Target data digital processing method and device and storage medium

Abstract

The invention relates to the technical field of oral cavity data processing, in particular to a target data digital processing method and device and a storage medium, and the method comprises the steps: collecting oral cavity scanning data of a user, building an oral cavity three-dimensional model of the user based on the oral cavity scanning data, and collecting oral cavity stress data of the user, establishing oral cavity dynamic occlusion characteristics of the user according to an acquisition result and an oral cavity three-dimensional model of the user, analyzing lateral stress vectors of teeth according to oral cavity stress data, updating the oral cavity dynamic occlusion characteristics according to an analysis result, and acquiring oral cavity biological data of the user; and determining a microbial risk factor of the user in combination with the oral biological judgment index, collecting historical oral data of the user, and performing oral health early warning on the user in combination with the oral dynamic occlusion characteristics of the user and the microbial risk factor. The oral cavity data processing efficiency is effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oral data processing, and in particular to a target data digital processing method and device and storage medium. BACKGROUND

[0002] In orthodontic treatment, the traditional tooth mold acquisition method relies on physical molding and manual measurement, which has problems such as complicated operation, low precision, and difficulty in long-term preservation. With the development of digital scanning technology and computer modeling, more and more clinical institutions have begun to use three-dimensional scanning equipment to acquire patients' dental morphology data to realize digital modeling and virtual treatment design.

[0003] Traditional orthodontic treatment relies on static models and manual experience, which has problems such as modeling distortion, single analysis dimension, and delayed warning. Therefore, it is urgent to propose an efficient, accurate, and highly scalable target data digital processing method to meet the growing digital needs of the orthodontic industry. SUMMARY

[0004] The present application relates to the technical field of oral data processing, and in particular to a target data digital processing method and device and storage medium.

[0005] To achieve the above-mentioned purpose, according to one aspect of the present application, a target data digital processing method is provided, comprising:

[0006] Collecting oral scanning data of a user, and establishing a three-dimensional oral model of the user based on the oral scanning data;

[0007] Collecting oral stress data of the user, and establishing a dynamic occlusion feature of the user according to the collection result and the three-dimensional oral model of the user;

[0008] Analyzing the lateral stress vector of the teeth according to the oral stress data, and updating the dynamic occlusion feature according to the analysis result;

[0009] Collecting oral biological data of the user, and determining the microbial risk factor of the user in combination with the oral biological determination index;

[0010] Collecting historical oral data of the user, and combining the dynamic occlusion feature and the microbial risk factor of the user to perform oral health warning to the user.

[0011] Further, an initial three-dimensional oral model is established based on oral tooth positioning data and three-dimensional point cloud data of oral gingiva;

[0012] A saliva interference correction coefficient is established to correct the initial three-dimensional oral model, and the process is as follows:

[0013] A calculation formula of the saliva interference correction coefficient is set as: β = exp{-k x (σ / σ0) x (v / v0)}; in the formula, β is the saliva interference correction coefficient, k is an attenuation gain coefficient, σ is the saliva conductivity, v is the saliva flow rate, σ0 is a reference saliva conductivity, and v0 is a reference saliva flow rate;

[0014] The saliva interference correction coefficient is used to correct each oral tooth positioning data, and a product of the oral tooth positioning data and the saliva interference correction coefficient β is taken as the corrected oral tooth positioning data.

[0015] Further, the method for constructing the oral dynamic occlusion feature comprises the following steps:

[0016] Based on the oral force data of the user and the three-dimensional model of the oral cavity of the user, the force features of the teeth and the force features of the gums of the user are analyzed.

[0017] The force features of the teeth and the force features of the gums of the user are fused with the three-dimensional model of the oral cavity to form the oral dynamic occlusion feature of the user.

[0018] Further, when the force displacement features of the teeth of the user are analyzed, the occlusion force data of each tooth of the user is compared with each preset standard tooth occlusion force range, when the occlusion pressure of the ith tooth exceeds the ith preset standard tooth occlusion force range and the occlusion offset distance of the ith tooth is 0, the force feature of the ith tooth is set as the offset proportion of the right value of the ith standard tooth occlusion force range; when the occlusion pressure of the ith tooth is within the ith preset standard tooth occlusion force range and the occlusion offset distance of the ith tooth is 0, the force feature of the ith tooth is set as 0.

