132 results about "Intervention protocols" patented technology

The invention discloses a cerebral apoplexy multi-mode early screening intelligent evaluation system based on a large model, and relates to the technical field of medical health information, the cerebral apoplexy multi-mode early screening intelligent evaluation system comprises an intelligent management platform, and the intelligent management platform is in communication connection with the following modules: a multi-source heterogeneous data fusion engine, the data integration module is used for integrating multi-modal data including clinical data and terminal health data and constructing a health portrait of a patient; and the cerebral apoplexy knowledge graph construction platform is used for constructing a cerebral apoplexy domain knowledge graph in combination with evidence-based medical knowledge. By combining the digital twinning technology and the intelligent risk assessment engine, the influence of different intervention schemes on the cerebral apoplexy risk can be simulated, personalized intervention suggestions are generated, a patient is helped to reduce the cerebral apoplexy risk and change from passive prediction to active intervention, the patient is helped to take effective measures earlier, the health condition is improved, and the patient experience is improved. The occurrence of cerebral apoplexy is prevented, so that the disability rate and the death rate caused by cerebral apoplexy are reduced.

The invention discloses a health management service system and method based on AI optimization. The method comprises the following steps: S1, collecting multi-source health data of a user and constructing a structured health data set; s2, constructing a health state graph containing node attributes, edge connection weights and time indexes; s3, inputting the health state atlas into a linear graph neural network to generate a health state embedded vector; s4, collecting context information of a user, and fusing to generate personalized state perception representation and a health target embedding vector; s5, inputting the improved general value layering model to generate a structured intervention action candidate set; s6, screening and outputting a personalized health intervention scheme based on the matching score; s7, constructing a state evolution sequence, and inputting the state evolution sequence into a semi-supervised anomaly detection algorithm for monitoring and recognition; and S8, when the risk threshold is exceeded, triggering early warning and dynamically adjusting the intervention scheme. According to the method, collaborative optimization of personalized modeling and intelligent intervention strategies is realized, and the method is suitable for health management service scenes.

The invention provides an integrated doctor-patient cooperation and health management platform and a data processing method thereof, and relates to the technical field of health management, and the integrated doctor-patient cooperation and health management platform comprises a multi-source data access layer, a credibility evaluation and correction engine, a semantic fusion and knowledge graph layer, a patient digital twin module, a cooperation interaction module, a privacy protection and joint training module and a contract and audit layer. Dynamic modeling of health data is realized through a space-time diagram neural network, a risk prediction and intervention scheme with a confidence interval is generated in combination with Bayesian reasoning and Monte Carlo simulation, and then digital twins of a patient are constructed and personalized simulation is performed; and meanwhile, the safety and performance of cross-mechanism joint modeling are guaranteed by adopting hybrid synchronous-asynchronous federal learning and a dynamic privacy budget mechanism, and the traceability and compliance of the whole process are realized through a block chain contract. The accuracy and transparency of medical data processing can be remarkably improved, close cooperation between doctors and patients is promoted, and comprehensive health management of the patients is achieved.

The invention relates to the technical field of old people health management, and discloses a personalized health auxiliary management system based on artificial intelligence, which comprises a multi-mode sensing module, an intelligent preprocessing module, a dynamic knowledge graph module, a personalized inference engine module and a self-adaptive communication and interaction module. The multi-mode sensing module is used for acquiring physiological, environmental and behavioral data; the intelligent preprocessing module desensitizes and cleans data through federal learning, and extracts core features; the dynamic knowledge graph module constructs and updates an exclusive graph containing basic and dynamic nodes and associated edges; the personalized inference engine module generates a layered intervention scheme; and the adaptive interaction module adapts to the cognitive ability and synchronizes the scheme. According to the invention, health information integration and dynamic intervention are realized, the management accuracy and compliance are improved, and the method is suitable for home non-clinical scenes.

The invention provides an evaluation-intervention integrated system and equipment oriented to personalized rehabilitation scheme optimization. The evaluation-intervention integrated system comprises a personalized scheme recommendation module, a curative effect evaluation module and an intervention scheme optimization and adjustment module. The personalized scheme recommendation module generates a personalized rehabilitation treatment scheme by collecting multi-dimensional data and combining with a machine learning algorithm. And the curative effect evaluation module monitors and comprehensively evaluates the rehabilitation effect of the patient in real time, and generates a detailed curative effect report. And the intervention scheme optimization and adjustment module dynamically adjusts the treatment scheme and optimizes the treatment content, intensity and frequency according to the curative effect evaluation result and patient feedback. Through intelligent analysis and a feedback mechanism, an accurate and customized rehabilitation treatment scheme is provided for each patient, dynamic adjustment is performed according to a real-time evaluation result, and the rehabilitation treatment accuracy and the treatment effect are remarkably improved. The system also supports real-time data sharing and cooperation among patients, doctors and rehabilitation experts, and can perform cross-region and cross-professional personalized treatment management.

