CN112652404A - Traditional Chinese medicine self-service dialectical monitoring method based on big data artificial intelligence and robot - Google Patents
Traditional Chinese medicine self-service dialectical monitoring method based on big data artificial intelligence and robot Download PDFInfo
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Abstract
A traditional Chinese medicine self-service dialectical monitoring method and a robot based on big data artificial intelligence comprise the following steps: determining a target user; acquiring a target disease model; the step of looking for and asking for the target disease. According to the method, the system and the robot, the traditional Chinese medicine and the artificial intelligence are combined, diagnosis is carried out from four aspects of the observation and the study, corresponding deep learning neural network models are respectively established, the training of the deep learning models which are observed and studied is respectively carried out according to the diagnosis results of the traditional Chinese medicine experts, the deep learning models which contain the experience of the traditional Chinese medicine experts are obtained, and the four models which are observed and studied are integrated into one model for training and prediction through the deep learning, so that the resources of the traditional Chinese medicine experts can be saved, the experience of the traditional Chinese medicine experts can be inherited, the traditional Chinese medicine can be inherited and developed, and the accuracy of the prediction is improved.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a traditional Chinese medicine self-service dialectical monitoring method and a robot based on big data artificial intelligence.
Background
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art: in the prior art, in the aspect of monitoring new major infectious diseases, if a large number of users distributed all over the country realize self-service report of the users and big data acquisition of monitoring results, the monitoring accuracy and reliability are low.
Accordingly, the prior art is yet to be improved and developed.
Disclosure of Invention
Based on this, it is necessary to provide a self-help dialectical traditional Chinese medicine monitoring method and a robot based on big data artificial intelligence to solve the problem of low accuracy and reliability in monitoring of newly-discovered major infectious diseases in the prior art, aiming at the defects or shortcomings in the prior art.
In a first aspect, an embodiment of the present invention provides an artificial intelligence method, where the method includes:
a target user determination step: taking a user to be monitored as a target user;
target disease determination step: taking a disease to be monitored as a target disease;
acquiring a target disease model: acquiring a deep learning neural network model for inspection and diagnosis of a target disease, a deep learning neural network model for auscultation and diagnosis of the target disease, an inquiry and diagnosis deep learning neural network model for the target disease and a cutting and diagnosis deep learning neural network model for the target disease;
the step of looking for and asking for the target disease: taking data of a target user as input, taking output obtained through calculation of a deep learning neural network model for inspection and diagnosis of a target disease as first output, taking output obtained through calculation of a deep learning neural network model for auscultation and diagnosis of the target disease as second output, taking output obtained through calculation of a deep learning neural network model for inquiry and diagnosis of the target disease as third output, and taking output obtained through calculation of a deep learning neural network model for cutting and diagnosis of the target disease as fourth output; taking the first output, the second output, the third output and the fourth output as input, taking the probability that the user has the target disease as expected output, and training a deep learning neural network model to obtain a looking and smelling deep learning neural network model of the target disease; when the target user probability learning neural network model is used, data of the target user are used as input, and the obtained output is used as the probability that the target user suffers from the target disease through the calculation of the target disease looking, smelling and deep learning neural network model; and if the probability that the target user has the target disease exceeds a preset probability threshold, the target user has the target disease.
Preferably, the method further comprises:
acquiring a disease investigation model: acquiring a disease investigation and inspection deep learning neural network model, a disease investigation and auscultation deep learning neural network model, a disease investigation and inquiry deep learning neural network model and a disease investigation and diagnosis deep learning neural network model;
the step of looking for and asking for disease investigation: taking data of a target user as input, taking output obtained through calculation of a disease investigation and inspection deep learning neural network model as first output, taking output obtained through calculation of a disease investigation and auscultation deep learning neural network model as second output, taking output obtained through calculation of a disease investigation and inspection deep learning neural network model as third output, and taking output obtained through calculation of a disease investigation and inspection deep learning neural network model as fourth output; taking the first output, the second output, the third output and the fourth output as inputs, taking a preset plurality of diseases possibly suffered by the user and the probability of suffering from each disease as expected outputs, and training a deep learning neural network model to obtain a disease investigation, observation and inquiry deep learning neural network model; when the device is used, data of a target user are used as input, and the obtained output is used as the probability that the user possibly suffers from a plurality of preset diseases and each disease through the calculation of a disease investigation, inquiry, study and deep learning neural network model.
Preferably, the method further comprises:
a health condition model obtaining step: acquiring a health condition inspection deep learning neural network model, a health condition auscultation deep learning neural network model, a health condition inquiry deep learning neural network model and a health condition palpation deep learning neural network model;
the step of observing and asking for health conditions: taking data of a target user as input, taking output obtained through calculation of the health condition inspection deep learning neural network model as first output, taking output obtained through calculation of the health condition auscultation deep learning neural network model as second output, taking output obtained through calculation of the health condition inspection deep learning neural network model as third output, and taking output obtained through calculation of the health condition inspection deep learning neural network model as fourth output; taking the first output, the second output, the third output and the fourth output as input, taking the health condition of the user as expected output, and training a deep learning neural network model to obtain a health condition looking and asking deep learning neural network model; when the health condition learning method is used, data of a target user are used as input, calculation of the neural network model is deeply learned through health condition observation and hearing, and obtained output is used as the health condition of the user.
