Oral health management data analysis method and system

Abstract

The invention relates to an oral health management data analysis method, and relates to the field of medical care information. The method comprises the following steps: executing multiple image acquisition at different positions along the horizontal distribution direction of each tooth of a current detection person in a mode of taking the shortest distance from the central position of a camera lens to the tooth of the current detection person as a set distance, and combining multiple frames of acquisition images to obtain a fixed-distance panoramic image; and intelligently analyzing oral health data by adopting an oral data analysis model according to the overall visual information of the teeth in the fixed-distance panoramic picture. The invention also relates to an oral health management data analysis system. According to the invention, aiming at the technical problems that the data analysis mode of manual film viewing is lagged and the accuracy is insufficient, various visual information of the whole teeth of the human body can be analyzed by adopting a customized visual acquisition mode comprising fixed-distance multi-position acquisition and multi-frame picture combination, and the oral health data is intelligently analyzed by adopting an artificial intelligence model; therefore, the technical problem is solved.

Description

Technical Field

[0001] The present invention relates to the field of medical care information, and in particular to an oral health management data analysis method and system. Background Art

[0002] Generally speaking, oral health standards include the following: First, number and arrangement of teeth: An adult should have 32 teeth, including 8 front teeth, 4 canines, 8 premolars, and 12 molars. All teeth should be aligned and free of significant misalignment or crowding. Second, tooth condition: Each tooth should be free of caries, cracks, or other obvious damage. Furthermore, attention should be paid to the thickness and smoothness of the enamel, as well as any issues surrounding gingivitis. Third, periodontal tissues: The periodontal tissues, including the gums, alveolar bone, and radicular membrane, should be healthy, free of significant inflammation or infection. The depth of periodontal pockets should also be within normal limits. Fourth, chewing function: The ability to chew and swallow normally, without noticeable chewing difficulty or pain. Fifth, fresh breath: Exhaled breath should be fresh and odorless, with no noticeable halitosis. These are the basic requirements for oral health. If any of these issues occur, seek treatment at a hospital or dental clinic as soon as possible. Regular oral examinations and health care are also crucial to effectively prevent the development of oral diseases.

[0003] For example, Chinese invention patent publication CN118692673A proposes an oral health management data analysis system, which includes the following modules: an information entry module, an information query and comparison module, an image acquisition module, an image analysis module, a gas entry module, a gas analysis module, a cloud server, a display screen, and a master control switch. In this oral health management data analysis system and method, the image acquisition and gas entry modules provided in the terminal device can respectively capture images of the user's oral cavity and exhaled gas, thereby enabling more comprehensive collection of the user's oral information. The image and gas analysis modules can also perform basic analysis of the images and gas, avoiding the traditional need to go to the hospital to collect and analyze oral data. Instead, oral data can be collected autonomously, allowing users to more clearly and conveniently understand the situation in their oral cavity.

[0004] For example, Chinese invention patent publication CN115830034A proposes a data analysis system for oral health management, which includes constructing a patient CBCT image dataset, eliminating dataset redundancy and noise, inputting the dataset into a feature extraction module, calibrating the position of the horizontal impacted wisdom tooth, further selecting the mandibular canal target in the image background, connecting the mandibular canal pixel points into a complete mandibular canal, constructing a three-dimensional model of the horizontal impacted wisdom tooth, fusing the three-dimensional reconstructed model of the horizontal impacted wisdom tooth with the mandibular canal identification and reconstruction model according to spatial coordinates, determining the surgical position for extraction of the horizontal impacted wisdom tooth, reconstructing the three-dimensional model of the horizontal impacted wisdom tooth based on a convolutional neural network, identifying and connecting the mandibular neural canal pixel points, and finally fusing the mandibular canal fusion model with the three-dimensional model of the horizontal impacted wisdom tooth based on the coordinate position, which can provide a precise operating space for the extraction of the horizontal impacted wisdom tooth, and further reduce the uncertainty risk of extraction of the mandibular impacted wisdom tooth.

[0005] It can be seen that the above-mentioned existing technologies either only involve simple and rough analysis of oral health management data based on visual data, or only involve three-dimensional modeling of horizontally impacted wisdom teeth. In the absence of sufficient and comprehensive visual data and various auxiliary data as basic data, and in the absence of a targeted data analysis model based on an artificial intelligence model, it is difficult to conduct reliable and stable automated analysis of the oral health status of each person to be tested. For example, to obtain the smoothness of the enamel of each person to be tested and to determine whether each person to be tested has caries, it is still necessary to rely on manual experience to judge the oral health status, resulting in a delay in the judgment results and a large error due to over-reliance on manual experience. Summary of the Invention

[0006] In order to solve the technical defects in the prior art, the present invention provides an oral health management data analysis method and system, which can adopt a customized visual acquisition mode including fixed-distance multi-point acquisition and multi-frame image combination to perform multi-directional and multi-angle visual information analysis of the overall teeth of each person to be tested, and based on the various visual information of the overall teeth of each person to be tested and multiple auxiliary data, a customized structurally designed artificial intelligence model is used to intelligently analyze the smoothness of the enamel of each person to be tested and determine whether each person to be tested has caries, so that an intelligent judgment mode can be used to replace the traditional manual film viewing judgment mode, thereby improving the speed, accuracy and intelligence level of oral health data analysis.

[0007] According to a first aspect of the present invention, a method for analyzing oral health management data is provided, the method comprising: The shortest distance from the center of the camera lens to the teeth of the current inspected person is used as the set distance. Multiple frames of image capture are performed at different positions along the horizontal distribution direction of each tooth of the current inspected person, and the multiple frames of captured images are combined to obtain a fixed-distance panoramic image. Identifying image blocks occupied by teeth in the fixed-distance panoramic image based on imaging characteristics of human teeth, and performing image fitting processing on the image blocks to obtain an overall image of the teeth; Obtaining respective red component values, respective vertical coordinate values, respective horizontal coordinate values, and respective imaging depth of field values ​​corresponding to respective pixel points constituting the overall tooth image, and outputting the values ​​as item-by-item visual capture information of the overall tooth image corresponding to the current inspector, wherein the red component values, vertical coordinate values, and horizontal coordinate values ​​of the pixel points are related to the presence of caries on the tooth, and the imaging depth of field values, vertical coordinate values, and horizontal coordinate values ​​of the pixel points are related to the smoothness of the tooth enamel on the tooth; Obtain the time interval between the last teeth cleaning time and the current time, gender, age, and number of existing teeth of the current inspector as output of various tooth-related parameters of the current inspector; An oral data analysis model is used to intelligently analyze the enamel smoothness level of the current inspector and the presence of caries of the current inspector based on the number of pixels occupied by the fixed-distance panoramic image, the number of pixels occupied by the overall tooth image corresponding to the current inspector, the item-by-item visual capture information of the overall tooth image corresponding to the current inspector, and the various tooth-related parameters of the current inspector.

