector of the corresponding reference data point; the amplitude abnormality degree of each point cloud data point is determined according to the mean of the displacement amplitude deviation values ​​of all reference data points corresponding to each point cloud data point; the data point matching degree of each point cloud data point and the corresponding matching data point is negatively correlated to determine the matching abnormality weight of each point cloud data point;

[0083] First, the displacement of normal hard tissue areas has an overall feature, that is, each point in the same hard tissue area has similar movement displacement. If the matching coefficient of the two points is smaller and the displacement size is significantly different from the other points in the corresponding area, the greater the abnormality of the current point is: Therefore, for the oral structure area representing the hard tissue area, the more inconsistent the displacement of the point cloud data point is compared with other point cloud data points therein, the more abnormal the point cloud data point is; therefore, this application characterizes the inconsistency of the displacement from two dimensions: displacement direction and displacement amount; for a point cloud data point, if the vector direction of its corresponding matching displacement vector is different from that of other point cloud data points in the oral structure area, the point cloud data point is more abnormal. The greater the directional deviation of the matching displacement vector, the more abnormal the displacement of the corresponding point cloud data point in the directional dimension; therefore, the greater the mean of the displacement directional deviation values ​​of all reference data points corresponding to the point cloud data point, that is, the greater the degree of directional abnormality, the more abnormal the displacement of the corresponding point cloud data point; if the length deviation of the modulus of the corresponding matching displacement vector is greater than the modulus of the matching displacement vectors of other point cloud data points in the oral structure area, the more abnormal the displacement of the corresponding point cloud data in the displacement dimension; therefore, the greater the mean of the displacement amplitude deviation values ​​of all reference data points corresponding to the point cloud data point, that is, the greater the degree of amplitude abnormality, the more abnormal the displacement of the corresponding point cloud data point.

[0084] Furthermore, the smaller the degree of data point matching between a point cloud data point and its corresponding matching data point, the more likely that the point cloud data point experienced an anomaly during its movement, resulting in an inability to find a matching data point with a higher degree of data point matching. Therefore, the smaller the degree of data point matching between the point cloud data point and the matching data point, the more abnormal the displacement of the corresponding point cloud data point. By combining the correlation between displacement anomalies and various parameters, the product of the matching anomaly weight, the degree of directional anomaly, and the degree of amplitude anomaly is further normalized to determine the probability of displacement anomaly for each point cloud data point. This results in a greater probability of displacement anomaly, indicating a higher probability of displacement anomaly for the corresponding point cloud data point.

[0085] In a specific implementation of the embodiment of the present invention, the process of obtaining the degree of directional anomaly is expressed by the formula: ;in, For the In the oral cavity area The degree of directional anomaly of each point cloud data point; For the In the oral cavity area The number of reference data points corresponding to each point cloud data point; For the In the oral cavity area The matching displacement vector of the point cloud data point is the The cosine value of the angle between the matching displacement vectors of the reference data points; For the In the oral cavity area The point cloud data point corresponds to the The displacement direction deviation value of a reference data point.

[0086] In a specific implementation of the embodiment of the present invention, the process of obtaining the amplitude abnormality degree is expressed by the formula: ;in, For the In the oral cavity area The degree of amplitude anomaly of each point cloud data point; For the In the oral cavity area The modulus of the matching displacement vector of each point cloud data point; For the In the oral cavity area The point cloud data point corresponds to the The magnitude of the matching displacement vector of the reference data point; For the In the oral cavity area The point cloud data point corresponds to the The displacement amplitude deviation value of the reference data point.

[0087] In a specific implementation of the embodiment of the present invention, the process of obtaining the possibility of displacement anomaly is expressed by the formula: ;in, For the In the oral cavity area The possibility of abnormal displacement of each point cloud data point; For the In the oral cavity area The degree of matching between a point cloud data point and the corresponding matching data point; For the In the oral cavity area The matching anomaly weight of each point cloud data point.

[0088] After determining the possibility of displacement anomaly of each point cloud data point, the abnormal areas can be further screened based on the characteristics that the point cloud data points of the soft tissue interference area and the hard tissue boundary false displacement area all show abnormalities, and further analysis can be performed to divide the soft tissue interference area and the hard tissue boundary false displacement area into regions; among them, due to the difference in scanning angle under different mouth opening amplitudes, the overlapping area of ​​the hard tissue boundary point cloud may have non-physiological displacement direction jumps, which is more unstable than the displacement direction of the soft tissue deformation interference area that is usually more in line with the laws of biomechanics, and the maximum principal curvature of the boundary area is more likely to produce local mutations. Therefore, based on this characteristic, the soft tissue interference area and the hard tissue boundary false displacement area are further distinguished and screened.