Preferably, the method further comprises:
a user image acquisition step: acquiring a tongue image or/and a face image or/and a hand image or/and an image of other parts of a body of a target user as an image of the target user;
a user sound acquisition step: acquiring speaking voice or/and breathing voice or/and lung voice of a target user or voice emitted or generated by other organs of a body as the voice of the target user;
a user condition obtaining step: acquiring answers of a target user to a preset question set as the condition of the target user;
a user sign obtaining step: acquiring pulse data or/and heartbeat data or/and blood oxygen data of a target user as physical signs of the target user;
target disease inspection step: taking an image of a user as input, taking the probability that the user has the target disease as expected output, and training a deep learning neural network model to obtain a prospective deep learning neural network model of the target disease; when the target disease detection method is used, the image of a target user is used as input, the calculation of the target disease observation deep learning neural network model is carried out, and the obtained output is used as the probability that the target user has the target disease; if the probability that the target user has the target disease exceeds a preset probability threshold, the target user has the target disease;
auscultation step of target diseases: taking the sound of a user as input, taking the probability that the user is suffered from the target disease as expected output, and training the deep learning neural network model to obtain an auscultation deep learning neural network model of the target disease; when the device is used, the sound of a target user is used as input, the calculation of the neural network model is learned through the auscultation depth of a target disease, and the obtained output is used as the probability that the target user suffers from the target disease; if the probability that the target user has the target disease exceeds a preset probability threshold, the target user has the target disease;
and (3) target disease inquiry step: taking the condition of a user as input, taking the probability that the user has the target disease as expected output, and training the deep learning neural network model to obtain an inquiry deep learning neural network model of the target disease; when the device is used, the condition of a target user is used as input, the calculation of the neural network model is deeply learned through the inquiry of a target disease, and the obtained output is used as the probability that the target user has the target disease; if the probability that the target user has the target disease exceeds a preset probability threshold, the target user has the target disease;
and (3) the step of diagnosing the target disease: taking the physical sign of a user as input, taking the probability that the user has the target disease as expected output, and training the deep learning neural network model to obtain a diagnosis deep learning neural network model of the target disease; when the device is used, the physical signs of a target user are used as input, and the obtained output is used as the probability that the target user suffers from the target disease through the calculation of the cutting diagnosis deep learning neural network model of the target disease; and if the probability that the target user has the target disease exceeds a preset probability threshold, the target user has the target disease.
Preferably, the method further comprises:
disease investigation and inspection: taking an image of a user as input, taking a plurality of preset diseases possibly suffered by the user and the probability of suffering from each disease as expected output, and training a deep learning neural network model to obtain a disease investigation and inspection deep learning neural network model; when the device is used, the image of a target user is used as input, and the obtained output is used as the probability that the target user possibly suffers from a plurality of preset diseases and each disease through the calculation of a disease examination and inspection deep learning neural network model;
disease investigation auscultation step: taking the sound of a user as input, taking a plurality of preset diseases possibly suffered by the user and the probability of suffering from each disease as expected output, and training a deep learning neural network model to obtain a disease investigation auscultation deep learning neural network model; when the device is used, the sound of a target user is used as input, the calculation of a neural network model is studied through disease investigation and auscultation depth, and the obtained output is used as the probability that the target user possibly suffers from a plurality of preset diseases and each disease;
disease investigation and inquiry step: taking the condition of a user as input, taking a plurality of preset diseases possibly suffered by the user and the probability of suffering from each disease as expected output, and training a deep learning neural network model to obtain a disease investigation and inquiry deep learning neural network model; when the device is used, the condition of a target user is used as input, and the output obtained by calculating the disease investigation, inquiry and deep learning neural network model is used as the probability that the target user possibly suffers from a plurality of preset diseases and each disease;
disease investigation and diagnosis step: taking the physical signs of a user as input, taking a preset plurality of diseases possibly suffered by the user and the probability of suffering from each disease as expected output, and training a deep learning neural network model to obtain a disease investigation and diagnosis deep learning neural network model; when the device is used, the physical signs of a target user are used as input, the calculation of the neural network model is deeply learned through disease investigation and diagnosis, and the obtained output is used as the probability that the target user possibly suffers from a plurality of preset diseases and each disease.
Preferably, the method further comprises:
a health condition inspection step: taking an image of a user as input, taking the health condition of the user as expected output, and training a deep learning neural network model to obtain a health condition inspection deep learning neural network model; when the system is used, the image of a target user is used as input, the calculation of the health condition inspection deep learning neural network model is carried out, and the obtained output is used as the health condition of the target user;
auscultation of health conditions: taking the sound of a user as input, taking the health condition of the user as expected output, and training a deep learning neural network model to obtain a health condition auscultation deep learning neural network model; when the health condition auscultation deep learning neural network model is used, the sound of a target user is used as input, the calculation of the neural network model is deeply learned through health condition auscultation, and the obtained output is used as the health condition of the target user;
the health condition inquiry step: taking the condition of a user as input, taking the health condition of the user as expected output, and training a deep learning neural network model to obtain a health condition inquiry deep learning neural network model; when the health condition query system is used, the condition of a target user is used as input, the calculation of the neural network model is deeply learned through health condition inquiry, and the obtained output is used as the health condition of the target user;
the health condition diagnosis step: taking the physical signs of a user as input, taking the health condition of the user as expected output, and training a deep learning neural network model to obtain a health condition diagnosis deep learning neural network model; when the health condition diagnosis device is used, the physical signs of a target user are used as input, the calculation of the deep learning neural network model is performed through health condition diagnosis, and the obtained output is used as the health condition of the target user.
In a second aspect, an embodiment of the present invention provides an artificial intelligence system, where the system includes:
a target user determination module: taking a user to be monitored as a target user;
a target disease determination module: taking a disease to be monitored as a target disease;
a target disease model acquisition module: acquiring a deep learning neural network model for inspection and diagnosis of a target disease, a deep learning neural network model for auscultation and diagnosis of the target disease, an inquiry and diagnosis deep learning neural network model for the target disease and a cutting and diagnosis deep learning neural network model for the target disease;
the target disease observation and inquiry module comprises: taking data of a target user as input, taking output obtained through calculation of a deep learning neural network model for inspection and diagnosis of a target disease as first output, taking output obtained through calculation of a deep learning neural network model for auscultation and diagnosis of the target disease as second output, taking output obtained through calculation of a deep learning neural network model for inquiry and diagnosis of the target disease as third output, and taking output obtained through calculation of a deep learning neural network model for cutting and diagnosis of the target disease as fourth output; taking the first output, the second output, the third output and the fourth output as input, taking the probability that the user has the target disease as expected output, and training a deep learning neural network model to obtain a looking and smelling deep learning neural network model of the target disease; when the target user probability learning neural network model is used, data of the target user are used as input, and the obtained output is used as the probability that the target user suffers from the target disease through the calculation of the target disease looking, smelling and deep learning neural network model; and if the probability that the target user has the target disease exceeds a preset probability threshold, the target user has the target disease.
