CN113178239A

CN113178239A - Method and system for guiding daily health care of patients with gingival atrophy

Info

Publication number: CN113178239A
Application number: CN202110505672.8A
Authority: CN
Inventors: 孙秋榕; 龚衍; 肖庆春; 聂智亮
Original assignee: First Affiliated Hospital of Gannan Medical University
Current assignee: First Affiliated Hospital of Gannan Medical University
Priority date: 2021-05-10
Filing date: 2021-05-10
Publication date: 2021-07-27

Abstract

The invention discloses a method and a system for guiding daily health care of a patient with gingival atrophy, wherein the method comprises the following steps: obtaining first gum image information of a first user according to the camera; performing convolution feature transformation extraction on the first gum image information to obtain a first convolution feature and a second convolution feature; inputting the first convolution characteristic and the second convolution characteristic into a first atrophy grade analysis model to obtain a first gingival atrophy grade; if the first user is a first associated disease user, obtaining a first guide characteristic based on incremental learning; obtaining first guidance content according to the obtained first gum atrophy health-care knowledge base and the first guidance inference rule base; and filling the first guidance frame based on the first guidance content to generate a first guidance scheme. The technical problems that the professional performance of daily health care is weak and the recovery effect of a patient is influenced due to the fact that the knowledge reserve of the patient with the gingival atrophy is not enough in the prior art are solved.

Description

Method and system for guiding daily health care of patients with gingival atrophy

Technical Field

The invention relates to the technical field of artificial intelligence, in particular to a method and a system for guiding daily health care of a patient with gingival atrophy.

Background

Along with popularization of oral health care knowledge, health consciousness of people is continuously enhanced, gingival atrophy is divided into physiological atrophy and pathological atrophy, atrophy can occur more or less along with the growth of age, tooth roots are exposed, due to the fact that the gingival atrophy occurs, harm can be caused to health of a patient, the patient can suffer pain, work and study in daily life are seriously affected, and therefore the delay through daily health care is needed.

However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:

the technical problems that the professional performance of daily health care is weak and the recovery effect of a patient is influenced due to insufficient knowledge reserve of a patient with gingival atrophy in the prior art exist.

Disclosure of Invention

The embodiment of the application provides a method and a system for guiding daily health care of patients with gingival atrophy, solves the technical problems that the implementation of daily health care is weaker due to insufficient knowledge reserve of patients with gingival atrophy and the recovery effect of patients is influenced in the prior art, achieves the aim of targeted professional guidance of gingival atrophy characteristics of patients based on the combination of a health care knowledge base and inference rules, and improves the technical effect of quality recovery of patients.

In view of the above problems, the present application provides a method and a system for guiding daily health care of a patient with gingival atrophy.

In a first aspect, the embodiment of the present application provides a method for guiding daily health care of a patient with gingival atrophy, wherein the method is applied to a system for guiding daily health care of a patient with gingival atrophy, the system is intelligently connected with a camera, and the method includes: according to the camera, first gum image information of a first user is obtained; performing convolution feature transformation extraction on the first gum image information to obtain a first convolution feature and a second convolution feature; inputting the first convolution characteristic and the second convolution characteristic into a first atrophy grade analysis model to obtain a first gingival atrophy grade; judging whether the first user is a first associated disease user; if the first user is a first associated disease user, obtaining a first guidance characteristic according to the first gingival atrophy grade; constructing a first guide frame according to the first guide characteristic; obtaining a first gingival atrophy health-care knowledge base; obtaining a first guiding inference rule base through a first doctor of the first user; obtaining first guidance content according to the first gum atrophy health-care knowledge base and the first guidance inference rule base; and filling the first guidance frame based on the first guidance content to generate a first guidance scheme.

In another aspect, the present application further provides a guidance system for daily health care of a patient with gingival atrophy, the system comprising: the first obtaining unit is used for obtaining first gum image information of a first user according to the camera; a second obtaining unit, configured to obtain a first convolution feature and a second convolution feature by performing convolution feature transformation extraction on the first gingival image information; a first input unit, configured to input the first convolution feature and the second convolution feature into a first atrophy level analysis model to obtain a first gingival atrophy level; the first judging unit is used for judging whether the first user is a first associated disease user or not; a third obtaining unit, configured to obtain a first guidance feature according to the first gingival atrophy level if the first user is a first associated disorder user; a first construction unit for constructing a first guiding frame according to the first guiding feature; a fourth obtaining unit for obtaining a first gingival atrophy healthcare knowledge base; a fifth obtaining unit, configured to obtain a first guidance inference rule base by a first referring physician of the first user; a sixth obtaining unit, configured to obtain first guidance content according to the first gingival atrophy healthcare knowledge base and the first guidance inference rule base; a first generating unit configured to populate the first guidance frame based on the first guidance content to generate a first guidance schedule.