[0089] Preferably, in some possible implementations of the embodiments of the present invention, the process of screening out the soft tissue interference region and the hard tissue boundary false displacement region includes:

[0090] Point cloud data points with a displacement abnormality probability greater than a preset abnormality threshold are regarded as displacement abnormal points; point cloud data points with a displacement abnormality probability less than or equal to the preset abnormality threshold are regarded as displacement normal points; cluster analysis is performed on the displacement abnormal points in each oral structure area to obtain at least two abnormal point clusters; in a specific implementation of an embodiment of the present invention, the preset abnormality threshold is set to 0.6, and the cluster analysis method adopts K-means cluster analysis. The cluster analysis method and the preset abnormality threshold can be adjusted according to the specific implementation environment, and no further details are given here.

[0091] The point cloud data points corresponding to the soft tissue interference area and the hard tissue boundary false displacement area are usually concentrated. Therefore, the cluster analysis method is used to determine the area where the displacement abnormality occurs corresponding to each abnormal point cluster, and on this basis, the hard tissue boundary false displacement area and the soft tissue interference area are divided.

[0092] The displacement normal point with the smallest Euclidean distance from each displacement abnormal point is used as the reference normal point for each displacement abnormal point; the overall displacement disorder of each abnormal point cluster is determined based on the displacement direction distribution disorder of the displacement abnormal points in each abnormal point cluster and the displacement direction distribution concentration of the corresponding reference normal points; wherein the overall displacement disorder is obtained by:

[0093] The corresponding displacement direction chaos is determined according to the variance of the displacement direction deviation values ​​of all displacement anomalies in each anomaly cluster; the displacement normal point with the smallest Euclidean distance to each displacement anomaly point is used as the reference normal point for each displacement anomaly point; the corresponding normal reference weight is determined according to the variance of the displacement direction deviation values ​​of all reference normal points corresponding to all displacement anomalies in each anomaly cluster; the overall displacement chaos of each anomaly cluster is determined according to the ratio between the displacement direction chaos and the normal reference weight.

[0094] First, the displacement direction of the false displacement area at the hard tissue boundary is unstable compared to that of the normal area. Therefore, the displacement direction deviation values ​​of the corresponding displacement abnormal points are usually relatively large and discrete. However, the unique direction of the normal displacement points is relatively stable, and the corresponding displacement direction deviation values ​​are usually relatively small and concentrated. Therefore, for each abnormal point cluster, the greater the chaos of its corresponding displacement direction and the smaller the normal reference weight, the more likely the area corresponding to the abnormal point cluster is a false displacement area at the hard tissue boundary.

[0095] In addition, the maximum principal curvature of the point cloud data points in the false displacement area of ​​the hard tissue boundary is more likely to produce local mutations, resulting in a strong fluctuation of the maximum principal curvature of the corresponding displacement anomaly points, and the value range is usually larger; further, the value range is characterized by the maximum value of the maximum principal curvature, and the maximum value of the maximum principal curvature of all displacement anomaly points in each anomaly cluster is taken as the curvature anomaly degree; so that when the curvature anomaly degree is greater, the area corresponding to the anomaly cluster is more likely to be a false displacement area of ​​the hard tissue boundary.

[0096] Further combining the correlation, the hard tissue boundary probability of each abnormal point cluster is determined according to the normalized value of the product between the volume displacement chaos and the curvature abnormality degree; and further dividing the hard tissue boundary false displacement area and the soft tissue interference area is performed according to the hard tissue boundary probability. Specifically, the area composed of the point cloud data points of the abnormal point cluster whose hard tissue boundary probability is greater than the preset interference threshold is regarded as the hard tissue boundary false displacement area; the area composed of the point cloud data points of the abnormal point cluster whose hard tissue boundary probability is less than or equal to the preset interference threshold is regarded as the soft tissue interference area. In a specific implementation of the embodiment of the present invention, the preset interference threshold is set to 0.5, which can be adjusted according to the specific implementation environment.

