17095results about "Health-index calculation" patented technology

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

The invention discloses an infrastructure risk operation optimization management system based on AI driving, and belongs to the field of computer data processing and commercial management, and the system comprises a multi-modal causal twinning construction module which integrates on-site multi-modal data streams to construct a dynamic space-time causal map; the risk evolution deduction module is used for performing anti-fact simulation based on a causal atlas to construct a prospective risk model; the collaborative configuration optimization module is used for solving an optimal collaborative defense strategy according to the risk model; the instruction analysis and digital prescription generation module is used for analyzing the defense strategy into a job digital prescription for a specific risk scene; and the intervention efficiency attribution and evolution correction module performs attribution analysis according to the execution effect of the digital prescription and adaptively updates the causal atlas. According to the method, a comprehensive method of constructing a dynamic causal map for risk deduction, coupling resource constraints for collaborative optimization and performing closed-loop feedback on a correction model is adopted, and active prediction, accurate intervention and continuous learning optimization of capital construction risks can be realized.

The invention discloses a scene interactive AI rehabilitation evaluation training and health monitoring system, and relates to the technical field of rehabilitation medical treatment and artificial intelligence, a semantic perception module is used for collecting and recognizing voice input, facial expressions, action tracks and eye movement paths of a user in a training process, and extracting context parameters; the knowledge-driven training generation module is used for calling a rehabilitation knowledge graph constructed by a graph neural network based on context parameters and individual training history, and generating a multi-path training scheme; training a feedback regulation engine, collecting posture offset, physiological stress and emotion feedback, and dynamically adjusting task difficulty, rhythm and prompt mode based on a dual-channel reinforcement learning model; the prediction module fuses training and monitoring data, and predicts a network identification function degradation risk through degradation driving; the cloud edge fusion platform is used for realizing task quick response and graph strategy iterative updating; according to the invention, the individuation, self-adaption and intelligent prediction capabilities of rehabilitation training are improved, and the rehabilitation effect and the system practicability are obviously optimized.

A federated distributed computational system enables secure oncological therapy optimization through multi-expert integration and advanced uncertainty quantification. The system implements a multi-expert integration framework that coordinates domain-specific knowledge through token-space communication for precision oncological treatment, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration. Through a distributed graph architecture, the system enables advanced fluorescence imaging with wavelength-specific targeting, multi-level uncertainty estimation combining epistemic and aleatoric approaches, and multi-scale tensor-based integration with adaptive dimensionality control. The system implements light cone search and planning for adaptive treatment strategy optimization, enabling medical institutions and research organizations to collaborate on complex oncological therapy projects while maintaining strict data privacy controls.

The invention discloses an accurate health risk early warning analysis system and method based on multi-modal medical data fusion. The system comprises a multi-source data acquisition module, a preprocessing module, a dynamic fusion module, a risk assessment module, an interpretability module and a dynamic early warning module. According to the method, multi-modal data are collected, feature vectors are generated through preprocessing and cross-modal fusion, a comprehensive health risk index is calculated through a double-flow model (time sequence LSTM + static GNN), abnormal association is analyzed in combination with causal reasoning, a threshold value is dynamically adjusted, grading early warning is triggered, and finally the model is optimized through reinforcement learning. According to the scheme, deep fusion and dynamic evaluation of multi-modal data are achieved, the accuracy, timeliness and interpretability of risk early warning are improved, the method is suitable for scenes such as chronic disease management and intensive care, and powerful support is provided for clinical decision making.

A spatiotemporal modeling system for Personal Health Database (PHDB) platforms integrates diverse health data types into a comprehensive 4D model of an individual's health status. By combining genomic, imaging, clinical, and real-time health data, the system creates a dynamic, time-based representation of the user's anatomy and physiology. This model enables real-time analysis, pattern recognition, and predictive forecasting of health outcomes. The system preprocesses and aligns data from various sources, constructs a detailed spatial framework, and continuously updates the model with new inputs. Through interactive visualizations, it provides users and healthcare providers with intuitive, personalized insights for improved health management and decision-making.

