330 results about "Dynamic assessment" patented technology

The invention provides a network attack risk mapping assessment method and system, and relates to the technical field of network attack risks, and the method comprises the steps: collecting a network flow data packet to extract a multi-dimensional attack feature vector, employing a deep neural network to carry out feature learning to obtain an attack type recognition result and an attack intensity assessment value, and constructing an attack feature standard model; based on the attack type identification result and the attack strength evaluation value, combining an asset weight coefficient, a vulnerability hazard level and a vulnerability utilization difficulty coefficient to construct an asset vulnerability evaluation model; and performing association analysis on the real-time attack feature data and the asset vulnerability assessment score, constructing a risk mapping matrix, calculating a comprehensive risk score, and generating risk early warning information. According to the invention, accurate identification, dynamic assessment and timely early warning of the network attack risk are realized, and the accuracy and real-time performance of risk assessment are improved.

The invention relates to the technical field of image processing, and discloses a video monitoring personnel behavior identification method and system. The method comprises the following steps: performing action node and joint angle extraction processing on a positioning area to obtain human body action skeleton sequence data; constructing an action connection graph and a time sequence association edge, and carrying out behavior pattern learning processing to obtain space-time behavior characteristics; performing action semantic mapping processing on the spatio-temporal behavior features and the human body behavior feature vectors, and performing behavior time sequence chain construction processing to obtain behavior semantic description features; and performing behavior pattern matching and anomaly measurement processing on the behavior semantic description features, performing feature discrimination processing to obtain a behavior type identifier and an anomaly degree index, performing behavior persistence analysis processing, and performing behavior risk level evaluation processing to obtain a behavior recognition result and early warning information. According to the invention, based on multi-modal feature fusion and spatio-temporal behavior modeling, accurate identification of personnel behaviors and dynamic assessment of risk levels are realized.

The invention discloses a submarine cable risk dynamic assessment method and system based on multi-modal deep learning, and belongs to the field of marine infrastructure operation and maintenance. Aiming at the problems of incomplete data coverage, unreal generated scene, low evaluation reliability and the like in the prior art, the method comprises the following steps of: 1) constructing a multi-source heterogeneous data set containing six types of data including geology, ocean, ships, biology and the like, and realizing data alignment by adopting space-time grid coding; 2) designing a physical constraint generative adversarial network, and generating risk scene data conforming to a fluid mechanics law through a Navier-Stokes equation constraint; 3) creating a hierarchical space-time fusion network (HST-Transform), and combining CNN spatial feature extraction, a time sequence attention mechanism and a dynamic memory module to realize multi-modal fusion; according to the method, the detection rate of rare risk events is increased by 62%, the evaluation accuracy rate reaches 91.7%, the false alarm rate is reduced by 34% compared with a traditional method, and submarine cable breakage accidents can be effectively prevented.

The invention relates to the technical field of biomass and carbon sink collaboration, in particular to a tropical rainforest biomass and carbon sink collaborative optimization decision-making system based on deep learning. According to the technical scheme, the system comprises a multi-modal data acquisition system, a space-time coupling preprocessing module, an ecological coupling deep learning model, a carbon sink dynamic evaluation system, a multi-objective decision optimization engine and an intelligent interaction execution platform. Data are comprehensively and safely acquired by means of a multi-modal data acquisition system, the data quality is optimized through a space-time coupling preprocessing module, biomass and carbon sink are accurately analyzed by means of an ecological coupling deep learning model and a carbon sink dynamic evaluation system, a scientific decision is made through a multi-objective decision optimization engine, execution is guaranteed, and the method is suitable for large-scale popularization and application. Convenient interaction and efficient execution are realized by means of the intelligent interaction execution platform, so that the accuracy, scientificity and execution efficiency of tropical rainforest biomass and carbon sink collaborative optimization decision making are remarkably improved, and ecological protection and sustainable development of tropical rainforests are powerfully promoted.

The invention provides a fire risk dynamic assessment and early warning method fused with a deep learning algorithm. According to the method, fire related data is collected in real time from various sources such as a sensor, data cleaning, standardization and feature extraction are carried out, and high-quality input data is generated. And based on the convolution and recurrent neural network, constructing a fire risk prediction model, and performing model training and verification by analyzing historical fire data. The system can monitor building and environment data in real time, assess fire risks and generate corresponding early warning information. And the early warning decision module compares model output with a set threshold value, and once the risk exceeds the threshold value, the system automatically pushes early warning information, so that managers and fire departments can take measures in time. The system also provides a user interface and a feedback mechanism to help a user to monitor a risk assessment result in real time and continuously optimize system performance. The method has the advantages of accurate data analysis and rapid early warning response, and the possibility of fire occurrence is reduced.