[0008] According to a second aspect of the present invention, there is provided an oral health management data analysis system, the system comprising: A fixed-distance acquisition device is used to perform multiple image acquisitions at different positions along the horizontal distribution direction of each tooth of the current inspected person, with the shortest distance from the center of the camera lens to the teeth of the current inspected person as the set distance, and combine the multiple frames of acquired images to obtain a fixed-distance panoramic image; a target recognition device connected to the fixed-distance acquisition device, configured to recognize the image blocks occupied by the respective teeth in the fixed-distance panoramic image based on the imaging characteristics of human teeth, and perform image fitting processing on the respective image blocks to obtain an overall image of the tooth; a visual capture device connected to the target recognition device, for obtaining respective red component values, respective vertical coordinate values, respective horizontal coordinate values, and respective imaging depth of field values ​​corresponding to respective pixel points constituting the overall image of the tooth portion, and outputting the obtained values ​​as item-by-item visual capture information of the overall image of the tooth portion corresponding to the current inspector, wherein the red component values, vertical coordinate values, and horizontal coordinate values ​​of the pixel points are related to the presence of caries on the tooth portion, and the imaging depth of field values, vertical coordinate values, and horizontal coordinate values ​​of the pixel points are related to the smoothness of the tooth enamel on the tooth portion; A parameter extraction device is used to obtain the interval time from the last teeth cleaning time to the current time, gender, age and number of existing teeth of the current tester to output as various tooth-related parameters of the current tester; The data analysis device is respectively connected to the visual capture device and the parameter extraction device, and is used to use an oral data analysis model to intelligently analyze the enamel smoothness level of the current detection person and the caries presence indication of the current detection person based on the number of pixels occupied by the fixed-distance panoramic image, the number of pixels occupied by the overall tooth image corresponding to the current detection person, the item-by-item visual capture information of the overall tooth image corresponding to the current detection person, and the various tooth-related parameters of the current detection person.

[0009] It can be seen that the present invention has at least the following four key inventive concepts: First, a custom-designed oral data analysis model intelligently analyzes the current examinee's enamel smoothness and caries presence based on the visually captured information about the examinee's teeth and various tooth-related parameters. This eliminates manual judgment errors by oral health managers and improves the intelligence level of oral health management data analysis. Secondly: in order to obtain the item-by-item visual capture information corresponding to the teeth of the current inspector as a whole, multiple image captures are performed at different positions along the horizontal distribution direction of each tooth of the current inspector in a mode with the shortest distance from the center position of the camera lens to the teeth of the current inspector as the set distance, and the obtained multiple frames of captured images are combined to obtain a fixed-distance panoramic image, and based on the imaging characteristics of human teeth, the image blocks occupied by each tooth in the fixed-distance panoramic image are identified, and the image blocks are subjected to image fitting processing to obtain an overall image of the teeth, and the red component values, vertical coordinate values, horizontal coordinate values ​​and imaging depth of field values ​​corresponding to each pixel point constituting the overall image of the teeth are obtained and used as the item-by-item visual capture information of the overall image of the teeth corresponding to the current inspector, thereby providing comprehensive and sufficient reference visual information for the overall status of the teeth of the current inspector, including multiple states such as tooth distribution state, tooth color state and tooth distance state, thereby ensuring the reliability and stability of the subsequent intelligent analysis of oral health status; Again: The oral data analysis model used for intelligent analysis of oral health status is a Hoffet neural network that has undergone multiple learning operations, and the number of learning operations the Hoffet neural network has undergone is monotonically positively correlated with the value of the set distance. The customized structural design of the above oral data analysis model further ensures the reliability and stability of subsequent intelligent analysis of oral health status; Finally: When performing each learning operation on the Hoffitt neural network, the known enamel smoothness level of a certain past tester and the known caries presence mark of the said past tester are used as the two output contents of the Hoffitt neural network, and the number of pixels occupied by the fixed-distance panoramic picture corresponding to the said past tester, the number of pixels occupied by the overall tooth image corresponding to the said past tester, the item-by-item visual capture information of the overall tooth image corresponding to the said past tester and the various tooth-related parameters of the said past tester are used as multiple input contents of the Hoffitt neural network to complete this learning operation, thereby ensuring the learning effect of each learning operation performed on the Hoffitt neural network. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The embodiments of the present invention will be described below with reference to the accompanying drawings, in which: Figure 1 This is a technical flow chart of an oral health management data analysis method and system according to the present invention.

[0011] Figure 2 This is a flowchart of the steps of the oral health management data analysis method according to Example 1 of the present invention.

[0012] Figure 3This is a flowchart of the steps of the oral health management data analysis method according to embodiment 2 of the present invention.

[0013] Figure 4 This is a flowchart of the steps of the oral health management data analysis method according to Example 3 of the present invention.

[0014] Figure 5 FIG. 4 is an internal structure diagram of an oral health management data analysis system according to embodiment 4 of the present invention.

[0015] Figure 6 FIG. 5 is a diagram showing the internal structure of an oral health management data analysis system according to Embodiment 5 of the present invention.

[0016] Figure 7 FIG. 1 is an internal structure diagram of an oral health management data analysis system according to embodiment 6 of the present invention. DETAILED DESCRIPTION

[0017] like Figure 1 As shown in FIG, a technical flow chart of an oral health management data analysis method and system according to the present invention is given.

[0018] like Figure 1 As shown, the specific technical process of the present invention is as follows: Step 1: Build a customized oral data analysis model for intelligent analysis of the current inspector's tooth enamel smoothness level and the presence of caries; exist Figure 1 In the test, the teeth of the current examinee are generally distributed horizontally, and the teeth include three different tooth bodies: maxillary teeth, wisdom teeth, and mandibular teeth. Specifically, the structural customization of the oral data analysis model is mainly reflected in the following aspects: First, the oral data analysis model is a Hoffitt neural network after multiple learning operations; Second, the number of learning operations the Hoffitt neural network undergoes is monotonically positively correlated with the set distance used when collecting visual data from the current examinee's teeth at a fixed distance. Consequently, more learning operations are used for visual data collected from farther distances, due to insufficient visual image accuracy, while fewer learning operations are used for visual data collected from closer distances, due to good visual image accuracy. This ensures the reliability and stability of subsequent intelligent analysis of the current examinee's oral health status. Third: When performing each learning operation on the Hoffitt neural network, the known enamel smoothness level of a certain past tester and the known caries presence mark of the said past tester are used as the two output contents of the Hoffitt neural network, and the number of pixels occupied by the fixed-distance panoramic picture corresponding to the said past tester, the number of pixels occupied by the overall tooth image corresponding to the said past tester, the item-by-item visual capture information of the overall tooth image corresponding to the said past tester and the various tooth-related parameters of the said past tester are used as multiple input contents of the Hoffitt neural network to complete this learning operation, thereby ensuring the learning effect of each learning operation performed on the Hoffitt neural network.

