1058 results about "Vital signs" patented technology

The invention belongs to the technical field of medical informatization, and provides an anesthesia medication management system with biological recognition, comprising: a biological recognition module verifies the identities of medical staff and patients through a vein recognition technology; the multi-source data acquisition module acquires vital sign data and equipment parameters of a patient in real time to obtain multi-source data; the intelligent drug management module authorizes to store and take drugs according to the biological recognition result and generates an electronic anesthesia prescription; the processing decision module receives the multi-source data and generates a medicine adjusting instruction through a preset rule and an algorithm in combination with an electronic anesthesia prescription; the execution control module carries out secondary confirmation on the medicine adjusting instruction through biological recognition and then sends the medicine adjusting instruction to anesthetic infusion equipment to be executed; according to the method, through the electroencephalogram double-frequency index, the entropy index and the hemodynamic parameters, in combination with the anesthesia depth evaluation result and the personalized drug metabolism and effect model, the medication scheme is dynamically adjusted, the optimal infusion rate is generated, the anesthesia depth control is more accurate, and the fluctuation range is narrowed.

The invention discloses a postoperative drainage device for clinical nursing and a monitoring system, and belongs to the technical field of biomedical engineering. The problems that an existing device manually monitors drainage liquid data, consequently, the monitoring precision is insufficient, and the complication probability cannot be predicted are solved, drainage liquid and patient vital sign data are collected in real time through the micro spectrograph, the character multi-parameter sensor, the micro pressure sensor and the intelligent bracelet and transmitted to the remote monitoring platform, and the monitoring accuracy is improved. Medical staff can check real-time data of a patient at any time and adjust a treatment scheme in time, so that the nursing efficiency and quality are improved; multi-parameter data such as color, metering and property of drainage liquid and vital sign data are analyzed through a risk prediction model, intervention measures are dynamically adjusted according to different complication types and probabilities in combination with patient condition changes and real-time monitoring data, accurate intervention on the complication is achieved, and the occurrence rate and severity of the complication are reduced.

The embodiment of the invention provides a multi-modal data fused postoperative plastic surgery infection early warning method and system. The method comprises the following steps: firstly, performing quality evaluation and restoration on postoperative wound images, clinical texts, wearable vital signs and baseline features; performing double-flow coding and priori segmentation on the repaired image to obtain a coarse mask; constructing a super-pixel graph, obtaining a topological consistent fine mask through a graph neural network, and quantifying the erythema / exudation area; the text and the vital signs are coded respectively and then fused with the image features and the masks through cross-attention and self-attention, and a fusion implicit vector and uncertainty are obtained through quality weight gating; an individualized dynamic threshold value is constructed by combining baseline risk, logarithmic probability calibration and a sliding window trend, a confidence band is output by using conformal prediction, and early reliable infection early warning is realized.

The invention discloses a millimeter wave radar vital sign detection method based on an HHO-CEEMDAM algorithm, and the method comprises the steps: building a millimeter wave radar experiment system, and collecting an intermediate frequency signal of a human body echo; performing data reading and recombination, extracting phase features, and enhancing a target signal through non-coherent accumulation to determine the distance between the chest of the human body and the radar; recovering the phase of the vital sign signal from the incoherent accumulation FFT result by using the linear characteristic of arc tangent demodulation, and performing phase unwrapping and phase difference to obtain optimized phase information; setting a CEEMDAN parameter initialization range and designing a fitness function; a CEEMDAN parameter is optimized by using an HHO algorithm; performing CEEMDAN decomposition by using the optimized parameters, and screening breathing and heartbeat IMF components; and according to the IMF component, obtaining estimated values of the respiratory rate and the heart rate. The method improves the estimation precision of the respiratory rate and the heart rate of the millimeter wave radar in fatigue driving detection.

