CN113555106A - Intelligent traditional Chinese medicine remote auxiliary diagnosis and treatment platform based on generation countermeasure network - Google Patents
Intelligent traditional Chinese medicine remote auxiliary diagnosis and treatment platform based on generation countermeasure network Download PDFInfo
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Abstract
The invention relates to the technical field related to computers, in particular to an intelligent traditional Chinese medicine remote auxiliary diagnosis and treatment platform based on a generated countermeasure network, which comprises a traditional Chinese medicine data collection module, a user conditioning scheme model module, a user physical condition data acquisition module, a traditional Chinese medicine health-preserving knowledge base module and an intelligent auxiliary diagnosis module; the user physical condition data acquisition module is used for acquiring state information of the physical condition of the user; the user conditioning scheme model module is based on a user conditioning scheme model module for generating a countermeasure network and is used for recommending a conditioning health-preserving scheme model for a user; the traditional Chinese medicine data collection module is used for collecting a sample data set from the traditional Chinese medicine health preserving knowledge base module and performing data enhancement and labeling on the collected sample data set; the intelligent auxiliary diagnosis module is used for receiving the state information and providing a conditioning scheme for the user or recommending a doctor of the user.
Description
Technical Field
The invention relates to the technical field of computers, in particular to an intelligent traditional Chinese medicine remote auxiliary diagnosis and treatment platform based on a generated countermeasure network.
Background
Along with the improvement of the living standard of people, the requirement of people on sanitary service is higher and higher. The Chinese medicine diagnosis is a unique cultural treasure in China, not only can accurately diagnose and treat diseases, but also can obviously contribute to the aspects of treating the diseases and the like, and the diagnosis and treatment method has a profound effect on dialectically analyzing the disease symptoms and health conditions of patients through the research on the correlation relationship presented among human viscera, whole body veins, blood and qi operation and body surface characteristics. However, since the diagnosis technique of traditional Chinese medicine relies on the long-term clinical diagnosis experience of the medical personnel, the traditional Chinese medicine experts with abundant experience are limited, massive one-to-one anytime and anywhere health guidance can not be provided, and the ordinary people can not identify the physical health condition of the people through an intuitive and effective means, and can not select a proper health preserving scheme according to the physical health condition of the people.
In the prior art, although remote online auxiliary diagnosis and treatment is carried out through the Internet, the diagnosis and treatment system does not simulate each link of traditional Chinese medicine diagnosis and treatment, and the health level of a user cannot be comprehensively and effectively evaluated. The traditional Chinese medicine intelligent remote auxiliary diagnosis and treatment platform with complete functions is researched and developed, the symptom information of a patient can be comprehensively acquired, the patient can monitor the self health condition for a long time by using the remote auxiliary diagnosis and treatment platform, and the health level of the user is improved.
Disclosure of Invention
The invention aims to provide an intelligent traditional Chinese medicine remote auxiliary diagnosis and treatment platform based on a generation countermeasure network, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
an intelligent traditional Chinese medicine remote auxiliary diagnosis and treatment platform based on a generated countermeasure network comprises a traditional Chinese medicine data collecting module, a user conditioning scheme model module, a user physical condition data collecting module, a traditional Chinese medicine health preserving knowledge base module and an intelligent auxiliary diagnosis module; wherein the content of the first and second substances,
the user physical condition data acquisition module is used for acquiring state information of the physical condition of the user;
the user conditioning scheme model module is based on a user conditioning scheme model module for generating a countermeasure network and is used for recommending a conditioning health-preserving scheme model for a user;
the traditional Chinese medicine data collection module is used for collecting a sample data set from the traditional Chinese medicine health preserving knowledge base module and performing data enhancement and labeling on the collected sample data set;
the intelligent auxiliary diagnosis module is used for receiving the state information and providing a conditioning scheme for the user or recommending a doctor of the user.
As a further scheme of the invention: the generation countermeasure network structure adopted by the user conditioning scheme model module is a deep convolution countermeasure network, wherein the deep convolution countermeasure network generation method comprises the following steps:
inputting random noise into a full-connection network, converting the random noise into a three-dimensional tensor through dimension transformation, sending the three-dimensional tensor into a generator network G, generating samples which are as close to real data distribution as possible, then inputting the generated samples and collected samples into a discriminator network D, outputting a classification result by the discriminator network D, transmitting errors of the classification result and the real result into the generator network G and the discriminator network D, and continuously iterating the whole process.
As a still further scheme of the invention: the random noise is random noise conforming to Gaussian distribution.