[0019] Step 2: Introducing comprehensive and comprehensive basic data for intelligent analysis of the current tester's enamel smoothness level and the presence of dental caries; Specifically, in order to obtain item-by-item visual capture information corresponding to the teeth of the current inspector as a whole, multiple image captures are performed at different positions along the horizontal distribution direction of each tooth of the current inspector in a mode with the shortest distance from the center position of the camera lens to the teeth of the current inspector as the set distance, and the obtained multiple frames of captured images are combined to obtain a fixed-distance panoramic image, and based on the imaging characteristics of human teeth, the image blocks occupied by each tooth in the fixed-distance panoramic image are identified, and the image blocks are subjected to image fitting processing to obtain an overall image of the tooth, and the red component values, vertical coordinate values, horizontal coordinate values ​​and imaging depth of field values ​​corresponding to each pixel point constituting the overall image of the tooth are obtained as the item-by-item visual capture information of the overall image of the tooth corresponding to the current inspector; Specifically, the red component value, vertical coordinate value, and horizontal coordinate value of the pixel point are related to the presence of caries on the tooth, and the imaging depth of field value, vertical coordinate value, and horizontal coordinate value of the pixel point are related to the smoothness of the tooth enamel. More specifically, for the entirety of all teeth in the oral cavity of the current test subject, a corresponding overall tooth image is obtained. By analyzing the red component value, vertical coordinate value, and horizontal coordinate value of each pixel in the overall tooth image, the caries status of the corresponding tooth position, i.e., the tooth component corresponding to the pixel point, is numerically represented. The reason is that, on the one hand, the vertical coordinate value and the horizontal coordinate value represent the distribution position of the tooth components corresponding to the constituent pixel points, and the red component value represents the color state of the tooth components corresponding to the constituent pixel points, and then combined with the vertical coordinate value and the horizontal coordinate value, it is possible to complete the digital representation of the caries existence state of the tooth components corresponding to each constituent pixel point; on the other hand, the vertical coordinate value and the horizontal coordinate value represent the distribution position of the tooth components corresponding to the constituent pixel points, and the imaging depth of field value represents the far and near state of the tooth components corresponding to the constituent pixel points, and then combined with the imaging depth of field value and the vertical coordinate value and the horizontal coordinate value, it is possible to complete the digital representation of the enamel smoothness state of the tooth components corresponding to each constituent pixel point; On this basis, the various tooth-related parameters of the current inspector, the number of pixels occupied by the fixed-distance panoramic image, and the number of pixels occupied by the overall tooth image corresponding to the current inspector are simultaneously input into the oral data analysis model. The oral data analysis model obtained after completing multiple learnings can stably and reliably obtain the digital representation of the caries status and enamel smoothness status of the current inspector's teeth as a whole, that is, the caries status represented by the binary value 0B10 or 0B11, and the enamel smoothness status represented by one of the 10 levels; In this way, Figure 1 As shown, it provides comprehensive and sufficient reference visual information of the overall dental status of the current tester, including tooth distribution, tooth color, and tooth distance, etc., that is, it captures information item by item visually, ensuring the reliability and stability of the subsequent intelligent analysis of oral health status; In addition, continue as Figure 1 As shown, the intelligent analysis of the current examinee's enamel smoothness level and the presence of dental caries also assists in the current examinee's various tooth-related parameters. The various tooth-related parameters of the current examinee are the interval time from the current examinee's most recent teeth cleaning time to the current time, gender, age, and number of existing teeth, thereby further improving the sufficiency and comprehensiveness of multiple basic data used for intelligent analysis; Step 3: The oral data analysis model designed with the customized structure in the first step is used to intelligently analyze the current tester's tooth enamel smoothness level and caries status based on the multiple basic data targeted and screened in the second step, thereby obtaining the current tester's oral health management data; For example, the caries presence status is indicated by a caries presence flag. When the caries presence flag of the current detection person obtained by intelligent analysis is 0B10, it indicates that the current detection person does not have caries. When the caries presence flag of the current detection person obtained by intelligent analysis is 0B11, it indicates that the current detection person has caries. And for example, the lower the tooth enamel smoothness grade of the current test person obtained by intelligent analysis is, the lower the degree of tooth enamel smoothness of the current test person is; Step 4: wirelessly transmitting the tooth enamel smoothness grade of the current test person and the caries presence indicator of the current test person obtained by intelligent analysis to the portable terminal of the nearest oral health manager via a wireless communication link; In this way, the oral health management data of each inspector can be obtained directly and intelligently, so that the oral health status of each inspector can be directly given without relying on manual experience, avoiding errors caused by manual review due to over-reliance on manual experience, and at the same time improving the speed and efficiency of obtaining oral health management data.

[0020] The key points of the present invention are: targeted acquisition of comprehensive and sufficient reference visual information including multiple states such as tooth distribution status, tooth color status and tooth distance status, customized structural design of oral data analysis model, and intelligent analysis and on-site direct transmission of the current tester's enamel smoothness level and the current tester's caries presence mark.

[0021] Hereinafter, an oral health management data analysis method and system of the present invention will be specifically described by way of an embodiment.

[0022] Example 1 Figure 2 This is a flowchart of the steps of the oral health management data analysis method according to embodiment 1 of the present invention.