[0097] In a specific implementation of the embodiment of the present invention, the process of obtaining the hard tissue boundary possibility is expressed as follows: ;in, Clustering of outliers Possibility of hard tissue boundaries; Clustering of outliers The maximum value of the maximum principal curvature of all displacement abnormal points in , that is, the corresponding curvature abnormality degree; Clustering of outliers The variance of the displacement direction deviation values ​​of all displacement abnormal points in , that is, the corresponding displacement direction chaos; Clustering of outliers The variance of the displacement direction deviation values ​​of all reference normal points corresponding to all displacement abnormal points in , that is, the corresponding normal reference weight; Clustering of outliers It should be noted that, in order to ensure that the calculation results are meaningful, when performing fractional operations in the embodiments of the present invention, when encountering a situation where the denominator is 0, it is necessary to add a parameter adjustment factor greater than 0 to the denominator to prevent the denominator from being 0. The value of the parameter adjustment factor is set by the implementer according to the actual situation, and this application does not impose any special restrictions.

[0098] Step S104: Based on the initial 3D point cloud, delete all point cloud data points in the soft tissue interference area and correct the point cloud data points in the hard tissue boundary false displacement area to obtain a corrected 3D point cloud; record the corrected 3D point cloud and generate a digital base.

[0099] Since only the anatomical structure of the hard tissue needs to be accurately preserved during the design and bite recording of the rigid base plate, the points in the soft tissue interference area are screened out. However, the false displacement area at the hard tissue boundary will interfere with the accuracy of the rigid base plate design and bite recording, so the displacement of each point in it needs to be corrected. The false displacement area at the edge of the hard tissue is usually manifested as a discontinuity in the direction of displacement from the surrounding normal displacement points, such as a sudden reversal or a sudden change in amplitude. Therefore, to avoid the local abnormal area causing a break in the obtained intraoral geometric structure, the displacement of each point in the false displacement area is corrected by moving the normal point in the adjacent area, that is, the area with similar surface deformation and force conditions to the real object during the change of the mouth opening amplitude. This allows for a more accurate correction of the three-dimensional point cloud.

[0100] Preferably, in some possible implementations of the embodiments of the present invention, the process of obtaining the corrected three-dimensional point cloud includes:

[0101] For each displacement anomaly point in the hard tissue boundary false displacement region:

[0102] The cosine value of the angle between the matching displacement vector of the displacement abnormal point and the matching displacement vector of each displacement normal point is used as the reference cosine value of each displacement normal point; the displacement normal point whose Euclidean distance to the displacement abnormal point is less than the preset distance threshold and whose reference cosine value is greater than the preset cosine threshold is used as the effective comparison point of the displacement abnormal point. In a specific implementation of an embodiment of the present invention, the preset distance threshold is set to the distance between the two farthest displacement abnormal points in the corresponding hard tissue boundary false displacement area; the preset cosine threshold is set to 0.5, which can be adjusted according to the specific implementation environment. It should be noted that when a displacement normal point whose Euclidean distance to the displacement abnormal point is less than the preset distance threshold and whose reference cosine value is greater than the preset cosine threshold is not found, the displacement normal point with the smallest reference cosine value is used as the only effective comparison point for analysis and calculation, which will not be further elaborated here.

[0103] The matching displacement vector of the effective comparison point characterizes the overall displacement trend of the corresponding neighborhood texture structure under normal circumstances. Therefore, for the corresponding displacement anomaly point, the displacement adjustment should be carried out based on the overall characteristics of the matching displacement vector of the effective comparison point. Therefore, the present application further determines the corresponding reference displacement amount according to the mean value of the modulus of the matching displacement vectors of all effective comparison points corresponding to the displacement anomaly point; the difference between the reference displacement amount and the modulus of the matching displacement vector of the displacement anomaly point is positively correlated to determine the displacement amplitude adjustment weight; the final displacement amplitude of the displacement anomaly point is determined according to the product between the modulus of the matching displacement vector of the displacement anomaly point and the displacement amplitude adjustment weight. Among them, the larger the modulus of the matching displacement vector of the displacement anomaly point is relative to the reference displacement, the smaller the displacement amplitude adjustment weight should be; and the smaller the modulus of the matching displacement vector of the displacement anomaly point is relative to the reference displacement, the larger the displacement amplitude adjustment weight should be; that is, the final displacement amplitude of the displacement anomaly point is adjusted to the size of the reference displacement amount.

[0104] In a specific implementation of the embodiment of the present invention, the process of obtaining the final displacement amplitude is expressed by the formula: ;in, False displacement area of ​​hard tissue boundary Middle The final displacement amplitude of each abnormal displacement point; False displacement area of ​​hard tissue boundary Middle The modulus of the matching displacement vector of each displacement outlier point; False displacement area of ​​hard tissue boundary Middle The mean value of the modulus of the matching displacement vectors of all valid comparison points of the displacement abnormal point is the corresponding reference displacement; for Normalization function, whose value range is -1 to 1; False displacement area of ​​hard tissue boundary Middle The displacement amplitude of each displacement outlier point is adjusted.

