73 results about "Health knowledge" patented technology

The invention relates to the technical field of health management and artificial intelligence, in particular to an old people health analysis system and method based on combination of medicine and nursing. The method comprises the steps of collecting and processing multi-modal health data, and generating a health data vector set; mapping the vector set to a standardized health knowledge ontology, constructing a health map initial structure and generating a map node vector; generating a health portrait and an evolution prediction result based on time sequence modeling; identifying risk features according to the health portrait and the prediction result, and generating a medical care service strategy set; and issuing the strategy set to an execution unit and collecting feedback, and finally updating a graph structure and optimizing model parameters based on feedback data. According to the invention, multi-modal data-driven old people health dynamic evolution modeling and personalized service strategy closed-loop optimization are realized, and the intelligent level and response efficiency of medical and nursing services are improved.

A computer-implemented system for personalized health optimization constructs a confidence-weighted personal health knowledge graph (PHKG) from heterogeneous data, including wearable sensors, medical devices, lab results, medication logs, and conversational inputs. A multi-stage causal-inference stack identifies modifiable drivers of outcomes using layered methods (e.g., MI, GAM, Neural Granger, DAG-GNN), and simulates candidate interventions. A recommendation engine ranks lifestyle or pharmacologic actions using a benefit-to-friction score, selecting a personalized intervention aligned with user readiness and clinical safety constraints. Interventions may include a minimum effective dose (MED), optimal level, adaptive low-dose, or behavioral challenge. Optional modules include reinforcement learning for timing adaptation and privacy-preserving on-device inference. The system operates across domains including metabolic, cardiovascular, renal, sleep, stress, and medication response, enabling cross-condition synergy evaluation. The architecture is modular, supports runtime plug-in targets, and adapts in real time with or without continuous clinical oversight, depending on deployment.

The invention belongs to the technical field of medical treatment and public health, and particularly relates to a hypertension group knowledge recommendation method and system based on a large language model multi-agent. Hypertension knowledge question-answer pair information, medical short video information and user personal health portraits are finely extracted by constructing a hypertension knowledge question-answer pair library; according to the method, video content quality evaluation and matching degree evaluation are performed, and user platform interaction data adjustment is combined, so that personalized and accurate knowledge recommendation services can be provided for hypertension users, the requirements of hypertension patients for health knowledge are met, the accuracy and effectiveness of knowledge recommendation are improved, and self-health management of the patients is facilitated.

The invention relates to the technical field of data processing and analysis, in particular to an intelligent large health system and a data processing method thereof, and the method comprises the steps: collecting the data of a multi-source heterogeneous data source in real time, and comprehensively capturing the multi-dimensional health data of a user; a multi-modal health knowledge graph is dynamically constructed and optimized through an efficient data preprocessing and feature alignment technology, and the integration value and the utilization efficiency of data are improved; a graph neural network and a time sequence analysis model are utilized to realize accurate evaluation of the health state of the user and timely prediction of future risks, and predictability and pertinence of health management are effectively improved; based on a reinforcement learning algorithm, a highly personalized intervention sequence can be generated according to the individual condition of a user, the accuracy of health intervention is enhanced, and the positive change of the health behavior of the user is promoted; an intervention instruction is executed through intelligent equipment, user feedback is monitored in real time, an intervention strategy is dynamically adjusted, and the flexibility and adaptability of intervention measures are ensured.

The invention provides a health state evaluation and product matching method and system based on a knowledge graph, and relates to the technical field of knowledge graphs, and the method comprises the steps: obtaining user health data, building a health knowledge graph, mapping the user data into a feature vector, and employing a multi-scale time convolution network to extract a time sequence change feature; and constructing a dynamic health feature association network, generating a health state evaluation result by using a graph convolutional neural network and a causal discovery algorithm, calculating a collaborative enhancement coefficient based on a product feature vector, and predicting an intervention effect to determine an optimal product combination. According to the method, personalized health assessment and precise product matching are realized, and the health management efficiency is improved.

The invention relates to the technical field of old-age care monitoring, and discloses an intelligent old-age care monitoring method and system based on wearable equipment. According to the method, original physiological data such as heart rate signals, oxyhemoglobin saturation and body movement frequency are collected through a plurality of wearable devices, multi-modal feature extraction is conducted on the original physiological data, and a physiological feature vector set is generated. A to-be-analyzed health event sequence containing a periodic abnormal mode and a non-periodic burst mode is screened out, and a health knowledge base is called to retrieve a matched candidate health event entry set. And inputting the two health events into a health event aggregator for cross-modal semantic fusion analysis, generating a health event decision coding matrix, obtaining an intervention measure instruction set for each health event to be analyzed through hierarchical decoding, and finally generating a response operation queue executable by the pension monitoring terminal. According to the method, the health condition of the old people can be comprehensively and accurately monitored, intervention measures can be timely and effectively provided, and the quality and efficiency of old-age care monitoring are greatly improved.