The invention relates to a digital chronic disease intelligent management platform based on an AI model and multi-dimensional data fusion, clinical diagnosis and treatment data, wearable equipment monitoring data, medication record data and environment monitoring data are acquired through a data acquisition module, and after standardized preprocessing is performed through a data fusion processing module, deep analysis is performed through an AI analysis module, and the data fusion processing module performs data fusion processing; in combination with medical knowledge of the knowledge base module, the intelligent decision-making module generates a personalized management scheme, and the personalized management scheme is implemented through the intervention execution module and the intelligent interaction module. Multi-dimensional health data are processed through an AI large model, a complex mode and an association relationship are automatically learned, and accurate disease prediction and risk assessment are realized; the pertinence of the scheme and the compliance of a patient are greatly improved; and real-time interaction and personalized guidance are provided, the participation degree and the self-management ability of the patient are effectively enhanced, and a benign health management cycle is formed.

The invention relates to the technical field of medical data processing systems, in particular to an artificial intelligence-driven medical diagnosis and treatment data processing method and system. The method comprises the steps that a multi-modal medical data acquisition module acquires and processes multi-source heterogeneous medical data of a patient, and a standardized data set is generated; a medical feature depth extraction module performs multi-dimensional feature extraction on the data set, and constructs a dynamic evolution feature matrix; a multi-dimensional health state space construction module constructs a patient health state multi-dimensional space according to the matrix and determines a key medical early warning index set; the real-time medical data fusion module maps real-time data to the space to generate real-time health risk factors; and the medical risk prediction and decision-making module establishes a personalized model, outputs a disease occurrence probability and generates personalized treatment suggestions. The system solves the problems that medical data processing is difficult, diagnosis analysis is not comprehensive, and a treatment scheme lacks personality, and diagnosis accuracy and treatment pertinence are improved.

The invention relates to the technical field of respiratory system risk prediction, and provides a respiratory system risk prediction method and system based on a graph neural network, and the method comprises the steps: collecting the multi-modal medical data of a patient, and constructing a multilayer heterogeneous graph based on the multi-modal medical data; constructing a weighted adjacency matrix and a node feature vector through the multi-layer heterogeneous graph; matrix product operation and convolution operation are carried out based on the weighted adjacent matrix and the node feature vector, splicing combination with historical moment state information is carried out, graph state representation is obtained, weighted aggregation of time dimensions is carried out, and time sequence attention features are obtained; performing coding processing based on the clinical examination data to obtain multi-modal fusion features; and inputting the multi-modal fusion features into a risk classifier for classification calculation to obtain a respiratory system risk level prediction result, generating a risk assessment report, and outputting respiratory risk early warning information. The accuracy and clinical practicability of respiratory system risk prediction 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.

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and / or track coronary artery disease.

The invention provides a method for establishing a diagnosis and treatment system for digestive system disease multi-modal information. The method comprises the following steps: S1, collecting multi-modal information for labeling and preprocessing; s2, extracting a feature vector and embedding a label into the multi-modal information according to the labeled information; s3, splicing and mapping the feature vector and the tag to a unified dimension to obtain an enhanced feature vector; s4, fusing the enhanced feature vectors to form a multi-modal feature matrix, performing linear mapping and weighted aggregation on the multi-modal feature matrix to obtain global fusion vectors, and collecting to generate a fusion vector sequence; s5, enhancing the time sequence information of the global fusion vector sequence, enhancing the spatial information of the spatial relevance of the specific feature of the part, and performing interactive fusion to obtain a spatio-temporal joint feature; s6, performing classification prediction on the disease stage or the specific pathological type, and outputting a diagnosis result; and S7, performing semantic association on the diagnosis result and the medical knowledge graph, sharing data to an online health intelligent platform, and providing a personalized decision basis for clinicians.

