376 results about "Health evaluation" patented technology

The invention relates to the technical field of power equipment, and discloses a digital twin operation monitoring system for power equipment, which comprises a data sensing and acquisition system for acquiring key operation parameters of temperature, current, voltage, partial discharge, vibration and humidity of the power equipment in real time, and performing multi-dimensional data acquisition through a sensor and a data transmission module; the state evaluation and prediction system is used for performing equipment health evaluation and residual life prediction by using a prediction model LSTM based on the collected data, and updating a prediction result in real time; provided is a digital twin modeling system. Through the combination of edge calculation, an LSTM model and a digital twinning technology, the precision and real-time performance of health management of power equipment are improved, data quality is optimized through edge calculation, the LSTM model captures an equipment degradation trend, virtual-real fusion is realized through digital twinning, and accurate monitoring and early warning of the health state of the equipment are ensured, so that intelligent operation and maintenance decisions are optimized, the failure rate is reduced, and the safety of power equipment health management is improved. The equipment life is prolonged.

The invention belongs to the technical field of bridge monitoring, and relates to a system and a method for fusing multi-source data of a bridge structure. Comprising a heterogeneous topological graph construction and manifold embedding technology module, a multi-scale space-time cognitive convolutional neural network module, a continuous manifold space-time alignment and Bayesian fusion module and a structure health index calculation and state evaluation module. The heterogeneous topological graph construction and manifold embedding technology module is used for obtaining a heterogeneous topological graph, a node embedding vector and a manifold model parameter; the multi-scale space-time cognitive convolutional neural network module is used for performing deep feature extraction on the heterogeneous topological graph to obtain multi-scale fusion features; the continuous manifold space-time alignment and Bayesian fusion module is used for obtaining space-time alignment parameters and fusion state vectors; the structure health index calculation and state evaluation module is used for carrying out structure health monitoring and state evaluation on the bridge to obtain a final health evaluation result; therefore, the intelligence, automation and reliability levels of the structure monitoring system are improved.

The invention relates to the technical field of data processing, and discloses a comprehensive evaluation method and system for the health degree of a distribution network cable. The method comprises the following steps: collecting cable joint multi-source monitoring signals, normalizing the monitoring signals to obtain a degradation degree feature vector, correcting multi-physics field coupling model parameters, obtaining a recessive degradation index through finite element calculation to obtain an enhanced feature vector, and performing time-frequency domain decomposition to extract multi-scale feature parameters to obtain a comprehensive feature matrix; a double attention mechanism calculates a feature weight and a time sequence correlation degree to obtain a deterioration trend prediction value, and fuzzy integral is fused with a multi-classifier output probability to obtain a health degree evaluation grade and an early warning result. According to the invention, the early defect identification accuracy and the degradation trend prediction precision are improved.

The invention provides a substation self-adaptive inspection method based on equipment health degree dynamic evaluation. The substation self-adaptive inspection method based on equipment health degree dynamic evaluation comprises the steps of S1, collecting electrical parameters, mechanical vibration parameters and environmental parameters of substation equipment in real time through a multi-source sensor network, and S2, performing data cleaning and time synchronization on the electrical parameters, the mechanical vibration parameters and the environmental parameters, and performing subset dynamic normalization processing. According to the substation self-adaptive inspection method based on equipment health degree dynamic assessment, electrical, mechanical and environmental parameters of the equipment are acquired in real time through the multi-source sensor network, and the real-time performance and accuracy of equipment health degree assessment are remarkably improved by combining dynamic normalization processing and real-time anomaly detection. And the LSTM neural network is used for analyzing the change trend of the equipment health index, so that the residual life of a key component can be predicted, and differentiated inspection and resource optimization allocation can be realized.

The invention discloses a glioma T cell prediction and prognosis evaluation method based on pathological images, particularly relates to the field of patient prognosis health evaluation, and aims to solve the problems that existing pathological evaluation is difficult to combine with tumor structure heterogeneity and immune infiltration distribution, and the future progress risk of a patient cannot be predicted based on follow-up visit pathological data. A spatial heterogeneity map of a tumor core area and an invasion edge is constructed in a digital pathological section, density gradients of T cells in different areas are calculated to generate a distribution heterogeneity coefficient, interaction processing is performed on the two types of characteristics in combination with historical follow-up visit records, and a time sequence neural network constructed based on a gating structure is combined to obtain a high-efficiency characteristic of the tumor. And outputting disease progress probabilities of a plurality of follow-up visit time points in the future to form a prognosis trajectory prediction curve, calculating a quantitative recurrence risk score according to curve slope change and an immune fluctuation mode, and finally generating an individualized management scheme, thereby realizing quantitative prediction evaluation and risk management of the prognosis trend of the glioma patient.