Preferably, the system further comprises:
a disease investigation model acquisition module: acquiring a disease investigation and inspection deep learning neural network model, a disease investigation and auscultation deep learning neural network model, a disease investigation and inquiry deep learning neural network model and a disease investigation and diagnosis deep learning neural network model;
the disease investigation is looked after and asked about the module: taking data of a target user as input, taking output obtained through calculation of a disease investigation and inspection deep learning neural network model as first output, taking output obtained through calculation of a disease investigation and auscultation deep learning neural network model as second output, taking output obtained through calculation of a disease investigation and inspection deep learning neural network model as third output, and taking output obtained through calculation of a disease investigation and inspection deep learning neural network model as fourth output; taking the first output, the second output, the third output and the fourth output as inputs, taking a preset plurality of diseases possibly suffered by the user and the probability of suffering from each disease as expected outputs, and training a deep learning neural network model to obtain a disease investigation, observation and inquiry deep learning neural network model; when the device is used, data of a target user are used as input, and the obtained output is used as the probability that the user possibly suffers from a plurality of preset diseases and each disease through the calculation of a disease investigation, inquiry, study and deep learning neural network model.
Preferably, the system further comprises:
a health condition model acquisition module: acquiring a health condition inspection deep learning neural network model, a health condition auscultation deep learning neural network model, a health condition inquiry deep learning neural network model and a health condition palpation deep learning neural network model;
health condition is looked after and is asked for module: taking data of a target user as input, taking output obtained through calculation of the health condition inspection deep learning neural network model as first output, taking output obtained through calculation of the health condition auscultation deep learning neural network model as second output, taking output obtained through calculation of the health condition inspection deep learning neural network model as third output, and taking output obtained through calculation of the health condition inspection deep learning neural network model as fourth output; taking the first output, the second output, the third output and the fourth output as input, taking the health condition of the user as expected output, and training a deep learning neural network model to obtain a health condition looking and asking deep learning neural network model; when the health condition learning method is used, data of a target user are used as input, calculation of the neural network model is deeply learned through health condition observation and hearing, and obtained output is used as the health condition of the user.
Preferably, the system further comprises:
a user image acquisition module: acquiring a tongue image or/and a face image or/and a hand image or/and an image of other parts of a body of a target user as an image of the target user;
a user sound acquisition module: acquiring speaking voice or/and breathing voice or/and lung voice of a target user or voice emitted or generated by other organs of a body as the voice of the target user;
a user condition acquisition module: acquiring answers of a target user to a preset question set as the condition of the target user;
the user sign acquisition module: acquiring pulse data or/and heartbeat data or/and blood oxygen data of a target user as physical signs of the target user;
target disease inspection module: taking an image of a user as input, taking the probability that the user has the target disease as expected output, and training a deep learning neural network model to obtain a prospective deep learning neural network model of the target disease; when the target disease detection method is used, the image of a target user is used as input, the calculation of the target disease observation deep learning neural network model is carried out, and the obtained output is used as the probability that the target user has the target disease; if the probability that the target user has the target disease exceeds a preset probability threshold, the target user has the target disease;
the target disease auscultation module: taking the sound of a user as input, taking the probability that the user is suffered from the target disease as expected output, and training the deep learning neural network model to obtain an auscultation deep learning neural network model of the target disease; when the device is used, the sound of a target user is used as input, the calculation of the neural network model is learned through the auscultation depth of a target disease, and the obtained output is used as the probability that the target user suffers from the target disease; if the probability that the target user has the target disease exceeds a preset probability threshold, the target user has the target disease;
target disease interrogation module: taking the condition of a user as input, taking the probability that the user has the target disease as expected output, and training the deep learning neural network model to obtain an inquiry deep learning neural network model of the target disease; when the device is used, the condition of a target user is used as input, the calculation of the neural network model is deeply learned through the inquiry of a target disease, and the obtained output is used as the probability that the target user has the target disease; if the probability that the target user has the target disease exceeds a preset probability threshold, the target user has the target disease;
the target disease diagnosis module: taking the physical sign of a user as input, taking the probability that the user has the target disease as expected output, and training the deep learning neural network model to obtain a diagnosis deep learning neural network model of the target disease; when the device is used, the physical signs of a target user are used as input, and the obtained output is used as the probability that the target user suffers from the target disease through the calculation of the cutting diagnosis deep learning neural network model of the target disease; and if the probability that the target user has the target disease exceeds a preset probability threshold, the target user has the target disease.
Preferably, the system further comprises:
disease investigation inspection module: taking an image of a user as input, taking a plurality of preset diseases possibly suffered by the user and the probability of suffering from each disease as expected output, and training a deep learning neural network model to obtain a disease investigation and inspection deep learning neural network model; when the device is used, the image of a target user is used as input, and the obtained output is used as the probability that the target user possibly suffers from a plurality of preset diseases and each disease through the calculation of a disease examination and inspection deep learning neural network model;
the auscultation module for disease investigation: taking the sound of a user as input, taking a plurality of preset diseases possibly suffered by the user and the probability of suffering from each disease as expected output, and training a deep learning neural network model to obtain a disease investigation auscultation deep learning neural network model; when the device is used, the sound of a target user is used as input, the calculation of a neural network model is studied through disease investigation and auscultation depth, and the obtained output is used as the probability that the target user possibly suffers from a plurality of preset diseases and each disease;
disease investigation module: taking the condition of a user as input, taking a plurality of preset diseases possibly suffered by the user and the probability of suffering from each disease as expected output, and training a deep learning neural network model to obtain a disease investigation and inquiry deep learning neural network model; when the device is used, the condition of a target user is used as input, and the output obtained by calculating the disease investigation, inquiry and deep learning neural network model is used as the probability that the target user possibly suffers from a plurality of preset diseases and each disease;
disease investigation and diagnosis module: taking the physical signs of a user as input, taking a preset plurality of diseases possibly suffered by the user and the probability of suffering from each disease as expected output, and training a deep learning neural network model to obtain a disease investigation and diagnosis deep learning neural network model; when the device is used, the physical signs of a target user are used as input, the calculation of the neural network model is deeply learned through disease investigation and diagnosis, and the obtained output is used as the probability that the target user possibly suffers from a plurality of preset diseases and each disease.