The invention relates to the technical field of intelligent diagnosis and treatment, and discloses a remote intelligent disease diagnosis and treatment system and method based on a block chain. The method comprises the steps that multi-modal physiological data such as real-time vital signs, historical medical record texts and medical images of a patient are collected, an encrypted data packet is generated, distributed storage is conducted through a block chain node network, and a data hash value and a timestamp are generated; and calling an intelligent contract to extract cross-modal features, generating a standardized feature vector set containing a multi-feature association identifier, and constructing a diagnosis and treatment decision tree capable of dynamically adjusting branch paths according to the standardized feature vector set. Verifying decision validity through a consensus mechanism, and generating a treatment instruction sequence containing drug configuration, instrument operation and a follow-up visit period; and monitoring feedback data in real time and updating the feedback data to the block chain, triggering an intelligent contract to generate a correction scheme when the deviation exceeds a threshold value, and writing the difference data as a new transaction record into the block chain, thereby realizing multi-modal data security management and control and dynamic precise diagnosis and treatment.

The invention discloses a multifunctional safety helmet management system and method based on the Internet of Things, relates to the field of safety production, and solves the problems that a traditional safety helmet is single in safety management function, insufficient in data processing precision and the like. A multi-dimensional state data sequence is continuously collected through a three-axis acceleration sensor, a temperature and humidity sensor, a positioning sensor, a vital sign sensor and the like integrated on the safety helmet terminal; terminal monitoring characteristics including an acceleration module value, a position drift distance and a physical sign fluctuation index are constructed, data reliability is evaluated through weighted fusion of dynamic fluctuation values, and an abnormal terminal is isolated. And carrying out space-time calibration on the reliable data to generate a unified coordinate system trajectory, analyzing and identifying abnormal behaviors through group trajectory consistency, and constructing a regional safety risk map in combination with a signal blind area coverage rate, thereby realizing safety risk level prediction based on historical data and real-time trajectory matching. According to the invention, comprehensive real-time monitoring of the state of the operator is realized, and the intelligent level of safety production management is improved.

The invention discloses an emergency patient information data matching method and system based on the Internet, and the method comprises the steps: obtaining multi-source heterogeneous data in real time according to an electronic medical record, image data and real-time vital sign data of an emergency patient, and generating a time-synchronized multi-source data set through a self-adaptive data format conversion algorithm; for the multi-source data set, using a feature extraction network to extract illness state features of patients, and generating patient priority scores; adjusting a resource allocation scheme in real time by adopting a medical resource matching algorithm based on a graph neural network according to the patient priority score and the illness state feature, and generating a preliminary medical resource matching result; and for a medical resource matching result, generating a final emergency patient information data matching scheme through a differential privacy protection mechanism by adopting a data security transmission protocol based on homomorphic encryption. According to the embodiment of the invention, the method can meet the requirements of quick, safe and effective processing of data in an emergency scene, and improves the quality and efficiency of emergency medical service.

The invention relates to the technical field of clinical auxiliary decision making, in particular to a vascular embolism clinical risk prognosis evaluation system based on multi-source data. The system comprises a patient multi-source data acquisition module, a clinical data feature processing module, a multi-source data fusion module and a clinical explanatory enhancement module. The method comprises the following steps: acquiring basic information, clinical examination results, vital signs and medical image data of a patient, and respectively performing structured and image feature extraction to form multi-modal feature representation; a key area is mined in combination with a self-attention mechanism, and the feature expression ability is improved; and through comparison of historical similar cases and contribution analysis of local features, interpretability enhancement of model output is realized, and an embolism risk prediction result with a clinical reference value is generated. The method effectively improves the prediction accuracy, stability and medical readability of the model, and is suitable for auxiliary diagnosis and treatment scenes of diseases such as vascular embolism.