In a third aspect, the present invention provides a guidance system for daily health care of a patient with gingival atrophy, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method according to the first aspect when executing the program.

One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

the method comprises the steps of acquiring a gingival image of a patient suffering from gingival atrophy by a camera, analyzing and extracting convolution characteristics based on acquired first gingival image information, wherein the extracted characteristics comprise a first convolution characteristic and a second convolution characteristic, and inputting the first convolution characteristic and the second convolution characteristic into a first atrophy grade analysis model to obtain a corresponding gingival atrophy grade. Furthermore, updated related guide characteristics are obtained after incremental learning is carried out on the information of the affected related symptoms by judging whether the first user suffers from other related symptoms, a first guide frame is further constructed based on the first guide characteristics, guide contents are formed through the obtained first gum atrophy health care knowledge base and a guide inference rule base of a doctor, and finally professional guidance of daily health care is carried out on the first user according to the way that the guide contents and the guide frame generate a first guide scheme, so that the aim of combining the health care knowledge base with the inference rule is achieved, the targeted professional guidance is carried out on the gum atrophy characteristics of the patient, and the technical effect of recovering the quality of the patient is improved.

The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.

Drawings

FIG. 1 is a schematic flow chart of a method for guiding daily health care of a patient with gingival atrophy according to an embodiment of the present application;

FIG. 2 is a schematic diagram of a guidance system for daily health care of a patient with gingival atrophy according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.

Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a first input unit 13, a first judging unit 14, a third obtaining unit 15, a first constructing unit 16, a fourth obtaining unit 17, a fifth obtaining unit 18, a sixth obtaining unit 19, a first generating unit 20, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 305.

Detailed Description

The embodiment of the application provides a method and a system for guiding daily health care of patients with gingival atrophy, solves the technical problems that the implementation of daily health care is weaker due to insufficient knowledge reserve of patients with gingival atrophy and the recovery effect of patients is influenced in the prior art, achieves the aim of targeted professional guidance of gingival atrophy characteristics of patients based on the combination of a health care knowledge base and inference rules, and improves the technical effect of quality recovery of patients. Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.

Summary of the application

Along with popularization of oral health care knowledge, health consciousness of people is continuously enhanced, gingival atrophy is divided into physiological atrophy and pathological atrophy, atrophy can occur more or less along with the growth of age, tooth roots are exposed, due to the fact that the gingival atrophy occurs, harm can be caused to health of a patient, the patient can suffer pain, work and study in daily life are seriously affected, and therefore the delay through daily health care is needed. However, the prior art has the technical problems that the professional performance of daily health care is weak and the recovery effect of a patient is influenced due to insufficient knowledge reserve of a patient with gingival atrophy.

In view of the above technical problems, the technical solution provided by the present application has the following general idea:

the embodiment of the application provides a method for guiding daily health care of a patient with gingival atrophy, wherein the method is applied to a system for guiding daily health care of a patient with gingival atrophy, the system is intelligently connected with a camera, and the method comprises the following steps: according to the camera, first gum image information of a first user is obtained; performing convolution feature transformation extraction on the first gum image information to obtain a first convolution feature and a second convolution feature; inputting the first convolution characteristic and the second convolution characteristic into a first atrophy grade analysis model to obtain a first gingival atrophy grade; judging whether the first user is a first associated disease user; if the first user is a first associated disease user, obtaining a first guidance characteristic according to the first gingival atrophy grade; constructing a first guide frame according to the first guide characteristic; obtaining a first gingival atrophy health-care knowledge base; obtaining a first guiding inference rule base through a first doctor of the first user; obtaining first guidance content according to the first gum atrophy health-care knowledge base and the first guidance inference rule base; and filling the first guidance frame based on the first guidance content to generate a first guidance scheme.

Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.

Example one

As shown in fig. 1, the present application provides a method for guiding daily health care of a patient with gingival atrophy, wherein the method is applied to a system for guiding daily health care of a patient with gingival atrophy, the system is intelligently connected to a camera, and the method includes:

step S100: according to the camera, first gum image information of a first user is obtained;

particularly, the camera is miniature intelligent camera, can visit into first user's oral cavity carries out the multi-angle of gum, many distances carry out the intelligent acquisition of image, and is further, because a plurality of image information that gather through the camera are to the comprehensive reaction of first user oral cavity image, consequently, need further carry out quality screening and processing through all image information to gathering to obtain first gum image information, wherein, because the camera with the daily health care's of first kind gum atrophy patient guidance system communication connection, thereby can accomplish data acquisition's storage and transmission, and then carry out intelligent image acquisition to user's gum, provide the basic data of guiding discernment.