[0023] like Figure 2 As shown, the oral health management data analysis method includes the following steps: Step S21: performing multiple image captures at different positions along the horizontal distribution direction of each tooth of the current person being tested, with the shortest distance from the center of the camera lens to the teeth of the current person being tested as the set distance, and combining the multiple frames of captured images to obtain a fixed-distance panoramic image; For example, a mode in which the shortest distance from the center of the camera lens to the teeth of the current inspected person is used as a set distance, multiple image captures are performed at different positions along the horizontal distribution direction of each tooth of the current inspected person, and the multiple frames of captured images are combined to obtain a fixed-distance panoramic image. The method includes: using a depth of field analysis mode or an infrared ranging mode to measure the shortest distance from the center of the camera lens to the teeth of the current inspected person in real time; Step S22: identifying the image blocks occupied by the teeth in the fixed-distance panoramic image based on the imaging characteristics of human teeth, and performing image fitting processing on the image blocks to obtain an overall image of the teeth; Specifically, identifying the image blocks respectively occupied by the teeth in the fixed-distance panoramic picture based on the imaging characteristics of human teeth, and performing image fitting processing on the image blocks to obtain the overall image of the teeth includes: pre-storing the imaging characteristics of the human teeth in a cache device; Step S23: Obtaining respective red component values, respective vertical coordinate values, respective horizontal coordinate values, and respective imaging depth of field values ​​corresponding to respective pixel points constituting the overall tooth image, and outputting the values ​​as item-by-item visual capture information of the overall tooth image corresponding to the current inspector, wherein the red component values, vertical coordinate values, and horizontal coordinate values ​​of the pixel points are related to the presence of caries on the tooth, and the imaging depth of field values, vertical coordinate values, and horizontal coordinate values ​​of the pixel points are related to the smoothness of the tooth enamel on the tooth; More specifically, for the entirety of all teeth in the oral cavity of the current test subject, a corresponding overall tooth image is obtained. By analyzing the red component value, vertical coordinate value, and horizontal coordinate value of each pixel in the overall tooth image, the caries status of the corresponding tooth position, i.e., the tooth component corresponding to the pixel point, is numerically represented. The reason is that, on the one hand, the vertical coordinate value and the horizontal coordinate value represent the distribution position of the tooth components corresponding to the constituent pixel points, and the red component value represents the color state of the tooth components corresponding to the constituent pixel points, and then combined with the vertical coordinate value and the horizontal coordinate value, it is possible to complete the digital representation of the caries existence state of the tooth components corresponding to each constituent pixel point; on the other hand, the vertical coordinate value and the horizontal coordinate value represent the distribution position of the tooth components corresponding to the constituent pixel points, and the imaging depth of field value represents the far and near state of the tooth components corresponding to the constituent pixel points, and then combined with the imaging depth of field value and the vertical coordinate value and the horizontal coordinate value, it is possible to complete the digital representation of the enamel smoothness state of the tooth components corresponding to each constituent pixel point; On this basis, the various tooth-related parameters of the current inspector, the number of pixels occupied by the fixed-distance panoramic picture, and the number of pixels occupied by the overall tooth image corresponding to the current inspector are simultaneously input into the oral data analysis model. The oral data analysis model obtained after completing multiple learnings can stably and reliably obtain the digital representation of the caries presence state and enamel smoothness state of the current inspector's teeth as a whole, that is, the caries presence state represented by the binary value 0B10 or 0B11, and the enamel smoothness state represented by a certain level among 10 levels; specifically, the red component values, vertical coordinate values, horizontal coordinate values, and imaging depth of field values ​​corresponding to each pixel constituting the overall tooth image are obtained and output as the item-by-item visual capture information of the overall tooth image corresponding to the current inspector, including: in the overall tooth image, the pixel point in the lower left corner is used as the origin of the two-dimensional coordinate system, the bottom pixel row is used as the positive horizontal axis of the two-dimensional coordinate system, and the leftmost pixel column is used as the positive vertical axis of the two-dimensional coordinate system to construct the two-dimensional coordinate system of the overall tooth image; Step S24: Obtain the time interval between the last teeth cleaning time and the current time, gender, age, and number of existing teeth of the current tester to output as various tooth-related parameters of the current tester; Specifically, a plurality of different parameter extraction components can be used to respectively obtain the interval time from the last teeth cleaning moment to the current moment, gender, age, and number of existing teeth of the current tester; Step S25: using an oral data analysis model to intelligently analyze the tooth enamel smoothness grade of the current examinee and the caries presence indicator of the current examinee based on the number of pixels occupied by the fixed-distance panoramic image, the number of pixels occupied by the overall tooth image corresponding to the current examinee, the item-by-item visually captured information of the overall tooth image corresponding to the current examinee, and various tooth-related parameters of the current examinee; Specifically, a numerical simulation mode may be selected to implement testing and simulation of a data processing process for intelligently analyzing the enamel smoothness grade of the current inspector and the caries presence indicator of the current inspector using an oral data analysis model based on the number of pixels occupied by the fixed-distance panoramic image, the number of pixels occupied by the overall tooth image corresponding to the current inspector, item-by-item visually captured information of the overall tooth image corresponding to the current inspector, and various tooth-related parameters of the current inspector; Among them, the oral data analysis model is used to intelligently analyze the enamel smoothness level of the current detection person and the caries presence identification of the current detection person according to the number of pixels occupied by the fixed-distance panoramic picture, the number of pixels occupied by the overall tooth image corresponding to the current detection person, the item-by-item visual capture information of the overall tooth image corresponding to the current detection person, and various tooth-related parameters of the current detection person, including: the oral data analysis model is a Hoffet neural network that has undergone multiple learning operations, and the number of learning operations undergone by the Hoffet neural network is monotonically positively correlated with the value of the set distance; For example, the oral data analysis model is a Hoffet neural network after multiple learning operations, and the number of learning operations undergone by the Hoffet neural network is monotonically positively correlated with the value of the set distance, including: in the macro state, when the set distance is 1 cm, the corresponding number of learning operations undergone by the Hoffet neural network is 200, when the set distance is 1.5 cm, the corresponding number of learning operations undergone by the Hoffet neural network is 350, and when the set distance is 2 cm, the corresponding number of learning operations undergone by the Hoffet neural network is 500, and so on; And wherein, the oral data analysis model is a Hoffitt neural network after multiple learning operations and the number of learning operations undergone by the Hoffitt neural network is monotonically positively correlated with the number of pixel points occupied by the fixed-distance panoramic picture, including: when each learning operation is performed on the Hoffitt neural network, the known enamel smoothness level of a certain past tester and the known caries presence identifier of the certain past tester are used as two output contents of the Hoffitt neural network, and the number of pixel points occupied by the fixed-distance panoramic picture corresponding to the certain past tester, the number of pixel points occupied by the overall tooth image corresponding to the certain past tester, the item-by-item visual capture information of the overall tooth image corresponding to the certain past tester, and the various tooth-related parameters of the certain past tester are used as multiple input contents of the Hoffitt neural network to complete this learning operation.

[0024] Example 2 Figure 3 This is a flowchart of the steps of the oral health management data analysis method according to embodiment 2 of the present invention.

[0025] like Figure 3 As shown, before performing multiple image captures at different positions along the horizontal distribution direction of each tooth of the current test person in a mode with the shortest distance from the center position of the camera lens to the teeth of the current test person as the set distance, and combining the obtained multiple frames of captured images to obtain a fixed-distance panoramic image, that is, before step S21, the oral health management data analysis method further includes: Step S26: performing multiple learning operations on the Hoffet neural network to obtain a Hoffet neural network after the multiple learning operations, and outputting the Hoffet neural network after the multiple learning operations as the oral data analysis model; Specifically, performing multiple learning operations on the Hoffitt neural network to obtain the Hoffitt neural network after the multiple learning operations, and outputting the Hoffitt neural network after the multiple learning operations as the oral data analysis model includes: the oral data analysis model is model-defined by its multiple model parameters.

[0026] Example 3 Figure 4 This is a flowchart of the steps of the oral health management data analysis method according to Example 3 of the present invention.

[0027] like Figure 4 As shown, after the oral data analysis model is used to intelligently analyze the enamel smoothness grade of the current detection person and the caries presence indicator of the current detection person based on the number of pixels occupied by the fixed-distance panoramic image, the number of pixels occupied by the overall tooth image corresponding to the current detection person, the item-by-item visual capture information of the overall tooth image corresponding to the current detection person, and the various tooth-related parameters of the current detection person, that is, after step S25, the oral health management data analysis method further includes: Step S27: wirelessly transmitting the tooth enamel smoothness grade of the current inspected person and the caries presence indicator of the current inspected person obtained by intelligent analysis to the nearest portable terminal of the oral health manager via a wireless communication link; For example, the enamel smoothness grade of the current inspector and the caries presence indicator of the current inspector obtained by intelligent analysis are wirelessly transmitted to the portable terminal of the nearest oral health manager via a wireless communication link, including: the wireless communication link is a time division duplex communication link.