The invention relates to the technical field of multi-modal data, provides a pilot competency dynamic evaluation method, system and device based on multi-modal data and a storage medium, and solves the problems of low accuracy of pilot competency evaluation and poor pertinence of training guidance. The method comprises the steps of collecting flight control data, physiological signal data, psychological assessment data, subjective scale data and international civil aviation organization core competency information, performing alignment fusion on multi-source data by adopting a time synchronization algorithm, and identifying an attention fixation mode based on a hidden Markov model. And constructing a workload index by fusing a subjective scale and a physiological entropy value, inputting the multi-modal features into a pre-trained competency assessment model, outputting three levels of psychological assessment indexes including a basic ability layer, a dynamic presentation layer and a risk early warning layer, and finally generating a personalized training report. According to the invention, the accuracy of pilot competency evaluation and the pertinence of training guidance are improved.

The invention discloses a lightweight concrete column beam durability evaluation method and device based on twinborn simulation, and relates to the technical field of twinborn simulation, and the method comprises the steps: collecting the degradation characteristics of a lightweight concrete column beam, and constructing a three-dimensional multi-scale structure model; executing coupling simulation to generate a twin simulation model; monitoring a column beam through a multi-source sensor, and determining a real-time performance degradation parameter; updating the twinborn simulation model by using the degradation parameters; and collecting future environment and load data, inputting the future environment and load data into the updated model, simulating a degradation evolution process, and generating a durability evaluation result. The technical problem that a prediction result and actual service performance have significant deviation due to the fact that a traditional durability evaluation method is difficult to dynamically couple a multi-physical field environment and a material microcosmic degradation mechanism is solved, and a dynamic evaluation system for virtual-real mapping is constructed through twinborn simulation. And the prediction precision of the degradation process of the lightweight concrete column beam and the dynamic deduction capability of the service life are improved.

The invention provides a multi-level supply chain optimization system based on a dynamic knowledge graph and a large model, and the system comprises a dynamic knowledge graph building module which employs historical supply chain data to build a graph, formulate a risk propagation rule, evaluate a supplier risk, and update the graph; the cloud manufacturing supplier optimization large model building module is used for building an optimization model by training and adjusting a BERT model which introduces an attention mechanism and combining with a dynamic knowledge graph; the supplier priority ranking tool establishing module is used for establishing a ranking tool according to the order information and supplier productivity and risk requirements; and the cloud manufacturing supplier optimization scheme prediction module integrates the dynamic knowledge graph, optimizes a large model and a sorting tool, inputs a language model development framework, predicts and optimizes a supply chain and updates the knowledge graph. Dynamic evaluation of supply chain risks and supplier optimization are realized, and the intelligent level of supply chain management is improved. The invention further provides a multi-stage supply chain optimization method based on the dynamic knowledge graph and the large model.

The invention discloses an educational resource image intelligent recommendation and multi-scale matching system and method based on machine learning, and relates to the technical field of intelligent education, a permeability evaluation model is constructed by collecting interactive behavior data of students and educational resource images to quantify cognitive stability and knowledge mastery parameters; synchronously performing multi-scale analysis on the image to generate a visual feature, a knowledge association feature and a cognitive guide feature; and determining an optimal recommendation level based on dynamic granularity selection processing, generating an enhanced image with a permeability feedback mark, and interactively generating learning evaluation data through the enhanced image. According to the method, cognitive dynamic evaluation and multi-scale feature matching are fused, so that the technical bottleneck of a traditional recommendation system on image granularity selection and cognitive adaptation is solved, and the educational resource recommendation accuracy and learning efficiency are remarkably improved.

The invention discloses a power transaction market risk dynamic assessment system, which comprises a multi-modal sensing module used for collecting physical equipment operation data, market transaction data and environmental parameters in real time, and performing space-time alignment processing through edge computing nodes to generate a fusion data stream; the self-adaptive evaluation engine is in data connection with the multi-modal sensing module and is used for analyzing multi-dimensional risk association in the fused data stream; the invention belongs to the technical field of electricity markets, and aims to solve the problems that in the prior art, historical data static analysis is relied on, spatial-temporal scales of multi-source data are not uniform, and data cannot be given in time. The technical effects are that a closed loop is formed through sensor data, a multi-mode sensing module (alignment processing), an adaptive evaluation engine (risk analysis), a digital twin system (simulation strategy) and a dynamic feedback module (optimization system), automatic calibration and learning are realized, and the risk prediction accuracy and the reliability of the scheme are improved.