The invention relates to the technical field of health assessment, and discloses a user climacteric physical and psychological health assessment and science popularization system, which comprises a science popularization plot and dynamic assessment process module, a multi-dimensional assessment engine and a general knowledge and personalized knowledge content push engine which are in closed-loop communication connection in sequence. According to the application, the science popularization garden and dynamic evaluation process module not only ensures data integrity, but also adapts to real-time updating requirements of medical guidelines; the multi-dimensional assessment engine fuses the multi-dimensional data and processes the medical scale, so that more comprehensive and accurate health assessment is realized; a general knowledge and personalized knowledge content pushing engine constructs a dynamic user portrait based on multi-dimensional data and evaluation results, and realizes accurate matching and interpretable pushing of health knowledge through a tag system and a four-dimensional evaluation system; the flexibility and the operation efficiency of the evaluation system and the acceptability of the user to the health management content are improved, and a finer and more efficient physical and psychological health doctor-patient management scheme is provided for climacteric people.

The invention provides a health education resource RPA interaction system and method based on a knowledge graph, and the system comprises a multi-source data fusion module which is used for summarizing health data of medical literatures, electronic cases and monitoring equipment, calling a pre-training model for entity recognition and relation extraction, and constructing a dynamically updated health knowledge graph; the RPA process management module is used for generating an automatic process according to the health knowledge graph and allowing a user to dynamically adjust a generation rule; the multi-modal interaction module provides texts, languages and / or visual charts for the user for real-time interaction according to the generated automatic process; and the feedback optimization module is used for optimizing the knowledge graph and the RPA process according to the user behavior log. According to the invention, the RPA system is combined with the AI, the health problem of the patient is intelligently analyzed, task scheduling is automatically carried out, personalized and accurate health education service is provided, and the intelligence and practicability of health education are improved.

A digital vaccine system, method and device that maintains a health knowledge base, inputs user characteristics, generates health scores based on the user characteristics and provides pathogen risk recommendations based on the user characteristics, health scores and knowledge base, wherein the recommendations are indicated by the knowledge base to be likely to improve the user's health.

The invention discloses a ward health education robot, and the robot is internally provided with a health education platform, and the health education platform comprises a personalized education module which is responsible for generating a personalized guidance scheme according to the personal information of a patient; the intelligent question and answer module is responsible for intelligently recommending related health knowledge and matters needing attention; the medicine use and treatment nursing management module is responsible for recording the medicine use condition of the patient and generating a schedule about the patient; the intelligent navigation and hospital guide module is responsible for realizing autonomous navigation of the patient from a ward to a designated place; the physical sign monitoring and analyzing module is responsible for collecting vital sign data of a patient and generating a health report and early warning information; according to the system, the emotion recognition and interaction optimization module recognizes the emotional state of the patient and adjusts the communication mode and content according to the emotional state, the anxiety and fear emotion of the patient can be relieved, and the confidence of the patient in fighting the disease is enhanced.

The invention discloses an intelligent recommendation system for health management, which belongs to the technical field of intelligent medical treatment and comprises an acquisition module, a knowledge analysis module, a calculation processing module and an interaction module. The system collects original health data, manages a health knowledge graph and a safety recommendation rule base, constructs an initial health portrait by using the original health data, generates a first recommendation list and a second recommendation list based on the initial health portrait and a logic rule base, generates a recommendation card through the first recommendation list and the second recommendation list, and sends the recommendation card to a user. And collecting user behavior data for feedback. According to the method, the problems of high cold start difficulty, insufficient safety guarantee and limited personalized effect in the existing health recommendation service are solved, the initial availability and recommendation scientificity of the system are improved, the health behavior of the user can be effectively guided, and the method has positive significance in improving the health level of people.

