110 results about "Disease early" patented technology

The invention belongs to supervisory technical field for distance treatment, particularly for applying far distance patient monitor system and centralized continuously monitoring process to high-risk people of heart disease. Said system comprising portable monitor carried by patients, hospital central monitor center, doctors' far distance group consultation terminal, ambulance monitor, communication net system, etc. The monitor process is, through communication network such as mobile , local, wireless and internet communication, making hospital central monitor center and portable monitor in and out of the hospital , ambulance carrying monitor and far distance group consultation real-time connecting, exchanging and transmitting information such as real-time ECG, blood pressure, etc., patient geographical location, doctor group consultation and advice, and far distance equipment remote measurement & program controlled in each terminal, realizing continuously monitor to high-risk people of heart disease, applying central remote measurements and program control of far distance mobile terminal, prior to coming on of heat disease early period proper diagnosis given and applying real-time treatment to serious patient, etc.

Real-time and individualized disease monitoring is central to rapidly evolving medical sciences and technologies, but for the vast majority of patients, disease progression and treatment are monitored only in an irregular and discontinuous fashion. Consequently, disease progression and relapse are often allowed to proceed too far before they are detected, compromising the possibility of any effective treatment. For one patient, this can mean becoming refractory to the few early drug treatments that are available; for another, missing early detection may be deadly. This invention provides a method for the detection of early signals of disease and recovery thereof comprising a universal yet personalized health-monitoring solution using cell phones or other wearable smart device data that generate extensive real-time data. The invention further provides a system and method to provide answers to a variety of questions related to the patient health status and health trajectory. Its flexibility and generality is designed for a preferred application to rare disorders and rare questions for which other analytical system are lacking.

The invention relates to a health risk assessment and disease early warning information system, and belongs to the technical field of health management. The system comprises an employee health management system and a personal health system; the employee health management system comprises 11 modules such as an employee basic information management module, an employee health data acquisition module,and an employee health questionnaire management module; the personal health system consists of an ordinary user registration sub-module, a personal information management sub-module, a personal health history management sub-module, a daily health monitoring sub-module, a medical sick rest management sub-module, a health questionnaire sub-module, an annual health examination sub-module, a health condition assessment sub-module, a disease risk assessment sub-module, a health promotion management sub-module, and a health consultation forum sub-module. The health risk assessment and disease earlywarning information system forms an integrated network management mode of health management, health risk assessment and health risk intervention control.

The invention relates to the technical field of a medical treatment information decision-making system and provides a method for extracting a morbidity key factor and early warning a disease. The method comprises the following steps: pre-treating a data: converting a medical record file data into a training value matrix; establishing a disease early warning model; and searching for an optimal early warning model through verification. The invention also provides a system for extracting the morbidity key factor and early warning the disease. Through the method and the system for extracting the morbidity key factor and early warning the disease provided by the invention, a high-risk factor of the disease is extracted, the early warning of disease mutation is performed, the realization is simple and the effect is good.

The invention discloses an intelligent system for healthy breeding of live pigs. The system comprises a live-pig precise liquid feeding subsystem, a disease early warning subsystem, an environmental monitoring and controlling subsystem, a remote monitoring subsystem, a live-pig breeding cloud big datum center and intelligent supporting subsystem, and a control system. The system disclosed by the invention uses a scientific live-pig breeding technology as a theoretical guide, and is combined with a high-integrated novel technology of sensor, internet and information processing technologies, sothat the system can sensitively detect health conditions of the live pigs, thereby realizing timely and controllable epidemic prevention, guiding farmers to perform healthy breeding of the live pigs,and greatly improving scale benefits of the farmers of the live pigs; the system realizes controllability of disease early warning and systematization of datum processing, and reduces the breeding risk of the farmers of the live pigs; and the system has high reliability and good real-time performance, replaces manual operation in a fully-automatic manner, greatly optimizes a structure of choosingand employing people in a pig farm, reduces pressure of choosing and employing the people in the pig farm, effectively saves manpower and material resources and reduces management costs.