The invention relates to the technical field of evaluation and management, in particular to a classroom learning dynamic evaluation and management system, which comprises a multi-source heterogeneous data acquisition module, an acoustic sensor array, an optical motion capture unit, an intelligent writing track acquisition device and a brain wave monitoring terminal, the data preprocessing and feature fusion module is used for eliminating environment interference signals and generating preprocessed data; the dynamic learning state evaluation module is used for extracting three-dimensional features and outputting a dynamic evaluation matrix; the group learning portrait generation module is used for generating a multi-dimensional learning portrait in combination with a preset knowledge graph topological structure; the self-adaptive management strategy generation module is used for dynamically generating a management strategy set; and the intelligent feedback execution module is used for driving the self-adaptive illumination system, the wearable vibration reminding device and the AR enhancement projection equipment to execute physical environment regulation and control. According to the invention, personalized strategy dynamic generation is realized, and adaptive regulation and control of the classroom environment and the teaching rhythm are realized in combination with the intelligent Internet of Things equipment, so that an intelligent collaborative feedback closed loop is formed.

The invention discloses a gate opening adaptive control method and system based on resonance risk dynamic assessment, and relates to the technical field of data processing, and the method comprises the steps: obtaining a current time point, obtaining a processing time period, and obtaining a historical water flow frequency and a historical gate frequency; obtaining a deviation degree corresponding to each to-be-processed time point, obtaining a gradient correction value corresponding to the current time point based on the prediction model and the deviation degrees corresponding to the plurality of to-be-processed time points, and obtaining a trend correction value corresponding to the current time point based on the trend model and the deviation degrees corresponding to the plurality of to-be-processed time points; obtaining the current water flow frequency and the current gate frequency at the current time point, and obtaining a risk index according to the current water flow frequency and the current gate frequency; and a target opening degree is obtained according to the risk index, the gradient correction value and the trend correction value, and the gate is controlled to be opened according to the target opening degree. The method has the advantages of dynamic perception, trend prediction and adaptive control.

The invention discloses an infection risk dynamic assessment method based on deep learning, and relates to the technical field of intelligent monitoring. Multi-source heterogeneous data are integrated by calling a pre-trained deep learning model, a basic regeneration number (R0) value set is calculated based on an SEIR infection model, the basic regeneration number (R0) value set serves as a node initial feature of a space-time diagram network, and a double-layer dynamic network is constructed to generate a regional vulnerability scoring matrix. And calculating a global infection risk index through the linear combination vulnerability score and the R0 numerical set, and generating a prediction infection density matrix and a hierarchical prevention and control strategy when the index exceeds a preset threshold. And further comparing a prediction result with actually measured infection data, dynamically adjusting a symptom keyword weight and a medical resource dependency parameter, and triggering online incremental learning of the model to optimize evaluation precision. Dynamic adaptation of multi-source data fusion modeling and prevention and control strategies is achieved, and the timeliness of infection risk assessment and the scientificity of prevention and control decisions are remarkably improved.

The invention relates to the technical field of risk assessment, in particular to an AI enterprise financial risk dynamic assessment method and system. The method comprises the following steps: obtaining enterprise multi-dimensional financial data, operation activity data, industry environment data and macroeconomic index data, carrying out cleaning, standardization and time sequence alignment processing, and integrating the data into a financial risk assessment multi-dimensional feature matrix; constructing a dynamic weight distribution model based on the financial risk assessment multi-dimensional feature matrix, endowing differentiated weights, and generating a time sequence weighted feature vector; risk level mapping and comparative analysis are carried out on the time sequence weighted feature vectors, risk points exceeding a threshold value are identified, and feature sources of the risk points are traced; and constructing a risk conduction path map based on the risk points and feature sources thereof, simulating risk evolution trends under different intervention measures, and evaluating and generating a dynamic evaluation report containing risk early warning levels, key influence factors and intervention measure suggestions. According to the invention, the enterprise financial risk assessment efficiency can be improved.

