1609 results about "Elderly people" patented technology

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal data fusion processing method and system, and the method comprises the steps: collecting a multi-modal data set containing the activities of the old through an intelligent old-age care robot; constructing a timestamp calibration matrix based on the multi-modal data set, and carrying out dynamic weight correction on the timestamp calibration matrix by adopting a three-modal data envelope delay compensation algorithm to obtain a three-modal data stream with a synchronous time sequence; performing hierarchical feature extraction through a nested feature extractor to obtain a three-mode tumble feature vector group; performing vector fusion on the three-mode tumble feature vector group to obtain tumble fusion feature vectors; and inputting the tumble fusion feature vector into a multi-modal forward prediction model to perform tumble risk trajectory prediction, and outputting a tumble detection result, the method realizes prediction analysis of body posture changes in a key time window before the old man tumble, and improves the ability to distinguish a slow getting-up action and a real tumble behavior of the old man.

The invention discloses an intelligent control system and method based on an AI intelligent home model, and the system comprises a data acquisition module which is used for collecting home related data in real time based on a multi-dimensional sensing system, and the data comprises indoor and outdoor environment parameters and habitant physiological feature data; the data analysis module is used for performing resident comfort demand prediction analysis on the home related data based on the AI smart home model to obtain the comfort demand of the resident; and the environment regulation and control module is used for carrying out working parameter setting and working mode adjustment on the household electrical equipment based on the comfort degree requirement, so as to realize personalized environment adjustment for different residents and scenes. The AI smart home model accurately predicts the comfort demands of the residents, and the system can provide customized environment adjustment schemes for individual differences of different residents, meets the differentiated demands of special groups such as old people, children, pregnant women and the like, and improves the living experience.

The invention discloses an old people cognitive ability assessment system and method based on multi-modal fusion, and relates to the field of old people cognitive assessment, and the method comprises the steps: firstly obtaining the current multi-modal data of a user, and fusing the current multi-modal data into a current feature vector; then, instead of being compared with the universality standard, the personal baseline portrait of the user is called, and the score of the difference degree between the current state and the historical baseline of the user is calculated; particularly, the core index of the drift speed is introduced, and the difference degree and the historical change rate are combined, so that quantitative modeling is carried out on the changed speed. By analyzing the amplitude and the speed of the change at the same time, normal aging with gentle change and low speed and pathological recession with violent change and high speed can be effectively distinguished, so that the key technical problem that the normal aging and the pathological recession are easy to be confused in the background technology is accurately solved.

The invention relates to the technical field of intelligent health, and discloses an intelligent health middle-aged and elderly people behavior data real-time monitoring and analysis method and system, and the method comprises the steps: collecting a biological resonance signal, a gas spectrum and a sound wave signal of middle-aged and elderly people; constructing a resonance characteristic spectrum of the middle-aged and elderly people, and analyzing physiological feedback characteristics of the middle-aged and elderly people by using the resonance characteristic spectrum; constructing an odor-behavior association map of the middle-aged and elderly people, and identifying gas feedback characteristics of the middle-aged and elderly people by using the odor-behavior association map; performing frequency domain decomposition on the sound wave signal to obtain an infrasonic frequency band and an ultrasonic frequency band, identifying internal organ vibration characteristics and body surface action characteristics of the middle-aged and elderly people, and analyzing voiceprint feedback characteristics of the middle-aged and elderly people; the physiological feedback features, the gas feedback features and the voiceprint feedback features are used for conducting behavior analysis on the middle-aged and elderly people, and a behavior monitoring report is obtained. The method can improve the reliability of behavior abnormality analysis of the elderly in smart health.

The invention relates to the technical field of data processing, and discloses an old people health state assessment data processing system based on multi-modal data, and the system comprises a medical data integration module which obtains electronic medical record data and a medical examination report through an FHIR interface, and extracts a structured health index; the cross-modal causal fusion processing module is used for fusing the monitoring data and the medical text through an image, text and image interlayer architecture; the health state evolution modeling module is used for mapping the health feature vectors into physiological function, cognitive level and athletic ability three-dimensional state indexes; an evaluation report backtracking module; and a decision output module. Through an image, text and image interlayer architecture, deep semantic fusion of multi-modal features is realized under the constraint of medical pathology rules, feature weight adaptive distribution is dynamically guided based on a medical causal atlas, a high-dimensional fusion vector retaining key pathology information is generated, the semantic integration ability of health data is remarkably improved, and the health data fusion efficiency is improved. And the reliability of discrimination and decision making is improved.