The invention discloses an aero-engine group health evaluation method based on multi-working-condition dynamic clustering, and belongs to the field of aero-engine health state evaluation. The method comprises the following steps: firstly, carrying out clustering analysis on set parameters in engine operation data, and carrying out merging processing on small-scale abnormal clusters to obtain a working condition category division result; secondly, constructing a health baseline data set, carrying out standardized preprocessing on sample data in a working condition category division result, and carrying out nonlinear dimensionality reduction to obtain a low-dimensional feature data set; thirdly, performing clustering analysis on the low-dimensional feature data set by adopting a Gaussian mixture model, and calculating an average mahalanobis distance between a sample of each clustering category and a health reference center to obtain multi-level health levels corresponding to different clustering categories; and finally, through fusing the membership soft probability and the sample individual mahalanobis distance, constructing a continuous health score and obtaining a health grade determination interval. According to the method, health state characteristics under different working conditions can be effectively identified, individual difference modeling and group transverse comparison evaluation are supported, and the accuracy is improved.

The invention discloses an intelligent electric meter box operation and maintenance management method and system, and relates to the field of electric power operation and maintenance management. The intelligent electric meter box operation and maintenance management method comprises the steps of multi-source data acquisition and preprocessing, equipment health state dynamic evaluation, fault root cause positioning and prediction, maintenance task dynamic scheduling, adaptive maintenance execution and closed-loop optimization, feedback of a maintenance result to a health evaluation model, updating of a dynamic weight coefficient, and monthly generation of an equipment health white book. And continuously optimizing the operation and maintenance strategy. According to the intelligent electric meter box operation and maintenance management method, the fault prediction model is established based on the LSTM neural network, the typical fault mode is matched in combination with the knowledge graph, the fault prediction accuracy is improved, and non-planned power failure is reduced. The improved genetic algorithm improves the utilization rate of maintenance resources and shortens the emergency response time. Predictive maintenance replaces regular maintenance, invalid inspection is reduced, and the spare part inventory turnover rate is increased. Human errors are reduced through AR assistance; and the maintenance operation accuracy is improved.

The invention relates to the technical field of millimeter-wave radars, in particular to a millimeter-wave radar action evaluation method, system and equipment based on a human skeleton model, and the method comprises the following steps: S1, millimeter-wave radar point cloud sampling: employing millimeter-wave radar equipment to collect three-dimensional point cloud data of a human body, and extracting target points related to human skeleton nodes; s2, skeleton node estimation: predicting three-dimensional coordinates of each joint of the human body by adjusting an output structure of a PointNet neural network model, so as to reconstruct and fit a skeleton structure of the human body; and S3, human body action evaluation: constructing the obtained three-dimensional coordinate data of the multiple frames of human body skeleton nodes into time sequence data, and analyzing a motion track by using an evaluation system based on an ST-GCN network. According to the invention, complex dynamic changes and behavior modes in human motion can be accurately captured, and high-precision real-time evaluation and feedback are provided for the fields of motion monitoring, rehabilitation training, health evaluation and the like.

The invention provides an equipment edge intelligent early warning method and system based on multi-dimensional data association, and the method comprises the steps: obtaining a current actual health index parameter of a health index of target equipment, and obtaining a current working condition index parameter of at least one working condition index associated with the health index, the health index is used for evaluating the health condition of the target equipment, and the working condition index is an index capable of influencing the parameter size of the health index; inputting each working condition index parameter into a pre-trained health index parameter prediction model, and outputting a reference health index parameter by the health index parameter prediction model; determining a target health evaluation parameter of the target device based on a difference value between the actual health index parameter and the reference health index parameter; and if the target health evaluation parameter is greater than a preset threshold, generating first abnormal early warning information, and sending the first abnormal early warning information to operation and maintenance personnel. According to the technical scheme, the accuracy of performing abnormal early warning on the transformer substation can be improved.