The invention discloses a wearable NIRS-EEG based schizophrenia early detection evaluation system. The system comprises an NIRS-EEG bimodal cerebral function monitor device, a mobile intelligent terminal and a network server, wherein the NIRS-EEG bimodal cerebral function monitor device is used for acquiring cerebral function information of schizophrenia patients or healthy persons in real time by adopting NIRS and EEG technologies, and transmitting the cerebral function information to the mobile intelligent terminal through bluetooth signals; the mobile intelligent terminal is used for uploading data to the network server for data processing via mobile internet; and the network server is used for saving the processed data, generating a corresponding diagnosis result, and feeding back to the mobile intelligent terminal for data waveform display. The system has the advantages of accurate detection evaluation result and extremely low power consumption, and can be used for quickly and conveniently performing early detection and disease early warning for schizophrenia patients and providing scientific and reasonable guidance for nursing schizophrenia patients, so that pains and psychological burden of schizophrenia patients and family members thereof can be reduced.

The invention relates to public health information acquisition technology, and discloses an electronic health station system and a using method thereof. The electronic health station system comprises an electronic health record management system and an electronic health station terminal, which are used for integrally detecting human body sign parameters. The electronic health station system is characterized in that the electronic health record management system is disposed at a networker server end, and the electronic health station terminal serves as a client and is connected with the electronic health record management system through a network. Compared with the prior art, the system and the method are used for detecting multiple parameters by centralized operation flow, resource waste in technical repeated development and incompatibility of equipment data are avoided, and health states of a user can be historically tracked and analyzed continuously through electronic records so as to perform disease early warning and health intervene in advance. The system can be installed at public places to acquire health information of different people and issue the information, people's health awareness and health information attainment can be guided to the maximum extent, and evidence for establishing sharing of public health data in the internet of things is provided.

The present invention relates to a detection chip for performing gene detection to various vibrio and its detection and applications. The invention provides 16S rRNA sequences corresponding to each vibrio of vibrio anguillarum, vibrio harveyi, vibrio alginolyticus, vibrio parahaemolyticus, brilliant vibrio and Fisher vibrio; heat shock protein hsp60 probe sequence; virulence gene probe sequence; 16S rRNA forward primer sequence; 16S rRNA reverse primer sequence; heat shock protein hsp60 forward primer sequence; heat shock protein hsp60 reverse primer sequence; virulence gene forward primer sequence and virulence gene reverse primer sequence. The present invention has specific, sensitive and high-throughput features, can simultaneously detect six kinds of bacteria virulence genes, and the invention will effectively guide the production as an important disease early-warning detection method used in clinical diagnosis of aquatic animals.

The invention relates to a pulse detector, a pulse monitoring device and a system for locally and remotely monitoring and warning, wherein the pulse detector comprises a sensor and a plurality of probes which are respectively arranged on the sensing surface of the sensor at preset intervals; each probe transfers the signals detected by each probe to the sensing surface of the sensor; and the signals received by the sensing surface are superposition of signals transferred by all the probes. The pulse monitoring device comprises the pulse detector, a signal processing circuit, an input device, an output device and a wireless communication module, wherein the signal processing circuit is connected with the pulse detector, and the input device, the output device and the wireless communication module are connected with the signal processing circuit. The pulse detector can be used for obtaining pulse signals with the advantages of high intensity, small interference, stability, reliability and insensitive positions, and can be used for establishing a reliable, practical and real-time pulse monitoring system which can be used all day long to pay close attention to patients every moment, thereby treating diseases early when some symptoms occur.

The invention provides a disease early-warning method based on a knowledge base. The disease early-warning method comprises the steps of building a kidney disease knowledge base; monitoring inspectiondata of a patient; and inputting the inspection data of the patient into the knowledge base, and outputting a diagnosis result. According to the invention, the knowledge base of diseases is established by combining authority data such as clinical consensuses, clinical guidelines and clinical pathways of specialized diseases on the basis of historical clinical data of hospitals, and various dangerous factors and association relations thereof are automatically integrated based on an early-warning method of the specialized disease knowledge base, so that problems of solidified indexes and poor flexibility of the traditional expert system are solved. Meanwhile, the knowledge base is generated under specialized medical treatment alliance based on mass personalized data, and probability based relevance reasoning under the mass data well solves a problem of low discrimination of the traditional machine learning method. The invention further discloses a disease early-warning system based on the knowledge base.