[0028] In any of the above embodiments 1-3, optionally, in the oral health management data analysis method: The oral data analysis model is a Hoffet neural network that has undergone multiple learning operations, and the number of learning operations that the Hoffet neural network has undergone is monotonically positively correlated with the value of the set distance. The method further includes: using a content mapping formula to express a content mapping relationship in which the number of learning operations that the Hoffet neural network has undergone is monotonically positively correlated with the value of the set distance; The content mapping relationship of using a content mapping formula to express the monotonically positive correlation between the number of learning operations of the Hoffet neural network and the value of the set distance includes: in the content mapping formula, the value of the set distance is the input content of the content mapping formula, and the number of learning operations of the Hoffet neural network corresponding to the value of the set distance is the output content of the content mapping formula; Among them, using the oral data analysis model to intelligently analyze the enamel smoothness grade of the current detection person and the caries presence identifier of the current detection person according to the number of pixels occupied by the fixed-distance panoramic picture, the number of pixels occupied by the overall tooth image corresponding to the current detection person, the item-by-item visual capture information of the overall tooth image corresponding to the current detection person, and various tooth-related parameters of the current detection person also includes: when the caries presence identifier of the current detection person obtained by intelligent analysis is 0B10, it indicates that the current detection person does not have caries; when the caries presence identifier of the current detection person obtained by intelligent analysis is 0B11, it indicates that the current detection person has caries; Here, 0B10 and 0B11 are both binary numerical representations, and different binary numerical values ​​are used to represent different states of the caries presence identification of the current detection person, namely, the absence of caries and the presence of caries, respectively, thereby completing the digital processing of the caries state of the current detection person; wherein, the oral data analysis model is used to intelligently analyze the enamel smoothness level of the current detection person and the caries presence identification of the current detection person according to the number of pixels occupied by the fixed-distance panoramic picture, the number of pixels occupied by the overall tooth image corresponding to the current detection person, the item-by-item visual capture information of the overall tooth image corresponding to the current detection person, and various tooth-related parameters of the current detection person, and further includes: the lower the enamel smoothness level of the current detection person obtained by the intelligent analysis, the lower the degree of enamel smoothness of the current detection person; Specifically, the lower the tooth enamel smoothness level of the current test person obtained by intelligent analysis, the lower the degree of tooth enamel smoothness of the current test person. The method includes: dividing the human body's tooth enamel smoothness level into 10 levels from 1 to 10, and the lower the level, the lower the degree of tooth enamel smoothness of the human body; Here, since the 10 levels from 1 to 10 can represent the different smoothness levels of human tooth enamel, different levels are used to represent the different smoothness levels of the current tester's tooth enamel, thereby completing the digital processing of the current tester's tooth enamel smoothness state; The method of identifying the image blocks respectively occupied by the teeth in the fixed-distance panoramic image based on the imaging characteristics of human teeth, and performing image fitting processing on the image blocks to obtain the overall image of the teeth includes: the imaging characteristics of the human teeth are the grayscale value distribution intervals corresponding to the human teeth; The method further comprises: identifying the image blocks respectively occupied by the teeth in the fixed-distance panoramic image based on the imaging characteristics of human teeth, and performing image fitting processing on the image blocks to obtain the overall image of the teeth; and further comprising: a grayscale value distribution interval corresponding to the human teeth being a grayscale value distribution interval numerically limited by an upper grayscale value threshold corresponding to the human teeth and a lower grayscale value threshold corresponding to the human teeth; The grayscale value distribution interval corresponding to the human teeth is a grayscale value distribution interval numerically limited by an upper grayscale value threshold corresponding to the human teeth and a lower grayscale value threshold corresponding to the human teeth, including: the upper grayscale value threshold corresponding to the human teeth is greater than the lower grayscale value threshold corresponding to the human teeth, and the upper grayscale value threshold corresponding to the human teeth and the lower grayscale value threshold corresponding to the human teeth are both within a range of 0-255; The step of obtaining respective red component values, respective vertical coordinate values, respective horizontal coordinate values, and respective imaging depth of field values ​​corresponding to respective pixel points constituting the overall tooth image and outputting the obtained values ​​as item-by-item visual capture information of the overall tooth image corresponding to the current inspector comprises: the red component value corresponding to each pixel point constituting the overall tooth image is the R component value of the pixel point in the RGB color space; And wherein, the red component value corresponding to each pixel point constituting the overall image of the tooth is the R component value of the pixel point in the RGB color space, including: the pixel point has an R component value, a G component value and a B component value in the RGB color space.

[0029] Example 4 Figure 5 FIG. 4 is an internal structure diagram of an oral health management data analysis system according to embodiment 4 of the present invention.

[0030] like Figure 5 As shown, the oral health management data analysis system includes the following components: A fixed-distance acquisition device is used to perform multiple image acquisitions at different positions along the horizontal distribution direction of each tooth of the current inspected person, with the shortest distance from the center of the camera lens to the teeth of the current inspected person as the set distance, and combine the multiple frames of acquired images to obtain a fixed-distance panoramic image; For example, a mode in which the shortest distance from the center of the camera lens to the teeth of the current inspected person is used as a set distance, multiple image captures are performed at different positions along the horizontal distribution direction of each tooth of the current inspected person, and the multiple frames of captured images are combined to obtain a fixed-distance panoramic image. The method includes: using a depth of field analysis mode or an infrared ranging mode to measure the shortest distance from the center of the camera lens to the teeth of the current inspected person in real time; a target recognition device connected to the fixed-distance acquisition device, configured to recognize the image blocks occupied by the respective teeth in the fixed-distance panoramic image based on the imaging characteristics of human teeth, and perform image fitting processing on the respective image blocks to obtain an overall image of the tooth; Specifically, identifying the image blocks respectively occupied by the teeth in the fixed-distance panoramic picture based on the imaging characteristics of human teeth, and performing image fitting processing on the image blocks to obtain the overall image of the teeth includes: pre-storing the imaging characteristics of the human teeth in a cache device; a visual capture device connected to the target recognition device, for obtaining respective red component values, respective vertical coordinate values, respective horizontal coordinate values, and respective imaging depth of field values ​​corresponding to respective pixel points constituting the overall image of the tooth portion, and outputting the obtained values ​​as item-by-item visual capture information of the overall image of the tooth portion corresponding to the current inspector, wherein the red component values, vertical coordinate values, and horizontal coordinate values ​​of the pixel points are related to the presence of caries on the tooth portion, and the imaging depth of field values, vertical coordinate values, and horizontal coordinate values ​​of the pixel points are related to the smoothness of the tooth enamel on the tooth portion; More specifically, for the entirety of all teeth in the oral cavity of the current test subject, a corresponding overall tooth image is obtained. By analyzing the red component value, vertical coordinate value, and horizontal coordinate value of each pixel in the overall tooth image, the caries status of the corresponding tooth position, i.e., the tooth component corresponding to the pixel point, is numerically represented. The reason is that, on the one hand, the vertical coordinate value and the horizontal coordinate value represent the distribution position of the tooth components corresponding to the constituent pixel points, and the red component value represents the color state of the tooth components corresponding to the constituent pixel points, and then combined with the vertical coordinate value and the horizontal coordinate value, it is possible to complete the digital representation of the caries existence state of the tooth components corresponding to each constituent pixel point; on the other hand, the vertical coordinate value and the horizontal coordinate value represent the distribution position of the tooth components corresponding to the constituent pixel points, and the imaging depth of field value represents the far and near state of the tooth components corresponding to the constituent pixel points, and then combined with the imaging depth of field value and the vertical coordinate value and the horizontal coordinate value, it is possible to complete the digital representation of the enamel smoothness state of the tooth components corresponding to each constituent pixel point; On this basis, the various tooth-related parameters of the current inspector, the number of pixels occupied by the fixed-distance panoramic image, and the number of pixels occupied by the overall tooth image corresponding to the current inspector are simultaneously input into the oral data analysis model. The oral data analysis model obtained after completing multiple learnings can stably and reliably obtain the digital representation of the caries status and enamel smoothness status of the current inspector's teeth as a whole, that is, the caries status represented by the binary value 0B10 or 0B11, and the enamel smoothness status represented by one of the 10 levels; Specifically, obtaining respective red component values, respective vertical coordinate values, respective horizontal coordinate values, and respective imaging depth of field values ​​corresponding to respective pixel points constituting the overall tooth image and outputting the obtained values ​​as item-by-item visual capture information of the overall tooth image corresponding to the current inspector includes: constructing a two-dimensional coordinate system for the overall tooth image by taking the pixel point at the lower left corner as the origin of the two-dimensional coordinate system, taking the bottommost pixel row as the positive horizontal axis of the two-dimensional coordinate system, and taking the leftmost pixel column as the positive vertical axis of the two-dimensional coordinate system in the overall tooth image; A parameter extraction device is used to obtain the interval time from the last teeth cleaning time to the current time, gender, age and number of existing teeth of the current tester to output as various tooth-related parameters of the current tester; Specifically, a plurality of different parameter extraction components can be used to respectively obtain the interval time from the last teeth cleaning moment to the current moment, gender, age, and number of existing teeth of the current tester; a data analysis device, connected to the visual capture device and the parameter extraction device, respectively, for using an oral data analysis model to intelligently analyze the tooth enamel smoothness grade of the current test person and the caries presence indicator of the current test person based on the number of pixels occupied by the fixed-distance panoramic image, the number of pixels occupied by the overall tooth image corresponding to the current test person, the item-by-item visual capture information of the overall tooth image corresponding to the current test person, and various tooth-related parameters of the current test person; Specifically, a numerical simulation mode may be selected to implement testing and simulation of a data processing process for intelligently analyzing the enamel smoothness grade of the current inspector and the caries presence indicator of the current inspector using an oral data analysis model based on the number of pixels occupied by the fixed-distance panoramic image, the number of pixels occupied by the overall tooth image corresponding to the current inspector, item-by-item visually captured information of the overall tooth image corresponding to the current inspector, and various tooth-related parameters of the current inspector; Among them, the oral data analysis model is used to intelligently analyze the enamel smoothness level of the current detection person and the caries presence identification of the current detection person according to the number of pixels occupied by the fixed-distance panoramic picture, the number of pixels occupied by the overall tooth image corresponding to the current detection person, the item-by-item visual capture information of the overall tooth image corresponding to the current detection person, and various tooth-related parameters of the current detection person, including: the oral data analysis model is a Hoffet neural network that has undergone multiple learning operations, and the number of learning operations undergone by the Hoffet neural network is monotonically positively correlated with the value of the set distance; For example, the oral data analysis model is a Hoffet neural network after multiple learning operations, and the number of learning operations undergone by the Hoffet neural network is monotonically positively correlated with the value of the set distance, including: in the macro state, when the set distance is 1 cm, the corresponding number of learning operations undergone by the Hoffet neural network is 200, when the set distance is 1.5 cm, the corresponding number of learning operations undergone by the Hoffet neural network is 350, and when the set distance is 2 cm, the corresponding number of learning operations undergone by the Hoffet neural network is 500, and so on; And wherein, the oral data analysis model is a Hoffitt neural network after multiple learning operations and the number of learning operations undergone by the Hoffitt neural network is monotonically positively correlated with the number of pixel points occupied by the fixed-distance panoramic picture, including: when each learning operation is performed on the Hoffitt neural network, the known enamel smoothness level of a certain past tester and the known caries presence identifier of the certain past tester are used as two output contents of the Hoffitt neural network, and the number of pixel points occupied by the fixed-distance panoramic picture corresponding to the certain past tester, the number of pixel points occupied by the overall tooth image corresponding to the certain past tester, the item-by-item visual capture information of the overall tooth image corresponding to the certain past tester, and the various tooth-related parameters of the certain past tester are used as multiple input contents of the Hoffitt neural network to complete this learning operation.