Step S200: performing convolution feature transformation extraction on the first gum image information to obtain a first convolution feature and a second convolution feature;

specifically, the first gingival image information is corresponding image information completed by the miniature intelligent camera, so that the image with the quality reaching a certain preset standard is further analyzed, wherein the first convolution feature and the second convolution feature are obtained by performing convolution calculation on two main features in the first gingival image and then identifying, in detail, obtaining a corresponding convolution layer corresponding to the first convolution feature and the second convolution feature, wherein the convolution layer comprises a plurality of different convolution kernel information, thereby completing convolution calculation of the first gum image information, further obtaining corresponding convolution characteristics, and then performing further detailed analysis, after all the convolution characteristics are classified, two main characteristics identified in the convolution characteristics need to be extracted, such as the slit distance and the root exposure of a patient with gingival atrophy.

Step S300: inputting the first convolution characteristic and the second convolution characteristic into a first atrophy grade analysis model to obtain a first gingival atrophy grade;

specifically, the obtaining of the first gingival atrophy grade is a training output result obtained by performing convolution feature extraction on the first user gingiva and completing training of a neural network, wherein the training process is a process of performing refined data supervised learning and accurate analysis, and invalid data in all convolution feature data needs to be removed before performing data supervised training, so that the training capacity and performance of the model are increased. Further, when first gum atrophy grade is higher, the corresponding expression first user's gum atrophy degree is comparatively serious, will first convolution characteristic with the study of carrying out the atrophy grade in the first atrophy grade analysis model of second convolution characteristic input, thereby obtain first gum atrophy grade, in detail, first gum atrophy grade can be right first user's gum atrophy degree carries out the accurate sign of datumization to the platform of being convenient for the computer to build carries out the processing of relevant data, and then has reached and has obtained the output data that has mathematical property and logicality, has further reached intelligent data learning's technological effect.

Step S400: judging whether the first user is a first associated disease user;

step S500: if the first user is a first associated disease user, obtaining a first guidance characteristic according to the first gingival atrophy grade;

specifically, by obtaining the specific physical condition information of the first user, further obtaining can be completed through a physical examination report and medical record information, so as to obtain the disease condition information suffered by the user in real time, and then, judging all the disease conditions, and comparing and judging the real-time disease condition information and the identified associated disease condition, wherein the first associated disease condition information is related disease condition information having a certain relevance to gingival atrophy, such as diabetes. If the first user suffers from a disease aggravating gingival atrophy, data training and learning are carried out on the severity of the gingival atrophy of the user again according to corresponding disease information, the process of carrying out disease condition training again is an incremental learning process, further refinement and determination of the grade are completed according to specific associated symptoms, health-care features in corresponding grades are obtained according to the first gingival atrophy grade, as the target gingival atrophy grade is a multi-grade set in advance, corresponding features are obtained by determining the real-time grade, the corresponding features are used as main features for guiding later, main guiding features are obtained, and accurate and effective information is provided for guiding later.

Step S600: constructing a first guide frame according to the first guide characteristic;

specifically, by classifying or multi-level dividing the first guidance feature, and wherein multiple classes and multiple levels have certain extensibility, the first guidance framework is a main framework constructed based on the first guidance feature, which specifies the architecture of all guidance features, clarifies the entire feature content, the dependency relationship between hierarchical relationships, content distribution and content skipping processes, and can perform the function setting of the framework based on the continuous correction of the first guidance feature, thereby achieving the technical effects of completing system fitting on all information by constructing the first guidance framework, and improving the total information management level and the process management quality.

Step S700: obtaining a first gingival atrophy health-care knowledge base;

specifically, the first gingival atrophy health-care knowledge base is a repository of gingival atrophy health-care knowledge obtained based on big data and a data acquisition device, further, the acquisition way of the health-care knowledge can be based on a professional communication platform or the integration of experience knowledge of multiple experts, and then all the acquired data are stored in a cloud so as to reduce the storage tension caused by too large data volume, and a data processor based on the cloud performs a preprocessing process on all the information so as to obtain accurate professional health-care knowledge.

Step S800: obtaining a first guiding inference rule base through a first doctor of the first user;

specifically, the first doctor of visiting a doctor in a hospital for the first user to visit the gingival atrophy, because the relevant doctors have a certain diagnosis and treatment process for the corresponding atrophy, and can provide better corresponding health care measures by combining medical records and state information of the patient, the first guidance and inference rule is obtained based on the system guidance experience of the first doctor, so that the specific experience is integrated, that is, the rule setting of logical judgment is completed by inputting information, and further a specific guidance and inference rule is obtained. The method achieves personalized guidance for the user through a special doctor for seeing a doctor, and improves the specialty and the fitness of the guidance method.