The invention discloses a power equipment health evaluation method and system based on time sequence analysis and probability modeling, equipment and a medium, and relates to the technical field of power equipment state monitoring and fault prediction, and the method comprises the steps: obtaining and preprocessing multi-source operation data of power equipment, outputting a predicted value and a confidence interval of a future parameter through a time sequence prediction model, calculating a residual sequence of an actual observation value and a predicted value, fitting distribution through a probability distribution model, establishing a statistical characteristic model of a normal operation state of the equipment, performing anomaly judgment, calculating a health degree index of the equipment based on a deviation degree and a dynamic weight of a monitoring parameter and weighted accumulation, and dividing equipment state grades according to the index. Quantitative evaluation of the health state of the equipment is realized. According to the method, accurate quantification and early abnormity identification of the health state of the power equipment are realized, a reliable basis is provided for predictive maintenance, and the intelligent level and the safety guarantee capability of power grid operation and maintenance are remarkably improved.

The invention relates to the technical field of power battery health state evaluation and anomaly recognition, in particular to a remote health state analysis and anomaly recognition method for an electric bicycle battery, which comprises the following steps of: under an access working condition, applying small-signal active excitation and synchronously acquiring to form an opportunity diagnosis excitation data window; time-frequency coupling modeling including equivalent impedance modeling, harmonic wave and intermodulation fingerprint modeling, Koopman operator modeling and entropy coefficient difference is carried out, and a spectral measurement geometric distance is constructed through optimal transmission to generate a multi-domain response packet, a physical invariant vector and an uncertainty matrix; according to the geometric distance and consistency score gating and hierarchical standardization, obtaining a health state estimation record and an abnormal event set by adopting a shape-limited health mapping model; and in combination with mapping Jacobian and dual potential, generating an opportunity diagnosis incentive plan of a next period by adopting D-optimal test design, and updating a group health manifold, thereby realizing cross-environment, cross-equipment and traceable remote health assessment and early anomaly recognition.

The invention relates to the technical field of health assessment, in particular to a psychological health assessment system and method for old people, and the system comprises a psychological monitoring module, a psychological analysis module, an adjustment strategy module and an intervention execution module. According to the method, the behaviors of the old people are monitored, data are recorded, abnormal indexes of the psychological state are accurately analyzed, the behaviors are matched with the psychological state through the psychological mode atlas, the accuracy of psychological health assessment and the response speed to changes are greatly improved, and the psychological health can be effectively monitored and timely adjusted by adjusting assessment parameters in real time; after the optimized psychological state data is deeply analyzed, the tuning effect can be more accurately evaluated, personalized psychological intervention measures are provided for the elderly, the personalized degree of the intervention measures is enhanced, the individual requirements are better met, and by analyzing feedback in the intervention period and recognizing key change points, the optimization effect is improved. And the accuracy and the effect of mental health intervention are obviously improved.

The invention relates to the field of intelligent evaluation, and particularly discloses a robot joint wear evaluation method and system based on a neural network, and the method comprises the steps: synchronously obtaining an original vibration time sequence and a working condition data time sequence of a robot joint, and constructing a feature extraction network with working condition data as a dynamic condition; and pure degradation feature codes only related to the internal wear state are decoupled and extracted. And then, based on the pure degeneration feature coding sequence, a degeneration trend time sequence modeling network is driven to accurately capture a long-term monotonous evolution law of the degeneration trend time sequence modeling network, and a time sequence hidden state representing the real abrasion degree of the joint is generated. And finally, decoding prediction is carried out on the hidden state of the time sequence, accurate evaluation of the remaining service life of the robot joint is achieved, it can be ensured that the real degradation state of the joint is accurately tracked all the time under the working condition of wide changes, and therefore accurate and intelligent health evaluation of the robot joint is achieved.

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 provides an on-line monitoring system and method for the grounding current of a transformer iron core, and belongs to the field of data processing, and the system comprises a magnetic adsorption type dual-channel current sensor which is used for synchronously collecting a transformer iron core grounding current channel signal and a transformer clamp grounding current channel signal; the self-adaptive signal preprocessing module is used for carrying out self-adaptive differential processing through a self-adaptive differential coefficient and carrying out variable gain adjustment and self-adaptive filtering; the multi-dimensional feature extraction module is used for performing multi-dimensional signal feature extraction on the purified grounding current signal to obtain a multi-dimensional feature parameter set; the feature fusion module is used for performing constraint fusion on the multi-dimensional feature parameter set based on the corrected iron core eddy current loss physical model, and generating a comprehensive health evaluation index through an adaptive weight adjustment mechanism; and the intelligent diagnosis module is used for carrying out transformer iron core fault type identification, health state evaluation and trend prediction according to the comprehensive health evaluation index and the multi-dimensional characteristic parameter set to obtain a transformer iron core state monitoring result.