The invention discloses a psychological health full-process intelligent evaluation and intervention system, and mainly relates to the technical field of psychological health evaluation and intervention. Comprising a gamification general measurement module used for collecting multi-modal initial data of a user through multiple gamification tasks; the multi-modal fine screening module is connected with the gamification general survey module and is used for collecting multi-dimensional fine data of the user when the general survey risk score reaches a preset threshold value; the self-adaptive intervention module is connected with the multi-modal fine screening module and used for matching a corresponding intervention scheme according to the risk level and continuously collecting physiological data of the user in the intervention process to dynamically adjust the intervention scheme; and the data interaction and control module is connected with the gamification general measurement module, the multi-mode fine screening module and the self-adaptive intervention module and is used for realizing data transmission and cooperative control among the modules. The method has the beneficial effects that the evaluation precision is remarkably improved, and meanwhile, the intervention response speed is increased.

The invention relates to the technical field of reinforcement learning, and discloses a chronic disease intervention scheme generation method and system based on reinforcement learning and multi-modal data, and the method comprises the steps: obtaining the multi-modal data of a patient, the multi-modal data at least comprising electronic medical record data, voice data, image data, text data and physiological time sequence data; performing feature extraction on the multi-modal data to obtain a multi-modal feature vector; on the basis of the multi-modal feature vectors, health state vectors are constructed, and the health state vectors at least comprise a physiological risk score, a treatment compliance score and a lifestyle health degree score; designing a reward function according to the dynamic change of the health state vector; optimizing the strategy network in a predefined intervention action space according to the reward function by utilizing a reinforcement learning algorithm so as to output an optimal intervention action; and converting the optimal intervention action into personalized natural language interaction content through a generative AI model. According to the invention, the efficiency, precision and patient compliance of chronic disease management can be significantly improved.

The invention discloses a postoperative monitoring system and method for a metabolic weight loss surgery patient, and the system comprises a multi-source data collection module which is used for obtaining physiological parameters, behavior data and environment data of the patient in real time, the physiological parameters comprise body weight, blood sugar and body composition indexes, and the behavior data comprise exercise amount and diet records; the environment data comprises temperature, humidity and geographic position; the central processing platform is connected with the multi-source data acquisition module and comprises a data fusion unit for performing space-time alignment and dimension reduction processing on the multi-source heterogeneous data by adopting a feature level fusion algorithm; the dynamic risk assessment unit is used for constructing a patient personalized metabolism model based on the fused data, and dynamically generating nutrition loss and complication recurrence risk indexes through a machine learning algorithm; and the intelligent early warning module triggers a graded early warning signal according to the risk index, generates a personalized intervention scheme and pushes the personalized intervention scheme to the doctor-patient terminal.

The invention relates to a precise health monitoring method based on multi-source data fusion. The method comprises the following steps of 1, collecting multi-source data; step 2, data preprocessing; step 3, multi-source data fusion; 4, health analysis and decision making; step 5, dynamic weight adjustment; and step 6, feedback optimization. The hierarchical fusion strategy is adopted to process heterogeneous data, a machine learning algorithm is combined to establish an individualized health model, physiological state changes are tracked in real time, a targeted intervention scheme is generated, and in terms of technical implementation, the system supports collaborative decision making of various analysis models, including rule-based behavior reasoning, time sequence feature analysis and environment threshold judgment, and the system has the advantages of being simple in structure and convenient to use. The contribution degrees of information of different sources are balanced through an adaptive weight distribution mechanism, time-frequency analysis and dimension reduction technologies are fused in the feature extraction process, the dynamic mode of the health state is effectively captured, cross validation optimization is adopted in model training, and the reliability of an evaluation result is ensured.