Step S900: obtaining first guidance content according to the first gum atrophy health-care knowledge base and the first guidance inference rule base;

step S1000: and filling the first guidance frame based on the first guidance content to generate a first guidance scheme.

Specifically, the process of obtaining the first guidance content is to use the first gingival atrophy health-care knowledge base as a total knowledge base, use the first guidance inference rule base as a rule set for inference by an inference engine, and further output all corresponding guidance contents based on the corresponding feature information of the first user, the first guidance inference rule base can infer the information of the user based on the inference engine, so as to obtain corresponding guidance content information, the first guidance contents are stored in the corresponding database, and further complete filling of corresponding frames based on the information obtained by these inferences, in other words, the process of obtaining the first guidance content is to input the gingival atrophy basic information of the user through a system, and output inference content results and related explanations, based on the inference process of simulating human experts, to improve the specialty of the output content, the frame filling process can systematize and process the guide information to finish effective guidance for the first user, so that the aim of performing targeted professional guidance on the gingival atrophy characteristics of the patient based on the combination of a health-care knowledge base and an inference rule is achieved, and the technical effect of improving the quality recovery of the patient is achieved.

Further, after obtaining the first gingival atrophy level based on the first convolution characteristic and the second convolution characteristic, step S200 of the embodiment of the present application further includes:

step S210: judging whether the first user has a first wearing tool or not according to the first gum image information;

step S220: when the first user has a first wearing tool, obtaining first mechanical damage information of the first wearing tool;

step S230: obtaining a first correction coefficient based on the first mechanical damage information, wherein the first correction coefficient is correction data based on a first influence factor;

step S240: and correcting the first gingival atrophy grade according to the first correction coefficient to obtain a second gingival atrophy grade.

Specifically, whether the first user wears the corresponding dental appliance or not may cause damage to the gums of the user due to some worn appliances, such as the aged periodontal tissue may be continuously aggravated by wearing dentures and inflammatory stimulation for a long time, or the gingival atrophy may be aggravated by a long-term stimulation of the gums due to partial alveolar bone thinning caused by malposition of the teeth and improper orthodontic treatment of the dental appliance. The first mechanical damage information is based on the current main gum damage condition information of the first user, such as bleeding, redness and swelling, bacterial plaque, damage and the like caused by the dental appliance, and further analysis is performed on the related influence factors to correspondingly complete specific grade correction, so that further grade correction is performed based on the main characteristic state of the user, and accurate and reliable atrophy grade information is obtained.

Further, if the first user is a first associated disease user, obtaining a first guidance feature according to the first gingival atrophy level, step S500 of the embodiment of the present application further includes:

step S510: if the first user is a first associated disease state user, obtaining a first disease state characteristic, wherein the first disease state characteristic is diabetes characteristic information of the first user, and the first disease state characteristic comprises a plurality of disease state characteristics;

step S520: performing incremental learning on the first atrophy grade analysis model according to the first disease characteristic to obtain a second atrophy grade analysis model;

step S530: obtaining a second gingival atrophy grade based on the second atrophy grade analysis model;

step S540: obtaining the first guidance feature according to the second gingival atrophy scale.

Specifically, the first disease characteristic is a corresponding disease characteristic obtained by extracting a characteristic of associated disease information of a user, such as an associated disease attribute characteristic, a user diagnosis and treatment process characteristic, the second atrophy level analysis model is a correspondingly updated level analysis model obtained by machine learning based on the first disease characteristic, and the second atrophy level analysis model needs to combine old training data of the first medical record error correction model and newly added first disease characteristic data to complete comprehensive incremental learning, so that basic performance of the first atrophy level analysis model can be retained and model performance can be updated after incremental learning of the first disease characteristic, and the second gingival atrophy level is obtained, wherein the second gingival atrophy level is atrophy level information obtained based on the newly trained model, therefore, the grade of the gingival atrophy of the user is corrected for the second time based on the first disease characteristic, and the technical effect that incremental learning is continuously performed based on the newly-added characteristic so as to improve the grade analysis accuracy performance of the model is achieved.

Further, step S520 in the embodiment of the present application further includes:

step S521: adding the first condition features to a first incremental features library according to a first add instruction;

step S522: inputting the data in the first incremental feature library into the first atrophy level analysis model to obtain a first predicted atrophy level;

step S523: obtaining first loss data by performing data loss analysis on the first predicted atrophy level;

step S524: and inputting the first loss data into a first atrophy grade analysis model for training to generate the second atrophy grade analysis model.