The invention discloses a detecting method for accurate risk early warning and accurate drug use of cardiovascular and cerebrovascular diseases and a specific primer. The invention provides a specific primer for detecting SNP site of drug resistance of the cardiovascular and cerebrovascular diseases, which comprises a primer group 1 and a primer group 2; the primer group 1 is composed of primer pair 1-primer pair 13; a primer group 2 is composed of single stranded extended primer 1- single stranded extended primer 13; the method can efficiently detect and complete the accurate early warning of the attack of the cardiovascular and cerebrovascular diseases and the accurate drug use after being attacked, namely, four kinds of cardiovascular and cerebrovascular disease early warning with huge health risk and accurate drug use of four clinical common drugs (aspirin, nitroglycerin, warfarin, and clopidogrel) for prevention and treatment of cardiovascular and cerebrovascular diseases.

The invention discloses a multi-modal fusion image sorting method based on RLS-ELM. The method is characterized by comprising the steps that 1. rs-fMRI, sMRI and DTI data of a plurality of tested objects are obtained, preprocessing is carried out, and tested data which do not accord with a provision are removed; 2. ReHo values of voxels in the rs-fMRI data are computed; 3. grey matter density values of voxels in the sMRI data are computed; 4. FA values of voxels in DTI data are computed; 5. the ReHo values, the grey matter density values and the FA values of the voxels are connected to form a new characteristic matrix A; 6. the new characteristic matrix A is subjected to PCA dimension reduction processing; and 7. an RLS-ELM sorter is subjected to training, and a trained RLS-ELM sorter is obtained. According to the multi-modal fusion image sorting method based on RLS-ELM, sorting accuracy and sorting speed are obviously improved, disease early discovering and early treating are achieved, and great significance is achieved in a clinical medicine study process for revealing disease progression is achieved.

The embodiment of the invention provides a disease prediction method and device, a medium, and electronic equipment. The method comprises the steps: obtaining the data, correlated with health, of a plurality of users; carrying out the clustering analysis of the plurality of users based on the data, correlated with the health, of the users, so as to dividing the users into at least one class; determining a target class where a diagnosed patient belongs, and predicting other users in the target class as a high-risk group of diseases. According to the technical scheme of the invention, the methodcan achieve the prediction of the group which may have diseases, can guarantee the timely finding of severe diseases, so as to find and treat a disease early, and improve the level of health management of users.

The invention discloses a sow breath system disease early warning method based on an acoustic analysis. The method includes following steps: S1, setting an audio acquisition node for acquiring a soundsignal; S2, establishing a classification for determining whether the sound signal is a cough audio; S3, extracting cough information to determine whether the sow is ill; S4, obtaining a sound sourceposition; S5, transmitting illness information by a GSM module; S6, receiving the information by an administrator; and S7, determining whether monitoring is continued: if yes, returning to step S2, and otherwise, ending the process. According to the method, the sound signals in a pig house can be directly acquired, the cough sound of the sow is recognized in real time, information carried by thecough audio is extracted and recognized, whether the sow has a breath system disease is determined, the automation level of a cultivation environment of the pig house can be improved, man-made subjective determination is reduced, and the recognition accuracy of the breath system disease is improved.

The invention is suitable for the technical field of disease prediction, and provides a disease early-warning method and device. The method comprises the following steps: acquiring first information of a target user, wherein the first information comprises image information; matching the first information with second information corresponding to early-warning stages in a preset database, and acquiring an early-warning stage corresponding to an early-warning feature of the target user and a corresponding risk factor according to a matching result, wherein the preset database comprises a plurality of early-warning stages and the second information corresponding to the early-warning stages, and each early-warning stage corresponds to at least one risk factor; and performing an early-warning analysis on the early-warning feature of the target user based on the risk factor in combination with a time sequence model according to the early-warning stage corresponding to the early-warning feature of the target user. The method has the advantages of high individual adaptability, low cost and high prediction accuracy.

The invention discloses a risk assessment method for wide-range crop diseases. The method comprises the following steps that: selecting the n pieces of meteorological parameters of a monitoring area,giving an attack earlier stage meteorological early waning, carrying out meteorological encryption monitoring, calculating each meteorological parameter to obtain a disease occurrence posterior probability, predicting a meteorological multi-factor disease probability, adopting an inverse distance weighted method to carry out spatial interpolation, generating an accumulated meteorological risk map,generating a remote sensing growth vigor map, and generating a disease occurrence appropriateness map. The method has the characteristics that comprehensive information, including meteorology, crop habitats, representation after crops are stressed and the like, can be comprehensively considered for giving a disease early warning, and the method can be reused by various main crop diseases.