The invention discloses an integrated pediatric vital sign intelligent monitoring and early warning method and system, and relates to the technical field of biomedical signal processing. The method is used for solving the problems of large monitoring interference and early warning lag in a dynamic environment. The method comprises the following steps: firstly, synchronously collecting a multi-modal signal to generate a motion intensity envelope signal and an R wave peak value interval sequence, carrying out orthogonal projection denoising on a photoelectric volume pulse wave signal by utilizing motion envelope based on a recursive least square algorithm, and outputting a reconstructed blood oxygen waveform; then, calculating a cross correlation coefficient of a heart rate trend and a motion envelope, quantifying a physiological coupling degree to generate an independent heart rate anomaly index, and accurately identifying a non-motion related risk; finally, the real-time blood oxygen value is analyzed, the index is used for dynamically increasing the low oxygen threshold value so as to tighten the alarm boundary, physiological parameter linkage grading early warning is achieved, and the recognition sensitivity of the compensation period risk is improved while interference resistance is guaranteed.

The invention provides a non-invasive intracranial pressure prediction method and system and a storage medium, and the method comprises the following steps: obtaining a multi-modal vital sign original signal from a subject in a non-invasive manner, embedding a timestamp of the multi-modal vital sign original signal, and generating a high-dimensional time sequence feature of each vital sign; based on a preset reference sequence, associating the time sequence features with the original signals by adopting an attention mechanism to obtain alignment features of each vital sign; inputting the alignment features into a gating circulation unit network, and extracting hidden features of each vital sign; and splicing and fusing the hidden vital sign features, sending the fused hidden vital sign features into a classifier, and outputting a judgment result about whether the intracranial pressure of the subject rises or not. The clinical application value of a non-invasive monitoring technology is improved, and then the treatment effect and prognosis quality of a patient are improved.

The invention discloses an anesthesia strategy generation system fusing time sequence data and a knowledge graph, and relates to the technical field of medical information processing. The method specifically comprises the steps that five types of vital sign data including arterial blood pressure, electrocardio and electroencephalogram are collected, correction is conducted in combination with traction marks and electrotome interference characteristics, and an analysis result is output; the inference analysis module executes double-track processing based on the analysis value, one track evaluates cortical suppression, shock and ventilation risks and generates a risk report, and the other track performs reverse arbitration when the blood oxygen data is abnormal; constructing a knowledge graph fusing the efficacy nonlinear cooperation equation, the age sensitive parameters and the drug administration sequence influence; and the strategy generation module integrates the multi-source data to generate a drug execution track, and executes fast and slow closed-loop regulation and control based on a risk report and an arbitration result. According to the invention, the adaptation capability of the system to individual differences, the abnormal signal identification and processing capability and the intelligent response level of the anesthesia strategy are improved, and the accuracy and safety of clinical anesthesia are significantly enhanced.

The invention relates to the technical field of fire fighting, in particular to a fire fighting safety technical-connection early warning and task trajectory tracking system, which comprises a safety technical-connection early warning subsystem and a task trajectory tracking subsystem, and is characterized in that the safety technical-connection early warning subsystem and the task trajectory tracking subsystem realize bidirectional linkage through a data middle table; through bidirectional linkage of the safety technical linkage early warning subsystem and the task trajectory tracking subsystem, an intelligent cooperative closed loop of early warning and rescue is constructed, millisecond-level data interaction between the multi-mode monitoring unit and the multi-mode tracking unit is achieved through the data middle platform, and when the early warning system recognizes fire point coordinates and a three-level risk level, the safety technical linkage early warning subsystem and the task trajectory tracking subsystem are combined to achieve early warning and rescue. An optimal internal attack path can be pushed to a trajectory tracking system in real time; on the contrary, when the vital signs of the rescuers are abnormal, emergency equipment such as a smoke exhaust fan can be reversely triggered to be started, and a full-link response mechanism of risk pre-judgment, path planning and dynamic intervention is formed.