Specifically, the first predicted atrophy level is characteristic information for level prediction in the first atrophy level analysis model based on the first disease feature in the first incremental feature library, and since the second atrophy level analysis model is a new model obtained by performing analysis on loss of new disease data based on an introduced loss function, the first loss data is loss data representing knowledge related to the first disease feature of the first atrophy level analysis model, and then performing incremental learning on the first atrophy level analysis model based on the first loss data, wherein the incremental learning means that a learning system can continuously learn new knowledge from a new sample and can save most of the previously learned knowledge. Incremental learning is very similar to the learning mode of human beings, and as the body information of a user or the corresponding oral cavity feature information is changed continuously, the system can learn newly added features continuously. Furthermore, the atrophy grade analysis model is obtained by forming a neural network by connecting a plurality of neurons, so that the second atrophy grade analysis model keeps the basic function of the first atrophy grade analysis model through the training of loss data, the grade analysis accuracy is improved, and the technical effect of intelligent updating and learning is achieved.

Further, in the obtaining a first correction coefficient based on the first mechanical damage information, embodiment S230 of the present application further includes:

step S231: obtaining correlation factor information based on the first mechanical damage information;

step S232: obtaining first correlation information according to the correlation factor information;

step S233: constructing a first correlation fitting curve based on the first correlation information;

step S234: performing principal component factor analysis on the first correlation fitting curve to obtain the first influence factor, wherein the first influence factor is a first principal component factor influencing mechanical damage;

step S235: and generating the first correction coefficient according to the first influence factor.

Specifically, when the gingival atrophy level is adjusted according to the damage of the appliance worn by the user, the main influence components are determined mainly by a main component analysis and statistics method, and then the determination of the corresponding correction coefficients is completed, wherein all damage stimulation influence factors in the first mechanical damage information are obtained, such as various factors of the type of the wearing appliance, the material of the appliance, the appliance correction age and the like, and then the most main first influence factor is determined from the influence factors and is used as a main analysis factor to correct the atrophy level. Therefore, the data analysis accuracy can be ensured and the data analysis efficiency is increased while the data processing amount is reduced by the method of analyzing and completing the corresponding dimension reduction processing based on the indexes.

Further, the step S1000 of the embodiment of the present application further includes that the first guidance frame is filled based on the first guidance content to generate a first guidance scheme:

step S1010: obtaining first updating information according to the basic information of the first user;

step S1020: judging whether first superposition information exists in the first guide content, wherein the first superposition information is information with a high superposition rate with the first updating information in the first guide content;

step S1030: if the first superposition information exists in the first guide content, a first substitute instruction is obtained;

step S1040: replacing the first superposition information according to the first replacement instruction and the first updating information to obtain second guidance content;

step S1050: and generating a second guidance scheme according to the second guidance content and the first guidance frame.

Specifically, when the basic information of the first user is updated to a certain extent, the corresponding guidance content also needs to be updated to a certain extent, that is, the knowledge base and the rule base need to be updated, for example, when a new research content is provided, so that better guidance content can be adopted to perform auxiliary guidance according to the basic information of the first user, and then the original guidance content needs to be replaced, wherein the process of performing the matching query is to complete traversal by comparing the matching degrees between the information, therefore, the previous corresponding content is replaced according to the first replacement instruction, the information base is updated in time, and further the technical effects of the user satisfaction and the guidance effect are improved.

Further, the step S300 of inputting the first convolution feature and the second convolution feature into a first atrophy level analysis model to obtain a first gingival atrophy level further includes:

step S310: constructing the first atrophy level analysis model by using the first convolution characteristics and the second convolution characteristics as input information;

step S320: obtaining the first atrophy level analysis model by training through a plurality of sets of training data, wherein each of the plurality of sets of training data comprises: the first convolution feature, the second convolution feature, and identification information identifying a first output result;

step S330: obtaining a first output of the first atrophy level analysis model, the first output being the first gingival atrophy level.

Specifically, the first gingival atrophy level is input into each set of training data as supervision data for supervised learning, the first atrophy level analysis model is a model established based on a neural network model, the neural network is an operation model formed by interconnection of a large number of neurons, and the output of the network is expressed according to a logic strategy of the connection mode of the network. Further, the training process is substantially a supervised learning process, each of the plurality of sets of training data includes the first convolution feature, the second convolution feature, and identification information identifying a first output result, the first atrophy level analysis model performs continuous self-correction and adjustment until the obtained output result is consistent with the identification information, and the supervised learning of the set of data is finished, and the supervised learning of the next set of data is performed. When the output information of the first atrophy grade analysis model reaches the preset accuracy rate/reaches the convergence state, the supervised learning process is ended, the first gum atrophy grade is more accurately output through the training of the first atrophy grade analysis model, and the technical effect of intelligent data analysis is achieved.