The invention discloses a hip and knee joint replacement postoperative rehabilitation method and system based on mobile medical treatment, relates to the technical field of medical rehabilitation, and discloses the hip and knee joint replacement postoperative rehabilitation method and system based on mobile medical treatment. The training plan is dynamically adjusted in combination with multi-dimensional indexes, the problems of compensation recognition deficiency, feedback lag and plan stiffness in a traditional rehabilitation scheme are effectively solved, the rehabilitation effect can be improved, and the injury risk of adjacent joints can be reduced.

The invention discloses a brain tumor multi-mode large model construction method, device and equipment and a storage medium, and is applied to the technical field of brain tumor imagines.The method comprises the steps that pixel-concept level alignment is conducted on a multi-mode MRI image and a pathological text; constructing a multi-modal feature fusion network for fusing image features and text features by adopting an attention mechanism of pathology perception and combining medical semantic information; training the multi-modal feature fusion network to generate an analysis report and a segmentation result; according to the technical scheme of multi-task cooperation, cross-modal pathological semantic accurate alignment, pathological knowledge graph injection and lightweight and continuous optimization parallelization, full-process coverage of brain tumor accurate segmentation, analysis report generation and prognosis prediction is achieved, the problems that a traditional model lacks pathological semantic support and is insufficient in clinical adaptability are solved, and the clinical adaptability of the traditional model is improved. And the deployment feasibility and the dynamic optimization capability are also considered.

The invention relates to the technical field of intelligent medical health management and artificial intelligence, in particular to a healthy life management system based on heterogeneous data collaboration, which comprises a cross-protocol heterogeneous data collaboration acquisition module, a causal perception digital twin construction module, an interpretable large model inference engine and a closed-loop intervention self-optimization system. Multi-protocol equipment data synchronous acquisition is realized through a differentiable equipment selection mechanism, healthy twins of a user are constructed based on a cross-modal causal discovery algorithm, an interpretable health assessment and intervention strategy is output in combination with a retrieval enhancement generation technology of meta-learning optimization, system parameters are optimized in real time through cross-equipment feedback reinforcement learning, and the system performance is improved. A complete closed loop of collection-modeling-reasoning-intervention is formed, the accuracy, personalization and reliability of health management are improved, and the method is suitable for personalized health monitoring and intervention scenes.

The invention provides a time sequence fusion and health state prediction method and system for multi-dimensional physiological data, and relates to the technical field of electrical digital data processing.The method includes the steps that high-precision sensing equipment and a parameter calibration model are adopted to calibrate data, time synchronization and error compensation are achieved through a weighted fusion algorithm, and the accuracy of physiological parameters is improved; meanwhile, a fatigue index and a pressure index are corrected in real time by using a closed-loop feedback mechanism, and a comprehensive state index is generated based on an evaluation result for further analysis and prediction; the change trend of the comprehensive state index is predicted through a machine learning technology, and a targeted dynamic rehabilitation scheme including training intensity optimization, diet adjustment, rest cycle planning and the like is provided in combination with physiological data of the user; and a timing feedback mechanism is established, so that the user can obtain health state change and adjustment suggestions in real time, thereby improving scientificity and timeliness of a rehabilitation scheme, remarkably optimizing the rehabilitation effect and efficiency, and realizing intelligent health management at the same time.

A programmatically generated AI avatar includes a customizable personality module, acting as the embodied interface for a powerful AI “mind” that delivers personalized coaching to improve user health, well-being, and longevity. The system uses machine learning, large language models, and biometric modeling to synthesize real-time, multi-modal health data—including sleep, nutrition, glucose, mood, and activity—and generate forward-prescribed KHAs. Unlike human coaches, it continuously adapts based on context and behavior, targeting the root cause: metabolic dysfunction—namely by restoring healthy, sustainable body composition through the preservation or building of lean muscle mass and reduction of excess fat. KHAs can also be shared with friends or programmatically generated AI avatars, allowing for coordinated action, emotional support, and accountability through social connection—further reinforcing positive behavior and adherence. The system's reinforcement learning engine incorporates both individual response data and anonymized population-level insights to optimize recommendations over time, learning which interventions are most effective for users with similar physiological and behavioral profiles. First validated with Olympic athletes—resulting in measurable improvements and medal-winning outcomes—this system offers a scalable, emotionally intelligent coaching engine that exceeds human capability, designed for the ultimate purpose of supporting sustainable health, resilience, and human thriving.