[0019] When the occlusion pressure of the ith tooth is within the ith preset standard tooth occlusion force range and the occlusion offset distance of the ith tooth is not 0, the force feature of the ith tooth is set as a vector feature, denoted as α1(i), and α1(i) = a(i) is set.

[0020] When the occlusion pressure of the ith tooth exceeds the ith preset standard tooth occlusion force range and the occlusion offset distance of the ith tooth is not 0, the force feature of the ith tooth is set as a vector feature, denoted as α2(i), and α2(i) = [a(i) x the offset proportion of the right value of the ith standard tooth occlusion force range] is set.

[0021] Wherein, a(i) represents the displacement vector of the ith tooth relative to the ith tooth in the three-dimensional model of the oral cavity when the teeth are occluded.

[0022] Further, in the analysis of the gingival stress characteristics, the gingiva in the three-dimensional oral cavity model is divided into gingival regions S(i) according to the tooth positions, and the gingival stress deformation data is compared with the positions of the gingival regions, and the position deformation index γ(i) of each gingival region is calculated, and by setting a deformation threshold, each gingival region is judged as a normally stressed gingival region and an abnormally stressed gingival region.

[0023] The gingival stress characteristics of the normally stressed gingival region are set to 0, and the gingival stress characteristics of the abnormally stressed gingival region are set to [γ(i)-deformation threshold] / deformation threshold.

[0024] Further, in the analysis of the tooth lateral stress vector, a compression safety threshold is set, and the tooth compression force data is compared with the compression safety threshold to determine the abnormally compressed tooth, and the lateral stress vector is set for the abnormally compressed tooth, and the setting process is as follows: the direction of the lateral stress vector is set to the overall lateral stress direction of the abnormally compressed tooth, and the size of the lateral stress vector is set to the offset proportion of the overall lateral stress size of the abnormally compressed tooth relative to the compression safety threshold.

[0025] The result of adding the lateral stress vector to the tooth stress characteristics is taken as the updated tooth stress characteristics, and a new oral dynamic occlusion characteristic is generated.

[0026] Further, in the determination of the microbial risk factor, the risk bacteria abundance offset index C(i) of the i th tooth is constructed according to the user's oral biological data.

[0027] By setting a microbial offset threshold, the risk bacteria abundance offset index is used to judge the state of the environment around the tooth, and a microbial risk factor is set. When the risk bacteria abundance offset index of the i th tooth exceeds the microbial offset threshold, the microbial risk factor is set to Rm(i), and Rm(i) is set to exp{[C(i)-microbial offset threshold] / microbial offset threshold}-1; otherwise, the microbial risk factor is set to 0.

[0028] Further, the oral health early warning method comprises: collecting the user's historical oral data;

[0029] According to the user's historical oral data, the predicted oral dynamic occlusion characteristics are determined;

[0030] The predicted oral dynamic occlusion characteristics are matched with the user's oral dynamic occlusion characteristics;

[0031] According to the matching results of the predicted oral dynamic occlusion characteristics and the user's oral dynamic occlusion characteristics and the microbial risk factor, the safety state of each tooth is analyzed, and the user is warned.

[0032] According to another aspect of the present application, a target data digital processing device is provided, comprising:

[0033] a data acquisition unit configured to acquire oral scanning data of a user, oral force data of the user, oral biology data, and historical oral data;

[0034] a model establishment unit configured to establish a three-dimensional oral model of the user based on the oral scanning data;

[0035] a feature analysis unit configured to analyze a lateral force vector of a tooth according to the oral force data, and update a dynamic occlusion feature of the oral cavity according to an analysis result;

[0036] a risk analysis unit configured to determine a microbial risk factor of the user in combination with an oral biological determination index;

[0037] an oral early warning unit configured to perform oral health early warning to the user according to the historical oral data of the user, the dynamic occlusion feature of the oral cavity, and the microbial risk factor.

[0038] According to still another aspect of the present application, a computer readable storage medium is provided, which stores a computer program, wherein the computer program is configured to control an electronic device in which the computer readable storage medium is located to execute the target data digital processing method when the computer program is run.

[0039] Compared with the prior art, the present application has the beneficial effects that the saliva interference correction coefficient and the temperature compensation mechanism are used to improve the accuracy of the three-dimensional oral model, the dynamic occlusion feature and the microbial risk factor are combined to realize cross-dimension collaborative analysis of mechanics and microbiology, the occlusion feature is predicted based on the historical data, and the processing efficiency of the oral data is effectively improved through the double-weight similarity matching and the three-level early warning mechanism. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0041] Figure 1 It is a flowchart of the target data digital processing method of the present embodiment.