In summary, the guidance method and system for daily health care of patients with gingival atrophy provided by the embodiments of the present application have the following technical effects:

1. the gingival atrophy characteristic acquisition method comprises the steps of acquiring a gingival image of a patient suffering from gingival atrophy by a camera, analyzing and extracting convolution characteristics based on acquired first gingival image information, inputting the extracted characteristics into a first atrophy grade analysis model, obtaining corresponding gingival atrophy grades, correcting the atrophy grades based on user information, constructing a first guide frame based on the acquired first guide characteristics, and performing targeted professional guidance on the gingival atrophy characteristics of the patient, so that the technical effect of quality recovery of the patient is improved.

2. The method comprises the steps of judging whether the first user suffers from other related diseases or not, extracting the characteristics of the related disease information, and performing incremental learning based on the old model to obtain the updated atrophy grade, so that the grade analysis accuracy is improved, and the technical effect of intelligent updating learning is realized.

3. Because the guidance content is formed by constructing the first gingival atrophy health-care knowledge base and the guidance inference rule base of the doctor, the logic inference of the inference machine is completed in the knowledge base based on expert experience, the mode of combining the health-care knowledge base and the inference rule is achieved, and the technical effect of improving the health-care guidance quality is achieved.

Example two

Based on the same inventive concept as the method for guiding daily health care of the patient with gingival atrophy in the previous embodiment, the present invention further provides a system for guiding daily health care of the patient with gingival atrophy, as shown in fig. 2, the system includes:

a first obtaining unit 11, where the first obtaining unit 11 is configured to obtain first gum image information of a first user according to a camera;

a second obtaining unit 12, where the second obtaining unit 12 is configured to obtain a first convolution feature and a second convolution feature by performing convolution feature transformation extraction on the first gingival image information;

a first input unit 13, where the first input unit 13 is configured to input the first convolution feature and the second convolution feature into a first atrophy level analysis model to obtain a first gingival atrophy level;

a first judging unit 14, where the first judging unit 14 is configured to judge whether the first user is a first associated disease user;

a third obtaining unit 15, wherein the third obtaining unit 15 is configured to obtain a first guidance feature according to the first gingival atrophy level if the first user is a first associated disorder user;

a first construction unit 16, said first construction unit 16 being adapted to construct a first guiding frame according to said first guiding features;

a fourth obtaining unit 17, the fourth obtaining unit 17 being configured to obtain a first gingival atrophy healthcare knowledge base;

a fifth obtaining unit 18, wherein the fifth obtaining unit 18 is configured to obtain a first guiding inference rule base by a first medical doctor of the first user;

a sixth obtaining unit 19, where the sixth obtaining unit 19 is configured to obtain a first guidance content according to the first gingival atrophy healthcare knowledge base and the first guidance inference rule base;

a first generating unit 20, the first generating unit 20 being configured to populate the first tutorial framework based on the first tutorial content to generate a first tutorial solution.

Further, the system further comprises:

the first judging unit is used for judging whether the first user has a first wearing tool or not according to the first gum image information;

a seventh obtaining unit, configured to obtain first mechanical damage information of a first wearing tool when the first user has the first wearing tool;

an eighth obtaining unit, configured to obtain a first correction coefficient based on the first mechanical damage information, where the first correction coefficient is correction data based on a first influence factor;

a ninth obtaining unit, configured to correct the first gingival atrophy level according to the first correction coefficient, and obtain a second gingival atrophy level.

Further, the system further comprises:

a tenth obtaining unit, configured to obtain a first condition feature if the first user is a first associated condition user, where the first condition feature is diabetes feature information that the first user has, and the first condition feature includes a plurality of condition features;

an eleventh obtaining unit, configured to perform incremental learning on the first atrophy level analysis model according to the first disease feature to obtain a second atrophy level analysis model;

a twelfth obtaining unit for obtaining a second gingival atrophy level based on the second atrophy level analysis model;

a thirteenth obtaining unit for obtaining the first guidance feature according to the second gingival atrophy level.

A first adding unit for adding the first condition feature to a first incremental feature library according to a first adding instruction;

a fourteenth obtaining unit, configured to input the data in the first incremental feature library into the first atrophy level analysis model, and obtain a first predicted atrophy level;

a fifteenth obtaining unit configured to obtain first loss data by performing data loss analysis on the first predicted atrophy level;

and the second generation unit is used for inputting the first loss data into a first atrophy grade analysis model for training and generating the second atrophy grade analysis model.