The invention provides an automatic image processing and displaying method based on a magnetic resonance cerebral perfusion image, which is used for assisting in evaluating cerebral ischemia disease risks and is characterized by comprising spatial deformation, multi-parameterization, brain map coverage and a method for evaluating ischemic cerebral disease risks. The image space deformation methodcomprises the following steps: head movement correction, which is used for adjusting a space difference generated by head movement in a cerebral perfusion imaging process; and standard brain registration: taking the standard brain as a reference to realize standardization of the imported cerebral perfusion image. The multi-parameterization is used for converting the original nuclear magnetic datainto cerebral image perfusion parameters such as cerebral blood flow, cerebral blood volume and artery arrival time, wherein the coverage brain map is used for enabling the parameterized cerebral perfusion image data to cover standard brain maps of different templates to form different regions of interest, and calculating average parameter values of the different regions of interest.The standard cerebral map comprises a cerebral artery blood supply area and an early CT scoring area of an Albetan stroke project. The ischemic brain disease risk assessment method is used for comparing the averageparameter value of the region of interest with a reference range, displaying the differentiation degree and automatically displaying the region of interest beyond the reference range. According to the invention, multi-parameter automatic partitioning and quantitative processing from nuclear magnetic cerebral perfusion original number to ischemic cerebral disease early-stage risk assessment are realized, and an effective assessment method is provided for early-stage screening, early-stage diagnosis and early-stage treatment of cerebral ischemic diseases.

The invention discloses a device for automatically detecting and recognizing human body temperature and an application method thereof. The front side of a shell of the device is provided with a face recognition camera, a human body temperature infrared detection head, a display screen and a body temperature alarm module, the shell is internally provided with a control module and a communication module which are connected with each other, the output ends of the face recognition camera and the human body temperature infrared detection head are separately connected to the control module, and thedisplay screen and the body temperature alarm module are separately connected to the control module. The method comprises the following steps: carrying out face recognition on a person through the face recognition camera to determine the identity of the person, and simultaneously detecting the body temperature of the person through the human body temperature infrared detection head, and recordingthe identity and the body temperature information of the person; and if the body temperature of the person exceeds a preset threshold value, outputting alarm information through the body temperature alarm module. The device can realize the automatic detection of human body temperature in a key area of disease early warning monitoring, has high detection accuracy and quick information transmission,and can provide complete and reliable data for realizing accurate disease early warning monitoring and prediction.

The invention discloses a multilayer information monitoring early warning method of early diseases of crops. The objective is to provide a lossless monitoring method of early diseases of crops. The method comprises steps of shooting the whole crop to obtain an object infrared image; calculating the average temperature of a monitoring object pixel point, the average temperature of a low-temperature pixel point and the average temperature of a high-temperature pixel point, and calculating the temperature difference between the average temperature of the low-temperature pixel point and the average temperature of the high-temperature pixel point; comparing the temperature difference with a monitoring threshold value and taking the temperature difference as the basis for determining whether the crops are infected with diseases; using a visible light-near infrared high spectral imaging device to monitor the crops suffering the diseases to obtain characteristic images; using a microscopy imaging device to monitor disease speckles in the characteristic images,= to obtain microscopy images; and according to a microscopy image gray scale histogram, estimating the early disease degree of the crops by combining the characteristic images. According to the invention, multilayer information of temperatures, pigments and textures of the crops is monitored; and disease detection and precise analysis in the incubation period and at the disease early stage are achieved.

The invention discloses a disease early-warning method, a disease early-warning device and a computer-readable storage medium, wherein the disease early-warning method, the disease early-warning device and the computer-readable storage medium are applied to the field of knowledge relation analysis, wherein the method comprises the steps of acquiring a knowledge atlas and high-frequency seasonal diseases, wherein medical data generated in diagnosis of a plurality of patients are stored in the knowledge atlas; extracting morbidity data of the high-frequency seasonal disease in the knowledge atlas, wherein the morbidity data comprise morbidity time of all patients in affecting the high-frequency seasonal disease; determining the morbidity time period and morbidity scale of the high-frequencyseasonal disease according to the morbidity data of the high-frequency seasonal disease; and reminding the disease early-warning information, wherein the disease early-warning information comprises the morbidity time period and the morbidity scale of the high-frequency seasonal disease. The disease early-warning method, the disease early-warning device and the computer-readable storage medium predict the morbidity time period and the morbidity scale of the high-frequency seasonal disease, thereby warning management personnel to perform corresponding prevention and medicament preparation, thereby supplying a quick and high-efficiency disease early-warning method.