The invention discloses a community elderly weakness screening and intervention training method based on artificial intelligence, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining dynamic physiological parameters and medical data, and generating a weakness evaluation parameter set; determining a target weakness level and generating a personalized training scheme in combination with the user tolerance parameter set; generating a user action quality evaluation result based on real-time action monitoring, and adjusting a training intensity parameter according to a baseline error threshold; and returning an evaluation result, updating the weakness evaluation parameter set, and generating a community-level weakness early warning signal according to a group risk threshold. The time sequence physiological data fusion and decision tree-transfer learning hybrid model of the wearable device is adopted, real-time optimization of a personalized training scheme and dynamic early warning of group weakness risks can be achieved, and screening accuracy and intervention effectiveness are remarkably improved.

The invention relates to the technical field of medical or health data information, in particular to an intelligent health old-age care safety monitoring system, which comprises an old-age information acquisition module for acquiring face and basic information; the body health monitoring module predicts health parameters through LSTM, and if the health parameters exceed a threshold value or change abnormally, one is added to the alarm frequency; the psychological health monitoring module uses a StyleGAN to generate an enhanced data set and performs emotion classification through a convolutional neural network, and if the emotion deviates from a baseline, one is added to the alarm frequency; the tumble posture monitoring module judges tumble through posture estimation, and if tumble is detected, one is added to the alarm frequency; the position anomaly monitoring module identifies abnormal movement or residence, and if an anomaly is detected, the number of alarms is increased by one. And guardians and medical staff can obtain alarm information and monitoring reports in time according to the alarm times, so that the efficiency of monitoring the health and safety of the elderly is improved.

The invention relates to the field of community old-age care, and discloses a community old-age care-oriented medical-care-and-care-combined data intercommunication and service coordination system, which comprises a sensing module used for acquiring physiological data of old people, old-age care service data, environment data and service demand information to form multi-modal information; the network module is used for carrying out multi-modal information transmission, protocol conversion and edge calculation preprocessing to obtain processed data; and the data module is used for constructing a standardized data lake according to the processed data by adopting a unified data standard and a main data management technology, and carrying out fusion storage, treatment and safety protection to obtain multi-source information. Multi-modal information is collected to provide a data basis for cross-mechanism cooperation, data transmission and preprocessing are performed, and a standardized data lake is constructed through a unified data standard and main data management, so that medical institutions, pension institutions and community grids can share global data such as health states, service records and the like of old people in real time.

The invention relates to the technical field of intelligent medical monitoring, in particular to a personalized health monitoring and early warning system for elderly people living alone fused with multi-dimensional data, and the system comprises a data collection and preprocessing module which collects multi-dimensional physiological parameter signals of a user and converts the multi-dimensional physiological parameter signals into multi-dimensional medical features; the health prediction module performs health category prediction on the multi-dimensional medical features of the current time node based on a pre-trained deep learning model, generates a data graph under each time node, and stores the data graph and a corresponding health category prediction result in a specified directory; the early warning module analyzes whether the multi-dimensional physiological parameter signals under each time node are abnormal or not in a mode of combining a hard threshold value and a soft threshold value, and if yes, an alarm is given out; and the abnormity output module calls and outputs the data maps of the preset duration before and after the abnormity alarm time node and the corresponding health categories. According to the invention, personalized monitoring and risk early warning of the health state of the elderly can be realized more accurately from multiple dimensions.

The invention, which relates to the field of the intelligent accompanying system, discloses an interactive digital person accompanying system for the old with cognitive impairment, comprising a data acquisition module, an emotion recognition module, a first matching module, a second matching module, a personalized interaction generation module and a reinforcement learning adaptive module. The data acquisition module acquires multi-modal sensing data of a target old person; the emotion recognition module outputs an emotion label and an intensity value through feature extraction and Transform fusion; a first matching module screens corpus fragments according to emotion intensity triggering conditions; the second matching module performs secondary screening based on the retrieval weight; the personalized interaction generation module generates digital twin interaction data through probability distribution sampling; the reinforcement learning adaptive module dynamically adjusts the corpus weight according to the interaction reward value; according to the method, the dual guarantee of emotion guarding and physiological safety is realized, the anxiety emotion of the old with cognitive impairment is remarkably relieved, and the novelty of interaction and high emotion relieving efficiency can be kept.