The invention discloses a method and system for predicting the health state of equipment based on large model AGENT capability. According to the method, equipment vibration, temperature, pressure, current and other data are acquired in real time through a multi-mode sensor, and data fusion and self-supervised learning are realized in combination with virtual data generated by digital twin simulation, so that equipment operation characteristics are extracted, and an equipment health index is calculated. According to the method, the health index and the change rate thereof are comprehensively considered, the equipment fault risk is dynamically evaluated, meanwhile, a dynamic weight self-adaptive updating mechanism is introduced, model parameters are optimized on line, and it is ensured that the model keeps high-precision prediction in long-term operation. The system can be widely applied to various heterogeneous devices, realizes full-process closed-loop management from data acquisition, intelligent analysis to risk early warning, and provides a scientific basis for device maintenance and preventive overhaul, so that the device failure rate is reduced, and the service life of the device is prolonged.

The invention relates to the technical field of intelligent water affairs, in particular to an intelligent water affairs management and control system based on the Internet of Things, and aims to solve the problems that in the prior art, multi-source water quality data and user behavior characteristics can be fused to construct a risk assessment model based on fuzzy logic and a Bayesian network, dynamic assessment of a complex water quality state cannot be achieved, and the risk assessment accuracy is poor. Abnormity cannot be quickly recognized through a similarity matching mechanism, and the response speed and accuracy of early warning are reduced; multi-source water quality data and user behavior characteristics are fused through the water quality intelligent early warning module, a risk assessment model based on fuzzy logic and a Bayesian network is constructed, the method has high nonlinear modeling and uncertainty processing capacity, dynamic assessment of a complex water quality state is achieved, abnormity is rapidly recognized through a similarity matching mechanism, and the risk assessment efficiency is improved. The method improves the early warning response speed and accuracy, combines the countercurrent tracking and GIS technology, accurately locates the pollution source, and enhances the emergency disposal and decision support capability.

The invention relates to the technical field of biodiversity assessment, and discloses a dynamic assessment method and system for the influence of engineering construction on biodiversity, and the method comprises the steps: obtaining a basic ecological data set, and carrying out the construction of an ecological patch graph structure and the edge building of hydrological connectivity according to the basic ecological data set, and obtaining a composite ecological graph model; performing plaque function heterogeneity aggregation and disturbance link simulation processing to obtain an ecological propagation path and a sensitivity pedigree of each node; performing spectral domain decomposition and key propagation node identification processing to obtain ecological intermediary nodes and propagation weights; performing reversible modeling and path entropy backtracking analysis processing to obtain a community reconstruction trend index and a function stability attenuation curve; and carrying out ecological response persistence analysis processing to obtain a time sequence biodiversity influence index. According to the method, the problems of static state, splitting, low coupling and the like in the prior art are solved, and the precision, timeliness and predictive capacity of ecological disturbance dynamic response evaluation are remarkably improved.

The invention relates to the technical field of computers, particularly discloses an engineering investment project multi-dimensional risk dynamic assessment and early warning system, and aims to solve the problems that existing risk assessment is single in dimension, insufficient in timeliness and lack of dynamic early warning and intelligent decision support. The system comprises a data acquisition and preprocessing module, a multi-dimensional risk feature construction module, a dynamic risk assessment and prediction module, a risk early warning and visualization module and an intelligent decision support and optimization module. Through integration of multivariate data, machine learning and deep learning algorithms, risk dynamic modeling, real-time evaluation and trend prediction are realized, and in combination with intelligent early warning and decision support, risk management is changed from post-remedy to beforehand prevention.

The invention discloses a multi-dimensional risk supervision method for bridge and underground pipe gallery engineering, and particularly relates to the field of underground pipe gallery engineering supervision, and the method comprises the steps: an underground structure slippage risk identification and prediction process based on pressure data driving: firstly, collecting an original pressure signal and screening effective data; establishing a standardized two-dimensional pressure field matrix by using the variation function; local gradient mutation features are recognized through Sobe l convolution, evaluation units are divided to calculate entropy distribution, entropy change abnormity is extracted, and a multi-dimensional phase space trajectory is constructed; the maximum Lyapunov index is further applied to estimate and recognize the stability state, gradient and entropy features are combined to construct a parameter inversion observation set, and a constrained optimization algorithm is adopted to iteratively solve an equivalent friction angle; constructing the time sequence into a time sequence, inputting the time sequence into an ARI MA model, executing slip rolling prediction, finally generating a risk thermodynamic diagram with confidence, and achieving the dynamic evaluation and early warning of the slip trend of the underground pipe gallery.