The invention discloses an airport disease early warning and prevention and control method. The method generally comprises the steps that (1) a preliminary disease early warning pool is established, and popular infectious disease symptoms and physiological value indicators are input thereto; data collectors are arranged at an airport entry and exit to collect the relevant physiological values of entry and exit personnel and the corresponding personnel information; (2) the relevant physiological values of entry and exit personnel and the corresponding personnel information are transmitted to alocalization relation database, and the localization relation database compares the data with symptoms and indicators in the disease early warning pool to screen out abnormal data; and (3) the information of infected entry and exit personnel, the infected infectious diseases, and the onset time are sent to the nearest medical institution, and the personnel and the surrounding population thereof are monitored to further prevent disease spread. According to the invention, infectious diseases and infectious diseases in an incubation period can be effectively and accurately detected, and isolationis adopted when necessary.

The invention discloses an alzheimer early diagnosis liquid chip which mainly includes the following components: coating microsphere, microsphere coated with t-tau capture antibody, microsphere coated with p-tau231 capture antibody, microsphere coated with p-tau181 capture antibody, microsphere coated with p-tau199 capture antibody, microsphere coated with A beta 1-42 capture antibody, these microspheres have codes with different colors; biotin-labeled test antibody; avidin-linked phycoerythrin. The invention alzheimer early diagnosis liquid chip has advantages of high detection efficiency, a small amount of sample, high specificity and high sensitivity. And at the same time, every neural biochemical injury marker can freely assemble for convenient usage. At the same time, because all the reactions are in liquid environment, which is good for maintaining native conformation of protein, the reaction between probe and tested material is faster and more complete, so the test sensitivity and the linear range are improved significantly.

The embodiment of the invention relates to the technical field of artificial intelligence, and particularly discloses a medical disease early warning method and system and a storage medium. The methodcomprises the steps of obtaining body parameter information related to a user; bringing the body parameter information into a pre-built optimal testing model for performing prediction, and obtainingthe body health state of the user; when it is determined that the user has an insidious disease or the disease has happened according to the body health state of the user, and sending the early warning information to the user in time. In this way, the insidious disease of the user can be known in advance or whether or not the user has a disease can be known in advance, and the early warning information is sent out to the user in time. In addition, the trouble of queueing in the hospital is omitted. A training model is obtained by combining a large amount of sample data and doctor experience, so that the diagnostic and treating level is not lower than doctors in the hospital, and even the diagnostic and treating accuracy is higher. No influence is caused by doctor emotions and fatigue, thepatient can be diagnosed in real time, and practicability is higher.

The invention relates to an online early warning method for plant diseases in facilities based on Terahertz spectroscopy. The method comprises the following steps: S1, selecting first and second sampling points of a plant for extracting air; S2, acquiring Terahertz spectrum intensity values of the first and second sampling points through Terahertz spectrometer detection; S3, calculating the difference of the Terahertz spectrum intensity values of the first and second sampling points; S4, judging whether the content of disease spores in air near the plant exceeds the standard or not according to comparison between the difference of the Terahertz spectrum intensity values of the first and second sampling points and an experience threshold. A point close to the middle lower part of the crop in the facility and a point which is far away from the crop are selected, the strong absorption characteristic of water on Terahertz waves is utilized, absorption of air at the sampling point on the Terahertz spectrum is measured in real time, the difference is found, the distribution number of the disease spores in the air near the crop is judged by comparing the difference with the experience threshold, and a real-time disease early warning aim is achieved. The invention also discloses an online early warning system for plant diseases in facilities based on Terahertz spectroscopy.

The invention provides a disease early warning method and system, relates to the technical field of the Internet of Things, and regards an Internet of Things server as an execution main body. The method includes steps: receiving disease description information transmitted by an acquisition terminal; processing the disease description information, and generating disease information; assessing the disease information according to a preset rule, and generating disease early warning information; and performing format conversion on the disease early warning information and transmitting the converted information to a user terminal to enable the user terminal to display the disease early warning information. According to the method and system, the Internet of Things server is adopted so that thedisease early warning time is shortened, and the disease prevention and control range is extended.