As a still further scheme of the invention: the generator network G comprises 1 full-connection layer and 5 micro-step convolution layers, wherein batch normalization and activation functions are carried out after each micro-step convolution layer, the first 4 activation functions are ReLU functions, and the last activation function is a Tanh function.
As a still further scheme of the invention: the batch normalization comprises the steps of calculating the data mean value of each batch, calculating the data variance of each batch, and carrying out normalization operation, scale transformation and migration on the data of the batch by using the mean value and the variance obtained by calculation in the previous two steps.
As a still further scheme of the invention: the G loss function of the generator network is a feature matching method for matching the statistical distribution of the generated sample and the real sample, and the expression is as follows:
where f denotes a feature value of the discriminator intermediate layer, and z denotes input noise.
As a still further scheme of the invention: the network structure of the discriminator network D comprises 5 convolutional layers and 3 full-connection layers, the size of a convolutional core selected by the convolutional layers in the network is 5 multiplied by 5, batch normalization processing and an activation function are carried out after each convolutional layer, the selected activation function is a LeakReLU function, the slope of the LeakReLU is reserved in a negative half shaft, the gradient disappearance in the training process can be avoided, Dropout is added in the full-connection layers in order to prevent overfitting, and finally, the result is output through a softmax function, wherein the expression is as follows:
wherein, K is the real sample category, K +1 is the generation sample category, and the maximum value is the output probability of the category.
As a still further scheme of the invention: the arbiter network D loss function is a cross entropy loss function of real class label distribution and prediction class labels, and the expression is as follows:
wherein, x is a real sample, y is a category corresponding to the sample data, and x, y-PdataIndicating that the input samples bear the label y and x-G indicating that x is taken from the generated samples.
As a still further scheme of the invention: the user body condition data acquisition module is used for acquiring state information including venous qi and blood conditions, arteriovenous blood conditions, cold and heat deficiency and excess conditions, facial diagnosis thorax conditions and electrocardio meridian conditions of a user.
As a still further scheme of the invention: the traditional Chinese medicine health preserving knowledge base comprises a traditional Chinese medicine health preserving principle, a traditional Chinese medicine health preserving method and a traditional Chinese medicine health preserving application.
Compared with the prior art, the invention has the beneficial effects that: the invention uses the traditional Chinese medicine intelligent auxiliary diagnosis and treatment method combining the method of generating the confrontation network and the traditional Chinese medicine knowledge base, can recommend a health maintenance scheme for the user on the basis of the health state of the user, carries out intelligent analysis by using the generated confrontation network on the basis of a remote platform and offline acquired data, and realizes the fusion of various characteristic information with the traditional Chinese medicine classic docking, so that the remote medical treatment is closer to the face diagnosis, the flexibility and the effectiveness of medical treatment are improved, the user is helped to complete self diagnosis and treatment and health management, and the health level of the user is effectively improved.
Drawings
Fig. 1 is a structural block diagram of an intelligent TCM remote auxiliary diagnosis and treatment platform based on a generation countermeasure network.
Fig. 2 is a structure diagram of an antagonistic network generation structure in an intelligent TCM remote auxiliary diagnosis and treatment platform based on an antagonistic network generation.
Fig. 3 is a schematic diagram of data acquisition in an intelligent traditional Chinese medicine remote auxiliary diagnosis and treatment platform based on a generated countermeasure network.
Fig. 4 is a flow chart used in the intelligent TCM remote auxiliary diagnosis and treatment platform based on generation of the countermeasure network.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In addition, an element of the present invention may be said to be "fixed" or "disposed" to another element, either directly on the other element or with intervening elements present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like as used herein are for illustrative purposes only and do not represent the only embodiments.
Referring to fig. 1, in an embodiment of the present invention, an intelligent remote auxiliary traditional Chinese medicine diagnosis and treatment platform based on a generated countermeasure network includes a traditional Chinese medicine data collecting module, a user conditioning scheme model module, a user physical condition data collecting module, a traditional Chinese medicine health preserving knowledge base module, and an intelligent auxiliary diagnosis module; wherein the content of the first and second substances,
the user physical condition data acquisition module is used for acquiring state information of the physical condition of the user;
the user conditioning scheme model module is based on a user conditioning scheme model module for generating a countermeasure network and is used for recommending a conditioning health-preserving scheme model for a user;
the traditional Chinese medicine data collection module is used for collecting a sample data set from the traditional Chinese medicine health preserving knowledge base module and performing data enhancement and labeling on the collected sample data set;
the intelligent auxiliary diagnosis module is used for receiving the state information and providing a conditioning scheme for the user or recommending a doctor of the user.
