CN114388089A - Personal health management method and system based on artificial intelligence - Google Patents
Personal health management method and system based on artificial intelligence Download PDFInfo
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- CN114388089A CN114388089A CN202111676054.6A CN202111676054A CN114388089A CN 114388089 A CN114388089 A CN 114388089A CN 202111676054 A CN202111676054 A CN 202111676054A CN 114388089 A CN114388089 A CN 114388089A
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
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- G06F16/252—Integrating or interfacing systems involving database management systems between a Database Management System and a front-end application
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
Abstract
The invention discloses a personal health management method and a system based on artificial intelligence, wherein the method comprises the following steps: the method comprises the steps of obtaining first physical measurement data of a human body in a healthy state and second physical measurement data of the human body in various disease states from a big data cloud storage server, obtaining third physical measurement data uploaded by a target user, determining whether the target user is in the healthy state according to the third physical measurement data, if yes, no subsequent operation is needed, otherwise, sending a request for obtaining uncomfortable symptoms to the target user, receiving the target uncomfortable symptoms fed back by the target user, determining the disease of the target user according to the target uncomfortable symptoms, determining the target treatment scheme determined to be the disease in the big data cloud storage server, and intelligently reminding the target user to execute the target treatment scheme. The health condition of the user can be known in real time only by uploading the physical measurement data of the user in any time period.
Description
Technical Field
The invention relates to the technical field of human health management, in particular to a personal health management method and system based on artificial intelligence.
Background
Along with the improvement of living standard of people, more and more people's clothes and food and resident rows are higher-end and higher-grade, but the health problems that follow also are various, because work is busy and other individual reasons, people can not make effective management to own health condition in time, can only know own health condition at the whole body physical examination of company unified organization or evacuate to go the physical examination, this kind of method makes people can not know own health condition and potential sick condition in time, and then can not carry out reasonable treatment scheme to potential sick condition, the health of people has seriously been influenced.
Disclosure of Invention
In view of the above-mentioned problems, the present invention provides a personal health management method and system based on artificial intelligence to solve the problems mentioned in the background art that people cannot know their own physical conditions and potential diseased conditions in time, and further cannot perform a reasonable treatment scheme for the potential diseased conditions, which seriously affects the physical health of people.
An artificial intelligence based personal health management method comprises the following steps:
acquiring first body measurement data of a human body in a health state and second body measurement data of the human body in various disease states from a big data cloud storage server;
acquiring third physical measurement data uploaded by a target user, determining whether the target user is in a healthy state or not according to the third physical measurement data, if so, not needing subsequent operation, and otherwise, sending a request for acquiring uncomfortable symptoms to the target user;
receiving target discomfort symptoms fed back by the target user, and confirming the diagnosis of the target user to be ill according to the target discomfort symptoms;
and determining the target treatment scheme confirmed to be sick in the big data cloud storage server, and intelligently reminding a target user to execute the target treatment scheme.
Preferably, the acquiring, from the big data cloud storage server, first body measurement data of a human body in a healthy state and second body measurement data of the human body in various disease states includes:
logging in an online system of the big data cloud storage server through a preset account and a password;
searching in the online system by taking the human health function as a keyword to obtain a first search result;
acquiring first body measurement data of a human body in a health state from the first search result, and searching by taking disease function change as a keyword again after the acquisition is finished to obtain a second search result;
acquiring second anthropometric data of the human body under various disease states from the second search result;
and converting the first body measurement data and the second body measurement data into a json format, and storing the json format into a preset database after conversion.
Preferably, the obtaining third physical measurement data uploaded by the target user, and determining whether the target user is in a healthy state according to the third physical measurement data, if so, no subsequent operation is required, otherwise, a request for obtaining an uncomfortable symptom is sent to the target user, and the method includes:
comparing the third physical measurement data with the first physical measurement data, and confirming that the target user is in a healthy state when all current physical function data in the third physical measurement data are within all first preset physical function data ranges in the first physical measurement data; when a target number of current body function data in the third body measurement data is not within a first preset body function data range in the first body measurement data, preliminarily confirming that the target user is in a non-healthy state;
confirming whether the target number of current physical function data is within a second preset physical function data range in second physical measurement data in various disease states, and if so, further confirming that the target user is in a non-healthy state;
and generating an uncomfortable symptom questionnaire, and feeding back the uncomfortable symptom questionnaire to the target user.