The invention relates to the technical field of TEM models, provides a pilot dynamic evaluation method, system and device based on a TEM model and a storage medium, and solves the problems of low accuracy of flight training evaluation and poor adaptability of a training scheme. The method comprises the steps that flight control, physiological monitoring and cockpit voice data are acquired, and a multi-modal data stream is formed through time synchronization; performing threat perception, error management and non-technical skill three-level evaluation on the feature vector by using a TEM model, generating corresponding indexes, and fusing the indexes into a comprehensive feature vector; respectively processing the time sequence and cognitive features by means of a dual-channel long-short-term memory network, and extracting deep features; performing decision tracing by adopting an SHAP value so as to locate a capability defect node; finally, defect types are matched through a personalized improvement scheme recommendation mechanism, and self-adaptive training content is generated. According to the invention, the accuracy of flight training evaluation and the adaptability of the training scheme are improved.

The invention discloses a colorectal cancer metastasis risk prediction method and system fusing radiomics and clinical characteristics, and the method comprises the steps: S1, collecting image data and clinical risk factor data of a patient, carrying out the preprocessing, and carrying out the feature extraction; s2, performing data expansion, and fusing expanded features and original features; s3, constructing and training a multi-modal large model, and outputting a first prediction result of the colorectal cancer metastasis risk of the patient; s4, layering the patients according to the first prediction result, and performing dynamic prediction; s5, performing interpretable analysis on a dynamic prediction result and generating a report; and S6, verifying the multi-modal large model. According to the method, the aspects of data fusion, prediction performance, dynamic assessment, clinical interpretation and the like are remarkably improved, key problems in the prior art are solved, an efficient, accurate and easy-to-use transfer risk assessment tool is provided for clinicians, and the method has important academic value and wide clinical application prospects.

The invention relates to the technical field of artificial intelligence and information, provides an AI-based campus security risk dynamic monitoring system, and aims to solve the limitations of data fragmentation, risk identification lagging, insufficient early warning, manual dependence and the like of an existing campus security monitoring system. The system comprises a multi-source data acquisition subsystem, an integrated preprocessing subsystem, an AI analysis subsystem, a dynamic evaluation prediction subsystem, an intelligent early warning response subsystem and a system self-learning optimization subsystem, and realizes real-time acquisition, deep fusion and intelligent analysis of campus heterogeneous data so as to realize dynamic evaluation, accurate prediction and active early warning. According to the technical scheme, the intelligent and automatic level of campus safety management can be remarkably improved, the manual load is relieved, active prediction and accurate early warning are achieved, false alarm and missing alarm are reduced, and the management efficiency is improved.

The invention belongs to the technical field of child growth and development evaluation, and particularly relates to a child development dynamic evaluation method and system based on multi-source data fusion. According to the method, the growth and development state of the child can be preliminarily judged through the growth and development sample curve generated through fusion, the second index data of the child are obtained through the non-intrusive acquisition equipment, and the acquisition frequency is adjusted based on the initial state type, so that the comprehensiveness and accuracy of the data are ensured, unnecessary frequent acquisition is avoided, and the accuracy of the data is improved. Processing the second index data through a pre-trained convolutional neural network model to generate individual growth fingerprints, screening out historical fingerprint data with high similarity with the individual growth fingerprints from a cloud database, constructing a personalized growth corridor, providing a personalized referable trajectory range for growth and development of children, and improving the growth and development efficiency of the children. And on the basis, whether the growth and development of the children are abnormal is judged, so that the growth and development conditions of the children are accurately evaluated.

The invention relates to the technical field of credit risk assessment, in particular to a credit risk dynamic feature extraction method based on hierarchical reinforcement learning. The layered reinforcement learning-based credit risk dynamic feature extraction method comprises the following steps of S1, obtaining credit risk data, and constructing a knowledge graph; s2, extracting candidate feature attributes, constructing an initial risk tag, and generating first-time multi-dimensional vector sample data; s3, performing dynamic feature engineering processing, and performing re-calibration and iterative updating on the initial risk label of the first-time multi-dimensional vector sample data; s4, performing spatio-temporal feature fusion processing, performing risk dynamic assessment, and calculating a risk prediction value corresponding to the time sequence feature; and S5, constructing a layered DQN framework, and dynamically outputting an interpretable risk feature set in combination with SHAP value quantification contribution verification. According to the method, high-precision and high-interpretability credit risk assessment is realized through multi-source data deep integration, self-attention driven feature engineering and time-space fusion risk quantification.