Further, the system further comprises:

a sixteenth obtaining unit, configured to obtain correlation factor information based on the first mechanical damage information;

a seventeenth obtaining unit, configured to obtain first correlation information according to the correlation factor information;

a second construction unit for constructing a first correlation fitting curve based on the first correlation information;

an eighteenth obtaining unit, configured to obtain the first influence factor by performing principal component factor analysis on the first correlation fit curve, where the first influence factor is a first principal component factor that affects mechanical damage;

a third generating unit configured to generate the first correction coefficient according to the first influence factor.

Further, the system further comprises:

a nineteenth obtaining unit, configured to obtain first update information according to the basic information of the first user;

a second determination unit, configured to determine whether first superposition information exists in the first guidance content, where the first superposition information is information that has a high superposition rate with the first update information in the first guidance content;

a twentieth obtaining unit, configured to obtain a first substitute instruction if the first coincidence information exists in the first guidance content;

a twenty-first obtaining unit, configured to replace, according to the first replacement instruction and according to the first update information, the first coincidence information to obtain second guidance content;

a fourth generating unit, configured to generate a second guidance scheme according to the second guidance content and the first guidance frame.

Further, the system further comprises:

a third construction unit, configured to construct the first atrophy level analysis model by using the first convolution feature and the second convolution feature as input information;

a twenty-second obtaining unit configured to obtain the first atrophy level analysis model through training of multiple sets of training data, wherein each of the multiple sets of training data includes: the first convolution feature, the second convolution feature, and identification information identifying a first output result;

a twenty-third obtaining unit for obtaining a first output result of the first atrophy level analysis model, the first output result being the first gingival atrophy level.

Various changes and embodiments of the method for guiding daily health care of a patient with gingival atrophy in the first embodiment of fig. 1 are also applicable to the system for guiding daily health care of a patient with gingival atrophy in the present embodiment, and the implementation method of the system for guiding daily health care of a patient with gingival atrophy in the present embodiment is clear to those skilled in the art from the foregoing detailed description of the method for guiding daily health care of a patient with gingival atrophy, and therefore, for the sake of brevity of the description, detailed description thereof is omitted here.

Exemplary electronic device

The electronic device of the embodiment of the present application is described below with reference to fig. 3.

Fig. 3 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.

Based on the inventive concept of the method for guiding daily health care of a patient with gingival atrophy, the invention further provides a system for guiding daily health care of a patient with gingival atrophy, wherein the system comprises a computer program stored thereon, and the computer program is used for implementing the steps of any one of the methods for guiding daily health care of a patient with gingival atrophy.

Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 305 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other systems over a transmission medium.

The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.

The embodiment of the invention provides a method for guiding daily health care of a patient with gingival atrophy, wherein the method is applied to a system for guiding daily health care of the patient with gingival atrophy, the system is intelligently connected with a camera, and the method comprises the following steps: according to the camera, first gum image information of a first user is obtained; performing convolution feature transformation extraction on the first gum image information to obtain a first convolution feature and a second convolution feature; inputting the first convolution characteristic and the second convolution characteristic into a first atrophy grade analysis model to obtain a first gingival atrophy grade; judging whether the first user is a first associated disease user; if the first user is a first associated disease user, obtaining a first guidance characteristic according to the first gingival atrophy grade; constructing a first guide frame according to the first guide characteristic; obtaining a first gingival atrophy health-care knowledge base; obtaining a first guiding inference rule base through a first doctor of the first user; obtaining first guidance content according to the first gum atrophy health-care knowledge base and the first guidance inference rule base; and filling the first guidance frame based on the first guidance content to generate a first guidance scheme. The technical problems that the professional performance of daily health care execution is weak and the recovery effect of a patient is influenced due to insufficient knowledge reserve of the patient with the gingival atrophy in the prior art are solved, the aim of performing targeted professional guidance on the gingival atrophy characteristic of the patient based on the combination of a health care knowledge base and inference rules is achieved, and the technical effect of improving the recovery quality of the patient is achieved.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims

1. A guidance method for daily health care of a patient with gingival atrophy, wherein the method is applied to a guidance system for daily health care of a patient with gingival atrophy, the system is intelligently connected with a camera, and the method comprises the following steps:

according to the camera, first gum image information of a first user is obtained;

performing convolution feature transformation extraction on the first gum image information to obtain a first convolution feature and a second convolution feature;

inputting the first convolution characteristic and the second convolution characteristic into a first atrophy grade analysis model to obtain a first gingival atrophy grade;

judging whether the first user is a first associated disease user;

if the first user is a first associated disease user, obtaining a first guidance characteristic according to the first gingival atrophy grade;

constructing a first guide frame according to the first guide characteristic;

obtaining a first gingival atrophy health-care knowledge base;

obtaining a first guiding inference rule base through a first doctor of the first user;

obtaining first guidance content according to the first gum atrophy health-care knowledge base and the first guidance inference rule base;

and filling the first guidance frame based on the first guidance content to generate a first guidance scheme.