[0031] Example 5 Figure 6 FIG. 5 is a diagram showing the internal structure of an oral health management data analysis system according to Embodiment 5 of the present invention.

[0032] like Figure 6 As shown, the oral health management data analysis system also includes: a model building device, connected to the data analysis device, for performing multiple learning operations on the Hoffet neural network to obtain a Hoffet neural network after the multiple learning operations, and outputting the Hoffet neural network after the multiple learning operations as the oral data analysis model; Specifically, performing multiple learning operations on the Hoffitt neural network to obtain the Hoffitt neural network after the multiple learning operations, and outputting the Hoffitt neural network after the multiple learning operations as the oral data analysis model includes: the oral data analysis model is model-defined by its multiple model parameters.

[0033] Example 6 Figure 7 FIG. 1 is an internal structure diagram of an oral health management data analysis system according to embodiment 6 of the present invention.

[0034] like Figure 7 As shown, the oral health management data analysis system also includes: a wireless transmission device connected to the data analysis device, and configured to wirelessly transmit the tooth enamel smoothness grade of the current test person and the caries presence indicator of the current test person obtained through intelligent analysis to a portable terminal of the nearest oral health manager via a wireless communication link; For example, the enamel smoothness grade of the current inspector and the caries presence indicator of the current inspector obtained by intelligent analysis are wirelessly transmitted to the portable terminal of the nearest oral health manager via a wireless communication link, including: the wireless communication link is a time division duplex communication link.

[0035] And in any one of the above embodiments 4-6, optionally, in the oral health management data analysis system: The oral data analysis model is a Hoffet neural network that has undergone multiple learning operations, and the number of learning operations that the Hoffet neural network has undergone is monotonically positively correlated with the value of the set distance. The method further includes: using a content mapping formula to express a content mapping relationship in which the number of learning operations that the Hoffet neural network has undergone is monotonically positively correlated with the value of the set distance; The content mapping relationship of using a content mapping formula to express the monotonically positive correlation between the number of learning operations of the Hoffet neural network and the value of the set distance includes: in the content mapping formula, the value of the set distance is the input content of the content mapping formula, and the number of learning operations of the Hoffet neural network corresponding to the value of the set distance is the output content of the content mapping formula; Among them, using the oral data analysis model to intelligently analyze the enamel smoothness grade of the current detection person and the caries presence identifier of the current detection person according to the number of pixels occupied by the fixed-distance panoramic picture, the number of pixels occupied by the overall tooth image corresponding to the current detection person, the item-by-item visual capture information of the overall tooth image corresponding to the current detection person, and various tooth-related parameters of the current detection person also includes: when the caries presence identifier of the current detection person obtained by intelligent analysis is 0B10, it indicates that the current detection person does not have caries; when the caries presence identifier of the current detection person obtained by intelligent analysis is 0B11, it indicates that the current detection person has caries; Here, 0B10 and 0B11 are both binary value representations. By using different binary values ​​to represent different states of the caries presence identification of the current person being tested, that is, the absence of caries and the presence of caries, the digital processing of the caries status of the current person being tested is completed. Wherein, the method of using an oral data analysis model to intelligently analyze the tooth enamel smoothness grade of the current detection person and the dental caries presence indicator of the current detection person according to the number of pixels occupied by the fixed-distance panoramic image, the number of pixels occupied by the overall tooth image corresponding to the current detection person, the item-by-item visual capture information of the overall tooth image corresponding to the current detection person, and various tooth-related parameters of the current detection person further includes: the lower the tooth enamel smoothness grade of the current detection person obtained by the intelligent analysis, the lower the degree of tooth enamel smoothness of the current detection person; Specifically, the lower the tooth enamel smoothness level of the current test person obtained by intelligent analysis, the lower the degree of tooth enamel smoothness of the current test person. The method includes: dividing the human body's tooth enamel smoothness level into 10 levels from 1 to 10, and the lower the level, the lower the degree of tooth enamel smoothness of the human body; Here, since the 10 levels from 1 to 10 can represent the different smoothness levels of human tooth enamel, different levels are used to represent the different smoothness levels of the current tester's tooth enamel, thereby completing the digital processing of the current tester's tooth enamel smoothness state; The method of identifying the image blocks respectively occupied by the teeth in the fixed-distance panoramic image based on the imaging characteristics of human teeth, and performing image fitting processing on the image blocks to obtain the overall image of the teeth includes: the imaging characteristics of the human teeth are the grayscale value distribution intervals corresponding to the human teeth; The method further comprises: identifying the image blocks respectively occupied by the teeth in the fixed-distance panoramic image based on the imaging characteristics of human teeth, and performing image fitting processing on the image blocks to obtain the overall image of the teeth; and further comprising: a grayscale value distribution interval corresponding to the human teeth being a grayscale value distribution interval numerically limited by an upper grayscale value threshold corresponding to the human teeth and a lower grayscale value threshold corresponding to the human teeth; The grayscale value distribution interval corresponding to the human teeth is a grayscale value distribution interval numerically limited by an upper grayscale value threshold corresponding to the human teeth and a lower grayscale value threshold corresponding to the human teeth, including: the upper grayscale value threshold corresponding to the human teeth is greater than the lower grayscale value threshold corresponding to the human teeth, and the upper grayscale value threshold corresponding to the human teeth and the lower grayscale value threshold corresponding to the human teeth are both within a range of 0-255; The step of obtaining respective red component values, respective vertical coordinate values, respective horizontal coordinate values, and respective imaging depth of field values ​​corresponding to respective pixel points constituting the overall tooth image and outputting the obtained values ​​as item-by-item visual capture information of the overall tooth image corresponding to the current inspector comprises: the red component value corresponding to each pixel point constituting the overall tooth image is the R component value of the pixel point in the RGB color space; And wherein, the red component value corresponding to each pixel point constituting the overall image of the tooth is the R component value of the pixel point in the RGB color space, including: the pixel point has an R component value, a G component value and a B component value in the RGB color space.