Preferably, the system further comprises:
health condition inspection module: taking an image of a user as input, taking the health condition of the user as expected output, and training a deep learning neural network model to obtain a health condition inspection deep learning neural network model; when the system is used, the image of a target user is used as input, the calculation of the health condition inspection deep learning neural network model is carried out, and the obtained output is used as the health condition of the target user;
health condition auscultation module: taking the sound of a user as input, taking the health condition of the user as expected output, and training a deep learning neural network model to obtain a health condition auscultation deep learning neural network model; when the health condition auscultation deep learning neural network model is used, the sound of a target user is used as input, the calculation of the neural network model is deeply learned through health condition auscultation, and the obtained output is used as the health condition of the target user;
health condition interrogation module: taking the condition of a user as input, taking the health condition of the user as expected output, and training a deep learning neural network model to obtain a health condition inquiry deep learning neural network model; when the health condition query system is used, the condition of a target user is used as input, the calculation of the neural network model is deeply learned through health condition inquiry, and the obtained output is used as the health condition of the target user;
health condition palpation module: taking the physical signs of a user as input, taking the health condition of the user as expected output, and training a deep learning neural network model to obtain a health condition diagnosis deep learning neural network model; when the health condition diagnosis device is used, the physical signs of a target user are used as input, the calculation of the deep learning neural network model is performed through health condition diagnosis, and the obtained output is used as the health condition of the target user.
In a third aspect, an embodiment of the present invention provides an artificial intelligence apparatus, where the apparatus includes the modules of the system in any one of the embodiments of the second aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method according to any one of the embodiments of the first aspect.
In a fifth aspect, an embodiment of the present invention provides a robot, including a memory, a processor, and an artificial intelligence robot program stored on the memory and executable on the processor, where the robot is the first artificial intelligence device in the first aspect, and the processor implements the steps of the method in any one of the first aspect when executing the program.
The traditional Chinese medicine self-service dialectical monitoring method and the robot based on big data artificial intelligence provided by the embodiment comprise the following steps: determining a target user; acquiring a target disease model; the step of looking for and asking for the target disease. According to the method, the system and the robot, the traditional Chinese medicine and the artificial intelligence are combined, diagnosis is carried out from four aspects of the observation and the study, corresponding deep learning neural network models are respectively established, the training of the deep learning models which are observed and studied is respectively carried out according to the diagnosis results of the traditional Chinese medicine experts, the deep learning models which contain the experience of the traditional Chinese medicine experts are obtained, and the four models which are observed and studied are integrated into one model for training and prediction through the deep learning, so that the resources of the traditional Chinese medicine experts can be saved, the experience of the traditional Chinese medicine experts can be inherited, the traditional Chinese medicine can be inherited and developed, and the accuracy of the prediction is improved.
Drawings
FIG. 1 is a flow chart of an artificial intelligence method provided by an embodiment of the invention;
FIG. 2 is a flow chart of an artificial intelligence method provided by an embodiment of the invention;
FIG. 3 is a flow chart of an artificial intelligence method provided by an embodiment of the invention;
FIG. 4 is a flow chart of an artificial intelligence method provided by an embodiment of the invention;
FIG. 5 is a flow chart of an artificial intelligence method provided by an embodiment of the invention;
FIG. 6 is a flow chart of an artificial intelligence method provided by an embodiment of the invention;
FIG. 7 is a block diagram of a system for big data based self-service forensic monitoring according to an embodiment of the present invention.
Detailed Description
The technical solutions in the examples of the present invention are described in detail below with reference to the embodiments of the present invention.
Basic embodiment of the invention
One embodiment of the present invention provides an artificial intelligence method, as shown in fig. 1, the method including: determining a target user; acquiring a target disease model; the step of looking for and asking for the target disease. The technical effects are as follows: the method combines the traditional Chinese medicine and the artificial intelligence, diagnoses from four aspects of looking for and asking for information, respectively establishes corresponding deep learning neural network models, respectively trains the deep learning models which are looked for and asked for according to the diagnosis results of the traditional Chinese medicine experts to obtain the deep learning models which contain the experience of the traditional Chinese medicine experts and are used for forecasting, thereby saving the resources of the traditional Chinese medicine experts, being capable of inheriting the experience of the traditional Chinese medicine experts, carrying out the diagnosis and forecasting of the looking for information, simultaneously determining whether the disease is ill or not by setting a probability threshold, integrating the four models which are looked for and asked for information into one model for training and forecasting through the deep learning, fully embodying the thought and evidence of the traditional Chinese medicine, inheriting and developing the traditional Chinese medicine, and improving the accuracy of the forecasting.