Preferably, the receiving the target discomfort symptom fed back by the target user and confirming the diagnosis of the target user suffering from the disease according to the target discomfort symptom includes:
preliminarily confirming N suspected diseases of the target user according to the target discomfort symptom and the third physical measurement data;
analyzing the N suspected diseases, and counting a plurality of first discomfort symptoms except the target discomfort symptom in the N suspected diseases;
feeding back the plurality of first discomfort symptoms to a target user, and confirming the diagnosis illness of the target user according to a second discomfort symptom selected by the target user from the plurality of first discomfort symptoms and the target discomfort symptom.
Preferably, the determining, in the big data cloud storage server, the target treatment plan confirmed to be ill, and intelligently prompting a target user to execute the target treatment plan includes:
calling the historical cases confirmed to be ill and the historical treatment medicines of the historical cases;
combining the historical treatment medication and online physician recommendations to form the target treatment regimen;
setting a preset planning table, and setting different reminding voices for each time interval in the preset planning table;
generating a preset daily life track questionnaire, feeding back the preset daily life track questionnaire to the target user, receiving the preset daily life track questionnaire filled and fed back by the target user, and confirming the preset daily life track questionnaire as the target daily life track questionnaire of the target user;
setting an activity instruction for each time interval in the preset planning table according to the target quality scheme and the target life track questionnaire;
and after the setting is finished, storing the preset planning table, and reminding the target user to execute the target activity instruction in the target time interval on time according to the preset planning table.
Preferably, the method further comprises:
detecting health information of a target user in real time;
confirming a health problem to be faced by a target user according to the health information;
and intelligently planning work and rest exercises and diet for the target user according to the health problems to be faced by the target user.
Preferably, prior to combining the historical treatment medication and online physician recommendations to form the target treatment plan, the method further comprises:
constructing a pathogenic factor database of each disease and a guidance suggestion database of each disease according to the medical information;
acquiring a target pathogenic factor database and a target guidance suggestion database for confirming the disease, and generating an initial online doctor suggestion according to the content in the target pathogenic factor database and the content in the target guidance suggestion database;
acquiring a historical electronic medical record of the target user with the disease, and adaptively modifying the initial online doctor suggestion according to the historical electronic medical record of the disease to obtain a first target online doctor suggestion;
determining the potential disease risk level of the target user according to the third physical measurement data uploaded by the target user;
adaptively modifying the first target online doctor suggestion according to the potential disease risk level of the target user to obtain a second target online doctor suggestion;
confirming the second target online doctor suggestion as a final online doctor suggestion for the target user.
Preferably, the determining the potential disease risk level of the target user according to the third physical measurement data uploaded by the target user includes:
generating a disease body measurement data set by collecting preset body measurement data;
counting uncertainty risk factors in the disease physical examination dataset;
constructing an emergent disease risk evaluation model by using the uncertain risk factors;
inputting the third physical measurement data into the sudden illness risk model to confirm the sudden illness evaluation probability of the target user;
confirming whether the sudden illness evaluation probability is larger than or equal to a preset probability, and if so, determining that the potential illness risk level of the target user is high;
if the sudden illness probability is smaller than the preset probability, analyzing the third physical measurement data, confirming whether each item of physical function data in the third physical measurement data is in the range of the standard physical function data of the human body, and if so, confirming that the potential illness risk of the target user is low;
if the third physical measurement data contains more than the preset number of physical function data which are not in the corresponding range, determining whether the target user has habit of smoking and drinking;
and if the target user is confirmed to have habits of smoking and drinking, determining the association degree of the preset number of physical function data and the smoking and drinking, confirming that the potential risk of the target user is medium when the association degree of the preset number of physical function data and the smoking and drinking is greater than or equal to a first preset threshold, and confirming that the potential risk of the target user is high when the association degree of the preset number of physical function data and the smoking and drinking is less than the first preset threshold.