The invention discloses a neonatal severe illness management system and system based on an AI large model and electronic equipment, and relates to the technical field of artificial intelligence and health cross, and the system comprises a data access integration module, a model training module and a personalized health suggestion generation module; the data access integration module is used for acquiring multi-modal medical data and performing cross-source heterogeneous fusion processing on the multi-modal medical data to generate a data set; the model training module is used for training a preset large language model based on the data set to obtain a neonatal severe health management model; and the personalized health suggestion generation module is used for generating a corresponding personalized health management scheme according to the vital sign data of the target newborn by using the newborn severe health management model. According to the invention, a health management scheme accurately fitting the individual condition can be provided for each newborn, and the effect and quality of newborn intensive care management are significantly improved.

The invention relates to the technical field of medical health monitoring, and particularly discloses a chronic disease patient physical examination data and complication nursing monitoring system based on the Internet of Things, multi-dimensional physical examination data such as vital signs and blood biochemical indexes of a patient are collected in real time through a multi-source sensing equipment network, and an individualized data stream is constructed; extracting parameter collaborative change characteristics by adopting multi-time scale analysis, and constructing a dynamic physiological characteristic spectrum; identifying the abnormal offset by calculating the deviation degree of the feature vector distribution and the reference distribution; based on intelligent matching of the spatial distribution features of the feature vectors and a clinical case library, generating structured early warning information including risk assessment and personalized nursing schemes; and finally, visual display and preventive nursing intervention of the complication risk evolution process are realized.

The embodiment of the invention provides a charging and discharging control method and system for an energy storage converter, and the method achieves the comprehensive perception of the current actual bearing capacity of a battery through the precise obtaining and fusion of three key vital signs, i.e., a battery health state (SOH), a thermal state characteristic temperature (SOT) and a state of charge (SOC). On the basis, a double-layer dynamic limit value system of a sustainable power boundary and a transient power boundary is defined: the sustainable power boundary and the transient power boundary are based on SOH and SOT, and long-term health and safety of the battery under any working condition are ensured; and the latter provides necessary short-time high-power response capability for the system in combination with moderate relaxation of the SOC state on the basis of the sustainable boundary. And finally, corresponding boundaries are intelligently selected according to the operation mode through correction logic, and fine correction is performed on the power instruction in combination with an SOC safety defense line, so that the optimal dynamic balance of the energy storage system among the performance, the safety and the service life is fundamentally realized.

The invention relates to the technical field of artificial intelligence medical diagnosis, and discloses a remote disease diagnosis system based on artificial intelligence driving. The system comprises five functional modules, a real-time medical data acquisition module acquires multi-modal physiological data of a patient from a remote terminal in real time, and the multi-modal physiological data comprises vital sign time sequence data, medical image data and symptom description text data; the cross-modal correlation analysis module carries out feature fusion on the data, extracts a correlation mode between modals and generates an abnormal feature data set with a weight mark; establishing a dynamic diagnosis baseline by the dynamic diagnosis baseline model, and adapting to individual physiological feature changes by updating parameters; the abnormal behavior detection module is used for comparing the real-time data with a baseline, identifying abnormal indexes and generating a marking result; the deep pathogenesis tracing module analyzes a potential disease mode and traces a pathogenesis path in combination with a patient's historical medical record. The system can optimize the remote diagnosis effect and assist clinical diagnosis and treatment.

The invention relates to the technical field of data processing, and discloses an emergency department patient information processing method and system based on big data, and the method comprises the steps: carrying out the standardization processing of an unstructured text and heterogeneous data through natural language processing and medical ontology mapping; the method comprises the following steps: dynamically monitoring vital sign data of a patient, extracting trend change and fluctuation amplitude, and mining a frequent pattern of illness state evolution from historical medical record data based on an FP-Growth algorithm; through a Transform-GNN-XGBoost model, the disease deterioration risk of the patient is predicted, and in combination with historical medical record data and the current state of the patient, the hospitalization duration and the treatment cost of the patient are predicted; dynamically generating an optimal triage strategy and a resource scheduling scheme through a genetic algorithm; the prediction result, the optimal triage strategy and the resource scheduling scheme are visually displayed to medical staff; the diagnosis and treatment efficiency and the resource utilization rate of the emergency department are remarkably improved.