In the embodiment of the invention, the traditional Chinese medicine intelligent auxiliary diagnosis and treatment method combining the method for generating the countermeasure network and the traditional Chinese medicine knowledge base is used, a health maintenance scheme can be recommended for a user on the basis of the health state of the user, the generated countermeasure network is utilized for intelligent analysis on the basis of a remote platform and offline acquired data, and fusion of various characteristic information is realized with traditional Chinese medicine classic docking, so that remote medical treatment is closer to the face diagnosis, the flexibility and the effectiveness of medical treatment are improved, the user is helped to complete self diagnosis and treatment and health management, and the health level of the user is effectively improved.
Referring to fig. 2, as an embodiment of the present invention, the structure of the generation countermeasure network adopted by the user conditioning scheme model module is a deep convolutional countermeasure network, wherein the deep convolutional countermeasure network generation method includes:
inputting random noise into a full-connection network, converting the random noise into a three-dimensional tensor through dimension transformation, sending the three-dimensional tensor into a generator network G, generating samples which are as close to real data distribution as possible, then inputting the generated samples and collected samples into a discriminator network D, outputting a classification result by the discriminator network D, transmitting errors of the classification result and the real result into the generator network G and the discriminator network D, and continuously iterating the whole process.
In the embodiment of the invention, the samples generated by the generator network G are close to the real data distribution and the classification capability of the discriminator network D on the input samples is also improved continuously.
In the embodiment of the present invention, the random noise is a random noise conforming to gaussian distribution.
In the embodiment of the present invention, the generator network G includes 1 fully connected layer and 5 micro-step convolutional layers, each micro-step convolutional layer is followed by a batch normalization and an activation function, where the first 4 activation functions are ReLU functions, and the last activation function is a Tanh function.
In the embodiment of the invention, the batch normalization comprises the steps of calculating the mean value of data of each batch, calculating the variance of the data of each batch, and carrying out normalization operation and scale transformation and migration on the data of the batch by using the mean value and the variance calculated in the previous two steps.
The batch normalization method can effectively improve the training speed and accelerate the convergence process.
In the embodiment of the present invention, the generator network G loss function is a feature matching method in which the statistical distribution of the generated sample and the real sample is matched, and the expression is:
where f denotes a feature value of the discriminator intermediate layer, and z denotes input noise.
In the training process, the feature matching result is used as a loss function of the generator network G, the generated sample and the real sample are matched through the minimized loss function, and the maximum fitting of the generator G to the distribution of the real data is achieved.
In the embodiment of the present invention, the network structure of the discriminator network D includes 5 convolutional layers and 3 fully-connected layers, the size of a convolutional kernel selected by a convolutional layer in the network is 5 × 5, each convolutional layer is followed by batch normalization processing and an activation function, the activation function is a leak relu function, the leak relu retains a slope in a negative half axis, which can avoid the disappearance of the gradient in the training process, in order to prevent overfitting, Dropout is added to the fully-connected layer, and finally, the result is output by a softmax function, and the expression is:
wherein, K is the real sample category, K +1 is the generation sample category, and the maximum value is the output probability of the category.
In the embodiment of the present invention, the arbiter network D loss function is a cross entropy loss function of the real class label distribution and the prediction class label, and the expression is:
wherein, x is a real sample, y is a category corresponding to the sample data, and x, y-PdataIndicating that the input samples bear the label y and x-G indicating that x is taken from the generated samples.
In the embodiment of the invention, the arbiter network D optimization algorithm selects an Adam optimization algorithm, the algorithm can quickly and iteratively update the weight, the needed memory is less, the algorithm has good practicability for high noise and sparse gradient, and the method can solve the problems of large memory needed by model calculation, noise in data and the like.
Referring to fig. 3 as an embodiment of the present invention, the user body condition data acquisition module is configured to acquire state information including venous qi and blood conditions, arteriovenous blood conditions, cold and heat deficiency and excess conditions, facial diagnosis thorax conditions, and cardiac and cerebral channels and collaterals conditions of a user, where:
the venous qi and blood condition is mainly measured by clamping fingers on fingertips to measure the waveform and the intensity of blood volume pulse, the information can reflect whether blood circulation is smooth, the clamping of different fingers or toes can reflect the qi and blood circulation conditions of different channels, and the rhythm change of the waveform can measure the activity intensity of sympathetic nerves and parasympathetic nerves;
the arteriovenous blood condition is mainly measured by a light source and a multispectral sensor (red, green, blue and near infrared) which are attached to the surface of the skin, the color information of the skin surface and the depth of a few millimeters under the skin is measured, the color information is one of the most important information for inspection diagnosis in the traditional Chinese medicine, the process can overcome the interference of external environment light, the fluctuation information of the color can reflect the proportion of arterial blood, venous blood and other tissues, and the added infrared channel provides more information;
the cold, heat, deficiency and excess conditions mainly pass through an infrared thermograph, the image comprises 388x284 pixel points, each point reflects the temperature of the position, and the cold, heat, deficiency and excess conditions of each part on the surface of the human body can be intuitively reflected;
the facial diagnosis thorax condition is mainly characterized in that richer facial information data acquisition points are obtained by a normal oblique facial image acquisition method, and facial morphological features are extracted;
the electrocardio meridian condition is mainly collected by a meridian detection instrument.