Preferably, the step of acquiring third physical measurement data uploaded by the target user and determining whether the target user is in a healthy state according to the third physical measurement data includes:
analyzing the third body measurement data, and counting target detection data which are not in a normal range in the third body measurement data;
determining a specific value of each target detection data;
acquiring the temperature and the humidity of the environment where a target user is located, and determining the influence factor of the external environment on each target detection data according to the temperature and the humidity;
calculating the truth degree of each target detection data according to the specific value of each target detection data and the influence factor of the external environment on the detection data:
wherein k isiExpressed as the degree of truth, h, of the ith target detection dataiExpressed as a specific value of the ith target detection data, TiExpressed as the maximum value in the normal range corresponding to the ith target detection data, e is expressed as a natural constant with the value of 2.72, and thetaiFor the ith target detection, expressed as the ambient environmentThe influence factor of the data, log represents logarithm, a represents the working intensity of the target user, and the value is [0.5,1 ]]And b represents the sleep quality of the target user and takes the value of [0.5, 1%]；
Determining whether the truth of each target detection data is greater than or equal to a preset truth, if so, determining that the reliability of each target detection data is high, otherwise, counting first target detection data with the truth less than the preset truth, detecting the first target detection data of a target user again, obtaining second target detection data of the target user, and replacing the first target detection data with the second target detection data;
after the replacement is finished, calculating the health degree of the target user according to the replaced third physical measurement data:
wherein F is the health degree of the target user, N is the number of the detection data in the third volume detection data, and RjIs expressed as the parameter value, R ', of the j-th detection data in the third volume measurement data'jThe j is represented as the parameter value of the j detection data in the first measured data, Y () is represented as a preset correlation degree calculation function, p is represented as the physique intensity of the target user, and the value is [0.8,1 ]]δ is expressed as the immune attenuation coefficient of the target user;
and determining whether the health degree of the target user is greater than or equal to a second preset threshold, if so, determining that the target user is in a healthy state, and otherwise, determining that the target user is in a non-healthy state.
An artificial intelligence based personal health management system, the system comprising:
the acquisition module is used for acquiring first body measurement data of a human body in a health state and second body measurement data of the human body in various disease states from the big data cloud storage server;
the determining module is used for acquiring third physical measurement data uploaded by the target user, determining whether the target user is in a healthy state or not according to the third physical measurement data, if so, not needing subsequent operation, and otherwise, sending a request for acquiring uncomfortable symptoms to the target user;
the confirmation module is used for receiving the target discomfort symptom fed back by the target user and confirming the diagnosis illness of the target user according to the target discomfort symptom;
and the reminding module is used for determining the target treatment scheme confirmed to be sick in the big data cloud storage server and intelligently reminding a target user to execute the target treatment scheme.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
FIG. 1 is a flow chart of the present invention for a personal health management method based on artificial intelligence;
FIG. 2 is another flowchart of the present invention for a personal health management method based on artificial intelligence;
FIG. 3 is a flowchart of another embodiment of a method for personal health management based on artificial intelligence;
FIG. 4 is a schematic structural diagram of an artificial intelligence-based personal health management system according to the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
Along with the improvement of living standard of people, more and more people's clothes and food and resident rows are higher-end and higher-grade, but the health problems that follow also are various, because work is busy and other individual reasons, people can not make effective management to own health condition in time, can only know own health condition at the whole body physical examination of company unified organization or evacuate to go the physical examination, this kind of method makes people can not know own health condition and potential sick condition in time, and then can not carry out reasonable treatment scheme to potential sick condition, the health of people has seriously been influenced. In order to solve the above problems, the present embodiment discloses a personal health management method based on artificial intelligence.
An artificial intelligence based personal health management method, as shown in fig. 1, comprises the following steps:
step S101, acquiring first body measurement data of a human body in a healthy state and second body measurement data of the human body in various disease states from a big data cloud storage server;
step S102, third physical measurement data uploaded by a target user is obtained, whether the target user is in a healthy state or not is determined according to the third physical measurement data, if yes, follow-up operation is not needed, and if not, a request for obtaining uncomfortable symptoms is sent to the target user;
step S103, receiving the target discomfort symptom fed back by the target user, and confirming the diagnosis illness of the target user according to the target discomfort symptom;
step S104, determining the target treatment scheme confirmed to be sick in the big data cloud storage server, and intelligently reminding a target user to execute the target treatment scheme.