[0042] Figure 2 It is a flowchart of the oral dynamic occlusion feature construction method of the present embodiment.

[0043] Figure 3 It is a flowchart of the oral environment health state determination method of the present embodiment.

[0044] Figure 4 A schematic diagram of the oral health early warning method of the present embodiment.

[0045] Figure 5 A schematic diagram of the target data digital processing device provided by the present embodiment. DETAILED DESCRIPTION

[0046] In order to more clearly illustrate the present application, the present application will be further described below in conjunction with preferred embodiments and drawings. Similar components are denoted by the same reference numerals in the drawings. Those skilled in the art should understand that the specific description below is illustrative rather than limiting, and should not limit the scope of protection of the present application.

[0047] It should be noted that although the terms first, second, third, etc. may be used in the embodiments of the present application, these descriptions should not be limited to these terms. These terms are only used to distinguish the description. For example, without departing from the scope of the embodiments of the present application, the first can also be called the second, and similarly, the second can also be called the first. The present application described above is greater than and not equal to the quantity relationship.

[0048] The acquisition, storage, use, processing, etc. of data in the technical solutions of the present application all comply with the relevant provisions of national laws and regulations.

[0049] Specifically, the target data digital processing method described in the present application is applied to the digital processing of oral data in the field of oral health; specifically, it is applied to the digital processing of oral data in the process of orthodontic treatment of teeth, to continuously monitor the changes of the user's oral cavity in the process of orthodontic treatment of teeth, to realize timely and accurate early warning and scheme correction.

[0050] Applied to the above application scenarios, the present application provides a target data digital processing method, please refer to Figure 1 As shown in the figure, the target data digital processing method comprises:

[0051] Step S101, collecting oral scanning data of a user; the oral scanning data is data obtained by scanning the oral cavity when the oral cavity is not in occlusion; the oral scanning data comprises oral tooth positioning data and oral gingival three-dimensional point cloud data; the oral tooth positioning data is coordinate data in a three-dimensional space, in the present application, multiple positioning coordinates can be used as positioning data of a single tooth, the more positioning coordinates, the more accurate, which can be freely set by the technical personnel of the present embodiment; the oral gingival three-dimensional point cloud data is point cloud data of the gingiva.

[0052] Exemplarily, the application does not specifically limit the collection process of the oral scanning data, and the person skilled in the art can freely set it as long as the collection requirement of the oral scanning data is met. In the application, the oral scanning data can be collected by an intraoral scanner.

[0053] Please continue to refer to Figure 1 As shown, the target data digitization processing method further comprises:

[0054] In step S102, the three-dimensional model of the user's oral cavity is established based on the oral scanning data.

[0055] Specifically, in the step S102, the process of establishing the three-dimensional model of the user's oral cavity is as follows:

[0056] An initial three-dimensional model of the oral cavity is established based on the oral tooth positioning data and the three-dimensional point cloud data of the oral gingival;

[0057] The initial three-dimensional model of the oral cavity is corrected by establishing a saliva interference correction coefficient, and the process is as follows:

[0058] The calculation formula of the saliva interference correction coefficient is set as β = exp{-k × (σ / σ0) × (v / v0)}; in the formula, β is the saliva interference correction coefficient, k is the attenuation gain coefficient, σ is the saliva conductivity, v is the saliva flow rate, σ0 is the reference saliva conductivity, and v0 is the reference saliva flow rate;

[0059] The saliva interference correction coefficient is used to correct each oral tooth positioning data, and the product of the oral tooth positioning data and the saliva interference coefficient β is taken as the corrected oral tooth positioning data.

[0060] Optionally, to establish a more accurate three-dimensional model of the oral cavity, the interference of the oral temperature on the saliva conductivity also needs to be excluded. In an exemplary embodiment, the saliva conductivity can be corrected by the formula σ' = σ × [1 + 0.02 × (T-37)] to exclude the interference of the temperature on the conductivity, wherein T is the measured oral temperature in degrees Celsius, and σ' is the corrected saliva conductivity. It is worth noting that the calculation formula of σ' only uses mathematical meaning and does not use physical meaning.

[0061] Specifically, the attenuation gain coefficient k in the application is a weight for regulating the contribution of conductivity and flow rate to the interference, and the value of k is inversely proportional to the saliva interference correction coefficient. In the application, the best value of k is set to 0.75. It should be noted that the values of the reference saliva conductivity and the reference saliva flow rate are 500 μS / cm and 0.3 mL / min, respectively.