The invention is suitable for the technical field of artificial intelligence, and provides a medical and cross-boundary fusion-based multimedia data generation method and system suitable for aging, and the method comprises the steps: constructing and training an adaptation degree evaluation model which takes health knowledge features and cross-boundary content features as input and takes the adaptation degree between the health knowledge features and the cross-boundary content features as output; obtaining target health knowledge data and a candidate cross-border content data set, and determining an adaptation degree predicted value of each candidate cross-border content data in the candidate cross-border content data set based on the adaptation degree evaluation model; and determining target cross-border content data based on the adaptation degree predicted value of each candidate cross-border content data, and generating aging-suitable multimedia data based on the target health knowledge data, the target cross-border content data and an adaptation degree evaluation model. According to the technical scheme provided by the invention, the accuracy of health knowledge and cross-border content suitability evaluation is improved, the quality of the multimedia data suitable for aging and the matching degree with the elderly user are improved, and the transmission effect of the multimedia data suitable for aging is improved.

The invention provides a hospital health education intelligent question and answer method based on a large language model and a knowledge base, and belongs to the technical field of intelligent question and answer, and the method comprises the steps: collecting hospital health education related document data for preprocessing, and converting a text block into a vector form through a vector embedding model, storing in an AI native database in combination with metadata, and constructing an index to form a hospital health education knowledge base; receiving health consultation questions input by a user, performing semantic analysis and keyword extraction, and retrieving related knowledge fragments from a hospital health education knowledge base; carrying out fusion reordering on the retrieved knowledge fragments, calculating correlation scores, and selecting the first K knowledge fragments with the highest scores; and substituting the selected knowledge fragments and the user question into a preset cue word engineering template, generating a cue word input large language model, generating an answer for the user question, and feeding back the answer to the user. The acceptability and practicability of the patient on health knowledge are enhanced, and the efficiency of hospital health education and the satisfaction degree of the patient are improved.

The invention discloses a multi-modal health knowledge generation and integration system, which is applied to the technical field of fusion and application of health data, and comprises a multi-modal data acquisition and preprocessing module used for acquiring multi-modal health data and preprocessing the multi-modal health data; the multi-level fusion framework design sub-module comprises data-level fusion based on a time alignment algorithm, feature-level fusion based on a Transform cross-modal attention mechanism and decision-making-level fusion based on an ensemble learning algorithm; and the intelligent knowledge generation sub-module comprises health knowledge graph construction based on a graph neural network, personalized health risk report and intervention scheme generation based on a conditional generative adversarial network, visual image feature contribution degree based on gradient weighted class activation mapping and text data key factor analysis based on an SHAP value. According to the method, the bottleneck of a data island is fundamentally broken through, and the generation quality and the application value of health knowledge are improved.

The invention discloses a health knowledge science popularization interaction system establishment method based on a virtual reality technology, and the method comprises the steps: S1, obtaining multi-dimensional data of a science popularization activity process, and constructing a health knowledge science popularization quality dynamic evaluation index system; s2, establishing a health knowledge recommendation engine, and distributing multi-modal science popularization data to students according to the health knowledge recommendation engine; s3, constructing a science popularization scene based on a virtual reality technology; and S4, acquiring popular science learning action and state data of the student, carrying out feature description on the student, and analyzing the learning process and effect data of the student. According to the method, the characteristics of the students can be accurately described, personalized science popularization experience meeting individual requirements is provided, precise measurement of the science popularization activity effect is achieved, the learning quality of audiences in the health knowledge science popularization process is remarkably improved, quantitative analysis is conducted on the science popularization effect, and powerful support is provided for improvement and optimization of science popularization education activities.

The invention relates to the technical field of education, in particular to a health education interaction system and method based on virtual reality and deep learning. The health education interaction system creatively integrates motion sensing games, VR video teaching, personalized reading and a body measurement motion module, and all-around and immersive learning experience is created; the system motivates the participation degree of a user through a motion sensing game, interactive learning is achieved through VR video teaching, personalized reading meets the requirements of the user, and the health state is monitored through motion measurement. The intelligent analysis module is used for deeply mining data and providing personalized health assessment and suggestions; the self-learning unit continuously optimizes a knowledge base and a recommendation algorithm, and constructs a dynamic health knowledge graph; the system breaks through the limitation of traditional health education, improves the knowledge propagation efficiency and acceptance, effectively promotes the formation and maintenance of health behaviors, and brings revolutionary change to the field of health education.

1. The name of the design product: playing cards (sports guide version). 2. The use of the design product: playing cards for popularizing diabetes health knowledge. 3. The design points of the design product: in the pattern. 4. The picture or photo that best indicates the design points: component 1 front view. 5. The design product is a thin product, and the top view, bottom view, left view, and right view of components 1-56 are omitted; the rear view of components 2-56 is the same as the rear view of component 1, and the rear view of components 2-56 is omitted.