The working principle of the technical scheme is as follows: acquiring first physical measurement data of a human body in a healthy state and second physical measurement data of the human body in various disease states from a big data cloud storage server, acquiring third physical measurement data uploaded by a target user, determining whether the target user is in a healthy state according to the third physical measurement data, if so, not needing subsequent operation, otherwise, sending a request for acquiring uncomfortable symptoms to the target user, receiving the target uncomfortable symptoms fed back by the target user, determining the illness of the target user according to the target uncomfortable symptoms, determining the objective treatment scheme for determining the illness in the big data cloud storage server, and intelligently reminding the target user to execute the objective treatment scheme;
in this embodiment, the third physical measurement data uploaded by the target user may be obtained by using ordinary medical devices, such as a thermometer, a blood glucose meter, a blood pressure meter, and the like, to measure health data of the target user, and upload the health data by using a mobile phone terminal.
The beneficial effects of the above technical scheme are: the health state of the target user can be quickly and accurately evaluated by acquiring the body measurement data uploaded by the target user in real time, and the definite illness of the target user is determined, so that the user can know the health condition of the user in real time only by uploading the body measurement data of the user in any time period, further, after the definite illness of the target user is confirmed, the treatment scheme corresponding to the definite illness is determined, the target user is intelligently reminded to execute the treatment scheme, the supervision effect can be achieved, the user is ensured to strictly execute the treatment scheme, the body health of the target user is ensured, and the problems that in the prior art, the user cannot timely know the body condition and the potential illness condition, the reasonable treatment scheme cannot be carried out according to the potential illness condition, and the body health of the user is seriously influenced are solved.
In one embodiment, the acquiring, from a big data cloud storage server, first measured data of a human body in a healthy state and second measured data of the human body in various disease states includes:
logging in an online system of the big data cloud storage server through a preset account and a password;
searching in the online system by taking the human health function as a keyword to obtain a first search result;
acquiring first body measurement data of a human body in a health state from the first search result, and searching by taking disease function change as a keyword again after the acquisition is finished to obtain a second search result;
acquiring second anthropometric data of the human body under various disease states from the second search result;
and converting the first body measurement data and the second body measurement data into a json format, and storing the json format into a preset database after conversion.
The beneficial effects of the above technical scheme are: all human body function data related to human health and diseases can be searched in a large range by adopting a keyword searching mode, and further the integrity and accuracy of the first body measurement data and the second body measurement data are ensured. The accuracy of the comparison parameters is ensured, and the human health state of the target user can be accurately evaluated subsequently.
In one embodiment, the obtaining third physical measurement data uploaded by the target user, determining whether the target user is in a healthy state according to the third physical measurement data, if so, no subsequent operation is required, otherwise, sending a request for obtaining an uncomfortable symptom to the target user includes:
comparing the third physical measurement data with the first physical measurement data, and confirming that the target user is in a healthy state when all current physical function data in the third physical measurement data are within all first preset physical function data ranges in the first physical measurement data; when a target number of current body function data in the third body measurement data is not within a first preset body function data range in the first body measurement data, preliminarily confirming that the target user is in a non-healthy state;
confirming whether the target number of current physical function data is within a second preset physical function data range in second physical measurement data in various disease states, and if so, further confirming that the target user is in a non-healthy state;
and generating an uncomfortable symptom questionnaire, and feeding back the uncomfortable symptom questionnaire to the target user.
The beneficial effects of the above technical scheme are: the health state of the target user is determined by carrying out double judgment on the third body measurement data of the target user, so that the situation that the body measurement data fluctuates and is mistakenly judged due to natural motion of the target user can be avoided, and the judgment accuracy is ensured.
In one embodiment, as shown in fig. 2, the receiving the target discomfort symptom fed back by the target user, and confirming the diagnosis of the disease of the target user according to the target discomfort symptom comprises:
step S201, preliminarily confirming N suspected diseases of the target user according to the target discomfort symptom and the third body measurement data;
step S202, analyzing the N suspected diseases, and counting a plurality of first uncomfortable symptoms except the target uncomfortable symptom in the N suspected diseases;
step S203, feeding back the plurality of first discomfort symptoms to a target user, and confirming the confirmed diagnosis of the target user to be ill according to a second discomfort symptom selected by the target user from the plurality of first discomfort symptoms and the target discomfort symptom.
The beneficial effects of the above technical scheme are: the confirmed diagnosis and illness of the target user can be determined in a checking mode, so that the uncomfortable symptoms of the target user can be comprehensively known, the confirmed diagnosis and illness of the target user can be accurately determined according to the uncomfortable symptoms, and the accuracy of judgment is further improved.