[0062] Exemplarily, the process of establishing the initial oral three-dimensional model by using the oral tooth positioning data and the oral gingival three-dimensional point cloud data in the present application can rely on professional software (such as CloudCompare, MeshLab, AutoCAD, etc.) to process the oral tooth positioning data and the oral gingival three-dimensional point cloud data, and the generated oral modeling is taken as the initial oral three-dimensional model.

[0063] Specifically, by introducing a saliva interference correction coefficient, the scanning distortion caused by the saliva conductivity and flow rate is dynamically corrected. Combined with the temperature compensation formula, the model accuracy is significantly improved. The traditional modeling ignores the interference of the oral environment, and the present solution quantitatively integrates the reference conductivity, flow rate and attenuation gain coefficient, thereby providing a reliable basis for subsequent mechanical analysis.

[0064] Please continue to refer to Figure 1 As shown in the figure, the target data digitization processing method further includes:

[0065] In step S103, the oral force data of the user is collected, and the dynamic occlusion characteristics of the user's oral cavity are established according to the collection result and the three-dimensional model of the user's oral cavity. The oral force data includes the force data of each tooth and the force deformation data of the gingiva.

[0066] Specifically, based on the three-dimensional model, the real-time occlusion data is fused, and the tooth force characteristics and the gingival deformation characteristics are synchronously analyzed. The traditional method only focuses on static pressure. In this step, the displacement vector and the preset standard occlusion range are dynamically associated with the tooth deviation and the pressure exceeding condition, and the gingival area is divided to calculate the deformation index, thereby realizing the multi-dimensional feature fusion of occlusion force-displacement-deformation, and accurately depicting the dynamic occlusion process.

[0067] In order to realize the establishment of the dynamic occlusion characteristics of the user's oral cavity, the specific implementation process of the above step S103 can refer to the oral dynamic occlusion characteristic construction method of the present application Figure 2 As shown in the figure, the oral dynamic occlusion characteristic construction method includes:

[0068] In step S301, the oral force data of the user is collected. The oral force data is the data during the process of the user's occlusion pressure sensing film. The oral force data includes the occlusion force data of the teeth, the force displacement data of the teeth, the force deformation data of the gingiva and the extrusion force data of the teeth. The occlusion force data of the teeth includes the occlusion pressure of each tooth. The force displacement data of the teeth includes the occlusion deviation distance of each tooth. The occlusion deviation distance of the tooth is the displacement distance of the corresponding tooth in the oral three-dimensional model during the occlusion process. The force deformation data of the gingiva is the position data of the deformed gingiva when the user occludes, and is of the same type as the oral gingival three-dimensional point cloud data. The extrusion force data of the teeth is the interproximal contact force, which can be collected by the existing technology of dental floss or flexible film pressure sensor.

[0069] Exemplarily, the collection of the oral force data in the present application can be collected by recording the force value distribution during the dynamic occlusion of the user, and the sampling frequency is greater than or equal to 200 Hz.

[0070] Please continue to refer to Figure 2 As shown, the method for constructing the dynamic occlusion characteristics of the oral cavity further comprises:

[0071] In step S302, based on the oral force data of the user and the three-dimensional model of the oral cavity of the user, the force characteristics of the teeth and the force characteristics of the gums of the user are analyzed.

[0072] Specifically, the process of analyzing the force displacement characteristics of the teeth of the user is as follows:

[0073] The occlusion force data of each tooth of the user is compared with each preset standard tooth occlusion force range. When the occlusion pressure of the ith tooth exceeds the ith preset standard tooth occlusion force range and the occlusion displacement distance of the ith tooth is 0, the force characteristics of the ith tooth are set to the offset proportion of the right value of the ith standard tooth occlusion force range; when the occlusion pressure of the ith tooth is within the ith preset standard tooth occlusion force range and the occlusion displacement distance of the ith tooth is 0, the force characteristics of the ith tooth are set to 0.

[0074] When the occlusion pressure of the ith tooth is within the ith preset standard tooth occlusion force range and the occlusion displacement distance of the ith tooth is not 0, the force characteristics of the ith tooth are set to a vector characteristic, denoted as α1(i), and α1(i) is set to a(i).