[0105] After determining the displacement adjustment amplitude of the displacement anomaly point, it is further necessary to correct its adjustment direction. Since the matching displacement vector of the effective comparison point represents the overall displacement trend of the corresponding neighborhood texture structure under normal circumstances, the sum vector of the matching displacement vectors of all effective comparison points is further normalized as an effective unit vector; the adjustment displacement vector of the displacement anomaly point is determined based on the product between the final displacement amplitude and the effective unit vector. Among them, the modulus value of the effective unit vector is 1, which will not be further elaborated here. At this point, the position correction of the displacement anomaly point in the false displacement area of ​​the hard tissue boundary is completed. Further, based on the initial three-dimensional point cloud, all point cloud data points in the soft tissue interference area are deleted, and each displacement anomaly point in the false displacement area of ​​the hard tissue boundary is corrected according to the corresponding adjustment displacement vector to obtain a corrected three-dimensional point cloud; wherein the position correction according to the adjustment displacement vector is specifically: the starting position of the adjustment displacement vector corresponding to the displacement anomaly point is used as the corresponding displacement anomaly point, and the end position of the corresponding adjustment displacement vector is used as the position of the corrected position of the displacement anomaly point.

[0106] After determining the corrected 3D point cloud data, the process of data recording and digital base generation must be completed. Specifically, this embodiment of the present invention transmits all corrected 3D point clouds to a recording device and then inputs them into the 3Shape Dental System to generate the corresponding digital base. It should be noted that the process of generating a digital base from a 3D point cloud using the 3Shape Dental System is a well-known technical means for those skilled in the art and will not be further described here.

[0107] In summary, a digital base model design method based on jaw relationship obtains the initial three-dimensional point cloud of the patient at different mouth opening amplitudes, obtains the soft tissue interference area where soft tissue movement may exist and the hard tissue boundary false displacement area corresponding to the point cloud overlap caused by the scanning angle based on the comparison of the initial three-dimensional point cloud at different mouth opening amplitudes, and corrects the movement trend of each point in the hard tissue boundary false displacement area in combination with the movement trend of the adjacent area in the oral cavity, so that the corrected three-dimensional point cloud obtained after correction is more accurate, the accuracy of recording based on the corrected three-dimensional point cloud is higher, and the accuracy of the generated digital base is improved.

[0108] This application also provides a digital base and its jaw relationship recording system, please refer to Figure 2 , which shows a structural diagram of a digital base and its jaw relationship recording system provided by an embodiment of the present invention, the system includes: a data acquisition and preprocessing module 201, a data point matching module 202, a region screening module 203 and a three-dimensional point cloud correction module 204.

[0109] The data acquisition and preprocessing module 201 is used to acquire an initial three-dimensional point cloud of the oral cavity of an oral patient at each mouth opening amplitude; obtain each oral structure region in the initial three-dimensional point cloud and its corresponding matching structure region at the next mouth opening amplitude;

[0110] The data point matching module 202 is configured to calculate a data point matching degree between each point cloud data point in the oral structure region and each point cloud data point in the corresponding matching structure region based on curvature similarity and position projection proximity; and determine a matching data point corresponding to each point cloud data point based on the data point matching degree;

[0111] The region screening module 203 is configured to determine the corresponding matching displacement vector based on the relative position between each point cloud data point and the corresponding matching data point; determine the corresponding displacement anomaly probability based on the degree of data point matching and the deviation of the matching displacement vector of each point cloud data point compared to other point cloud data points in the oral structure region; and screen out soft tissue interference regions and hard tissue boundary false displacement regions based on the overall distribution of displacement anomaly probabilities of each point cloud data point in each oral structure region;

[0112] The 3D point cloud correction module 204 is used to delete all point cloud data points in the soft tissue interference area and correct the point cloud data points in the hard tissue boundary false displacement area based on the initial 3D point cloud to obtain a corrected 3D point cloud; the corrected 3D point cloud is recorded and then a digital base is generated.

[0113] It should be noted that the system provided in the above embodiment is merely an example of the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the digital base and its jaw relationship recording system provided in the above embodiment and the embodiment of a digital base model design method based on jaw relationship are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0114] The present application also provides a computer device. Figure 3 , which shows a schematic diagram of the structure of a computer device provided by an embodiment of the present invention, the computer device includes a memory 301, a processor 302, and a computer program 303 stored in the memory 301 and running on the processor 302, wherein when the processor 302 executes the computer program 303, the computer device can execute any one of the digital base model design methods based on jaw relationship introduced above.