The present invention reveals a comprehensive health evaluation method and system for the top cover drainage system of a hydroelectric generating set. It analyzes health evaluation factors at multiple levels, processing the drainage system into a component evaluation set and a state variable evaluation set. A combined weighting method, incorporating both initial weight and objective weighting methods, is used for comprehensive weighting across the levels. Standard requirements and scoring criteria are established, enabling sequential calculation of scores from state variables to components, then to the top cover drainage system, progressing from low to high levels. This process evaluates the health degrees at each level, resulting in a comprehensive and reliable health evaluation outcome. Consequently, it effectively addresses technical issues in prior art, such as limitations in health evaluation methods and the low reliability and universality of evaluation results.

The invention discloses a power distribution station equipment monitoring and health evaluation method based on multi-modal information, and the method comprises the steps: collecting multi-dimensional parameters of power distribution station equipment, carrying out the dynamic clustering through an improved fuzzy C-means clustering algorithm, and carrying out the aggregation of related parameter subsets; and constructing a hypergraph neural network model, taking the parameter subset as a node, generating a hypergraph adjacency matrix containing a multi-parameter coupling relationship, extracting deep features through multilayer hyperedge convolution, generating a quantitative evaluation result through reasoning, and dividing health levels. According to the method, parameter adaptability is enhanced through a model formula, key steps are refined, parameter association is mined, the defects that traditional clustering precision is insufficient and a conventional neural network cannot model high-order association are overcome, and comprehensiveness and accuracy of health management of power distribution station equipment are improved.

The invention discloses a boiler tube group diagnosis and maintenance method, and relates to the technical field of boiler monitoring, and the method comprises the following steps: building a thermodynamic simulation model, and carrying out reference simulation to obtain a reference system response parameter and a reference thermodynamic parameter; based on the simulation model, multiple times of simulation are carried out by adjusting the operation condition and the fault state, and a tube panel group health evaluation model is constructed by utilizing machine learning; introducing a thermal medium into the target tube group system, collecting actual measurement data, and screening out a problem tube panel group through the health evaluation model; and performing three-dimensional laser scanning and thermal imaging image acquisition on the defective tube panel group, fusing temperature information and space coordinates through coordinate mapping to construct a three-dimensional temperature field data set, positioning an abnormal tube section needing to be maintained according to the three-dimensional temperature field data set, and performing local maintenance. According to the method, step-by-step positioning and targeted maintenance from system-level abnormity to pipe section-level defects can be realized, and the diagnosis accuracy and maintenance efficiency of the boiler pipe group are remarkably improved.

The invention discloses a tunnel structure health dynamic monitoring system and method, and relates to the technical field of tunnel state monitoring. A tunnel structure health dynamic monitoring system comprises a data acquisition and processing module, a structure health evaluation module, a structure health prediction module, a score fusion and correction module and a monitoring scheme planning module. According to the method, weighted summation is performed on the recent structure health score and the predicted structure health score, and the comprehensive structure health score is obtained, so that the real-time state and the future development trend of the tunnel can be comprehensively considered, and the real health condition of the tunnel can be more comprehensively reflected; therefore, evaluation deviation caused by one-sided attention to a certain aspect at present or in the future is avoided, and insight of potential risks in advance is facilitated, so that resource allocation is optimized, risk areas are monitored emphatically, decision perspectiveness and planning rationality of monitoring work are improved, and the method is suitable for popularization and application. And the planning rationality of the tunnel structure health dynamic monitoring system and method on the monitoring scheme is improved.

The invention belongs to the technical field of electric energy metering box performance testing, and particularly relates to an electric energy metering box performance testing system based on data analysis, which comprises a multi-dimensional data acquisition module, a self-adaptive preprocessing module, a multi-modal feature fusion module, a dynamic health assessment module, an intelligent decision output module and an operation supervision center. The multi-dimensional data acquisition module outputs an original signal, the original signal is corrected by the self-adaptive preprocessing module and then flows into the multi-modal feature fusion module to generate a high-order feature, the dynamic health assessment module generates a health index based on a feature vector, and a data acquisition strategy is reversely regulated according to an assessment result. A complete test and analysis link is formed from original data collection to final maintenance decision generation, quantitative characterization and trend pre-judgment of the performance of the electric energy metering box are achieved, the real-time performance is guaranteed, meanwhile, the long-term state pre-estimation precision is improved, and the intelligent level and the automation level are high.