In one embodiment, the determining, in the big data cloud storage server, the target treatment plan confirmed to be ill intelligently prompting a target user to execute the target treatment plan includes:
calling the historical cases confirmed to be ill and the historical treatment medicines of the historical cases;
combining the historical treatment medication and online physician recommendations to form the target treatment regimen;
setting a preset planning table, and setting different reminding voices for each time interval in the preset planning table;
generating a preset daily life track questionnaire, feeding back the preset daily life track questionnaire to the target user, receiving the preset daily life track questionnaire filled and fed back by the target user, and confirming the preset daily life track questionnaire as the target daily life track questionnaire of the target user;
setting an activity instruction for each time interval in the preset planning table according to the target quality scheme and the target life track questionnaire;
and after the setting is finished, storing the preset planning table, and reminding the target user to execute the target activity instruction in the target time interval on time according to the preset planning table.
The beneficial effects of the above technical scheme are: the daily preset planning table of the target user is intelligently set, and planning is performed in a targeted manner according to the living habits and the living modes of the target user, so that the experience of the target user is improved.
In one embodiment, as shown in fig. 3, the method further comprises:
s301, detecting health information of a target user in real time;
step S302, confirming the health problem to be faced by the target user according to the health information;
and S303, intelligently planning work and rest exercises and diet for the target user according to the health problems to be faced by the target user.
The beneficial effects of the above technical scheme are: the health problems to be faced by the target user can be evaluated by detecting the health information of the target user in real time, so that the target user can do preventive work, and further, the target user can avoid the health problems from the aspect of daily behaviors to a certain extent by planning work and rest exercises and diet for the target user, so that the experience of the target user is further improved.
In one embodiment, prior to combining the historical treatment medications and online physician recommendations to form the target treatment regimen, the method further comprises:
constructing a pathogenic factor database of each disease and a guidance suggestion database of each disease according to the medical information;
acquiring a target pathogenic factor database and a target guidance suggestion database for confirming the disease, and generating an initial online doctor suggestion according to the content in the target pathogenic factor database and the content in the target guidance suggestion database;
acquiring a historical electronic medical record of the target user with the disease, and adaptively modifying the initial online doctor suggestion according to the historical electronic medical record of the disease to obtain a first target online doctor suggestion;
determining the potential disease risk level of the target user according to the third physical measurement data uploaded by the target user;
adaptively modifying the first target online doctor suggestion according to the potential disease risk level of the target user to obtain a second target online doctor suggestion;
confirming the second target online doctor suggestion as a final online doctor suggestion for the target user.
The beneficial effects of the above technical scheme are: medicine contraindications or contraindications of the target user can be known according to historical disease conditions of the target user, and the initial online doctor suggestion can be adaptively modified according to the influence reasons so as to ensure that the final online doctor suggestion meets the requirements of the target user and does not have other influences on the health of the target user, and further ensure the physical health of the target user.
In one embodiment, the determining the potential disease risk level of the target user according to the third measurement data uploaded by the target user includes:
generating a disease body measurement data set by collecting preset body measurement data;
counting uncertainty risk factors in the disease physical examination dataset;
constructing an emergent disease risk evaluation model by using the uncertain risk factors;
inputting the third physical measurement data into the sudden illness risk model to confirm the sudden illness evaluation probability of the target user;
confirming whether the sudden illness evaluation probability is larger than or equal to a preset probability, and if so, determining that the potential illness risk level of the target user is high;
if the sudden illness probability is smaller than the preset probability, analyzing the third physical measurement data, confirming whether each item of physical function data in the third physical measurement data is in the range of the standard physical function data of the human body, and if so, confirming that the potential illness risk of the target user is low;
if the third physical measurement data contains more than the preset number of physical function data which are not in the corresponding range, determining whether the target user has habit of smoking and drinking;
and if the target user is confirmed to have habits of smoking and drinking, determining the association degree of the preset number of physical function data and the smoking and drinking, confirming that the potential risk of the target user is medium when the association degree of the preset number of physical function data and the smoking and drinking is greater than or equal to a first preset threshold, and confirming that the potential risk of the target user is high when the association degree of the preset number of physical function data and the smoking and drinking is less than the first preset threshold.
The beneficial effects of the above technical scheme are: whether the target user has the risk of the sudden illness can be effectively evaluated according to the third physical measurement data of the target user by constructing the sudden illness risk evaluation model, so that the target user can prepare according to an evaluation result, the life safety of the target user is further ensured, further, whether abnormal data in the third physical measurement data are normal or not can be analyzed according to the daily behavior habit of the target user, the occurrence of the false recognition condition can be avoided, the potential illness risk level of the target user can be effectively and accurately evaluated, and the accuracy of the data is improved.