[0115] An embodiment of the present application further provides a computer program product, which, when executed on a computer device, enables the computer device to execute any one of the aforementioned methods for designing a digital base model based on jaw position relationships.

[0116] An embodiment of the present application also provides a computer-readable storage medium, in which computer program code is stored. When the computer program code is run on a computer device, the computer device can execute any one of the digital base model design methods based on jaw relationship introduced above.

[0117] In the embodiments provided in the present application, it should be understood that the provided computer devices, computer program products and computer-readable storage media are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the methods provided above and will not be repeated here.

[0118] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0119] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A digital base model design method based on jaw position relationship, characterized in that: The method comprises: Collecting the initial three-dimensional point cloud of the oral cavity of the patient at each mouth opening amplitude; obtaining each oral structure area in the initial three-dimensional point cloud and its corresponding matching structure area at the next mouth opening amplitude; Calculating a data point matching degree between each point cloud data point in the oral structure region and each point cloud data point in the corresponding matching structure region based on curvature similarity and position projection proximity; determining a matching data point corresponding to each point cloud data point according to the data point matching degree; Determine the corresponding matching displacement vector based on the relative position between each point cloud data point and the corresponding matching data point; determine the corresponding displacement anomaly probability based on the degree of data point matching and the deviation of the matching displacement vector of each point cloud data point compared to other point cloud data points in the oral structure area; and screen out soft tissue interference areas and hard tissue boundary false displacement areas based on the overall distribution of displacement anomaly probabilities of each point cloud data point in each oral structure area; Based on the initial three-dimensional point cloud, all point cloud data points in the soft tissue interference area are deleted and the point cloud data points in the hard tissue boundary false displacement area are corrected to obtain a corrected three-dimensional point cloud; the corrected three-dimensional point cloud is recorded to generate a digital base; The process of obtaining the matching degree of data points includes: The vector corresponding to the centroid point in each oral structure area to each point cloud data point is used as the reference direction vector of each point cloud data point; each point cloud data point in each oral structure area is used as the target data point in turn; each point cloud data point in the matching structure area corresponding to the target data point is used as the comparison data point in turn; Normalizing the cosine value of the angle between the reference direction vector of the target data point and the reference direction vector of the comparison data point to determine the corresponding direction similarity; Determining a corresponding curvature deviation value according to a difference between the maximum principal curvature of the target data point and the maximum principal curvature of the comparison data point; Determining a corresponding center displacement deviation value according to a difference between the modulus of the reference direction vector of the target data point and the modulus of the reference direction vector of the comparison data point; A negative correlation mapping value of the product of the center displacement deviation value and the curvature deviation value is multiplied by the direction similarity to determine a data point matching degree between the target data point and the comparison data point.

2. The method for designing a digital base model based on jaw position relationship according to claim 1, characterized in that: The process of obtaining the matching structure region includes: The PFFH algorithm is used to determine the matching structural area of ​​each oral structure area under each mouth opening amplitude and the matching structural area under the next mouth opening amplitude.

3. The method for designing a digital base model based on jaw position relationship according to claim 1, characterized in that: The process of obtaining the matching data points includes: In the matching structure region corresponding to the oral structure region of the target data point, the comparison data point with the greatest matching degree of the corresponding data point is used as the matching data point of the target data point in the corresponding matching structure region.

4. The method for designing a digital base model based on jaw position relationship according to claim 1, characterized in that: The process of obtaining the matching displacement vector includes: The reference direction vector of the matching data point corresponding to each point cloud data point is used as the matching vector; the matching vector is vector-subtracted from the reference direction vector of each point cloud data point to obtain the matching displacement vector of each point cloud data point.