[0036] In addition, in an oral health management data analysis method and system according to the present invention: Adopting an oral data analysis model to intelligently analyze the tooth enamel smoothness grade of the current detection person and the caries presence identification of the current detection person based on the number of pixels occupied by the fixed-distance panoramic picture, the number of pixels occupied by the overall tooth image corresponding to the current detection person, the item-by-item visual capture information of the overall tooth image corresponding to the current detection person, and the various tooth-related parameters of the current detection person further includes: synchronously inputting the number of pixels occupied by the fixed-distance panoramic picture, the number of pixels occupied by the overall tooth image corresponding to the current detection person, the item-by-item visual capture information of the overall tooth image corresponding to the current detection person, and the various tooth-related parameters of the current detection person into the oral data analysis model, and executing the oral data analysis model to obtain the tooth enamel smoothness grade of the current detection person and the caries presence identification of the current detection person output by the oral data analysis model; For example, the number of pixels occupied by the fixed-distance panoramic picture, the number of pixels occupied by the overall image of the teeth corresponding to the current detection person, the item-by-item visual capture information of the overall image of the teeth corresponding to the current detection person, and the various tooth-related parameters of the current detection person are synchronously input into the oral data analysis model, and the oral data analysis model is executed to obtain the tooth enamel smoothness grade of the current detection person and the caries presence identifier of the current detection person output by the oral data analysis model, including: using a synchronous control device to synchronously input the number of pixels occupied by the fixed-distance panoramic picture, the number of pixels occupied by the overall image of the teeth corresponding to the current detection person, the item-by-item visual capture information of the overall image of the teeth corresponding to the current detection person, and the various tooth-related parameters of the current detection person into the oral data analysis model; Specifically, a synchronous control device is used to synchronously input the number of pixels occupied by the fixed-distance panoramic image, the number of pixels occupied by the overall tooth image corresponding to the current detection person, the item-by-item visual capture information of the overall tooth image corresponding to the current detection person, and various tooth-related parameters of the current detection person into the oral data analysis model, including: the synchronous control device is an FPGA chip; And wherein, the number of pixels occupied by the fixed-distance panoramic picture, the number of pixels occupied by the overall tooth image corresponding to the current detection person, the item-by-item visual capture information of the overall tooth image corresponding to the current detection person, and the various tooth-related parameters of the current detection person are synchronously input into the oral data analysis model, and the oral data analysis model is executed to obtain the enamel smoothness grade of the current detection person and the caries presence identifier of the current detection person output by the oral data analysis model, including: before the number of pixels occupied by the fixed-distance panoramic picture, the number of pixels occupied by the overall tooth image corresponding to the current detection person, the item-by-item visual capture information of the overall tooth image corresponding to the current detection person, and the various tooth-related parameters of the current detection person are synchronously input into the oral data analysis model, the number of pixels occupied by the fixed-distance panoramic picture, the number of pixels occupied by the overall tooth image corresponding to the current detection person, the item-by-item visual capture information of the overall tooth image corresponding to the current detection person, and the various tooth-related parameters of the current detection person are respectively binary digitized.

[0037] Obviously, many modifications and variations will be apparent to those skilled in the art. The exemplary embodiments were chosen and described in order to best explain the principles of the invention and its practical application, thereby enabling others skilled in the art to understand the invention for various embodiments and various modifications as are suited to the specific use contemplated.

Claims

1. A method for analyzing oral health management data, characterized in that: The method comprises: The shortest distance from the center of the camera lens to the teeth of the current inspected person is used as the set distance. Multiple frames of image capture are performed at different positions along the horizontal distribution direction of each tooth of the current inspected person, and the multiple frames of captured images are combined to obtain a fixed-distance panoramic image. Based on the imaging characteristics of human teeth, the image blocks occupied by each tooth in the fixed-distance panoramic image are identified, and each image block is subjected to image fitting processing to obtain the overall image of the tooth; Obtaining respective red component values, respective vertical coordinate values, respective horizontal coordinate values, and respective imaging depth of field values ​​corresponding to respective pixel points constituting the overall tooth image and outputting these values ​​as item-by-item visual capture information of the overall tooth image corresponding to the current inspector, wherein the red component values, vertical coordinate values, and horizontal coordinate values ​​of the pixel points are related to the presence of caries on the tooth, and the imaging depth of field values, vertical coordinate values, and horizontal coordinate values ​​of the pixel points are related to the smoothness of the tooth enamel on the tooth; Obtain the time interval between the last teeth cleaning time and the current time, gender, age, and number of existing teeth of the current inspector as output of various tooth-related parameters of the current inspector; An oral data analysis model is used to intelligently analyze the current examinee's enamel smoothness level and the presence of caries based on the number of pixels occupied by the fixed-distance panoramic image, the number of pixels occupied by the overall tooth image corresponding to the current examinee, the item-by-item visual capture information of the overall tooth image corresponding to the current examinee, and the various tooth-related parameters of the current examinee.

2. The oral health management data analysis method according to claim 1, wherein: The oral data analysis model is a Hoffet neural network that has undergone multiple learning operations, and the number of learning operations that the Hoffet neural network has undergone is monotonically positively correlated with the value of the set distance; Among them, when performing each learning operation on the Hoffitt neural network, the known enamel smoothness level of a certain past tester and the known caries presence mark of a certain past tester are used as the two output contents of the Hoffitt neural network, and the number of pixels occupied by the fixed-distance panoramic picture corresponding to the certain past tester, the number of pixels occupied by the overall tooth image corresponding to the certain past tester, the item-by-item visual capture information of the overall tooth image corresponding to the certain past tester, and the various tooth-related parameters of the certain past tester are used as multiple input contents of the Hoffitt neural network to complete this learning operation.