The invention relates to the technical field of medical data processing, and discloses an operation period treatment risk intelligent prompting method, which comprises the following steps of: acquiring physiological index time sequence data and a treatment scheme execution parameter matrix of a perioperative period patient, generating a composite state tensor through multi-source feature correlation calculation, constructing a risk-intervention correlation nodal topological map, and calculating the risk-intervention correlation nodal topological map. Inputting a risk transfer model to simulate a risk transfer path and outputting an association influence probability value set, establishing a bidirectional association equation through space-time association mapping operation to generate a risk-intervention coupling prediction curve, calculating a risk prompt factor, and performing operation on the risk prompt factor and an association influence probability value to generate a standardized thermodynamic diagram; and extracting the operation quantitative index set, dynamically comparing the operation quantitative index set with a preset safety threshold value, and generating an intelligent prompt scheme containing a risk early warning level and an intervention scheme adjustment strategy when the index breaks through the threshold value. According to the method, the accuracy and effectiveness of risk prompting during the operation period are improved.

The invention provides a trauma memory integration evaluation and treatment system. Comprising the following steps: acquiring patient description information, generating a trauma event scene based on the description information, collecting multi-modal data of a patient in the scene, identifying a trauma recall activation state by utilizing the multi-modal data, combining the activation state with individual psychological characteristics and database contents to generate an intervention scheme, and circularly updating the scene and the scheme until a termination condition is met. The importance of the multi-modal features is determined through a machine learning model, a trauma recall activation score is generated through weighted fusion, and quantitative and objective evaluation of the activation state is achieved; and a personalized intervention scheme is dynamically retrieved or generated in combination with psychological characteristics of the patient, so that the pertinence and effectiveness of treatment are improved. The technical problems that existing trauma recall activation evaluation depends on subjective judgment, and an intervention strategy lacks individuation and dynamic optimization can be solved.

The invention relates to a risk assessment method and system for acute attack of asthma in children. The method comprises the following steps: acquiring multi-modal data of a target child and preprocessing; extracting specified features to form basic feature vectors, adjusting dynamic feature weights in combination with individual differences of children and real-time scenes, generating weighted feature vectors, fusing the weighted feature vectors into recent medication compliance to obtain fused feature vectors, and obtaining the acute attack probability of asthma in a specified time period in the future. Pushing a conventional nursing scheme or an emergency intervention scheme according to the comparison between the probability and a first threshold value and a second threshold value, and if the threshold value is not reached, inputting the fusion feature vector into an XGBoost model to output a preliminary risk level and calibrating the preliminary risk level; and generating a dynamic intervention scheme. Through accurate integration of multi-modal data, adaptation of individuals and scenes by dynamic weights and combination of hierarchical model prediction and individual historical calibration, the accuracy and personalization of risk assessment are remarkably improved, deep adaptation of an assessment result and an intervention scheme is realized, and the problems of missed judgment, misjudgment rate and insufficient or excessive intervention of acute attack of children asthma are effectively reduced.

The invention relates to the technical field of health monitoring, in particular to a health state alarm response system based on multi-dimensional monitoring, which comprises a dynamic threshold module, a physiological monitoring module, an anomaly positioning module, an inducement output module, an anomaly intervention module and an early warning response module, according to the method, multi-dimensional original data of a user is collected, meanwhile, a timestamp alignment and scene label binding technology is adopted, an exclusive physiological index normal fluctuation interval library is constructed for different large classes of scenes, then the problem of misjudgment of a static threshold value is fundamentally solved, the cause of'abnormity 'is deeply and accurately positioned, and the accuracy of the abnormal condition is improved. The abnormity-scene-behavior-cause full-link association not only enables the user to clearly know the cause of the abnormity, but also provides data support for the medical personnel to formulate a targeted intervention scheme, and reasonably formulates the targeted intervention scheme based on the intervention list, thereby avoiding the ineffectiveness of general suggestions, enabling resources to be inclined to high-risk users, and improving the user experience. The complication occurrence rate is effectively reduced.

The invention relates to the technical field of personalized intelligent dialogue and digital accompanying for old people, and discloses an AI interaction-based personalized knowledge graph dialogue generation method for old people, which comprises the following steps of: reading a role conversion event record from a family structure sub-graph of a personalized knowledge graph for old people, and generating a role fall feeling feature template; aI dialogue initiation records of the old in recent late night periods are obtained, the number of days of continuous late night dialogues, the average dialogue duration and the active listing frequency are counted, and an aloneness level label is generated; generating a personalized spiral blocking intervention scheme based on the spiral strength coefficient and the spiral type label; and executing the collaborative psychological support dialogue script to generate a dialogue, and outputting a continuous personalized psychological support dialogue sequence. According to the method, the technical challenge that in the AI interaction-based elderly dialogue service, when the psychological state is worsened spirally due to the fact that the sense of fall and the sense of loneliness of roles are enhanced mutually, it is difficult to generate effective intervention dialogues by means of the personalized knowledge graph is solved.