The invention discloses a postoperative sub-delirium syndrome risk prediction system for a cardiac surgery patient, and relates to the technical field of medical treatment, and the system comprises a clinical data collection module which is used for collecting multi-dimensional clinical data of the cardiac surgery patient in a perioperative period under extracorporeal circulation, the data comprises patient baseline information, preoperative neurocognitive function evaluation indexes, intraoperative physiological parameters and postoperative early-stage vital signs; and the risk identification module is used for utilizing a single-factor statistical analysis mechanism. According to the postoperative sub-delirium syndrome risk prediction system for the cardiac surgery patient, accurate prediction of the sub-delirium syndrome risk is achieved by collecting multi-dimensional clinical data of the patient in the perioperative period, such as baseline information, preoperative neurocognitive function, intraoperative physiological parameters and postoperative early-stage vital signs; the independent risk factors are scientifically screened and quantified by using single factor analysis and a multivariate Logistic regression model, and the problem of incomplete data dimensions in traditional evaluation is effectively solved.

The invention discloses a breath and heartbeat detection method and system based on an FMCW (Frequency Modulated Continuous Wave) radar. The method comprises the following steps: acquiring a continuous wave signal sent by an FMCW millimeter wave radar to detect a target object so as to obtain a reflected echo signal; the echo signals are preprocessed; separating a respiration signal from a heartbeat signal of the preprocessed echo signal based on an EPH multi-stage cooperative signal processing algorithm; and carrying out anomaly correction on the separated instantaneous frequency anomaly data based on a local weighted regression algorithm. Therefore, the vital sign detection accuracy of the radar can be improved under the interference conditions of background noise, signal harmonic waves and the like.

The invention relates to the technical field of drainage control, in particular to an intelligent cerebrospinal fluid drainage rate and pressure cooperative control system and method. The system comprises a drainage pipeline module, a positioning adjustment module, an intelligent pump control module, a monitoring sensing module, a cooperative control module and a man-machine interaction module, and can be used for establishing a cerebrospinal fluid drainage channel and being connected with a drainage bag; a laser positioning pen fixed on the infusion rod emits a laser beam which is horizontal to a preset drainage plane and adjusts the hanging height; an infusion pump and a pump body controller drive cerebrospinal fluid to flow according to set parameters and generate flow velocity, flow and drainage time control signals, meanwhile, real-time intracranial pressure data, real-time flow and flow velocity data and physical sign data are collected, and a drainage height adjusting instruction or a pump control parameter adjusting instruction is generated; cooperative control over the drainage rate and the intracranial pressure is achieved, and system operation parameters, patient monitoring data and the drainage state are displayed. The drainage device can accurately control the drainage amount of cerebrospinal fluid.

The invention discloses a thoracic surgery postoperative complication assessment method and system based on multi-dimensional analysis, and relates to the technical field of thoracic surgery postoperative complication. Key sign signals such as heart rate fluctuation amplitude Cxv, respiratory rhythm drift coefficient Chx, pulse waveform disturbance degree Cmd and epidermal temperature change rate Cbt of a postoperative patient are continuously collected and denoised through a postoperative sign data acquisition module, and a structured sign data set TW is formed. By means of the method, tiny physiological fluctuation of a postoperative patient within 24 hours can be captured, early risk signals are prevented from being neglected, and the time sensitivity of complication recognition is improved from the source. A dynamic sliding window regression technology is introduced to carry out trend modeling on the sign data, so that a miniature instability section can be accurately extracted; through the improvement, the system can capture precursor signals of potential infection and bleeding earlier, and the possibility of early intervention is improved.