5. The method for designing a digital base model based on jaw position relationship according to claim 1, characterized in that: The process of obtaining the possibility of displacement anomaly includes: The other point cloud data points with matching data points in the oral structure area where each point cloud data point is located are used as corresponding reference data points; The cosine value of the angle between the matching displacement vector of each point cloud data point and the matching displacement vector of the corresponding reference data point is negatively correlated to determine the displacement direction deviation value of each reference data point; the degree of directional anomaly of each point cloud data point is determined based on the mean of the displacement direction deviation values ​​of all reference data points corresponding to each point cloud data point; Determine the displacement amplitude deviation value of each reference data point based on the difference between the modulus of the matching displacement vector of each point cloud data point and the modulus of the matching displacement vector of the corresponding reference data point; determine the amplitude anomaly degree of each point cloud data point based on the average of the displacement amplitude deviation values ​​of all reference data points corresponding to each point cloud data point; Negatively correlate the degree of matching between each point cloud data point and the corresponding matching data point to determine the matching anomaly weight of each point cloud data point; The product of the matching anomaly weight, the direction anomaly degree, and the amplitude anomaly degree is normalized to determine the displacement anomaly possibility of each point cloud data point.

6. The method for designing a digital base model based on jaw position relationship according to claim 5, characterized in that: The process of screening out the soft tissue interference area and the hard tissue boundary false displacement area includes: Point cloud data points with a displacement abnormality probability greater than a preset abnormality threshold are considered as displacement abnormal points; point cloud data points with a displacement abnormality probability less than or equal to the preset abnormality threshold are considered as displacement normal points; cluster analysis is performed on the displacement abnormal points in each oral structure area to obtain at least two abnormal point clusters; The displacement normal point with the smallest Euclidean distance to each displacement abnormal point is used as the reference normal point of each displacement abnormal point; According to the chaotic distribution of displacement directions of the displacement abnormal points in each abnormal point cluster and the concentrated distribution of displacement directions of the corresponding reference normal points, the overall displacement chaos of each abnormal point cluster is determined; The maximum value of the maximum principal curvature of all displacement abnormal points in each abnormal point cluster is used as the curvature abnormality degree; the hard tissue boundary possibility of each abnormal point cluster is determined according to the normalized value of the product between the body displacement disorder and the curvature abnormality degree; The area composed of point cloud data points of the abnormal point clusters whose hard tissue boundary probability is greater than the preset interference threshold is taken as the hard tissue boundary false displacement area; the area composed of point cloud data points of the abnormal point clusters whose hard tissue boundary probability is less than or equal to the preset interference threshold is taken as the soft tissue interference area.

7. The method for designing a digital base model based on jaw position relationship according to claim 6, characterized in that: The process of obtaining the overall displacement disorder includes: The corresponding displacement direction chaos is determined according to the variance of the displacement direction deviation values ​​of all displacement anomalies in each anomaly cluster; the displacement normal point with the smallest Euclidean distance to each displacement anomaly point is used as the reference normal point for each displacement anomaly point; the corresponding normal reference weight is determined according to the variance of the displacement direction deviation values ​​of all reference normal points corresponding to all displacement anomalies in each anomaly cluster; the overall displacement chaos of each anomaly cluster is determined according to the ratio between the displacement direction chaos and the normal reference weight.

8. The method for designing a digital base model based on jaw position relationship according to claim 6, characterized in that: The process of obtaining the corrected three-dimensional point cloud includes: For each displacement anomaly point in the hard tissue boundary false displacement region: The cosine value of the angle between the matched displacement vector of the displacement abnormal point and the matched displacement vector of each normal displacement point is used as the reference cosine value of each normal displacement point; the normal displacement point whose Euclidean distance to the displacement abnormal point is less than the preset distance threshold and whose reference cosine value is greater than the preset cosine threshold is used as the effective comparison point of the displacement abnormal point; Determine the corresponding reference displacement amount based on the mean value of the modulus of the matching displacement vectors of all valid comparison points corresponding to the displacement abnormal point; perform positive correlation mapping on the difference between the reference displacement amount and the modulus of the matching displacement vector of the displacement abnormal point to determine the displacement amplitude adjustment weight; determining a final displacement amplitude of the displacement abnormal point according to the product of the modulus of the matched displacement vector of the displacement abnormal point and the displacement amplitude adjustment weight; Normalizing the sum of the matching displacement vectors of all valid comparison points as a valid unit vector; determining an adjusted displacement vector of the displacement abnormal point according to the product of the final displacement amplitude and the valid unit vector; Based on the initial three-dimensional point cloud, all point cloud data points in the soft tissue interference area are deleted, and each displacement abnormal point in the hard tissue boundary false displacement area is corrected according to the corresponding adjustment displacement vector to obtain a corrected three-dimensional point cloud.

9. The method for designing a digital base model based on jaw position relationship according to claim 1, characterized in that: The process of generating a digital base after recording the corrected three-dimensional point cloud includes: The corrected 3D point cloud is transferred to the recording device and then input into the 3Shape Dental System to generate the corresponding digital base.

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