As an embodiment of the present invention, the health-preserving knowledge base of traditional Chinese medicine includes a health-preserving principle of traditional Chinese medicine, a health-preserving method of traditional Chinese medicine and a health-preserving application of traditional Chinese medicine.
As an embodiment of the invention, the intelligent auxiliary diagnosis recommends the conditioning scheme for the user by combining the user conditioning scheme recommendation model and the traditional Chinese medicine health-preserving knowledge base, and recommends the doctor for the user with serious illness.
In the embodiment of the invention, the user recommended conditioning scheme comprises four categories of diet health preserving, emotion health preserving, sports health preserving and massage health preserving.
As an embodiment of the present invention, a method for using an intelligent remote auxiliary diagnosis and treatment platform for traditional Chinese medicine based on a generated countermeasure network is also provided, which includes the following steps: after the user logs in the platform, the user is connected with signal acquisition external equipment, and then the user can use the signal acquisition external equipment to perform diagnosis operation; the user can clearly carry out corresponding operation according to the content of the software operation area to provide a remote diagnosis request, and the acquisition equipment starts to acquire the physical condition of the user; when the physical condition of a user is collected, the user needs to sit quietly after the sensor is connected, and after the body is stable, the user clicks a start collecting button to start collecting the body data; the acquisition time lasts for several minutes as far as possible, so that the information parameters in the information can be conveniently analyzed, and the large-amplitude actions which can aggravate the heart beating instantly are avoided as far as possible, so that the information is flawed, and the final accuracy is reduced; after the user finishes collecting, clicking a collection stopping button; the acquired data can be uploaded to a cloud platform, the cloud platform carries out remote intelligent auxiliary diagnosis and recommends a user conditioning scheme; after the diagnosis operation is finished, the diagnosis result and some important parameters related in the diagnosis process can be stored by clicking the storage button, and the user data can be stored in the database so as to be convenient to inquire at any time.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (10)
1. An intelligent traditional Chinese medicine remote auxiliary diagnosis and treatment platform based on a generated countermeasure network is characterized by comprising a traditional Chinese medicine data collecting module, a user conditioning scheme model module, a user physical condition data collecting module, a traditional Chinese medicine health preserving knowledge base module and an intelligent auxiliary diagnosis module; wherein the content of the first and second substances,
the user physical condition data acquisition module is used for acquiring state information of the physical condition of the user;
the user conditioning scheme model module is based on a user conditioning scheme model module for generating a countermeasure network and is used for recommending a conditioning health-preserving scheme model for a user;
the traditional Chinese medicine data collection module is used for collecting a sample data set from the traditional Chinese medicine health preserving knowledge base module and performing data enhancement and labeling on the collected sample data set;
the intelligent auxiliary diagnosis module is used for receiving the state information and providing a conditioning scheme for the user or recommending a doctor of the user.
2. The intelligent TCM remote auxiliary diagnosis and treatment platform based on generation of countermeasure network according to claim 1, wherein the generation of countermeasure network structure adopted by the user conditioning scheme model module is a deep convolution countermeasure network, wherein the deep convolution countermeasure network generation method comprises:
inputting random noise into a full-connection network, converting the random noise into a three-dimensional tensor through dimension transformation, sending the three-dimensional tensor into a generator network G, generating samples which are as close to real data distribution as possible, then inputting the generated samples and collected samples into a discriminator network D, outputting a classification result by the discriminator network D, transmitting errors of the classification result and the real result into the generator network G and the discriminator network D, and continuously iterating the whole process.
3. The intelligent remote assistant diagnosis and treatment platform for traditional Chinese medicine based on generation of countermeasure network as claimed in claim 2, wherein the random noise is a gaussian distribution-compliant random noise.
4. The intelligent remote assistant diagnosis and treatment platform for traditional Chinese medicine based on generation countermeasure network as claimed in claim 2, wherein the generator network G comprises 1 fully connected layer and 5 micro-step convolution layers, each micro-step convolution layer is followed by batch normalization and activation functions, wherein the first 4 activation functions are ReLU functions and the last activation function is Tanh function.