The invention provides an hemorrhagic brain injury intracranial pressure prediction and early warning system based on a CT image, and the system comprises a data collection module which is used for collecting the original data of a target data source of a target patient; the hybrid model comprises a 3DCNN model used for extracting hematoma spatial features of the head CT image sequence; the LSTM model is used for extracting hematoma time sequence characteristics of the physiological signals; the multi-modal fusion module is used for fusing the hematoma space features and the hematoma time sequence features and outputting hematoma fusion features; the prediction module is used for predicting an ICP sequence of the target patient in a future preset time period based on the hematoma fusion features; the self-adaptive early warning module is used for outputting a visual result based on the predicted ICP sequence in the future preset time period; and through a dynamic threshold adjustment mechanism, adjusting an early warning threshold of the target patient in combination with the case information, and triggering graded early warning in real time. Through personalized data processing and dynamic early warning, accurate, efficient and real-time patient risk management is realized.

The invention relates to the technical field of medical decision, and discloses a medical decision support system based on a knowledge graph, and the system comprises a knowledge graph construction module which constructs an initial knowledge graph based on a medical ontology library, and the knowledge graph comprises entities and association relationships of diseases, symptoms and drugs; the data acquisition module is used for acquiring data from an electronic medical record, wearable equipment, a medical literature library and a hospital information system and normalizing the data through a standardized protocol; the dynamic knowledge updating module is used for processing normalized data through an incremental graph neural network; a multi-source knowledge fusion module; a context awareness module; a dynamic deduction module; and a decision optimization closed loop module. And triggering a preset clinical rule in real time based on the pathological state of the patient, dynamically adjusting the intensity value of the related edge in the factor graph, and persistently storing the intensity value back to the knowledge graph, so that logic adaptation and individualized experience precipitation of general medical knowledge in a special pathological state are realized, and the individualized treatment accuracy is ensured.

The invention relates to the technical field of tumor treatment, in particular to an intelligent decision-making system for tumor precise targeted medication based on individual mutation information, which comprises a data processing layer, a variation annotation and function prediction layer, a knowledge base integration layer and a scheme decision-making engine and report visualization module. According to the intelligent decision-making system for tumor precise targeted medication based on individual mutation information, a rule engine and a prediction model are combined, dynamic priority ranking is output, multi-model fusion decision making is achieved, and clinical scene deep adaptation is achieved by predicting primary and secondary drug resistance, calculating liver and kidney function adjusting dosage and generating a combined medication time sequence scheme; through an individualized drug delivery scheme, combination drug use optimization is achieved, a visual clinical report is generated, clinical executable operation is further strengthened, and through algorithm quantification, a dynamic knowledge graph, AI auxiliary decision making and a clinical operation closed loop, the next-generation technical research direction of a tumor precise drug use system can be represented.

The invention belongs to the field of artificial intelligence, and provides a multi-modal depression recognition system based on an MFE-CCANNN model, which comprises a data acquisition unit, a data preprocessing unit and an MFE-CCANNN model unit. The data acquisition unit synchronously acquires multi-mode data such as videos, audios, texts and fNIRS when a subject performs the same interview task. The data preprocessing unit comprises a video preprocessing unit, an audio preprocessing unit, a text preprocessing unit and an fNIRS preprocessing unit. The MFE-CCARNN model unit comprises a video, audio, text and fNIRS neural signal feature extraction module, a multi-modal feature fusion module and a classification module, and depression recognition and classification result output are achieved. The system supports four-level depression degree discrimination, is high in recognition precision, portable in deployment, high in interpretability and the like, and is suitable for psychological health screening and clinical auxiliary evaluation scenes.