[0075] When the occlusion pressure of the ith tooth exceeds the ith preset standard tooth occlusion force range and the occlusion displacement distance of the ith tooth is not 0, the force characteristics of the ith tooth are set to a vector characteristic, denoted as α2(i), and α2(i) is set to [a(i) x offset proportion of right value of ith standard tooth occlusion force range].

[0076] Wherein, a(i) represents the displacement vector of the ith tooth relative to the ith tooth in the three-dimensional model of the oral cavity when occluding.

[0077] Exemplarily, the preset standard tooth occlusion force range in the present application can be assigned or obtained by referring to the occlusion force range of the healthy teeth at each position in the database; when obtaining each preset standard tooth occlusion force range, the influence of the difference parameters such as the age and gender of the user should be noted.

[0078] In step S302, the analysis process of the force characteristics of the gums is as follows:

[0079] The gingival in the three-dimensional model of the oral cavity is divided by tooth position to obtain each gingival region S(i), and the stress deformation data of the gingival is positionally compared with each gingival region, and the position deformation index γ(i) of each gingival region is calculated, and by setting a deformation threshold, each gingival region is judged as a normally stressed gingival region and an abnormally stressed gingival region;

[0080] The gingival stress feature of the normally stressed gingival region is set to 0, and the gingival stress feature of the abnormally stressed gingival region is set to [γ(i)-deformation threshold] / deformation threshold.

[0081] Specifically, the calculation process of the position deformation index γ(i) in the application is that the average absolute distance of the three-dimensional point cloud data of the gingival of the oral cavity is offset by the average absolute distance of the stress deformation data of the gingival. Proportion; the average absolute distance is the average value of the distance of each coordinate point from the origin.

[0082] Specifically, the value of the deformation threshold in the application is not specifically limited, and those skilled in the art can freely set it, as long as the value of the deformation threshold meets the requirement, such as setting the value of the deformation threshold to 0.15.

[0083] Specifically, the process of "dividing the gingival in the three-dimensional model of the oral cavity by tooth position" in the application can be divided by straight line segmentation, or can be divided by analyzing the stress related region of the gingival. The embodiment does not make specific limitation; the scheme described in the application solves the problem that the traditional method does not distinguish the displacement and pressure interaction, so that the feature value directly reflects the clinical abnormal type.

[0084] Please continue to refer to Figure 2 As shown, the oral dynamic occlusion feature construction method further comprises:

[0085] Step S303, the tooth stress feature and the gingival stress feature of the user are fused with the three-dimensional model of the oral cavity to form the oral dynamic occlusion feature of the user.

[0086] Please continue to refer to Figure 1 As shown, the target data digital processing method further comprises:

[0087] Step S104, analyzing the lateral stress vector of the tooth according to the stress data of the oral cavity, and updating the dynamic occlusion feature of the oral cavity according to the analysis result.

[0088] Specifically, in the step S104, the process of analyzing the lateral stress vector of the tooth is as follows:

[0089] The extrusion safety threshold is set, and the tooth extrusion force data is compared with the extrusion safety threshold to determine the abnormal extrusion tooth, and a lateral force vector is set for the abnormal extrusion tooth, and the setting process is as follows: the direction of the lateral force vector is set as the overall lateral force direction of the abnormal extrusion tooth, and the size of the lateral force vector is set as the offset proportion of the overall lateral force size of the abnormal extrusion tooth relative to the extrusion safety threshold;

[0090] The result of adding the lateral force vector to the tooth force feature is taken as the updated tooth force feature, and a new oral dynamic occlusion feature is generated.

[0091] Specifically, the optimal value of the extrusion safety threshold in the present application is 52N / mm 2 ; The extrusion safety threshold is added to identify the abnormal extrusion tooth and quantify the lateral force vector: the direction is the overall lateral force direction, and the size is the offset proportion. The vector and the original tooth force feature are superimposed to update the model, solving the problem of ignoring the interproximal extrusion force in the traditional scheme. Experiments show that the risk of root resorption caused by lateral force can be warned in advance.

[0092] Please continue to refer to Figure 1 As shown in the figure, the target data digitization processing method further includes:

[0093] Step S105, collect the user's oral biology data, and determine the user's microbial risk factor in combination with the oral biological determination index; the oral biological determination index is the health determination data of the corresponding data in oral medicine, which can be obtained through an oral medicine database.