In one embodiment, the step of acquiring third physical measurement data uploaded by the target user and determining whether the target user is in a health state according to the third physical measurement data includes:
analyzing the third body measurement data, and counting target detection data which are not in a normal range in the third body measurement data;
determining a specific value of each target detection data;
acquiring the temperature and the humidity of the environment where a target user is located, and determining the influence factor of the external environment on each target detection data according to the temperature and the humidity;
calculating the truth degree of each target detection data according to the specific value of each target detection data and the influence factor of the external environment on the detection data:
wherein k isiExpressed as the degree of truth, h, of the ith target detection dataiExpressed as a specific value of the ith target detection data, TiExpressed as the maximum value in the normal range corresponding to the ith target detection data, e is expressed as a natural constant with the value of 2.72, and thetaiThe data is expressed as an influence factor of an external environment on ith target detection data, log is expressed as logarithm, a is expressed as the working intensity of a target user, and the value is [0.5, 1%]And b represents the sleep quality of the target user and takes the value of [0.5, 1%]；
Determining whether the truth of each target detection data is greater than or equal to a preset truth, if so, determining that the reliability of each target detection data is high, otherwise, counting first target detection data with the truth less than the preset truth, detecting the first target detection data of a target user again, obtaining second target detection data of the target user, and replacing the first target detection data with the second target detection data;
after the replacement is finished, calculating the health degree of the target user according to the replaced third physical measurement data:
wherein F is the health degree of the target user, N is the number of the detection data in the third volume detection data, and RjExpressed as the parameter of the jth detected data in the third measured dataValue, R'jThe j is represented as the parameter value of the j detection data in the first measured data, Y () is represented as a preset correlation degree calculation function, p is represented as the physique intensity of the target user, and the value is [0.8,1 ]]δ is expressed as the immune attenuation coefficient of the target user;
and determining whether the health degree of the target user is greater than or equal to a second preset threshold, if so, determining that the target user is in a healthy state, and otherwise, determining that the target user is in a non-healthy state.
The beneficial effects of the above technical scheme are: whether each item of detection data accords with the actual condition of the target user under the influence of the external environment factor and the self function condition of the target user can be guaranteed by calculating the truth of each item of detection data in the third physical measurement data, and then the physical measurement data is obtained again when the actual condition is not met, so that guarantee is provided for subsequently judging whether the target user is healthy.
This embodiment also discloses a personal health management system based on artificial intelligence, as shown in fig. 4, the system includes:
an obtaining module 401, configured to obtain, from a big data cloud storage server, first measured data of a human body in a healthy state and second measured data of the human body in various disease states;
a determining module 402, configured to obtain third physical measurement data uploaded by a target user, and determine whether the target user is in a healthy state according to the third physical measurement data, if so, no subsequent operation is required, and otherwise, a request for obtaining an uncomfortable symptom is sent to the target user;
a confirmation module 403, configured to receive the target discomfort symptom fed back by the target user, and confirm that the diagnosis of the target user is ill according to the target discomfort symptom;
a reminding module 404, configured to determine the target treatment plan confirmed to be ill in the big data cloud storage server, and intelligently remind a target user to execute the target treatment plan.
The working principle and the advantageous effects of the above technical solution have been explained in the method claims, and are not described herein again.
It will be understood by those skilled in the art that the first and second terms of the present invention refer to different stages of application.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A personal health management method based on artificial intelligence is characterized by comprising the following steps:
acquiring first body measurement data of a human body in a health state and second body measurement data of the human body in various disease states from a big data cloud storage server;
acquiring third physical measurement data uploaded by a target user, determining whether the target user is in a healthy state or not according to the third physical measurement data, if so, not needing subsequent operation, and otherwise, sending a request for acquiring uncomfortable symptoms to the target user;
receiving target discomfort symptoms fed back by the target user, and confirming the diagnosis of the target user to be ill according to the target discomfort symptoms;
and determining the target treatment scheme confirmed to be sick in the big data cloud storage server, and intelligently reminding a target user to execute the target treatment scheme.