5. The intelligent TCM remote auxiliary diagnosis and treatment platform based on generation of countermeasure network according to claim 3, wherein the batch normalization comprises calculating the mean of data of each batch, calculating the variance of data of each batch, and performing normalization operation and scaling and migration on the data of each batch by using the mean and variance calculated in the previous two steps.
6. The intelligent TCM remote auxiliary diagnosis and treatment platform based on the generative confrontation network as claimed in claim 3, wherein the G loss function of the generator network is a feature matching method for matching the statistical distribution of the generative sample and the real sample, and the expression is as follows:
where f denotes a feature value of the discriminator intermediate layer, and z denotes input noise.
7. The intelligent remote traditional Chinese medicine diagnosis and treatment platform based on the generation countermeasure network as claimed in claim 2, wherein the network structure of the discriminator network D includes 5 convolutional layers and 3 fully connected layers, the convolutional kernel size selected by the convolutional layers in the network is 5 × 5, each convolutional layer is followed by batch normalization processing and an activation function, the activation function is a LeakReLU function, the LeakReLU has a slope in the negative half axis, the loss of the gradient in the training process can be avoided, Dropout is added to the fully connected layers to prevent overfitting, and finally the result is output by a softmax function, and the expression is:
wherein, K is the real sample category, K +1 is the generation sample category, and the maximum value is the output probability of the category.
8. The intelligent TCM remote auxiliary diagnosis and treatment platform based on the generative countermeasure network of claim 7, wherein the discriminator network D loss function is a cross entropy loss function of real class label distribution and prediction class label, and the expression is as follows:
wherein, x is a real sample, y is a category corresponding to the sample data, and x, y-PdataIndicating that the input samples bear the label y and x-G indicating that x is taken from the generated samples.
9. The intelligent TCM remote auxiliary diagnosis and treatment platform based on the generative confrontation network of claim 1, wherein the user physical condition data acquisition module is used for acquiring the state information of the user, including venous qi and blood state, arteriovenous blood state, cold and heat deficiency and excess state, facial diagnosis thoracic cavity state and electrocardio meridian state.
10. The intelligent remote assistant diagnosis and treatment platform based on traditional Chinese medicine based on generation of countermeasure network of claim 1, wherein the knowledge base of traditional Chinese medicine health preservation comprises traditional Chinese medicine health preservation principle, traditional Chinese medicine health preservation method and traditional Chinese medicine health preservation application.
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| CN107506603A (en) * | 2017-09-08 | 2017-12-22 | 深圳市前海安测信息技术有限公司 | Remote Chinese medical assisting in diagnosis and treatment system and method |
| CN107818822A (en) * | 2017-10-14 | 2018-03-20 | 深圳市前海安测信息技术有限公司 | The traditional Chinese medical science is preventiveed treatment of disease assisting in diagnosis and treatment system and its data processing equipment |
| CN107887024A (en) * | 2017-12-14 | 2018-04-06 | 华子昂 | Intelligent diagnosis system and traditional Chinese medical science robot based on traditional Chinese medical science robot |
| CN108829855A (en) * | 2018-06-21 | 2018-11-16 | 山东大学 | It is worn based on the clothing that condition generates confrontation network and takes recommended method, system and medium |
| CN109222914A (en) * | 2018-11-06 | 2019-01-18 | 北京三医智慧科技有限公司 | The information collection of Chinese medicine facial diagnosis and intelligent processing method based on deep learning and multi-optical spectrum imaging technology |
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| CN107506603A (en) * | 2017-09-08 | 2017-12-22 | 深圳市前海安测信息技术有限公司 | Remote Chinese medical assisting in diagnosis and treatment system and method |
| CN107818822A (en) * | 2017-10-14 | 2018-03-20 | 深圳市前海安测信息技术有限公司 | The traditional Chinese medical science is preventiveed treatment of disease assisting in diagnosis and treatment system and its data processing equipment |
| CN107887024A (en) * | 2017-12-14 | 2018-04-06 | 华子昂 | Intelligent diagnosis system and traditional Chinese medical science robot based on traditional Chinese medical science robot |
| CN108829855A (en) * | 2018-06-21 | 2018-11-16 | 山东大学 | It is worn based on the clothing that condition generates confrontation network and takes recommended method, system and medium |
| CN109222914A (en) * | 2018-11-06 | 2019-01-18 | 北京三医智慧科技有限公司 | The information collection of Chinese medicine facial diagnosis and intelligent processing method based on deep learning and multi-optical spectrum imaging technology |
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