[0094] In order to accurately determine the health status of the user's oral environment, the specific implementation process of the step S105 can refer to the oral environment health status determination method shown in Figure 3 As shown in the figure, the oral environment health status determination method includes:

[0095] Step S501, collect the user's oral biology data; the oral biology data includes Streptococcus mutans abundance and Porphyromonas gingivalis abundance; the oral biology data can be collected and obtained by a biological detection device.

[0096] Please continue to refer to Figure 3 As shown in the figure, the oral environment health status determination method further includes:

[0097] Step S502, determine the microbial risk factor around different teeth according to the user's oral biology data and the oral biological determination index.

[0098] Specifically, in the step S402, the process of determining the microbial risk factor is as follows:

[0099] A risk bacteria abundance offset index C(i) of the i th tooth is constructed according to the user's oral biology data, and C(i) is set as c1*Cs(i) / Csmax+c2*Cp(i) / Cpmax; wherein c1 and c2 are Streptococcus mutans risk weights and Porphyromonas gingivalis risk weights respectively, Cs(i) and Cp(i) are the abundance of Streptococcus mutans and Porphyromonas gingivalis around the i th tooth respectively, and Csmax and Cpmax are the abundance index of Streptococcus mutans and Porphyromonas gingivalis respectively;

[0100] The state of the environment around the tooth is judged by setting a microbial offset threshold for the risk bacteria abundance offset index, and a microbial risk factor is set, and when the risk bacteria abundance offset index of the i th tooth exceeds the microbial offset threshold, the microbial risk factor is set as Rm(i), and Rm(i) is set as exp{[C(i)-microbial offset threshold] / microbial offset threshold}-1; otherwise, the microbial risk factor is set to 0.

[0101] For example, the value of the microbial offset threshold is not specifically limited in the present application, and those skilled in the art can freely set it, as long as it meets the value requirement of the microbial offset threshold. In the present application, the best value of the microbial offset threshold is set to 0.17 after statistical arrangement of empirical data.

[0102] Specifically, according to the local aggregation characteristics of Streptococcus mutans and Porphyromonas gingivalis, the risk of the environment around each tooth is evaluated, which is more specific than traditional qualitative judgment, and can quantitatively locate high-risk teeth, thereby improving the prediction specificity of dental caries and periodontitis.

[0103] Please continue to refer to Figure 1 As shown, the target data digitization processing method further comprises:

[0104] In step S106, the user's historical oral data is collected, and the user is warned about oral health in combination with the user's dynamic occlusion characteristics and the microbial risk factor.

[0105] Please continue to refer to Figure 4 As shown, it is a flowchart of the oral health warning method provided by the present application, which comprises:

[0106] In step S601, the user's historical oral data is collected; the user's historical oral data is the oral scanning data and the oral stress data collected by the user at different stages of tooth correction.

[0107] Please continue to refer to Figure 4 As shown, the oral health warning method further comprises:

[0108] In step S602, the predicted dynamic occlusion characteristics are determined according to the user's historical oral data.

[0109] Specifically, the process of determining the predicted oral dynamic occlusion feature is as follows:

[0110] According to the historical oral data, the displacement vectors of each tooth are extracted, and the mean value of the displacement vectors of each tooth is taken as the predicted tooth stress displacement feature;

[0111] According to the historical oral data, the gingival stress features of each gingival region are extracted, and the extraction results are taken as the predicted gingival stress features;

[0112] The predicted tooth stress displacement feature and the predicted gingival stress feature are fused with the oral three-dimensional model obtained from the last historical oral data to obtain the predicted oral dynamic occlusion feature.

[0113] Step S603, the predicted oral dynamic occlusion feature is matched with the user's oral dynamic occlusion feature.

[0114] Specifically, in the step S603, the specific process of calculating the feature deviation is as follows:

[0115] The feature similarity between each tooth in the predicted oral dynamic occlusion feature and each tooth in the user's oral dynamic occlusion feature is calculated, and when the calculation result is less than the feature similarity threshold, it is determined that the matching is successful; otherwise, it is determined that the matching fails.

[0116] Specifically, when calculating the feature similarity between each tooth in the predicted oral dynamic occlusion feature and each tooth in the user's oral dynamic occlusion feature, the cosine similarity between the ith predicted tooth stress feature and the ith tooth stress feature, the difference ratio between the ith predicted gingival stress feature and the ith gingival stress feature are calculated, and the two calculation results are weighted and summed to obtain the feature similarity of the ith tooth, and the weights are 0.65 and 0.35 respectively.