In a preferred embodiment, as shown in fig. 2, the method further comprises: acquiring a disease investigation model; the step of looking for and asking for the disease investigation. The technical effects are as follows: said method comprising
In a preferred embodiment, as shown in fig. 3, the method further comprises: acquiring a health condition model; the health condition is looked after and asked for. The technical effects are as follows: said method comprising
In a preferred embodiment, as shown in fig. 4, the method further comprises: a user image acquisition step; a user sound acquisition step; a user condition obtaining step; a user sign obtaining step; a target disease inspection step; auscultation of target diseases; a target disease inquiry step; and (5) cutting and diagnosing the target disease. The technical effects are as follows: said method comprising
In a preferred embodiment, as shown in fig. 5, the method further comprises: disease investigation and inspection; auscultation step of disease investigation; disease investigation and inquiry; and (5) disease investigation and diagnosis. The technical effects are as follows: said method comprising
In a preferred embodiment, as shown in fig. 6, the method further comprises: a health condition inspection step; auscultation of health conditions; a health condition inquiry step; and (5) cutting the health condition. The technical effects are as follows: said method comprising
PREFERRED EMBODIMENTS OF THE PRESENT INVENTION
A target user determination step: taking a user to be monitored as a target user;
target disease determination step: taking a disease to be monitored as a target disease;
a user image acquisition step: acquiring a tongue image or/and a face image or/and a hand image or/and an image of other parts of a body of a target user as an image of the target user;
target disease inspection step: taking an image of a user as input, taking the probability that the user has the target disease as expected output, and training a deep learning neural network model to obtain a prospective deep learning neural network model of the target disease; when the target disease detection method is used, the image of a target user is used as input, the calculation of the target disease observation deep learning neural network model is carried out, and the obtained output is used as the probability that the target user has the target disease; if the probability that the target user has the target disease exceeds a preset probability threshold, the target user has the target disease;
disease investigation and inspection: taking an image of a user as input, taking a plurality of preset diseases possibly suffered by the user and the probability of suffering from each disease as expected output, and training a deep learning neural network model to obtain a disease investigation and inspection deep learning neural network model; when the device is used, the image of a target user is used as input, and the obtained output is used as the probability that the target user possibly suffers from a plurality of preset diseases and each disease through the calculation of a disease examination and inspection deep learning neural network model;
a health condition inspection step: taking an image of a user as input, taking the health condition of the user as expected output, and training a deep learning neural network model to obtain a health condition inspection deep learning neural network model; when the system is used, the image of a target user is used as input, the calculation of the health condition inspection deep learning neural network model is carried out, and the obtained output is used as the health condition of the target user;
a user sound acquisition step: acquiring speaking voice or/and breathing voice or/and lung voice of a target user or voice emitted or generated by other organs of a body as the voice of the target user;
auscultation step of target diseases: taking the sound of a user as input, taking the probability that the user is suffered from the target disease as expected output, and training the deep learning neural network model to obtain an auscultation deep learning neural network model of the target disease; when the device is used, the sound of a target user is used as input, the calculation of the neural network model is learned through the auscultation depth of a target disease, and the obtained output is used as the probability that the target user suffers from the target disease; if the probability that the target user has the target disease exceeds a preset probability threshold, the target user has the target disease;
disease investigation auscultation step: taking the sound of a user as input, taking a plurality of preset diseases possibly suffered by the user and the probability of suffering from each disease as expected output, and training a deep learning neural network model to obtain a disease investigation auscultation deep learning neural network model; when the device is used, the sound of a target user is used as input, the calculation of a neural network model is studied through disease investigation and auscultation depth, and the obtained output is used as the probability that the target user possibly suffers from a plurality of preset diseases and each disease;
auscultation of health conditions: taking the sound of a user as input, taking the health condition of the user as expected output, and training a deep learning neural network model to obtain a health condition auscultation deep learning neural network model; when the health condition auscultation deep learning neural network model is used, the sound of a target user is used as input, the calculation of the neural network model is deeply learned through health condition auscultation, and the obtained output is used as the health condition of the target user;
a user condition obtaining step: acquiring answers of a target user to a preset question set as the condition of the target user;
and (3) target disease inquiry step: taking the condition of a user as input, taking the probability that the user has the target disease as expected output, and training the deep learning neural network model to obtain an inquiry deep learning neural network model of the target disease; when the device is used, the condition of a target user is used as input, the calculation of the neural network model is deeply learned through the inquiry of a target disease, and the obtained output is used as the probability that the target user has the target disease; if the probability that the target user has the target disease exceeds a preset probability threshold, the target user has the target disease;
disease investigation and inquiry step: taking the condition of a user as input, taking a plurality of preset diseases possibly suffered by the user and the probability of suffering from each disease as expected output, and training a deep learning neural network model to obtain a disease investigation and inquiry deep learning neural network model; when the device is used, the condition of a target user is used as input, and the output obtained by calculating the disease investigation, inquiry and deep learning neural network model is used as the probability that the target user possibly suffers from a plurality of preset diseases and each disease;
the health condition inquiry step: taking the condition of a user as input, taking the health condition of the user as expected output, and training a deep learning neural network model to obtain a health condition inquiry deep learning neural network model; when the health condition query system is used, the condition of a target user is used as input, the calculation of the neural network model is deeply learned through health condition inquiry, and the obtained output is used as the health condition of the target user;
a user sign obtaining step: acquiring pulse data or/and heartbeat data or/and blood oxygen data of a target user as physical signs of the target user;
and (3) the step of diagnosing the target disease: taking the physical sign of a user as input, taking the probability that the user has the target disease as expected output, and training the deep learning neural network model to obtain a diagnosis deep learning neural network model of the target disease; when the device is used, the physical signs of a target user are used as input, and the obtained output is used as the probability that the target user suffers from the target disease through the calculation of the cutting diagnosis deep learning neural network model of the target disease; if the probability that the target user has the target disease exceeds a preset probability threshold, the target user has the target disease;
disease investigation and diagnosis step: taking the physical signs of a user as input, taking a preset plurality of diseases possibly suffered by the user and the probability of suffering from each disease as expected output, and training a deep learning neural network model to obtain a disease investigation and diagnosis deep learning neural network model; when the device is used, the physical signs of a target user are used as input, and the obtained output is used as the probability that the target user possibly suffers from a plurality of preset diseases and each disease through the calculation of a disease investigation and diagnosis deep learning neural network model;