The invention discloses a dementia intervention scheme recommendation method and system based on a knowledge graph, and relates to the technical field of intelligent medical treatment and artificial intelligence. And constructing a dementia intervention knowledge graph containing patients, symptoms, intervention measures and environmental elements. And performing intelligent semantic reasoning on the short-term state, the long-term behavior mode and the nursing environment of the patient by fusing multi-level reasoning and situational reasoning, generating a candidate intervention scheme matched with the current state of the patient, and outputting a personalized recommendation result. In the intervention implementation process, the knowledge graph association relation is dynamically updated based on patient behavior feedback and symptom changes, and continuous optimization and self-adaptive adjustment of an intervention scheme are achieved. According to the method, the individuation level, long-term adaptability and safety of dementia intervention recommendation are improved, and the method is suitable for clinical and long-term nursing scenes.

The invention discloses a medical critical risk prediction method based on physical sign data monitored by a smart watch, and relates to the technical field of critical monitoring, and the method comprises the steps: constructing a multi-dimensional physical sign fusion analysis model, collecting dynamic physiological parameters in real time through the smart watch, and updating a personalized health baseline; based on the personalized health baseline, detecting a pathological fluctuation mode in the dynamic physiological parameters through a time sequence convolutional network; inputting the identified abnormal waveform features into a risk prediction model optimized by transfer learning, and calculating a coupling risk index of the multi-organ system; establishing a three-level response early warning mechanism based on the coupling risk index of the multi-organ system; and generating an intervention scheme based on the coupling risk index of the multi-organ system. Compared with the prior art, the intelligent wearable device has the advantages that the multi-organ risk conduction model is combined, the severe risk is accurately predicted, a risk identification-early warning-disposal dynamic management system is formed, and the clinical practical value of the intelligent wearable device in a pre-hospital emergency scene is effectively improved.

The invention relates to the technical field of medical auxiliary evaluation, in particular to a perioperative pain evaluation system for a brain tumor surgery patient, which comprises a surgery pain data acquisition module for acquiring an HRV signal, an intracranial pressure waveform, a subjective VAS score and pain part data of the patient; the feature extraction module processes the data through abnormal value detection and sliding window filtering, extracts HRV and ICP features in combination with time domain analysis, and outputs standardized data after integrating the HRV and ICP features with subjective feedback features; the AI pain quantification module is used for generating a pain risk probability value based on an AI model constructed by an XGBoost algorithm, triggering three-level early warning and associating pain properties and causes of a patient; the intervention pushing module triggers a multi-terminal prompt and pushes an intervention scheme, and a patient performs graphic interaction and feedback through a mobile phone terminal; and the management and tracing module is used for associating single-time whole-process data, recording an intervention effect and automatically upgrading an unexpected scheme. Therefore, the problems that in the prior art, intracranial pressure and pain coupling is not quantified, intervention lags and the like are solved.

The invention provides a big data resource service-based patient hierarchical intervention method and system, which are applied to the technical field of medical information, and are used for acquiring full-cycle health data of a patient, generating and updating a patient disease course trajectory sequence, analyzing health index change and trend under short, medium and long time scales, and calculating health scores and steady-state coefficients, so as to realize hierarchical intervention of the patient. A layered intervention scheme is generated, detection and personalized intervention of the health state of the patient are achieved, the timeliness and pertinence of medical intervention can be improved, and the disease management effect is optimized.

The invention relates to the technical field of health management, in particular to an obese patient remote dynamic intervention system based on AI early warning, which comprises a multi-dimensional data acquisition module, an AI risk layering and early warning module, a personalized intervention scheme generation module, a remote collaborative management module and an effect evaluation and optimization module, the multi-dimensional data acquisition module is used for acquiring patient related data; the AI risk layering and early warning module constructs a multi-dimensional dynamic score card model to carry out risk scoring and layering, and obesity health risk early warning is generated; a personalized intervention scheme generation module constructs a multidisciplinary intervention strategy matrix, and dynamically generates an intervention scheme according to an early warning result and individual features of a patient; the remote collaborative management module realizes information interaction and collaborative intervention of multi-party resources; and the effect evaluation and optimization module tracks the intervention effect and optimizes the model and the intervention scheme. Therefore, the problems of data lag, intervention scheme solidification, untimely early warning and the like in obese patient management are solved.