The invention discloses a waistband type falling protection early warning device for old people living alone, and belongs to the technical field of intelligent wearable equipment. The problems that an existing device is single in early warning mode and cannot effectively guarantee the safety of old people living alone are solved, corresponding action results are matched for various data of the old people through the convolutional neural network and the lightweight decision tree model, and if a preliminary judgment result conforms to a normal mode, a judgment result is directly output; if not, triggering a secondary analysis mechanism to further judge whether the old people fall down or not; the GPS positioner is also used for analyzing whether falling behaviors occur at the same position for many times, and if a high-risk position is found, when the old people approach the high-risk position, the warning lamp and the alarm are used for reminding and paying attention to action behaviors; the fall detection accuracy is improved, the possibility of false alarm and missing alarm is reduced, the old people can be actively prevented from falling again in a high-risk area, and a more comprehensive safety guarantee is provided for the old people living alone.

The invention provides a radar-based off-bed detection method, system and product, and the method comprises the steps: carrying out the signal preprocessing of a collected radar echo signal, and obtaining a multi-dimensional echo signal containing the information of a distance dimension, a speed dimension and an angle dimension; performing target detection based on the multi-dimensional echo signal, and generating a target 2D point cloud based on a target detection result; clustering processing is carried out based on the target 2D point cloud, feature extraction is carried out based on a clustering result, and a feature vector is generated; and inputting the feature vector into a pre-trained human body in-bed state judgment model to carry out off-bed event detection. According to the off-bed detection method provided by the invention, actual image data is not needed, real-time point cloud of nursing personnel on the bed can be provided under the condition of not invading privacy, the off-bed event can be reported in time, rapid detection of the off-bed event of long-term bedridden old people can be realized, the off-bed detection response time can be effectively shortened, and the detection efficiency is improved. And the accuracy and the real-time performance of off-bed event detection are effectively improved.

The invention discloses a smart old-age service platform based on Internet of Things big data, and belongs to the technical field of Internet of Things old-age care, and the platform specifically comprises a voice instruction collection module which is used for collecting the interaction voice data of an old person and smart home equipment in real time; the voice feature extraction module is used for carrying out feature separation processing on the collected original voice data and extracting quantized instruction indexes; the baseline model construction module is used for establishing a voice health baseline model based on user characteristics and generating a personalized voice index reference interval according to the historical voice data of the old; the voice instruction comparison module is used for dynamically comparing the real-time instruction index with the baseline model, generating a voice health deviation degree curve, and triggering early warning when curve fluctuation exceeds a preset morphological characteristic; the interaction mode adjusting module is used for adjusting the interaction mode of the smart home equipment according to the voice health deviation degree; the health of the elderly is judged through the voice index change, and the smart home service quality is improved.

The invention relates to the technical field of intelligent control, in particular to an internet-of-things-based old-age care robot intelligent control system, which comprises a multi-mode sensing layer, a safety decision center, an execution driving module, a voice interaction optimization module and an internet-of-things communication module. According to the method, force sense data such as contact force of the mechanical arm, voice data such as voices of old people and view data such as obstacles are collected in real time through a multi-mode sensing layer, a displacement force coupling risk value is calculated through a hyperbolic tangent function coupling model of a safety decision center, and a deviation angle risk value is analyzed and calculated through an improved Lyapunov index spectrum; a total risk value is generated through fusion and is scheduled according to hierarchical intervals, mechanical operation, old people behaviors and environmental risks can be dynamically avoided, and the use safety is remarkably improved; through sub-ambiguity quantitative analysis, comprehensive ambiguity evaluation and intention correction of the voice interaction optimization module, instruction intention is optimized in combination with multi-modal data, the problem of fuzzy voice recognition of old people is effectively solved, and interaction accuracy and response efficiency are greatly improved.