The invention discloses a man-machine cooperation system and method for disease early warning and intervention management. The man-machine cooperation system comprises: a monitoring data collection device, a clinical data gateway, a score calculation server, an observation data recording gateway, an observation data recording terminal, an intervention plan server, an intervention activity record terminal, a score calculation monitoring terminal, and an intervention plan monitoring terminal; the monitoring data collection device is connected to the clinical data gateway; the score calculation server is respectively connected to the clinical data gateway, the observation data record gateway, the intervention plan server and the score calculation monitoring terminal; the observation data recording terminal is connected with the observation data recording gateway; and the intervention activity recording terminal and the intervention plan monitoring terminal are connected to the intervention plan server. The man-machine cooperation system and method for disease early warning and intervention management proposed by the invention can reduce the workload of medical staff on patient assessment, and improve work efficiency and accuracy.

The invention provides an intelligent robot-type patient care bed, which comprises a bed body, wherein the front end and the back end of the bed body are connected with a bedside turnover plate and a bed end turnover plate separately; the lower surface of the bedside turnover plate is connected with a hinge mechanism so as to drive the bedside turnover plate to complete upward turnover within an angle of 0 to alpha; the lower surface of the bed end turnover plate is connected with the hinge mechanism so as to drive the bed end turnover plate to complete downward turnover within an angle of 0 to beta; an intelligent closestool and a sewage water locking system communicating with the intelligent closestool are arranged at the lower part of the patient care bed; guardrail brackets and flushing devices are arranged at two sides of the bed body; and a control box and a bluetooth remote control device are arranged in the patient care bed. The patient care bed can achieve a nursing function in a bedroom, and is capable of automatically lifting the angles of the bedside and the bed end, thus promoting the user experience of a user. In addition, the intelligent closestool is arranged at the lower part of the bed body, so that the convenience of the user is improved. Robot arms are arranged at the side parts of the bed body and are capable of assisting an air cushion in rollover, local washing and the like. A bluetooth controller is matched with a film and television product, so that the human-computer interaction functions such as online diagnosis, disease early warning and off-bed early warning can be achieved.

The invention provides to a disease early warning system and a method based on medical big data, the method comprises the steps: a medical information packet is acquired from a hospital information system, and the medical information packet is cut into a lot of medical data; disease date in each piece of medical data information is analyzed; historical weather information corresponding to the disease date in each medical data is acquired from a weather information platform, and each piece of medical data information is related to the corresponding historical weather information; the medical data related to the historical weather information is analyzed to acquire patients influenced by weather factors; weather forecast information is acquired from the weather information platform, so that if the weather influence degree of the patients is greater than disease influence factor can be judged; when the weather influence factor is greater than the disease influence factor, early warning information is generated and sent to client terminals corresponding to the patients. According to the invention, the system and the method can obtain the patients influenced by the weather factor and can warn the patients in time, so that the risk of patients of sick again influenced by the weather factor can be reduced.

The invention discloses a disease public opinion monitoring method and device, electronic equipment and a computer readable storage medium, and relates to the technical fields of computers, artificialintelligence, data processing, public opinion monitoring and disease early warning. The specific implementation scheme is as follows: acquiring disease query information within a preset time, summarizing to generate a query information set, determining a disease information set hit in the query information set based on a pre-constructed disease knowledge graph, and aggregating different types ofdisease information in the disease information set by adopting a pre-determined similarity algorithm to obtain a plurality of pieces of classified disease information, and generating a monitoring listbased on the hit times of the plurality of pieces of classified disease information in the query information set. The disease knowledge graph is used for determining the disease information existingin the disease query information, and disease public opinion monitoring and disease early warning are achieved based on the disease query information.

The invention discloses an early-warning system and method for senile dementia, and belongs to the technical field of medical process management artificial intelligence. The invention solves the problem of how to quickly evaluate the influence of personal daily life and risk factors on the cognitive function and perform early warning. A data acquisition and processing module is used for acquiringbody parameter information related to an acquisition object and performing data processing on the information; a data classification processing and analysis module is used for carrying out storage, data indexing and engine and data deep learning on the processed data and carrying out correlation analysis on the processed data, health medical big data and biomolecular big data; and an early-warningmodule is used for constructing a cognitive function test score and correlation analysis of a brain area function state and daily life habits and medical health detection data, and carrying out riskfactor-based disease occurrence early warning and drug treatment-based curative effect evaluation before and after drug treatment. According to the invention, the influence of personal daily life andrisk factors on the cognitive function is rapidly evaluated, and the disease early warning is carried out.