2. The method for personal health management based on artificial intelligence of claim 1, wherein the obtaining of the first measured data of the human body in the health state and the second measured data of the human body in various disease states from the big data cloud storage server comprises:
logging in an online system of the big data cloud storage server through a preset account and a password;
searching in the online system by taking the human health function as a keyword to obtain a first search result;
acquiring first body measurement data of a human body in a health state from the first search result, and searching by taking disease function change as a keyword again after the acquisition is finished to obtain a second search result;
acquiring second anthropometric data of the human body under various disease states from the second search result;
and converting the first body measurement data and the second body measurement data into a json format, and storing the json format into a preset database after conversion.
3. The method for personal health management based on artificial intelligence of claim 1, wherein the obtaining of third physical measurement data uploaded by a target user, determining whether the target user is in a healthy state according to the third physical measurement data, if yes, no subsequent operation is required, otherwise, sending a request for obtaining an uncomfortable symptom to the target user comprises:
comparing the third physical measurement data with the first physical measurement data, and confirming that the target user is in a healthy state when all current physical function data in the third physical measurement data are within all first preset physical function data ranges in the first physical measurement data; when a target number of current body function data in the third body measurement data is not within a first preset body function data range in the first body measurement data, preliminarily confirming that the target user is in a non-healthy state;
confirming whether the target number of current physical function data is within a second preset physical function data range in second physical measurement data in various disease states, and if so, further confirming that the target user is in a non-healthy state;
and generating an uncomfortable symptom questionnaire, and feeding back the uncomfortable symptom questionnaire to the target user.
4. The method for personal health management based on artificial intelligence of claim 1, wherein the receiving the target discomfort symptom fed back by the target user, and confirming the diagnosis of the disease of the target user according to the target discomfort symptom comprises:
preliminarily confirming N suspected diseases of the target user according to the target discomfort symptom and the third physical measurement data;
analyzing the N suspected diseases, and counting a plurality of first discomfort symptoms except the target discomfort symptom in the N suspected diseases;
feeding back the plurality of first discomfort symptoms to a target user, and confirming the diagnosis illness of the target user according to a second discomfort symptom selected by the target user from the plurality of first discomfort symptoms and the target discomfort symptom.
5. The artificial intelligence based personal health management method of claim 1, wherein the determining the target treatment plan confirming the illness in the big data cloud storage server intelligently prompts a target user to execute the target treatment plan comprises:
calling the historical cases confirmed to be ill and the historical treatment medicines of the historical cases;
combining the historical treatment medication and online physician recommendations to form the target treatment regimen;
setting a preset planning table, and setting different reminding voices for each time interval in the preset planning table;
generating a preset daily life track questionnaire, feeding back the preset daily life track questionnaire to the target user, receiving the preset daily life track questionnaire filled and fed back by the target user, and confirming the preset daily life track questionnaire as the target daily life track questionnaire of the target user;
setting an activity instruction for each time interval in the preset planning table according to the target quality scheme and the target life track questionnaire;
and after the setting is finished, storing the preset planning table, and reminding the target user to execute the target activity instruction in the target time interval on time according to the preset planning table.
6. The artificial intelligence based personal health management method of claim 1, further comprising:
detecting health information of a target user in real time;
confirming a health problem to be faced by a target user according to the health information;
and intelligently planning work and rest exercises and diet for the target user according to the health problems to be faced by the target user.
7. The artificial intelligence based personal health management method of claim 5, wherein prior to combining the historical treatment medications and online physician recommendations to form the target treatment plan, the method further comprises:
constructing a pathogenic factor database of each disease and a guidance suggestion database of each disease according to the medical information;
acquiring a target pathogenic factor database and a target guidance suggestion database for confirming the disease, and generating an initial online doctor suggestion according to the content in the target pathogenic factor database and the content in the target guidance suggestion database;
acquiring a historical electronic medical record of the target user with the disease, and adaptively modifying the initial online doctor suggestion according to the historical electronic medical record of the disease to obtain a first target online doctor suggestion;
determining the potential disease risk level of the target user according to the third physical measurement data uploaded by the target user;
adaptively modifying the first target online doctor suggestion according to the potential disease risk level of the target user to obtain a second target online doctor suggestion;
confirming the second target online doctor suggestion as a final online doctor suggestion for the target user.