The invention discloses a method for predicting an abnormal behavior state of an elderly living at home based on digital twinning, and the method comprises the steps: obtaining a social relation network data flow and a non-intrusive energy consumption data flow of the elderly living at home, and constructing a time sequence dynamic social relation graph based on the social relation network data flow, meanwhile, a semantic activity event chain is generated based on energy consumption data flow overload decomposition, cross-modal fusion processing is carried out on the social relation graph and the activity event chain, a composite state vector is generated, and based on a time sequence of the composite state vector, the social relation graph and the activity event chain are subjected to cross-modal fusion processing; and predicting the probability of entering a preset abnormal behavior state (such as social isolation, depression tendency or decline of self-care ability) of the home elderly. According to the invention, the privacy of the user is protected through non-intrusive data acquisition, and the social communication and exclusive activity information of the elderly are fused, so that the transformation from post-event alarm to pre-event early warning is realized, a precious time window is won for guardians or service institutions to take preventive intervention measures, and remarkable social and humanistic values are achieved.

The invention discloses a health monitoring system of an old-age care robot, which relates to the technical field of health monitoring, and comprises the steps of constructing an indoor multi-modal data pool, generating an encrypted feature vector, establishing a steady-state physiological behavior baseline according to a historical encrypted feature vector, and outputting a health risk level and an intervention instruction set. According to the invention, all-time and all-scene health monitoring is realized by integrating a multi-mode sensor, old people do not need to wear equipment, feature compression and homomorphic encryption technologies are adopted, data security is ensured, a threshold value can be updated in real time, and the method is suitable for being applied to the health monitoring of the old people. The health state of the old people is accurately monitored, health risk prediction and personalized intervention are carried out in combination with analysis of behaviors such as falling and static abnormity, the prompt mode is dynamically adjusted according to the ability of the old people, the data processing efficiency is optimized through the edge cloud collaborative architecture, the data privacy and stability are guaranteed, and the life quality and safety are improved.

The invention discloses an old people abnormal expression recognition method based on gradient covariance analysis. The method comprises the steps of old people expression data set construction, time sequence optical flow feature-based facial expression region screening, abnormal expression enhancement loss estimation, gradient covariance-based facial expression region feature enhancement, micro-expression model training and micro-expression model testing. Aiming at the problems that the expression movement of the elderly is not obvious and the abnormal expression is easy to neglect, the facial expression key region is positioned by using the time sequence optical flow feature, the abnormal expression is introduced to enhance the loss so as to punish the leak detection condition, the back propagation gradient of the loss and the covariance thereof are calculated, and the region with larger covariance has more obvious feature change, so that the detection accuracy is improved. According to the method, facial expression region features based on gradient covariance are fully considered, key region analysis is enhanced, and the accuracy of abnormal expression recognition of the old people is effectively improved.

The invention provides an elderly nursing method and system, which is applied to elderly nursing services, and comprises the following steps: receiving a video link generated when a monitoring camera monitors an identification object through a message push service; acquiring user configuration information from a database; sending the video link and the user configuration information to an artificial intelligence cloud service, so that the artificial intelligence cloud service analyzes the dangerous behavior and the daily behavior of the identified object to obtain an analysis result; an analysis result sent by the artificial intelligence cloud service is received, and daily state information of the recognition object is counted according to the analysis result; comparing the daily state information with the user configuration information to obtain a comparison result; through combination of the intelligent camera and AI behavior analysis, abnormal or high-risk behaviors of the elderly can be fed back in real time and pushed to family members in time for first-time processing, so that the family members can comprehensively master the living state of the elderly, potential health risks or living abnormalities can be found in time, and the user experience is improved.

The invention belongs to the technical field of medical treatment, and discloses a wearable intelligent method and device for monitoring the mental health of community old people, and the method comprises the steps: collecting the physiological indexes of the community old people in real time through an intelligent chest card, and achieving the dynamic collection of multi-dimensional data; data are transmitted to the cloud platform through Bluetooth or Wi-Fi, the equipment supports a data caching mechanism, and key data are temporarily stored when connection is abnormal; a dynamic evaluation model is constructed based on AI learning, and a multi-factor decision tree model and a natural language processing NLP algorithm are combined; a group psychological health trend and an environment risk thermodynamic diagram are presented in real time through a chest card APP, main factors influencing psychological health of the old are analyzed, and community managers are assisted in making an intervention plan. By making and applying the community old people mental health monitoring and early warning model, a unified mental health assessment system is provided for old people in different states, the applicability, universality and compliance of mental assessment are improved, home rehabilitation is achieved, and the overall medical cost is reduced.