[0117] Step S604, according to the matching result of the predicted oral dynamic occlusion feature and the user's oral dynamic occlusion feature and the microbial risk factor, the safety state of each tooth is analyzed, and then the user is warned.

[0118] Specifically, the process of warning the user is as follows:

[0119] When the ith tooth matching fails and Rm(i)≠0, the user is sent the ith tooth correction risk warning; when the ith tooth matching succeeds and Rm(i) is less than the correction risk threshold, the user is sent the ith tooth health prevention warning; when the ith tooth matching fails and Rm(i)=0, the user is sent the misplacement warning.

[0120] Specifically, the historical data and real-time features are integrated: by matching the predicted dynamic occlusion features and the current features, the three-level early warning is realized in combination with the microbial risk factor (Rm(i)): the orthodontic risk early warning prompts the orthodontic deviation superimposed infection risk; the health prevention early warning warns the microbial over-standard; the misposition early warning warns the abnormal pure mechanics; at the same time, when the matching is successful and Rm(i)=0, no early warning is given to the user.

[0121] For example, the value of the orthodontic risk threshold is not specifically limited in the present application, and those skilled in the art can freely set it as long as the value requirement of the orthodontic risk threshold is met. In the present application, the orthodontic risk threshold can be set to 0.4.

[0122] Referring to Figure 5 As shown in the figure, the structure schematic diagram of the target data digital processing device provided by the present application comprises:

[0123] The data acquisition unit is used to acquire the oral scanning data of the user, the oral stress data of the user, the oral biology data and the historical oral data;

[0124] The model establishment unit is used to establish the three-dimensional model of the user's oral cavity based on the oral scanning data;

[0125] The feature analysis unit is used to analyze the lateral stress vector of the teeth according to the oral stress data, and update the dynamic occlusion features of the oral cavity according to the analysis result;

[0126] The risk analysis unit is used to determine the microbial risk factor of the user in combination with the oral biological determination index;

[0127] The oral early warning unit is used to perform oral health early warning to the user according to the historical oral data of the user, the dynamic occlusion features of the oral cavity of the user and the microbial risk factor.

[0128] The target data digital processing device provided by the present application can execute the target data digital processing method provided by any embodiment of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0129] In the present application, the computer readable storage medium is a tangible physical storage medium, which can store the above computer program and various types of data used in the program; the physical storage medium includes but is not limited to random access memory, read-only memory, optical disc, hard disk and other existing physical storage media or combinations of media.

[0130] The technical scheme of the present application has been described in combination with the preferred embodiments shown in the drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical schemes after the changes or replacements will all fall within the protection scope of the present application.

Claims

1. A method for digital processing of target data, characterized in that: include: Collecting the user's oral scan data and building the user's oral three-dimensional model based on the oral scan data; Collect the user's oral force data and establish the user's oral dynamic occlusal characteristics based on the collected results and the user's oral 3D model; Analyze the lateral force vector of the teeth based on the oral force data, and update the dynamic occlusal characteristics of the oral cavity based on the analysis results; Collect the user's oral biological data and determine the user's microbial risk factors based on oral biological determination indicators; Collect the user's historical oral data and provide oral health warnings to the user based on the user's oral dynamic occlusal characteristics and microbial risk factors.

2. The target data digital processing method according to claim 1, characterized in that: An initial oral 3D model is established using oral teeth positioning data and oral gum 3D point cloud data; The saliva interference correction coefficient is established to correct the initialized oral 3D model. The process is as follows: The calculation formula for setting the saliva interference correction coefficient is: β = exp{-k × (σ / σ0) × (v / v0)}; where β is the saliva interference correction coefficient, k is the attenuation gain coefficient, σ is the saliva conductivity, v is the saliva flow rate, σ0 is the reference saliva conductivity, and v0 is the reference saliva flow rate; The oral tooth positioning data are corrected using the saliva interference correction coefficient, and the product of the oral tooth positioning data and the saliva interference coefficient β is used as the corrected oral tooth positioning data.

3. The target data digital processing method according to claim 2, characterized in that: The method for constructing oral dynamic occlusal features includes: collecting oral force data of the user; Analyze the stress characteristics of the user's teeth and gums based on the user's oral stress data and the user's oral 3D model; The user's tooth force characteristics and gum force characteristics are fused with the oral three-dimensional model to form the user's oral dynamic occlusion characteristics.