8. The method for personal health management based on artificial intelligence of claim 7, wherein the determining the risk level of the target user according to the third physical measurement data uploaded by the target user comprises:
generating a disease body measurement data set by collecting preset body measurement data;
counting uncertainty risk factors in the disease physical examination dataset;
constructing an emergent disease risk evaluation model by using the uncertain risk factors;
inputting the third physical measurement data into the sudden illness risk model to confirm the sudden illness evaluation probability of the target user;
confirming whether the sudden illness evaluation probability is larger than or equal to a preset probability, and if so, determining that the potential illness risk level of the target user is high;
if the sudden illness probability is smaller than the preset probability, analyzing the third physical measurement data, confirming whether each item of physical function data in the third physical measurement data is in the range of the standard physical function data of the human body, and if so, confirming that the potential illness risk of the target user is low;
if the third physical measurement data contains more than the preset number of physical function data which are not in the corresponding range, determining whether the target user has habit of smoking and drinking;
and if the target user is confirmed to have habits of smoking and drinking, determining the association degree of the preset number of physical function data and the smoking and drinking, confirming that the potential risk of the target user is medium when the association degree of the preset number of physical function data and the smoking and drinking is greater than or equal to a first preset threshold, and confirming that the potential risk of the target user is high when the association degree of the preset number of physical function data and the smoking and drinking is less than the first preset threshold.
9. The method for personal health management based on artificial intelligence of claim 1, wherein the step of obtaining third physical measurement data uploaded by the target user and determining whether the target user is in a healthy state according to the third physical measurement data comprises:
analyzing the third body measurement data, and counting target detection data which are not in a normal range in the third body measurement data;
determining a specific value of each target detection data;
acquiring the temperature and the humidity of the environment where a target user is located, and determining the influence factor of the external environment on each target detection data according to the temperature and the humidity;
calculating the truth degree of each target detection data according to the specific value of each target detection data and the influence factor of the external environment on the detection data:
wherein k isiExpressed as the degree of truth, h, of the ith target detection dataiExpressed as a specific value of the ith target detection data, TiExpressed as the maximum value in the normal range corresponding to the ith target detection data, e is expressed as a natural constant with the value of 2.72, and thetaiThe data is expressed as an influence factor of an external environment on ith target detection data, log is expressed as logarithm, a is expressed as the working intensity of a target user, and the value is [0.5, 1%]And b represents the sleep quality of the target user and takes the value of [0.5, 1%]；
Determining whether the truth of each target detection data is greater than or equal to a preset truth, if so, determining that the reliability of each target detection data is high, otherwise, counting first target detection data with the truth less than the preset truth, detecting the first target detection data of a target user again, obtaining second target detection data of the target user, and replacing the first target detection data with the second target detection data;
after the replacement is finished, calculating the health degree of the target user according to the replaced third physical measurement data:
wherein F is the health degree of the target user, N is the number of the detection data in the third volume detection data, and RjIs expressed as the parameter value, R ', of the j-th detection data in the third volume measurement data'jThe j is represented as the parameter value of the j detection data in the first measured data, Y () is represented as a preset correlation degree calculation function, p is represented as the physique intensity of the target user, and the value is [0.8,1 ]]δ is expressed as the immune attenuation coefficient of the target user;
and determining whether the health degree of the target user is greater than or equal to a second preset threshold, if so, determining that the target user is in a healthy state, and otherwise, determining that the target user is in a non-healthy state.
10. An artificial intelligence based personal health management system, comprising:
the acquisition module is used for acquiring first body measurement data of a human body in a health state and second body measurement data of the human body in various disease states from the big data cloud storage server;
the determining module is used for acquiring third physical measurement data uploaded by the target user, determining whether the target user is in a healthy state or not according to the third physical measurement data, if so, not needing subsequent operation, and otherwise, sending a request for acquiring uncomfortable symptoms to the target user;
the confirmation module is used for receiving the target discomfort symptom fed back by the target user and confirming the diagnosis illness of the target user according to the target discomfort symptom;
and the reminding module is used for determining the target treatment scheme confirmed to be sick in the big data cloud storage server and intelligently reminding a target user to execute the target treatment scheme.
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| CN115938540A (en) * | 2022-12-14 | 2023-04-07 | 正德(海南)康复医疗中心管理有限责任公司 | Scene interaction children rehabilitation training system based on cloud platform |
| CN115938540B (en) * | 2022-12-14 | 2024-02-27 | 中科创新(深圳)技术控股有限公司 | Scene interaction children rehabilitation training system based on cloud platform |
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