The invention discloses an intelligent popular science knowledge pushing system based on inspection report data. The system comprises a report data acquisition module, a data analysis and feature extraction module, a knowledge graph construction and management module, an intelligent matching and reasoning engine, a user portrait module and a personalized pushing and feedback module. The method comprises the following steps: acquiring a structured or unstructured examination report of a user, and analyzing and extracting key medical entities and numerical features by using a natural language processing technology; precisely screening out popular science knowledge most related to the current health condition of the user; and in combination with a user portrait, personalized pushing is carried out through an optimal channel at an optimal opportunity, and user feedback is collected to optimize the system. According to the method and the system, the problems of generalization of existing health science popularization pushing and disjunction with a personal examination result of the user are solved, accurate, timely, dynamic and personalized health knowledge service is realized, and the health management consciousness and compliance of the user are remarkably improved.

The application discloses a personalized pet nutrition and health care product recommendation method and system, and relates to the field of artificial intelligence. The specific steps comprise the following steps: collecting pet related information and labels from multiple source systems; performing normalization and nonlinear interaction feature construction on numerical features, and performing adaptive embedding and category cross on category type features to obtain enhanced original vectors; screening features by using label association information, and performing clustering fusion to obtain a fusion feature matrix; adopting an improved feature cross network, introducing low-rank decomposition and pet health knowledge prior to construct a classification model; constructing a loss function introducing a rule constraint regular term, training the model and updating parameters; and recommending nutrition and health care products to pets by using the trained model. The application can accurately capture the complex relationship between features, and significantly improve the accuracy of model prediction.

A personal health system, method and device that maintains a health knowledge base, inputs user characteristics, generates health scores based on the user characteristics and provides recommendations based on the user characteristics, health scores and knowledge base, wherein the recommendations are indicated by the knowledge base to be likely to improve the user's health.

A digital vaccine system, method and device that maintains a health knowledge base, inputs user characteristics, generates health scores based on the user characteristics and provides pathogen risk recommendations based on the user characteristics, health scores and knowledge base, wherein the recommendations are indicated by the knowledge base to be likely to improve the user's health.

The invention provides an information recommendation method and system based on artificial intelligence, and belongs to the technical field of information recommendation. Comprising the steps of 1, constructing a first data set; 2, constructing a second data set; step 3, based on the first data set, constructing a health knowledge preference coefficient of the ith old person; 4, constructing a health behavior adaptation coefficient and a participation depth coefficient of the ith old person based on the second data set; and step 5, associating the ith old people health knowledge preference coefficient with the ith old people health behavior adaptation coefficient and the participation depth coefficient, constructing a comprehensive health participation index, and performing evaluation and optimization. Through data analysis in which online health knowledge and offline medical lectures are combined, the health behavior tendency of the elderly user can be evaluated more accurately, and the optimized recommendation can help the user to obtain appropriate health knowledge.

The present application relates to the medical and health field and discloses a method for generating a diet plan, comprising: in response to a diet plan generation request, sending a data acquisition request to a health detection device or health detection software, and combining the feedback physical indicator data and user input information into a pre-trained natural language processing model for patient information input; in the natural language processing model, processing the patient information based on the health knowledge information obtained from a pre-built health knowledge base to obtain nutrient element intake constraints, and determining a target diet plan and a plan description corresponding to the target diet plan based on the nutrient element intake constraints, wherein the diet plan includes a menu for each meal within a preset time period, and each menu includes the type of ingredients, the quantity of ingredients, and the cooking method, and the plan description is used to indicate the adaptability of the target diet plan to the patient information. The present application solves the problem that the patient data analyzed by the existing method is not real-time enough and the user trust in the output results is low.

The invention discloses a health knowledge social platform management method and system based on a block chain, and relates to the technical field of social platform management, and the method comprises the steps: at a management method level, initially receiving and storing user registration and authorization information, constructing a user basic information matrix by means of one-hot coding, calculating a true index, encrypting the registration information according to the true index, and storing the encrypted registration information; and generating a unique authentication identifier. And then, locking health knowledge data published by the user by virtue of an identifier, auditing factors such as repetition and sensitivity by the system, judging whether the data can be published for the first time, collecting interactive information after publishing, establishing an influence matrix, and performing professional auditing. Meanwhile, interaction information is collected in real time to generate a real-time influence matrix, and the user is rewarded in combination with registration information. The system matched with the system comprises a user module, a knowledge module, an incentive module and a data management module, subdivision units of the modules cooperate in a division mode, force is generated from multiple links, the platform management efficiency is comprehensively improved, and the knowledge quality and the user experience are guaranteed.