The invention discloses a sliding bearing health management system and method based on vibration analysis, and relates to the field of sliding bearing health management, and the method comprises the steps: firstly obtaining an original vibration signal and a real-time working condition parameter of a sliding bearing, and carrying out the multi-domain feature extraction of the vibration signal after time sequence alignment and windowing processing; and then introducing a double-flow attention coding mechanism, performing deep interaction and joint coding processing on the extracted vibration multi-domain features and working condition feature vectors, and further performing adjustment and decoding reconstruction on the joint potential space representation by taking the real-time working condition feature vectors as dynamic conditions, thereby generating a theoretical healthy vibration mode under the current working condition. And comparing the vibration with the actual vibration to calculate the health index. In this way, dynamic self-adaption of the health assessment standard can be achieved, and therefore the accuracy and robustness of health management under complex and changeable working conditions are remarkably improved.

The invention provides a management method and system of a vehicle networking control unit. The method comprises the steps of obtaining various operation data of a vehicle-mounted T-BOX; the types of the operation data comprise hardware state data, software operation data and vehicle operation state data; extracting a plurality of operation feature representations from the plurality of operation data; inputting the plurality of operation feature representations into a pre-trained fault prediction set model to obtain a fault prediction result of the vehicle-mounted T-BOX; wherein the fault prediction set model comprises a fault prediction overall model and at least one fault prediction sub-model for a specific fault type of the vehicle-mounted T-BOX; inputting the various operation data into a pre-constructed health state evaluation model, and determining a health state evaluation result of the vehicle-mounted T-BOX; and integrating the fault prediction result and the health state evaluation result of the vehicle-mounted T-BOX, and executing a fault processing action. In this way, the accuracy of vehicle-mounted T-BOX fault prediction, the comprehensiveness of health assessment and the initiative of fault processing can be improved, and the fault occurrence rate is effectively reduced.

The invention relates to the technical field of health evaluation, in particular to a group image health evaluation method and system based on intestinal flora, and the method comprises the steps: collecting intestinal flora samples of a target group through a standardized process, and obtaining microbial sequence information through a high-throughput sequencing technology; microflora diversity characteristics and core flora characteristics are extracted through strain identification and relative abundance analysis, individual flora characteristics are compared with a healthy population reference database, health state scores are calculated, health grades are divided, population health grade distribution is counted, and the population health grade distribution is calculated. And generating a group health evaluation report containing a visual chart and text analysis. According to the method, a complete technical system from sample collection to health strategy making is established, systematic evaluation of the group health state from the perspective of intestinal flora is achieved, and the limitation of a traditional method in group health early evaluation is overcome. The method is suitable for health monitoring of different scales of groups, and provides effective technical support for public health management and health service.

The invention discloses a remote intelligent control system for a drip irrigation head water pump based on the Internet of Things, relates to the technical field of agricultural intelligent irrigation, and is used for solving the problems of low precision and poor management efficiency of a traditional drip irrigation technology. The system comprises a drip irrigation area parameter acquisition module which acquires soil temperature and humidity, equipment operation parameters and meteorological data in a layered manner through a multi-source sensor network; the data processing module performs differential filling, space-time alignment and integration on the data; the intelligent analysis module realizes drip irrigation demand judgment, quantity value calculation, opportunity optimization and equipment health assessment; and the control execution module converts the instruction into equipment operation and supports remote intervention, so that precise management and control of drip irrigation are realized, and the precision and management efficiency of drip irrigation are improved.

The invention discloses a laser scanning type overhead conductor health detection system and method, and relates to the technical field of power equipment detection, and the method comprises the steps that a laser scanning module scans the contour of an overhead conductor according to a laser scanning line, detects the environmental interference, and corrects the scanning track of the laser scanning line in real time based on the environmental interference information; acquiring wire scanning data; the data processing module extracts wire contour data based on the wire scanning data, fits a wire diameter according to the wire contour data, and matches a standard wire diameter library to obtain wire diameter data; and the data analysis module calculates the wear rate according to the wire diameter data of the wire, evaluates the health state of the wire in combination with the multi-element wire information, and obtains a health evaluation result. According to the invention, the track deviation of the laser under environmental disturbance can be compensated in a complex scene, the lead diameter measurement precision is improved, the health state of the lead is evaluated by fusing the lead diameter data with the multi-element lead information, and the reliability of the lead health state evaluation result is improved.