the health condition diagnosis step: taking the physical signs of a user as input, taking the health condition of the user as expected output, and training a deep learning neural network model to obtain a health condition diagnosis deep learning neural network model; when the health condition diagnosis device is used, the physical signs of a target user are used as input, the calculation of the health condition diagnosis deep learning neural network model is carried out, and the obtained output is used as the health condition of the target user;
the step of looking for and asking for the target disease: taking data of a target user as input, taking output obtained through calculation of a deep learning neural network model for inspection and diagnosis of a target disease as first output, taking output obtained through calculation of a deep learning neural network model for auscultation and diagnosis of the target disease as second output, taking output obtained through calculation of a deep learning neural network model for inquiry and diagnosis of the target disease as third output, and taking output obtained through calculation of a deep learning neural network model for cutting and diagnosis of the target disease as fourth output; taking the first output, the second output, the third output and the fourth output as input, taking the probability that the user has the target disease as expected output, and training a deep learning neural network model to obtain a looking and smelling deep learning neural network model of the target disease; when the target user probability learning neural network model is used, data of the target user are used as input, and the obtained output is used as the probability that the target user suffers from the target disease through the calculation of the target disease looking, smelling and deep learning neural network model; if the probability that the target user has the target disease exceeds a preset probability threshold, the target user has the target disease;
the step of looking for and asking for disease investigation: taking data of a target user as input, taking output obtained through calculation of a disease investigation and inspection deep learning neural network model as first output, taking output obtained through calculation of a disease investigation and auscultation deep learning neural network model as second output, taking output obtained through calculation of a disease investigation and inspection deep learning neural network model as third output, and taking output obtained through calculation of a disease investigation and inspection deep learning neural network model as fourth output; taking the first output, the second output, the third output and the fourth output as inputs, taking a preset plurality of diseases possibly suffered by the user and the probability of suffering from each disease as expected outputs, and training a deep learning neural network model to obtain a disease investigation, observation and inquiry deep learning neural network model; when the device is used, data of a target user are used as input, and the obtained output is used as the probability that the user possibly suffers from a plurality of preset diseases and each disease through the calculation of a disease investigation, inquiry, study and deep learning neural network model;
the step of observing and asking for health conditions: taking data of a target user as input, taking output obtained through calculation of the health condition inspection deep learning neural network model as first output, taking output obtained through calculation of the health condition auscultation deep learning neural network model as second output, taking output obtained through calculation of the health condition inspection deep learning neural network model as third output, and taking output obtained through calculation of the health condition inspection deep learning neural network model as fourth output; taking the first output, the second output, the third output and the fourth output as input, taking the health condition of the user as expected output, and training a deep learning neural network model to obtain a health condition looking and asking deep learning neural network model; when the health condition learning method is used, data of a target user are used as input, calculation of the neural network model is deeply learned through health condition observation and hearing, and obtained output is used as the health condition of the user.
Other embodiments of the invention
Self-service dialectical monitoring based on big data.
Firstly, a big data acquisition system related to the listening and listening query is built based on a big data platform, so that tongue picture, face picture, voice, question and answer and index data of a user can be analyzed in real time after reaching the system, a big data framework is built by adopting a general and advanced SPARK platform, and a big data analysis algorithm in the listening and listening query process is developed based on the SPARK big data platform.
The artificial intelligence algorithm can be used for tongue picture monitoring of newly-discovered major infectious disease infection symptoms, and is also used for face picture monitoring, and tongue diagnosis and face diagnosis constitute inspection diagnosis in the traditional Chinese medicine dialectical diagnosis method. Inspection is mainly based on deep learning and expert systems.
The medical care knowledge question-answering system based on deep learning can be used for the inquiry of newly-sent serious infectious disease infection symptoms. Some research is carried out on the infection symptoms of the new major infectious diseases, and a knowledge base of the infection symptoms of the new major infectious diseases is established to discuss, confirm, supplement and perfect with cooperative doctors. The knowledge base can support an inquiry module, and the inquiry module is mainly based on a knowledge map and combined with deep learning.
The auscultation can be based on deep learning and expert system to identify the characteristics of the sound.
The palpation is carried out by self-test through the user by using the recommended mobile phone app (all free), then the heart rate and the blood oxygen content are input, and then diagnosis is carried out through the palpation deep learning and expert system.
And finally, integrating the diagnosis results by a decision tree and other big data machine learning methods to obtain a final diagnosis result. FIG. 7 shows a block diagram of a system for big data based self-service forensic monitoring.
A large number of users distributed all over the country can perform self-service monitoring through the self-service monitoring system of the embodiment, so that big data acquisition of self-service monitoring results of the users can be realized. The diagnosis and treatment method based on syndrome differentiation, also called four diagnosis, namely inspection, auscultation, inquiry and treatment (which is a method for diagnosing diseases in traditional Chinese medicine), is adopted, and the data of the user is acquired by using a mobile phone camera, a mobile phone microphone, a mobile phone APP and user input, so that the data acquisition of the inspection, auscultation and treatment is carried out.
When the number of users is large, large data is formed, and the large data continuously improves the model, so that the diagnosis accuracy of the system can be gradually improved. The big data platform adopts the current universal and advanced SPARK platform, unstructured data are accessed on the HDFS in a file form so as to improve the safety and processing speed of the big data, and knowledge analyzed and acquired from the big data is accessed in a knowledge base and a database form so as to facilitate real-time query of a user.
The big data analysis and processing of the inspection monitoring and the auscultation monitoring mainly uses a deep learning method based on big data and combines a knowledge base and an expert system to complete the processes of image and audio big data feature extraction and classification prediction. The big data analysis processing of the inquiry monitoring mainly uses a knowledge map and a deep learning method based on big data and combines a knowledge base and an expert system to complete the processes of character big data feature extraction and classification prediction. The big data analysis and processing of the palpation monitoring mainly uses machine learning and deep learning methods based on big data and combines a knowledge base and an expert system to complete the processes of digital index big data feature extraction and classification prediction. And finally, comprehensively looking for and asking for four monitoring results, and making decisions through machine learning methods such as a decision tree based on big data.
Inspection and monitoring based on big data: the probability of infecting new serious infectious diseases is identified and diagnosed by a big data deep learning algorithm and an expert system according to tongue characteristics (the part is completed, whether the tongue is cold or heated, whether moisture or pathogenic gas exists or not can be seen from the tongue image of a person infected with the new serious infectious diseases, and the tongue image of the person infected with the new serious infectious diseases also can have characteristics), facial characteristics (identified from the facial complexion of the person), and hand characteristics (identified from palm prints, fingerprints, the facial complexion, the nail color and the texture of the person). The tongue feature is a necessary feature, the face feature is an optional feature, and whether the hand feature is incorporated into the system or not needs to be demonstrated in the research process.