The invention discloses a large-model wearable electrocardiogram monitoring system and method based on a BSS-CNN-RF-DT-LLM fusion technology. The system comprises hardware equipment and a software platform. The hardware equipment integrates various core algorithm chips and sensing, transmission and interaction modules, and the software platform comprises a plurality of knowledge bases, a plurality of intelligent algorithm systems and a plurality of electrocardiosignal monitoring systems. According to the method, electrocardiosignals are collected through a wearable device, artifacts are removed through a BSS algorithm, features are extracted through a CNN algorithm, a classification model is constructed through an RF-DT algorithm, a professional knowledge report is generated in combination with an LLM algorithm and a health knowledge base, and the professional knowledge report is visually displayed in a partition mode on a display screen. All-dimensional monitoring of multiple electrocardiogram indexes and three-level evaluation of'normal-abnormity-danger 'are achieved, the anti-interference capacity is high, diagnosis is accurate, active monitoring and historical data comparison are supported, operation is convenient and fast, and a reliable solution is provided for daily health management and chronic disease tracking.

1. Name of the product in this design: Playing Cards (Nutritional Science Edition). 2. Purpose of this design: Playing cards for popularizing nutritional and health knowledge. 3. The key design feature of this product is its pattern. 4. The image or photograph that best illustrates the design highlights: Component 1 main view. 5. This design is for a thin product, so the top view, bottom view, left view, and right view of components 1-55 are omitted; the rear view of components 2-55 is the same as the rear view of component 1, so the rear view of components 2-55 is omitted.

The invention relates to the technical field of health monitoring, in particular to an intelligent wearable health monitoring method and system based on artificial intelligence, and the method comprises the steps: collecting multi-modal physiological parameters and behavior data of a user through an intelligent wearable device, constructing a nonlinear mapping relation between health indexes and behavior data, and forming a personalized health state model; and based on the model, optimizing and solving a health early-warning threshold adaptive to the individual user, generating a target early-warning parameter and configuring the target early-warning parameter to the equipment, thereby realizing real-time dynamic monitoring and abnormal early-warning of the physiological parameters. In daily monitoring, physiological and behavior data are analyzed through trends, and intervention suggestions are generated for abnormal fluctuation or potential risks; and meanwhile, user historical health data and a medical diagnosis result are integrated, a health state model is corrected by using big data analysis, and the modeling precision is improved. And in combination with different user group characteristics, a personalized health knowledge base and a case base are constructed, a standardized health assessment scheme is formed, and the accuracy and the intelligent level of personalized health management are improved.

This application discloses a central robot and its home-based health and elderly care implementation method. The method includes: acquiring a user's health events based on multimodal perception data from multiple non-visual sensors, detection data from health monitoring devices, and a digital twin space; activating visual sensors when preset conditions are met, and performing fusion processing with the digital twin space and the user's health profile to obtain the user's scene context; generating health intervention content based on the scene context and a pre-stored health knowledge graph, and mobilizing corresponding execution modules for multimodal output; determining the user's interaction information based on the perception data from the sensing modules and the digital twin space, and adjusting the corresponding execution modules for multimodal output. Implementing the technical solution of this application can avoid errors in perception from a single device or module, reduce the risk of user privacy leakage, provide more professional closed-loop health services, and eliminate the need for complex user operations.

1.The name of the design product: rotating bulletin board (mental health education). 2.The use of the design product: display and dissemination of mental health knowledge. 3.The design points of the design product: in the perspective. 4.The picture or photo that best indicates the design points: perspective. 5.The design contains colors.

The application relates to the technical field of old-age care monitoring, and discloses a smart old-age care monitoring method and system based on wearable devices. The method collects original physiological data such as heart rate signals, blood oxygen saturation and body motion frequency through multiple wearable devices, carries out multi-modal feature extraction on the original physiological data, and generates a physiological feature vector set. A to-be-analyzed health event sequence containing a periodic abnormal mode and a non-periodic burst mode is screened out from the physiological feature vector set, a matched candidate health event item set is searched from a health knowledge base, and the two are input into a health event aggregator for cross-modal semantic fusion analysis to generate a health event decision encoding matrix. The health event decision encoding matrix is subjected to hierarchical decoding to obtain an intervention measure instruction set for each to-be-analyzed health event, and finally a response operation queue executable by an old-age care monitoring terminal is generated. The method can comprehensively and accurately monitor the health status of the elderly, timely and effectively provide intervention measures, and greatly improves the quality and efficiency of old-age care monitoring.