According to the clinical nursing patient information monitoring and inputting method and system, the data accuracy, the data inputting efficiency and the privacy protection safety are improved; the method comprises the following steps: firstly, collecting and preprocessing vital sign data of a patient in real time through various physiological sensors; then, acquiring a nursing record image, acquiring nursing record data through an OCR model, acquiring nursing information voice, transwriting the nursing record data and the nursing information voice into nursing information data through a medical scene voice transwriting model, and performing unified standardization processing on the three groups of data to generate patient information data; and finally, when patient information data is input, symmetric encryption is carried out by adopting a national cryptographic SM4 algorithm, encrypted data is obtained, a dynamic encryption key is generated through an ECDH key distribution technology, and the dynamic encryption key can be called to carry out decryption operation after authentication of a dual protection mechanism of Hash value pre-verification and identity authentication is passed.

The invention relates to the technical field of intelligent medical treatment, in particular to a urinary surgery postoperative risk layering intelligent nursing management system which comprises the steps of preprocessing dynamic sign flow data of a patient to obtain regular sign time sequence data; and separating a core vital sign track, a wound exudation quantitative sequence and an autonomous perception discomfort record through modal analysis. The feature calculation module compares the vital sign trajectory with a standard recovery trajectory library to generate a sign bias spectrum, analyzes the exudation sequence to obtain a wound humidity distribution vector, and calculates a time attenuation factor of the pain event according to the discomfort record. And the space construction and layering module combines the features to construct a multi-dimensional risk base space, and identifies boundary coordinates of high-risk, medium-risk and low-risk risk clusters through clustering. And the strategy generation module divides risk levels and allocates corresponding monitoring and intervention measures according to the risk levels. According to the system, dynamic and multi-dimensional quantitative evaluation of the postoperative recovery state and objective automatic layering of risks are achieved.

The invention relates to the technical field of intelligent glasses, and discloses an intelligent glasses wearing identification method based on multi-modal perception, and the method comprises the steps: collecting the capacitance change rate, the facial contour matching degree, the body surface temperature, the vital signs, the head attitude angle and other multi-source feature data in real time, generating five groups of feature data, and carrying out the standardization processing and weight fusion, forming a comprehensive feature vector; calculating a preliminary confidence score by combining a dynamic weight adjustment mechanism and a rule engine, and correcting a deviation caused by head movement; judging a wearing state according to the corrected confidence score, outputting a label, and realizing function control in linkage with a gesture interaction module; the abnormal state triggers local or cloud alarm; the sampling frequency of the sensor is dynamically adjusted according to the wearing state, the frequency is reduced to 1Hz when the sensor is not worn, the frequency is improved to 10Hz when the sensor is worn so as to enhance the response precision, and the decision process is only triggered when the feature is suddenly changed in combination with an event-driven strategy. According to the invention, the precision and the reliability can be improved, and low-power-consumption design is realized.

The invention relates to the technical field of medical information, in particular to a multi-source data health intervention method, which comprises the following steps: firstly, acquiring texts, vital signs, image vectors and wearable signals, generating a unified vector through multi-modal coding, and extracting a topological life vector; constructing a spiking neuro-causal diagram based on a unified vector, and injecting a life vector as an external field into a Sheng differential equation to generate a dynamic pathological manifold; compressing the manifold into a tensor network state, constructing a quantum optimization model in combination with a causal diagram, minimizing energy expectation and topological risk to obtain an optimal intervention sequence, and reinjecting a target gradient to adjust the manifold in real time; after the sequence is subjected to clinical logic verification, a commitment value and a zero-knowledge proof are generated and written into a Byzantine account book, and meanwhile a control instruction is issued to an execution terminal. According to the method, millisecond-level decision making is achieved, the false alarm rate is reduced, and chain traceability is provided.