Auscultation monitoring based on big data: the probability of infecting new serious infectious diseases is obtained by identifying and diagnosing the speaking sound of a user (namely, the user is made to intentionally breathe or cough and speak a specified word such as 'o' because the sound before infection and the sound after infection are different) through a big data deep learning algorithm and an expert system.
Interrogation monitoring based on big data: the big data chat robot is used for providing questions related to the new major infectious disease symptoms for the user in an auxiliary diagnosis mode, and then the probability that the user is infected with the new major infectious disease is diagnosed through a knowledge map and deep learning according to the answers of the user.
And (4) performing clinical monitoring based on big data. The probability of infecting new serious infectious diseases is diagnosed through large data deep learning and an expert system according to input data of the user by prompting the user to utilize indexes such as free app self-test heartbeat speed and blood oxygen content and inputting the indexes into the system.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for those skilled in the art, various changes and modifications can be made without departing from the spirit of the present invention, and these changes and modifications are within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An artificial intelligence method, the method comprising:
a target user determination step: taking a user to be monitored as a target user;
target disease determination step: taking a disease to be monitored as a target disease;
acquiring a target disease model: acquiring a deep learning neural network model for inspection and diagnosis of a target disease, a deep learning neural network model for auscultation and diagnosis of the target disease, an inquiry and diagnosis deep learning neural network model for the target disease and a cutting and diagnosis deep learning neural network model for the target disease;
the step of looking for and asking for the target disease: taking data of a target user as input, taking output obtained through calculation of a deep learning neural network model for inspection and diagnosis of a target disease as first output, taking output obtained through calculation of a deep learning neural network model for auscultation and diagnosis of the target disease as second output, taking output obtained through calculation of a deep learning neural network model for inquiry and diagnosis of the target disease as third output, and taking output obtained through calculation of a deep learning neural network model for cutting and diagnosis of the target disease as fourth output; taking the first output, the second output, the third output and the fourth output as input, taking the probability that the user has the target disease as expected output, and training a deep learning neural network model to obtain a looking and smelling deep learning neural network model of the target disease; when the target user probability learning neural network model is used, data of the target user are used as input, and the obtained output is used as the probability that the target user suffers from the target disease through the calculation of the target disease looking, smelling and deep learning neural network model; and if the probability that the target user has the target disease exceeds a preset probability threshold, the target user has the target disease.
2. The artificial intelligence method of claim 1, wherein the method further comprises:
acquiring a disease investigation model: acquiring a disease investigation and inspection deep learning neural network model, a disease investigation and auscultation deep learning neural network model, a disease investigation and inquiry deep learning neural network model and a disease investigation and diagnosis deep learning neural network model;
the step of looking for and asking for disease investigation: taking data of a target user as input, taking output obtained through calculation of a disease investigation and inspection deep learning neural network model as first output, taking output obtained through calculation of a disease investigation and auscultation deep learning neural network model as second output, taking output obtained through calculation of a disease investigation and inspection deep learning neural network model as third output, and taking output obtained through calculation of a disease investigation and inspection deep learning neural network model as fourth output; taking the first output, the second output, the third output and the fourth output as inputs, taking a preset plurality of diseases possibly suffered by the user and the probability of suffering from each disease as expected outputs, and training a deep learning neural network model to obtain a disease investigation, observation and inquiry deep learning neural network model; when the device is used, data of a target user are used as input, and the obtained output is used as the probability that the user possibly suffers from a plurality of preset diseases and each disease through the calculation of a disease investigation, inquiry, study and deep learning neural network model.
3. The artificial intelligence method of claim 1, wherein the method further comprises:
a health condition model obtaining step: acquiring a health condition inspection deep learning neural network model, a health condition auscultation deep learning neural network model, a health condition inquiry deep learning neural network model and a health condition palpation deep learning neural network model;
the step of observing and asking for health conditions: taking data of a target user as input, taking output obtained through calculation of the health condition inspection deep learning neural network model as first output, taking output obtained through calculation of the health condition auscultation deep learning neural network model as second output, taking output obtained through calculation of the health condition inspection deep learning neural network model as third output, and taking output obtained through calculation of the health condition inspection deep learning neural network model as fourth output; taking the first output, the second output, the third output and the fourth output as input, taking the health condition of the user as expected output, and training a deep learning neural network model to obtain a health condition looking and asking deep learning neural network model; when the health condition learning method is used, data of a target user are used as input, calculation of the neural network model is deeply learned through health condition observation and hearing, and obtained output is used as the health condition of the user.
4. The artificial intelligence method of claim 1, wherein the method further comprises:
a user image acquisition step: acquiring a tongue image or/and a face image or/and a hand image or/and an image of other parts of a body of a target user as an image of the target user;
a user sound acquisition step: acquiring speaking voice or/and breathing voice or/and lung voice of a target user or voice emitted or generated by other organs of a body as the voice of the target user;
a user condition obtaining step: acquiring answers of a target user to a preset question set as the condition of the target user;
a user sign obtaining step: acquiring pulse data or/and heartbeat data or/and blood oxygen data of a target user as physical signs of the target user;
target disease inspection step: taking an image of a user as input, taking the probability that the user has the target disease as expected output, and training a deep learning neural network model to obtain a prospective deep learning neural network model of the target disease; when the target disease detection method is used, the image of a target user is used as input, the calculation of the target disease observation deep learning neural network model is carried out, and the obtained output is used as the probability that the target user has the target disease; if the probability that the target user has the target disease exceeds a preset probability threshold, the target user has the target disease;
auscultation step of target diseases: taking the sound of a user as input, taking the probability that the user is suffered from the target disease as expected output, and training the deep learning neural network model to obtain an auscultation deep learning neural network model of the target disease; when the device is used, the sound of a target user is used as input, the calculation of the neural network model is learned through the auscultation depth of a target disease, and the obtained output is used as the probability that the target user suffers from the target disease; if the probability that the target user has the target disease exceeds a preset probability threshold, the target user has the target disease;
and (3) target disease inquiry step: taking the condition of a user as input, taking the probability that the user has the target disease as expected output, and training the deep learning neural network model to obtain an inquiry deep learning neural network model of the target disease; when the device is used, the condition of a target user is used as input, the calculation of the neural network model is deeply learned through the inquiry of a target disease, and the obtained output is used as the probability that the target user has the target disease; if the probability that the target user has the target disease exceeds a preset probability threshold, the target user has the target disease;
and (3) the step of diagnosing the target disease: taking the physical sign of a user as input, taking the probability that the user has the target disease as expected output, and training the deep learning neural network model to obtain a diagnosis deep learning neural network model of the target disease; when the device is used, the physical signs of a target user are used as input, and the obtained output is used as the probability that the target user suffers from the target disease through the calculation of the cutting diagnosis deep learning neural network model of the target disease; and if the probability that the target user has the target disease exceeds a preset probability threshold, the target user has the target disease.