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 an injury condition simulation system based on treatment feedback and a treatment evaluation method, and relates to the technical field of injury condition treatment simulation, and the system comprises a simulation definition module which generates initial vital sign parameters and natural evolution increment under a non-intervention condition according to an injury condition type defined in a treatment scene, the physical injury condition simulation dummy comprises a physical injury condition simulation dummy, a treatment detection module for analyzing and quantifying treatment actions of practicers and generating corresponding intervention signals, a central control module for calculating and outputting vital sign parameters and body surface performance parameters based on pathophysiology laws and an intervention mapping model, and a state feedback module for driving each execution unit of the physical injury condition simulation dummy. The system can analyze and score execution parameters of each treatment action of practicers, generate action quality indexes and intervention signals, provide quantifiable and comparable operation feedback for students, and map treatment action effects to dynamic update of injury states, so that vital signs and body surface manifestation change along with operation.

The invention discloses a pregnant and lying-in woman full-cycle health management method and device based on multi-source data fusion. The method comprises the steps that multi-source data are collected in real time; constructing a space-time alignment engine, performing timestamp calibration and space normalization processing on the clinical data, the behavior data and the environment data, and generating a fusion data set of a unified space-time reference; calculating the weight of each data source through a preset credibility evaluation function, inputting the weight into the multi-dimensional risk assessment model, and then outputting a pregnancy risk index; and generating a multi-modal instruction set containing the physiological intervention scheme and the psychological intervention scheme. Uniform coordinate reference of hospital clinical data, user behavior data and environment IoT data is realized through a space-time alignment engine, the problem of data splitting is solved, and the data utilization efficiency is improved; using a credibility weighting model to dynamically fuse latest data, and combining with time sequence correction to realize risk early screening; and the compliance of antenatal care of pregnant and lying-in women is optimized.

The invention relates to the technical field of data processing, in particular to a data processing method and system for a health management platform, and the method comprises the steps: connecting a wearable device, a medical institution information system and a user manual input channel through a multi-interface data collection unit, collecting multi-source health data, and carrying out the standardized integration; after the integration is completed, encrypting the data, and constructing a distributed storage architecture based on a block chain technology to store the multi-source health data; constructing a user health baseline model according to the multi-source health data to calculate various health indexes of the user, analyzing the health data of the user through a machine learning technology, and performing real-time monitoring; and generating a personalized intervention scheme according to an analysis result, establishing an intervention effect feedback mechanism, and optimizing the personalized intervention scheme according to a feedback result. According to the invention, the service level of the health management platform is improved through standardized data integration, privacy protection enhancement and user personalized analysis model construction.

The invention discloses a neural network-based old people internal ability multi-dimensional intervention scheme construction method. The method comprises the steps of obtaining preprocessed multi-modal sensory data; generating an edge exception marking result and uploading the edge exception marking result; outputting a second multi-scale feature representation by using a channel sensing scale selection mechanism; outputting a third self-supervised representation vector by adopting a mask rate dynamic adjustment mechanism driven by sensory characteristics; generating a fourth fusion feature tensor; outputting a fifth time sequence feature sequence, and obtaining a sensory decline trend regression result, a sensory decline risk grading result and a sensory pathway priority protection list; and generating an intervention matrix including an intervention scene, an intervention action, an intervention dose and an intervention frequency, and defining the intervention matrix as an old people internal ability multi-dimensional intervention scheme. According to the method, the high-risk sensory pathway and the critical decline moment can be recognized earlier and more accurately, and support is provided for continuous health management and health care service of the old.

The invention discloses a rehabilitation nursing system for treating autism spectrum disorder children based on AI intelligent accompanying. The rehabilitation nursing system comprises an AI intelligent accompanying robot, a multi-mode interaction module, a personalized intervention scheme generation module, a real-time monitoring module and a cloud data platform. The system obtains facial expressions, voices, actions and electroencephalogram signals of a child patient in real time through a camera, a sensor, electroencephalogram equipment and other multi-mode data acquisition modules, emotion recognition is conducted in combination with a convolutional neural network, the emotion state is analyzed through an emotion calculation model, and an autism diagnosis and treatment knowledge base is constructed through a knowledge graph. The system generates a personalized intervention scheme based on a deep learning algorithm, and dynamically adjusts the training difficulty through real-time intervention means such as virtual coaches, action correction, dialogue guidance and the like in combination with reinforcement learning. The system provides an efficient and personalized home rehabilitation solution for children with autism, the social ability is improved, and the rehabilitation period is shortened.