The invention discloses a sepsis patient organ function damage early warning judgment method based on machine learning, and the method comprises the steps: obtaining the multi-modal clinical data of a sepsis patient, including vital sign time sequence data, inspection data, treatment intervention data and static patient basic information; after data standardization processing, organ function associated features are extracted through a multi-scale feature fusion strategy, and an organ-level feature set is constructed; the method comprises the following steps of: obtaining a multi-organ collaborative early warning model, inputting the multi-organ collaborative early warning model into a pre-trained multi-organ collaborative early warning model, respectively constructing an inter-organ compensatory relation map by double branches of the model, quantifying organ injury risk contribution degree, and outputting a multi-organ functional injury risk matrix by combining with attention mechanism weighted fusion; and generating an early warning result based on the risk matrix and the organ specificity early warning threshold, including the injury risk level of each organ and the dominant risk key feature identifier, monitoring data update in real time, and dynamically adjusting the interval to update the early warning result. According to the invention, the limitation of traditional single-source data and single-organ early warning is broken through, and the early warning accuracy and real-time performance are improved.

The invention provides a vehicle collision processing method and device, an electronic device and a storage medium, and relates to the technical field of vehicles. The method comprises the following steps: acquiring vehicle state data of a vehicle; under the condition that the vehicle state data meets the first-level collision condition, vital sign data and audio and video data of a user in the vehicle are collected; the first-level collision condition is used for indicating whether a collision possibility exists or not; judging whether a secondary collision condition is met or not based on the vehicle state data and the audio and video data; the secondary collision condition is used for indicating whether collision occurs; and determining the injury degree of the user based on the vehicle state data, the vital sign data and the audio and video data under the condition that the secondary collision condition is met. The method can be used for identifying whether the vehicle has the collision accident or not, and is used for solving the problem of inaccurate identification of the vehicle collision accident.

The invention provides a non-invasive LVEF monitoring method and system based on millimeter wave radar chest displacement signals. The method comprises the steps that S1, millimeter wave radar signals are transmitted; s2, thoracic cavity micro-motion signals are collected; s3, preprocessing the thoracic cavity micro-motion signal; step S4, separating vital signs; s5, extracting heart failure features; step S6, risk assessment; and S7, outputting a result. According to the invention, a set of non-contact and continuous monitoring cardiac function evaluation system is constructed through creative combination of a radar sensing technology and medical diagnosis, and compared with a traditional LVEF detection method (such as echocardiography, cardiac MRI and the like), breakthrough improvement in clinical practicability, data continuity and technical universality is realized.

The invention belongs to the technical field of medical auxiliary diagnosis, and particularly relates to an AI-based LoRA fine-tuning medical decision Agent system and an implementation method. The multi-modal data acquisition module is used for acquiring vital sign data, medical image data, laboratory inspection data and electronic medical record text data of a patient in real time; the dynamic weight adjustment LoRA fine adjustment module is used for carrying out low-rank adaptive fine adjustment on a pre-trained medical decision model by introducing a time attenuation factor, a data importance weight and a modal emergency degree coefficient based on the multi-modal data so as to generate a personalized decision model for the current patient; the multi-modal risk assessment module is used for fusing feature vectors output by the fine-tuned personalized decision-making model, constructing a comprehensive assessment function in combination with an attention mechanism and a feature interaction coefficient, and realizing dynamic quantitative assessment of the illness state risk of the patient; according to the method, the timeliness, the importance and the scene emergency degree of the multi-modal data can be dynamically balanced.

The invention discloses wearable pet multifunctional physical therapy health equipment and a use method thereof. The wearable pet multifunctional physical therapy health equipment comprises a biocompatible flexible substrate and wearing system, an intelligent moxibustion warm physical therapy module, a multifunctional phototherapy module, a bionic micro-vibration massage module, a vital sign real-time monitoring system, an intelligent control and energy system and an omnibearing safety protection system. A biocompatible flexible substrate and a wearable system are adopted as carriers, multiple modules such as intelligent moxibustion warm physical therapy, multifunctional phototherapy, bionic micro-vibration massage and vital sign real-time monitoring are integrated, cooperative operation is achieved through intelligent control and an energy system, and meanwhile an all-around safety protection system runs through all the time. The integrated design breaks through the limitation that traditional pet physiotherapy equipment is single in function. The device has the core advantages that the accuracy of cross-species treatment is improved, the concealment of the treatment process is guaranteed, and human-pet-medical collaborative management is realized.