To provide a large-capacity, thin bottle with a cap, which can prevent a situation in which a body is greatly dented by the force of a user's hand when the bottle is lifted by hand, causing contents to fly out of an opening, and is easy to hold stably even for women or elderly people with weak grip strength, and is easy to hold and use even in a large capacity.SOLUTION: Provided is a large-capacity, thin bottle with a cap in which a pair of right and left convex rib parts 61 that extend continuously in a vertical direction and project outward in a cross-sectional view are provided on each of a front surface 41 and a rear surface 42 of the body part 4.SELECTED DRAWING: Figure 1

The invention relates to the technical field of intelligent old-age care and health monitoring. The invention provides a tumble detection and emergency response system for the elderly at home based on multi-modal sensing fusion, and the system comprises a sensing module which is used for collecting the multi-modal data of the elderly and at least comprises a depth camera array, an inertial measurement unit, a millimeter wave radar, an audio sensor array, and a pressure sensing pad; the decision-making module is used for receiving, synchronizing and processing the multi-modal data, comprises a dynamic confidence fusion engine and is used for calculating a dynamic confidence score for the data streams acquired by the sensing module in real time and carrying out information fusion through a collaborative game algorithm based on the dynamic confidence score of each data stream to obtain a multi-modal data fusion result; generating a fall detection decision with a situation tag; and the response module is used for executing grading emergency response operation and comprises a local alarm unit, a remote communication unit and an interface unit linked with a community service and emergency center.

The invention relates to the technical field of elderly care, solves the problems that in the prior art, due to the fact that noisy and intermittent elderly voice is lack of robust sentence bound recognition and interruption repair, misinformation and missing information of calling-for-help triggering are prone to occurring, and time delay is high, and provides an audio processing method and device for elderly calling-for-help, equipment and a medium. The method comprises the following steps: preprocessing a voice signal of the elderly to obtain a preprocessed voice signal; performing statement boundary recognition and labeling on the preprocessed voice signal to obtain a voice fragment set; carrying out interruption segment and silent interval detection on the voice segment set to obtain an initial voice segment sequence, and carrying out intelligent recombination and time sequence repair on the initial voice segment sequence to obtain a target voice segment sequence; and carrying out emergency call recognition on the target voice segment sequence to obtain an emergency call judgment result. According to the invention, the accuracy of distress call identification of the elderly is improved.

The invention provides an edge multi-mode grading detection elder falling early warning method comprising the following steps: deploying an edge computing network comprising a master control node and a data node, and loading a context model according to a position; binding the face information of the user with the RFID tag through an identity confirmation camera; the data node triggers a millimeter wave radar to collect point cloud data through a human body infrared sensor; performing contour change rate primary detection, context model deep learning secondary detection and voice module state confirmation tertiary response; and after the tumble is confirmed, the master control node gives an early warning to the emergency center. The invention relates to the technical field of elderly monitoring.

The invention discloses a safety condition monitoring method and device for old people, and relates to the technical field of heart rate monitoring. Acquiring a face video and an infrared video of a target user to obtain a first final space-time diagram and a second final space-time diagram; respectively inputting the first final space-time diagram and the second final space-time diagram into a space-time feature extraction module for feature extraction to obtain RGB features and NIR features, and fusing the RGB features and the NIR features to obtain fused features; the heart rate of the target user is determined according to the fusion features, and state monitoring is conducted. Acquiring a face and an infrared video of a target user, and extracting spatio-temporal features of the face video according to a preset rule to obtain a first spatio-temporal diagram; the infrared video features are extracted and amplified frame by frame to obtain a second space-time diagram, RGB and NIR features are extracted through a space-time feature extraction module and fused into fused features, non-contact heart rate monitoring is achieved, the accuracy and efficiency of heart rate monitoring are improved, the heart rate is determined according to the fused features, and the health condition of a user is monitored and evaluated in real time.

The invention relates to the technical field of intelligent medical dispatching, in particular to a multi-terminal collaborative nursing worker resource intelligent allocation method, which comprises the following steps of: firstly, acquiring positioning, road, nursing worker physiology and old people demand data and generating multi-modal standardized data; constructing a three-dimensional digital twin potential field, predicting a corrected potential field by using photons, constructing a matching model by using the corrected potential field, a nursing worker capability vector and an old man demand vector, and performing annealing optimization to obtain initial matching; constructing a nursing worker-old person-time period tripartite graph based on a matching result, and obtaining optimal matching by adopting tension diffusion and gradient projection iteration; double digital signatures are executed on each piece of matching, and a non-homogeneous commitment is cast in the block chain, so that credible performance is realized; the wearing end spiking neural network continuously outputs fatigue probabilities, the fatigue probabilities are mapped into potential energy increments and written back to the potential field, and sub-potential field resolution and zero-knowledge post replacement are triggered and closed-loop updating is carried out. According to the method, the scheduling real-time performance and fairness are improved, the fatigue risk is reduced, and the whole service process is traceable.