2. The method of claim 1, after obtaining a first gingival atrophy level based on the first convolution feature and the second convolution feature, the method further comprising:

judging whether the first user has a first wearing tool or not according to the first gum image information;

when the first user has a first wearing tool, obtaining first mechanical damage information of the first wearing tool;

obtaining a first correction coefficient based on the first mechanical damage information, wherein the first correction coefficient is correction data based on a first influence factor;

and correcting the first gingival atrophy grade according to the first correction coefficient to obtain a second gingival atrophy grade.

3. The method of claim 1, wherein if the first user is a first associated disorder user, obtaining a first guidance characteristic based on the first gingival atrophy rating, the method further comprising:

if the first user is a first associated disease state user, obtaining a first disease state characteristic, wherein the first disease state characteristic is diabetes characteristic information of the first user, and the first disease state characteristic comprises a plurality of disease state characteristics;

performing incremental learning on the first atrophy grade analysis model according to the first disease characteristic to obtain a second atrophy grade analysis model;

obtaining a second gingival atrophy grade based on the second atrophy grade analysis model;

obtaining the first guidance feature according to the second gingival atrophy scale.

4. The method of claim 3, further comprising:

adding the first condition features to a first incremental features library according to a first add instruction;

inputting the data in the first incremental feature library into the first atrophy level analysis model to obtain a first predicted atrophy level;

obtaining first loss data by performing data loss analysis on the first predicted atrophy level;

and inputting the first loss data into a first atrophy grade analysis model for training to generate the second atrophy grade analysis model.

5. The method of claim 2, the obtaining a first correction factor based on the first mechanical damage information, the method further comprising:

obtaining correlation factor information based on the first mechanical damage information;

obtaining first correlation information according to the correlation factor information;

constructing a first correlation fitting curve based on the first correlation information;

performing principal component factor analysis on the first correlation fitting curve to obtain the first influence factor, wherein the first influence factor is a first principal component factor influencing mechanical damage;

and generating the first correction coefficient according to the first influence factor.

6. The method of claim 1, the populating the first guidance framework based on the first guidance content generating a first guidance plan, the method further comprising:

obtaining first updating information according to the basic information of the first user;

judging whether first superposition information exists in the first guide content, wherein the first superposition information is information with a high superposition rate with the first updating information in the first guide content;

if the first superposition information exists in the first guide content, a first substitute instruction is obtained;

replacing the first superposition information according to the first replacement instruction and the first updating information to obtain second guidance content;

and generating a second guidance scheme according to the second guidance content and the first guidance frame.

7. The method of claim 1, said inputting said first convolution feature and said second convolution feature into a first atrophy level analysis model, obtaining a first gingival atrophy level, said method further comprising:

constructing the first atrophy level analysis model by using the first convolution characteristics and the second convolution characteristics as input information;

obtaining the first atrophy level analysis model by training through a plurality of sets of training data, wherein each of the plurality of sets of training data comprises: the first convolution feature, the second convolution feature, and identification information identifying a first output result;

obtaining a first output of the first atrophy level analysis model, the first output being the first gingival atrophy level.

8. A guidance system for routine health care of a patient with gingival atrophy, wherein the system comprises:

the first obtaining unit is used for obtaining first gum image information of a first user according to the camera;

a second obtaining unit, configured to obtain a first convolution feature and a second convolution feature by performing convolution feature transformation extraction on the first gingival image information;

a first input unit, configured to input the first convolution feature and the second convolution feature into a first atrophy level analysis model to obtain a first gingival atrophy level;

the first judging unit is used for judging whether the first user is a first associated disease user or not;

a third obtaining unit, configured to obtain a first guidance feature according to the first gingival atrophy level if the first user is a first associated disorder user;

a first construction unit for constructing a first guiding frame according to the first guiding feature;

a fourth obtaining unit for obtaining a first gingival atrophy healthcare knowledge base;

a fifth obtaining unit, configured to obtain a first guidance inference rule base by a first referring physician of the first user;

a sixth obtaining unit, configured to obtain first guidance content according to the first gingival atrophy healthcare knowledge base and the first guidance inference rule base;

a first generating unit configured to populate the first guidance frame based on the first guidance content to generate a first guidance schedule.

9. A guidance system for routine health care of a patient with gingival atrophy, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the method according to any one of claims 1-7.