There are provided methods, systems and non-transitory storage mediums for predicting growth of an abdominal aortic aneurysm (AAA) of a patient having been diagnosed with AAA. Segmented regions of interest (ROI) comprising the aorta and adjacent structures are received by segmenting a set of images. A wall shear stress parameter and intraluminal thickness parameter is determined. A 3D parametric mesh comprising a plurality of concentric 3D mesh layers is generated, where each concentric 3D mesh layer includes a same predetermined number of nodes. The generation includes encoding the segmented ROIs, the wall shear stress parameter and the intraluminal thickness parameter as features at respective node locations in the 3D parametric mesh. A trained growth prediction machine learning model predicts, based at least on a subset of features of the 3D parametric mesh, if the given patient will show AAA growth. The training of the growth prediction model is also disclosed.

The invention provides a neurology clinical nursing potential safety hazard analysis method and device, and relates to the technical field of neurology clinical nursing, and the method comprises the steps: inputting a risk feature matrix generated based on multi-source heterogeneous nursing data into a rule engine and graph neural network model, and outputting a preliminary screening risk event set; taking the primary screening risk event set and the risk feature matrix as input, and constructing a causal conduction map through a causal discovery algorithm; performing risk conduction quantitative integration on each risk event in the primarily screened risk event set based on a causal conduction map to generate a quantitative risk list; based on the dynamic risk priority number and the conduction path chain, reversely tracing to a root cause node along a directed conduction edge, and generating a targeted intervention strategy packet bound with the root cause node; the targeted intervention strategy package is pushed to the nursing responsible person terminal, the strategy execution effect and the risk evolution data are recorded, and a closed-loop disposal database is generated, so that the dynamics and the effectiveness of potential safety hazard analysis of clinical nursing of the neurology department are improved.

The invention relates to the technical field of physiological parameter monitoring of intelligent wearable equipment, in particular to an intelligent ring health monitoring method based on multi-sensor cooperation and related equipment. The method comprises the following steps: acquiring motion, optical and temperature sensing data, determining a scene in combination with a multi-dimensional rule and a user preset log, executing differential data acquisition, and generating a health assessment result matched with the scene after signal noise reduction, feature extraction and fusion analysis. According to the invention, monitoring accuracy, low energy consumption and individuation can be considered, and the effectiveness of health monitoring is improved.

The invention discloses a renal clear cell carcinoma prognosis prediction method based on multi-mode MRI (Magnetic Resonance Imaging) image and digital pathological omics fusion. The method comprises the following steps: S1, collecting a training data set based on an MR image and a pathological image; s2, feature extraction of MR radiomics; s3, deep learning feature extraction of the pathological image; s4, an MR-pathological feature fusion module; and S5, deploying the network. According to the method, depth features with prognosis information are obtained from two scales of pre-treatment images and post-operation pathology, effective features are extracted by adopting image omics and a convolutional neural network mode according to data characteristics of MR images and pathology images, and depth fusion of the two types of features is completed in a hidden space through a multi-task guiding mode, so that the accuracy of the MR image and the pathology image is improved. A precise prognosis model with multi-scale information is provided, and the method has a relatively strong clinical application prospect and is of great significance for realizing precise immunotherapy and improving prognosis of a patient.

The invention discloses a cerebral apoplexy risk and prognosis prediction system and method, and relates to the field of intelligent medical treatment, and the system comprises a data processing and knowledge construction layer which is used for extracting, cleaning and constructing a space-time multi-modal knowledge graph and structured clinical features from multi-source heterogeneous medical data; the feature engineering and fusion layer is used for deeply fusing dynamic semantic information in the space-time multi-modal knowledge graph and the structured clinical features through a graph embedding and attention mechanism to generate a fusion feature vector for a cerebral apoplexy prediction task; and the prediction model and output layer is used for performing cerebral apoplexy risk and prognosis prediction based on the fusion feature vector to obtain a prediction result, and generating a decision result for assisting a doctor in understanding the model through an interpretable mechanism. The method provided by the invention can improve the accuracy of stroke recurrence, bleeding transformation or function prognosis prediction, and provides a new way for accurate stroke management.