3. The oral health management data analysis method according to claim 2, characterized in that: Before performing multiple image acquisitions at different positions along the horizontal distribution direction of the teeth of the current person being examined in a mode in which the shortest distance from the center of the camera lens to the teeth of the current person being examined is set as the distance, and combining the multiple frames of acquired images to obtain a fixed-distance panoramic image, the method further includes: Multiple learning operations are performed on the Hoffet neural network to obtain the Hoffet neural network after the multiple learning operations, and the Hoffet neural network after the multiple learning operations is output as an oral data analysis model.

4. The oral health management data analysis method according to claim 2, wherein: After intelligently analyzing the current examinee's tooth enamel smoothness grade and the current examinee's caries presence indicator using the oral data analysis model based on the number of pixels occupied by the fixed-distance panoramic image, the number of pixels occupied by the overall tooth image corresponding to the current examinee, item-by-item visually captured information of the overall tooth image corresponding to the current examinee, and various tooth-related parameters of the current examinee, the method further includes: The tooth enamel smoothness level of the current inspected person and the caries presence identification of the current inspected person obtained by intelligent analysis are wirelessly transmitted to the portable terminal of the nearest oral health manager through a wireless communication link.

5. The oral health management data analysis method according to any one of claims 2 to 4, characterized in that: The content mapping formula is used to express the content mapping relationship in which the number of learning operations of the Hoffet neural network is monotonically positively correlated with the value of the set distance. In the content mapping formula, the value of the set distance is the input content of the content mapping formula, and the number of learning operations of the Hoffet neural network corresponding to the value of the set distance is the output content of the content mapping formula. When the dental caries presence flag of the current test person obtained by intelligent analysis is 0B10, it indicates that the current test person does not have dental caries; when the dental caries presence flag of the current test person obtained by intelligent analysis is 0B11, it indicates that the current test person has dental caries; The lower the tooth enamel smoothness level of the current tester obtained by intelligent analysis is, the lower the tooth enamel smoothness of the current tester is; Among them, the imaging characteristics of human teeth are the grayscale value distribution interval corresponding to human teeth, the grayscale value distribution interval corresponding to human teeth is the grayscale value distribution interval numerically limited by the grayscale value upper limit threshold corresponding to human teeth and the grayscale value lower limit threshold corresponding to human teeth, the grayscale value upper limit threshold corresponding to human teeth is greater than the grayscale value lower limit threshold corresponding to human teeth, and the value ranges of the grayscale value upper limit threshold corresponding to human teeth and the grayscale value lower limit threshold corresponding to human teeth are both between 0 and 255; Among them, the red component value corresponding to each pixel constituting the overall image of the tooth is the R component value of the pixel in the RGB color space, and the pixel has an R component value, a G component value and a B component value in the RGB color space.

6. An oral health management data analysis system, characterized in that: The system comprises: A fixed-distance acquisition device is used to perform multiple image acquisitions at different positions along the horizontal distribution direction of each tooth of the current inspected person, with the shortest distance from the center of the camera lens to the teeth of the current inspected person as the set distance, and combine the multiple frames of acquired images to obtain a fixed-distance panoramic image; The target recognition device is connected to the fixed-distance acquisition device and is used to identify the image blocks occupied by each tooth in the fixed-distance panoramic image based on the imaging characteristics of human teeth, and perform image fitting processing on each image block to obtain the overall image of the tooth; a visual capture device connected to the target recognition device, for obtaining respective red component values, respective vertical coordinate values, respective horizontal coordinate values, and respective imaging depth of field values ​​corresponding to respective pixel points constituting the overall image of the tooth, and outputting the obtained information as item-by-item visual capture information of the overall image of the tooth corresponding to the current inspector, wherein the red component values, vertical coordinate values, and horizontal coordinate values ​​of the pixel points are related to the presence of caries on the tooth, and the imaging depth of field values, vertical coordinate values, and horizontal coordinate values ​​of the pixel points are related to the smoothness of the tooth enamel on the tooth; A parameter extraction device is used to obtain the interval time from the last teeth cleaning time to the current time, gender, age and number of existing teeth of the current tester to output as various tooth-related parameters of the current tester; The data analysis device is respectively connected to the visual capture device and the parameter extraction device, and is used to use the oral data analysis model to intelligently analyze the enamel smoothness level of the current inspector and the presence of caries of the current inspector based on the number of pixels occupied by the fixed-distance panoramic picture, the number of pixels occupied by the overall tooth image corresponding to the current inspector, the item-by-item visual capture information of the overall tooth image corresponding to the current inspector, and the various tooth-related parameters of the current inspector.

7. The oral health management data analysis system according to claim 6, wherein: The oral data analysis model is a Hoffet neural network that has undergone multiple learning operations, and the number of learning operations that the Hoffet neural network has undergone is monotonically positively correlated with the value of the set distance; Among them, when performing each learning operation on the Hoffitt neural network, the known enamel smoothness level of a certain past tester and the known caries presence mark of a certain past tester are used as the two output contents of the Hoffitt neural network, and the number of pixels occupied by the fixed-distance panoramic picture corresponding to the certain past tester, the number of pixels occupied by the overall tooth image corresponding to the certain past tester, the item-by-item visual capture information of the overall tooth image corresponding to the certain past tester, and the various tooth-related parameters of the certain past tester are used as multiple input contents of the Hoffitt neural network to complete this learning operation.

8. The oral health management data analysis system according to claim 7, wherein: The system further comprises: The model building device is connected to the data analysis device, and is used to perform multiple learning operations on the Hoffet neural network to obtain the Hoffet neural network after the multiple learning operations, and output the Hoffet neural network after the multiple learning operations as an oral data analysis model.

9. The oral health management data analysis system according to claim 7, wherein: The system further comprises: The wireless transmission device is connected to the data analysis device and is used to wirelessly transmit the tooth enamel smoothness level of the current inspector and the caries presence identification of the current inspector obtained by intelligent analysis to the portable terminal of the nearest oral health manager through a wireless communication link.

10. The oral health management data analysis system according to any one of claims 7 to 9, characterized in that: The content mapping formula is used to express the content mapping relationship in which the number of learning operations of the Hoffet neural network is monotonically positively correlated with the value of the set distance. In the content mapping formula, the value of the set distance is the input content of the content mapping formula, and the number of learning operations of the Hoffet neural network corresponding to the value of the set distance is the output content of the content mapping formula. When the dental caries presence flag of the current test person obtained by intelligent analysis is 0B10, it indicates that the current test person does not have dental caries; when the dental caries presence flag of the current test person obtained by intelligent analysis is 0B11, it indicates that the current test person has dental caries; The lower the tooth enamel smoothness level of the current tester obtained by intelligent analysis is, the lower the tooth enamel smoothness of the current tester is; Among them, the imaging characteristics of human teeth are the grayscale value distribution interval corresponding to human teeth, the grayscale value distribution interval corresponding to human teeth is the grayscale value distribution interval numerically limited by the grayscale value upper limit threshold corresponding to human teeth and the grayscale value lower limit threshold corresponding to human teeth, the grayscale value upper limit threshold corresponding to human teeth is greater than the grayscale value lower limit threshold corresponding to human teeth, and the value ranges of the grayscale value upper limit threshold corresponding to human teeth and the grayscale value lower limit threshold corresponding to human teeth are both between 0 and 255; Among them, the red component value corresponding to each pixel constituting the overall image of the tooth is the R component value of the pixel in the RGB color space, and the pixel has an R component value, a G component value and a B component value in the RGB color space.

Images