4. The target data digital processing method according to claim 3, characterized in that: When analyzing the force-displacement characteristics of the user's teeth, the bite force data of each tooth of the user is compared with each preset standard tooth bite force range. When the bite pressure of the i-th tooth exceeds the i-th preset standard tooth bite force range and the i-th tooth bite offset distance is 0, the force characteristic of the i-th tooth is set to the offset ratio of the right value exceeding the i-th standard tooth bite force range; When the occlusal pressure of the i-th tooth is within the i-th preset standard tooth occlusal force range and the occlusal offset distance of the i-th tooth is 0, the force characteristic of the i-th tooth is set to 0; When the occlusal pressure of the i-th tooth is within the i-th preset standard tooth occlusal force range and the occlusal offset distance of the i-th tooth is not 0, the force characteristic of the i-th tooth is set as a vector characteristic, recorded as α1(i), and α1(i)=a(i); When the occlusal pressure of the i-th tooth exceeds the i-th preset standard tooth occlusal force range and the i-th tooth occlusal offset distance is not 0, the force characteristic of the i-th tooth is set as a vector characteristic, recorded as α2(i), and α2(i) is set as [a(i) × the offset ratio of the right value exceeding the i-th standard tooth occlusal force range]; Wherein, a(i) represents the displacement vector of the i-th tooth relative to the i-th tooth in the oral 3D model during occlusion.

5. The target data digital processing method according to claim 4, characterized in that: When analyzing the gingival stress characteristics, the gingiva in the oral 3D model is divided according to the tooth position to obtain each gingival region S(i). The gingival stress deformation data is then compared with the position of each gingival region, and the position deformation index γ(i) of each gingival region is calculated. By setting the deformation threshold, each gingival region is judged as a normal stress-bearing gingival region or an abnormal stress-bearing gingival region. The gingival stress characteristics of the normal stressed gingival area are set to 0; the gingival stress characteristics of the abnormal stressed gingival area are set to [γ(i)-deformation threshold] / deformation threshold.

6. The target data digital processing method according to claim 5, characterized in that: When analyzing the lateral force vector of the tooth, an extrusion safety threshold is set, and the tooth extrusion force data is compared with the extrusion safety threshold to determine the abnormally extruded tooth, and a lateral force vector is set for the abnormally extruded tooth. The setting process is as follows: the direction of the lateral force vector is set to the overall lateral force direction of the abnormally extruded tooth, and the magnitude of the lateral force vector is set to the offset ratio of the overall lateral force magnitude of the abnormally extruded tooth relative to the extrusion safety threshold; The result of adding the lateral force vector to the tooth force characteristic is used as the updated tooth force characteristic, and a new oral dynamic occlusal feature is generated.

7. The target data digital processing method according to claim 2, characterized in that: When determining the microbial risk factor, the risk bacteria abundance deviation index C(i) of the i-th tooth is constructed based on the user's oral biological data; The state of the tooth environment is judged by setting the risk bacteria abundance deviation index based on the microbial deviation threshold, and a microbial risk factor is set. When the risk bacteria abundance deviation index of the i-th tooth exceeds the microbial deviation threshold, the microbial risk factor is set to Rm(i), and Rm(i) is set to exp{[C(i)-microbial deviation threshold] / microbial deviation threshold}-1; otherwise, the microbial risk factor is set to 0.

8. The target data digital processing method according to claim 6, characterized in that: The oral health early warning method includes: collecting historical oral data of users; Determine and predict oral dynamic occlusal features based on the user's historical oral data; Perform feature matching between the predicted oral dynamic occlusion features and the user's oral dynamic occlusion features; The safety status of each tooth is analyzed based on the matching results between the predicted oral dynamic occlusal characteristics and the user's oral dynamic occlusal characteristics and the microbial risk factors, and then an early warning is issued to the user.

9. A target data digital processing device, characterized in that: include: A data acquisition unit, used to collect the user's oral scan data, the user's oral force data, oral biology data and historical oral data; A model building unit, used to build a three-dimensional model of the user's oral cavity based on the oral scanning data; A feature analysis unit, used to analyze the lateral force vector of the teeth based on the oral force data, and update the oral dynamic occlusal features based on the analysis results; Risk analysis unit, used to determine the user's microbial risk factors in combination with oral biological determination indicators; The oral early warning unit is used to provide oral health early warnings to users based on their historical oral data, dynamic oral occlusal characteristics, and microbial risk factors.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein the computer program is used to control the electronic device where the computer-readable storage medium is located to execute the target data digital processing method according to any one of claims 1 to 8 during operation.

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