A computer implemented method for training a whole medical image foundation model, including: receiving a plurality of medical image datasets; extracting local sections of image data from the plurality of medical image datasets; obtaining one or more causal variables associated with the local sections and / or patient; training one or more self-supervised learning models based on the local sections of image data and the causal variables; combining the one or more trained self-supervised learning models with a deep learning network configured to combine a latent representation of the local sections of image data from the one or more trained self-supervised learning models into a patient-level representation; and combining, with the one or more trained self-supervised learning models and the deep learning network, at least one further network or function configured to accept the patient-level representation as input, the at least one further network or function operable to perform one or more patient-specific prediction tasks.

The embodiment of the invention provides a method and system for generating medical suggestions based on multi-modal data fusion, and the method comprises the steps: integrating a medical image, a physical examination report and dynamic physiological parameters of a patient through a multi-source data fusion module, generating a multi-modal data set, and synchronously inputting the multi-modal data set into a hybrid reasoning module and a dynamic knowledge graph engine. And the dynamic knowledge graph engine accurately recall a target diagnosis and treatment guide associated with the current multi-modal data set. The rule reasoning sub-module generates a first diagnosis suggestion containing a diagnosis conclusion, a treatment scheme and an evidence level based on a guide structured rule, and meanwhile, the neural network reasoning sub-module analyzes a multi-modal data set by relying on a triple topological structure and an edge weight; and generating a second diagnosis suggestion comprising the disease risk probability, the differentiated treatment suggestion and the evidence source. And finally, the interactive output module fuses the two suggestions to generate a medical suggestion report covering the diagnosis basis, the evidence level and the treatment scheme, so that the diagnosis and treatment precision of chronic disease management and health risk assessment is remarkably improved.

A programmatically generated AI avatar includes a customizable personality module, acting as the embodied interface for a powerful AI “mind” that delivers personalized coaching to improve user health, well-being, and longevity. The system uses machine learning, large language models, and biometric modeling to synthesize real-time, multi-modal health data—including sleep, nutrition, glucose, mood, and activity—and generate forward-prescribed KHAs. Unlike human coaches, it continuously adapts based on context and behavior, targeting the root cause: metabolic dysfunction—namely by restoring healthy, sustainable body composition through the preservation or building of lean muscle mass and reduction of excess fat. KHAs can also be shared with friends or programmatically generated AI avatars, allowing for coordinated action, emotional support, and accountability through social connection—further reinforcing positive behavior and adherence. The system's reinforcement learning engine incorporates both individual response data and anonymized population-level insights to optimize recommendations over time, learning which interventions are most effective for users with similar physiological and behavioral profiles. First validated with Olympic athletes—resulting in measurable improvements and medal-winning outcomes—this system offers a scalable, emotionally intelligent coaching engine that exceeds human capability, designed for the ultimate purpose of supporting sustainable health, resilience, and human thriving.

The invention discloses a health status assessment method and system based on a multi-modal biosensor, and the method comprises the steps: synchronously collecting a plurality of physiological signals of a plurality of target species objects, and generating a multi-modal physiological data flow with consistent time and space; inputting the multi-modal physiological data stream into a deep learning model, and outputting a high-dimensional feature vector fused with time-space characteristics; inputting the high-dimensional feature vector into a cross-species adaptation model based on transfer learning, and outputting a cross-species universality state vector representing the current health abnormal state and confidence of the target object; inputting the cross-species universality state vector into a lightweight risk assessment model, and outputting a quantitative assessment result reflecting the instant health risk level of the target object; and according to a quantitative evaluation result, automatically generating and triggering a personalized health intervention instruction for the target object. By utilizing the embodiment of the invention, the physiological difference of different species can be dynamically adapted, and the accuracy and the universality of health state evaluation are improved.