The application belongs to the technical field of disease risk prediction, and discloses a computer network-based risk prediction system for senile dementia, which comprises the following steps: collecting multi-modal data of a target user to obtain social interaction data, physiological parameter data and cognitive behavior data; extracting cognitive fluctuation nodes, social degradation nodes and physiological disorder nodes through dynamic fluctuation feature analysis to form a potential key node feature set; constructing an individualized dementia conversion risk map based on time sequence correlation and cross-modal coupling characteristics of the potential key node feature set to identify key risk nodes; determining a risk inflection point and predicting a senile dementia risk grade by analyzing the topological structure change and time evolution mode of the dementia conversion risk map; and providing a personalized risk intervention scheme and monitoring intervention effects in real time; the application realizes early and accurate prediction and active intervention of dementia risk, and significantly improves prevention efficiency and clinical decision value.

This invention discloses a weight management system, method, device, and storage medium for overweight and obese individuals based on standardized operating procedures, belonging to the field of health management technology. It includes an information collection and standardized input module; a health risk analysis and assessment module; a personalized intervention plan configuration module; a dynamic intervention execution and tracking management module; and an effect evaluation and optimization feedback module. The system of this invention achieves standardized collection of multi-dimensional basic data of users, health risk assessment and classification, standardized health risk assessment and classification, and effect evaluation and optimization feedback by constructing a collaborative operating system of the information collection and standardized input module, the health risk analysis and assessment module, the personalized intervention plan configuration module, the dynamic intervention execution and tracking management module, and the effect evaluation and optimization feedback module. Simultaneously, it identifies abnormal behaviors and generates early warning information through early warning rules, and uses the effect evaluation and optimization feedback module to comprehensively evaluate and dynamically optimize the intervention plan execution process.

The application relates to the technical field of medical information, and discloses a postoperative pain rehabilitation nursing data management system for a rehabilitation medical department, which comprises nine processing modules. A multidimensional data dynamic acquisition module is responsible for collecting postoperative pain related data of patients. A cross-platform data interaction module realizes data intercommunication and synchronization among multiple platforms in the system. A data encryption module performs encryption processing on collected sensitive data. A data analysis module calculates a pain comprehensive score and a rehabilitation progress index. A pain risk real-time early warning module performs real-time monitoring on the pain condition of a patient. An intelligent intervention scheme generation module generates a personalized pain intervention scheme. A pain rehabilitation evaluation module performs systematic evaluation on the pain rehabilitation process of a patient. A nursing feedback module collects feedback information of a patient on pain nursing service. A system emergency disaster recovery module provides a system-level disaster recovery backup and emergency response mechanism.

This invention discloses a method and system for processing COPD pulmonary rehabilitation data based on an integrated TPB and HAPA model, belonging to the field of pulmonary rehabilitation data processing technology. The method involves collecting TPB dimensional data, HAPA stage data, and pulmonary rehabilitation behavior and physiological data in association, and forming an association analysis dataset through quantitative processing. An integrated model with bidirectional feedback is constructed, clarifying the module hierarchy and connection relationships, and iteratively training and optimizing it. Real-time, quantified, and standardized patient data is substituted into the model for path coefficient calculation, stage fit analysis, and stage-specific adaptation matching. Finally, key model parameters, patient rehabilitation stage assessment reports, and dynamic pulmonary rehabilitation intervention plans are generated. This method achieves systematic processing and personalized guidance of COPD pulmonary rehabilitation data, improves the accuracy of rehabilitation behavior prediction and intervention plan formulation, and provides reliable technical support for clinical pulmonary rehabilitation guidance.

The invention relates to the technical field of health data management, and discloses a cloud data-based multi-point scheduling intelligent health management system, which comprises a multi-modal data acquisition and twinborn body construction module, a cloud data management module, a cloud data management module, a cloud data management module and a cloud data management module, the federated causal inference cloud brain module trains a global causal model under the condition of guaranteeing data privacy through federated learning; the collaborative decision-making and intervention scheduling module generates an intelligent intervention scheme based on the model; the abnormal resonance and risk traceability module identifies individual abnormity by comparing twinborn body states and finds a group resonance mode to trace unknown risks; and the closed-loop self-evolution module performs model iteration updating by using the execution result and the traceability risk. According to the technical scheme, the federated learning framework and the cloud causal inference model are combined, and the technical effects of global knowledge sharing and causal relationship mining are achieved on the premise that original data of all nodes are not directly accessed.