5. The artificial intelligence method of claim 2, wherein the method further comprises:
disease investigation and inspection: taking an image of a user as input, taking a plurality of preset diseases possibly suffered by the user and the probability of suffering from each disease as expected output, and training a deep learning neural network model to obtain a disease investigation and inspection deep learning neural network model; when the device is used, the image of a target user is used as input, and the obtained output is used as the probability that the target user possibly suffers from a plurality of preset diseases and each disease through the calculation of a disease examination and inspection deep learning neural network model;
disease investigation auscultation step: taking the sound of a user as input, taking a plurality of preset diseases possibly suffered by the user and the probability of suffering from each disease as expected output, and training a deep learning neural network model to obtain a disease investigation auscultation deep learning neural network model; when the device is used, the sound of a target user is used as input, the calculation of a neural network model is studied through disease investigation and auscultation depth, and the obtained output is used as the probability that the target user possibly suffers from a plurality of preset diseases and each disease;
disease investigation and inquiry step: taking the condition of a user as input, taking a plurality of preset diseases possibly suffered by the user and the probability of suffering from each disease as expected output, and training a deep learning neural network model to obtain a disease investigation and inquiry deep learning neural network model; when the device is used, the condition of a target user is used as input, and the output obtained by calculating the disease investigation, inquiry and deep learning neural network model is used as the probability that the target user possibly suffers from a plurality of preset diseases and each disease;
disease investigation and diagnosis step: taking the physical signs of a user as input, taking a preset plurality of diseases possibly suffered by the user and the probability of suffering from each disease as expected output, and training a deep learning neural network model to obtain a disease investigation and diagnosis deep learning neural network model; when the device is used, the physical signs of a target user are used as input, the calculation of the neural network model is deeply learned through disease investigation and diagnosis, and the obtained output is used as the probability that the target user possibly suffers from a plurality of preset diseases and each disease.
6. The artificial intelligence method of claim 3, wherein the method further comprises:
a health condition inspection step: taking an image of a user as input, taking the health condition of the user as expected output, and training a deep learning neural network model to obtain a health condition inspection deep learning neural network model; when the system is used, the image of a target user is used as input, the calculation of the health condition inspection deep learning neural network model is carried out, and the obtained output is used as the health condition of the target user;
auscultation of health conditions: taking the sound of a user as input, taking the health condition of the user as expected output, and training a deep learning neural network model to obtain a health condition auscultation deep learning neural network model; when the health condition auscultation deep learning neural network model is used, the sound of a target user is used as input, the calculation of the neural network model is deeply learned through health condition auscultation, and the obtained output is used as the health condition of the target user;
the health condition inquiry step: taking the condition of a user as input, taking the health condition of the user as expected output, and training a deep learning neural network model to obtain a health condition inquiry deep learning neural network model; when the health condition query system is used, the condition of a target user is used as input, the calculation of the neural network model is deeply learned through health condition inquiry, and the obtained output is used as the health condition of the target user;
the health condition diagnosis step: taking the physical signs of a user as input, taking the health condition of the user as expected output, and training a deep learning neural network model to obtain a health condition diagnosis deep learning neural network model; when the health condition diagnosis device is used, the physical signs of a target user are used as input, the calculation of the deep learning neural network model is performed through health condition diagnosis, and the obtained output is used as the health condition of the target user.
7. An artificial intelligence system, the system comprising:
a target user determination module: taking a user to be monitored as a target user;
a target disease determination module: taking a disease to be monitored as a target disease;
a target disease model acquisition module: acquiring a deep learning neural network model for inspection and diagnosis of a target disease, a deep learning neural network model for auscultation and diagnosis of the target disease, an inquiry and diagnosis deep learning neural network model for the target disease and a cutting and diagnosis deep learning neural network model for the target disease;
the target disease observation and inquiry module comprises: taking data of a target user as input, taking output obtained through calculation of a deep learning neural network model for inspection and diagnosis of a target disease as first output, taking output obtained through calculation of a deep learning neural network model for auscultation and diagnosis of the target disease as second output, taking output obtained through calculation of a deep learning neural network model for inquiry and diagnosis of the target disease as third output, and taking output obtained through calculation of a deep learning neural network model for cutting and diagnosis of the target disease as fourth output; taking the first output, the second output, the third output and the fourth output as input, taking the probability that the user has the target disease as expected output, and training a deep learning neural network model to obtain a looking and smelling deep learning neural network model of the target disease; when the target user probability learning neural network model is used, data of the target user are used as input, and the obtained output is used as the probability that the target user suffers from the target disease through the calculation of the target disease looking, smelling and deep learning neural network model; and if the probability that the target user has the target disease exceeds a preset probability threshold, the target user has the target disease.
8. An artificial intelligence device, wherein the device is configured to implement the steps of the method of any of claims 1-6.
9. A robot comprising a memory, a processor and an artificial intelligence robot program stored on the memory and executable on the processor, wherein the steps of the method of any one of claims 1 to 6 are carried out when the program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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| CN111724895A (en) * | 2020-05-16 | 2020-09-29 | 张东 | Personalized traditional Chinese medicine diagnosis and treatment robot system based on artificial intelligence |
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