The invention relates to the technical field of intelligent operation and maintenance of industrial equipment, and discloses an industrial equipment predictive maintenance method based on an adaptive neural network. According to the method, equipment operation data and sensor ontology data are acquired through a multi-source sensor network, an equipment health index and sensor data confidence coefficient are calculated based on principal component analysis, and a comprehensive health feature vector is constructed in combination with change rate and correlation analysis; inputting the feature vector and the state identifier into a hybrid neural network model, extracting a time sequence feature through an attention mechanism, and outputting an equipment health state, a residual life prediction value and a prediction confidence coefficient in parallel; based on a multi-dimensional evaluation result, a hierarchical maintenance strategy is generated and executed through a decision of data confidence management; according to the invention, through dual health assessment of the equipment and the sensor, the attention mechanism enhanced prediction model and data-driven closed-loop optimization, the accuracy and reliability of fault prediction of the industrial equipment and the self-adaptive capability of the system are improved.

The invention discloses a traditional Chinese medicine four diagnosis comprehensive diagnosis method and device integrated with AI analysis, and relates to the field of intelligent medical treatment, and the method comprises the steps: obtaining user identity information and basic physiological parameters at least comprising age, gender, height and weight, carrying out the correlation binding of the two, and constructing a user exclusive diagnosis file; a preset camera is combined with a color card correction mechanism to collect tongue surface, tongue bottom and face images of a user, the shooting angle and focusing parameters are automatically adjusted in the collection process, and ambient light interference is eliminated through the color card correction mechanism; according to the method, the traditional Chinese medicine syndrome element mapping matrix is converted into the syndrome score, the personalized health report containing multi-dimensional information is generated based on the analysis result, and the file is associated to support multi-mode acquisition, so that traditional Chinese medicine four-diagnosis data acquisition precision, analysis intelligence and diagnosis personalization are integrally realized, the traditional Chinese medicine four-diagnosis efficiency and accuracy are effectively improved, and the traditional Chinese medicine four-diagnosis efficiency and accuracy are improved. And comprehensive and adaptive health assessment is provided for the user.

The invention discloses a computer-aided old-age language erosion assessment method and assessment system, and relates to the technical field of old-age health assessment, and the method comprises the following steps: S1, collecting the voice data, text input data and interactive behavior data of an old-age user; s2, preprocessing the data, and extracting voice acoustic features, language structure features and cognitive behavior features; s3, inputting the extracted features into a pre-trained language erosion evaluation model, and outputting a language ability score and an erosion type classification result; and S4, generating a visual evaluation report, wherein the visual evaluation report comprises the language ability degradation degree, key obstacle points and intervention suggestions. According to the method, the score and classification result is automatically output through the multi-modal data acquisition and pre-trained deep learning model, so that the evaluation time is greatly shortened, the subjective deviation is eliminated, the result objectivity is ensured, the problems of low manual evaluation efficiency and high subjectivity are solved, and the effect of automatic evaluation is realized.

The invention discloses a management method and system for coping with nursing rehabilitation service, and relates to the technical field of nursing management. The method comprises the following steps: collecting and maintaining personal information of all users; health assessment is conducted on the target user regularly, and a rehabilitation state data set is obtained; the technical key points are as follows: the daily average nursing duration fine adjustment operation when the nursing grade reaches the limit is constructed, and the Barthel index, the PEI value and the ideal range deviation degree of the physical state evaluation value are introduced, so that the whole scheme can flexibly adjust the nursing duration to adapt to the rehabilitation trend change on the premise that the nursing grade is not changed or cannot be continuously adjusted, and the rehabilitation effect is improved. On one hand, personalized nursing resource configuration is carried out according to functional state differences, service flexibility is enhanced while nursing grade stability is maintained, on the other hand, the nursing disjunction phenomenon caused by grade limitation is avoided, and optimal management operation on nursing operation is completed.

The application relates to the physiological parameter monitoring technical field of intelligent wearable devices, 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, combining multi-dimensional rules and a user preset log to determine a scene, performing differentiated data acquisition, and generating a scene-matched health evaluation result after signal noise reduction, feature extraction and fusion analysis. The application can balance monitoring accuracy, low energy consumption and personalization, and improve the effectiveness of health monitoring.