The invention discloses an old people accident prevention comprehensive alarm system based on a multi-modal artificial intelligence model. The system comprises a data acquisition module, a multi-modal fusion module, an artificial intelligence analysis module, an alarm module and a background management module. The data acquisition module is used for acquiring multi-modal data such as video image data, wearable equipment data and environment data of old people; the multi-modal fusion module carries out fusion processing on the data to generate a multi-modal feature vector; the artificial intelligence analysis module analyzes the feature vectors based on a multi-modal artificial intelligence model, and outputs the probability and confidence of occurrence of various accident risks; when the alarm condition is met, the alarm module is started and sends alarm information to related personnel; the background management module carries out all-around management on the system. Various modal data are fused, the artificial intelligence technology is utilized to realize early warning and timely alarm of various accident risks of the elderly, related personnel can be prompted to take measures in time, the occurrence probability of accidents is reduced, the life safety of the elderly is guaranteed, and the system is suitable for various old-age care institutions and old-age living places. The method has important safety protection value.

The invention discloses an intelligent anti-falling risk management system based on artificial intelligence, and relates to the technical field of artificial intelligence, the intelligent anti-falling risk management system comprises a management center, and the management center is connected with a monitoring acquisition module, a transmission processing module, a body position analysis module and a multi-dimensional supervision module; constructing a resting virtual space for the bedridden old person, and performing position marking on the collected bedridden state data through the resting virtual space based on the intelligent anti-falling bed to obtain a continuous track of the bedridden old person; boundary distance statistics is carried out according to the continuous track of the bedridden patient, the boundary distance is converted into a real-time track fluctuation map, range division is carried out on the real-time track fluctuation map, the range proportion is counted, and a curve judgment range is obtained; carrying out simulation diagnosis through the static lying virtual space to obtain an abnormal identification state, and carrying out dynamic feedback adjustment on the bedridden old people according to the abnormal identification state based on the static lying virtual space to obtain an anti-falling intervention measure; the occurrence of falling-off accidents of old people is effectively reduced, the nursing efficiency is improved, and the burden of nursing personnel is relieved.

An antihypertensive peptide probiotic goat milk powder and a preparation method thereof are provided. Goat milk is used as a raw material, the goat milk is sterilized and then cooled, and the screened probiotic bacteria for fermenting goat milk to produce antihypertensive peptides, namely Lacticaseibacillus rhamnosus KD5 (which is preserved in CCTCC on Sep. 7, 2023, with a preservation number of CCTCC NO: M20231641), is added and stirred evenly, and then fermented at a constant temperature. After the fermentation is completed, probiotic fermented goat milk containing antihypertensive peptides is obtained. After mixing the probiotic fermented goat milk evenly with or without nutritional fortifiers for middle-aged and elderly people, it is subjected to vacuum low-temperature spray-drying to obtain antihypertensive peptide probiotic goat milk powder, which has a high ACE inhibition rate and can be used to assist consumers in lowering blood pressure.

The application relates to the technical field of data processing, and discloses an old person health state evaluation data processing system based on multi-modal data, which comprises a medical data integration module, an across-modal causal fusion processing module, a health state evolution modeling module, a backtracking evaluation report module and a decision output module. The medical data integration module acquires electronic medical record data and medical examination reports through a FHIR interface and extracts structured health indexes. The across-modal causal fusion processing module fuses monitoring data and medical texts through an image, a text and an image sandwich architecture. The health state evolution modeling module maps a health feature vector into three-dimensional state indexes of physiological functions, cognitive levels and motor abilities. The backtracking evaluation report module and the decision output module are used for backtracking evaluation and decision output. Through the image, the text and the image sandwich architecture, deep semantic fusion of multi-modal features is realized under the constraint of medical pathology rules, feature weight adaptive distribution is dynamically guided based on a medical causal graph, a high-dimensional fusion vector retaining key pathological information is generated, the semantic integration capability of health data is significantly improved, and the reliability of discrimination and decision is improved.