The invention relates to the technical field of intelligent assessment of psychological states, and discloses an intelligent dynamic assessment method for psychological states of teenagers based on multi-modal data. The method comprises the steps that multi-modal psychological data of a target assessment object is acquired, the multi-modal psychological data comprises voice emotion information and expression behavior relations, and dynamic features are extracted from the multi-modal psychological data; performing feature fusion processing on the psychological data by adopting a trained machine learning model to generate a multi-modal feature set containing a mapping relation between modal type identifiers and time sequence codes; based on the multi-modal feature set, performing time sequence correlation analysis on the dynamic features through a state analysis network to obtain a fusion result containing emotion dimension features and cognitive mode features; and inputting a fusion result into an evaluation model for dynamic state deduction processing, and outputting psychological state evaluation elements. According to the invention, comprehensive and dynamic assessment of the psychological state can be realized.

A fire risk dynamic assessment and early warning system based on K-class mean value clustering relates to the technical field of big data and artificial intelligence, and comprises a data acquisition module, a data processing module, a fire risk classification module, a fire risk assessment module, an early warning decision module and a feedback mechanism module. Through combination of clustering analysis and multi-factor evaluation, the system can quickly generate a risk evaluation result and timely give an early warning according to a preset threshold value. The efficient response mechanism can quickly inform related departments when the fire risk is aggravated, so that the safety of personnel and property is guaranteed.

The invention discloses a news image value evaluation method and system based on predicate logic and multi-modal fusion, and the method comprises the steps: obtaining multi-modal data of a news image, and extracting an audio and visual data key frame through a self-adaptive triggering mechanism; semantic analysis is performed on the key frame through a predicate logic analysis engine, a subject-action-scene triple is generated in combination with text data, and dual verification is performed through spatial position and semantic association; calculating a news value score of the verified triple data based on a three-dimensional dynamic evaluation model; the three-dimensional dynamic evaluation model fuses propagation intensity, information entropy compensation and ontology value; and performing visual adjustment and optimization on evaluation parameters and rules to realize optimization of the dynamic evaluation model. According to the method, massive multi-modal data can be efficiently processed, and the response time to emergencies is greatly shortened; the overall event checking efficiency is improved; the evaluation result is accurate, efficient, transparent and reliable, and the user can understand and trust the model decision conveniently.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an old people falling risk dynamic assessment and real-time early warning system and method based on multi-modal deep learning. The system comprises a multi-modal data acquisition module used for synchronously acquiring behavior data, physiological data and environmental data; the data preprocessing module is used for preprocessing the video data and the sensor data; the multi-modal feature fusion module is used for realizing cross-modal feature interaction of multi-modal data by adopting a hierarchical network architecture and fusing generated comprehensive features; the dynamic risk assessment module is used for realizing long-time-sequence risk prediction based on a prediction model and outputting a risk probability; the real-time early warning module performs real-time early warning based on the predicted risk probability by constructing a multi-level response mechanism; and the model adaptive updating module is used for constructing a closed-loop optimization mechanism and realizing adaptive updating of the model by adopting a transfer learning fine tuning model. The problem of misjudgment caused by one-sided data in the prior art is solved.

The invention relates to the technical field of information, in particular to a sleep health dynamic assessment method and system based on user feedback driving and a medium. The method comprises the following steps: constructing an individual knowledge graph reflecting an individual health causal relationship by adopting a time sequence causal analysis method; constructing a group knowledge graph reflecting a group health causal relationship in combination with an agglomerated hierarchical clustering method and a hierarchical federated learning method; based on user feedback information and health factor data which are acquired in real time, triggering individual knowledge graph updating, and triggering group knowledge graph delay increment reconstruction; and dynamically fusing the weight of the updated double atlases based on the accumulated quantity of the user feedback information to obtain the comprehensive weight of the user health factor, and carrying out weighted calculation on the newly obtained health factor data to obtain a sleep health assessment score. Therefore, real-time feedback data is deeply utilized, dynamic, accurate and explainable sleep health assessment is realized, and the technical prejudice that groups and individuals are irreconcilable is broken through.

The invention discloses a meteorological-based geological disaster risk early warning method, a server and a storage medium, and the method comprises the steps: constructing a two-factor dynamic evaluation framework, scientifically quantifying a disaster-bearing body vulnerability index reflecting a geological background and a disaster-causing inoculation factor reflecting a recent rainfall accumulation effect, and carrying out the inverse coupling of the disaster-bearing body vulnerability index and the disaster-causing inoculation factor, and generating a current rainfall disaster threshold dynamically adjusted in real time for each grid unit. By implementing the method and the device, the problem of insufficient early warning accuracy caused by the fact that traditional early warning cannot adapt to geological environment time-space difference is effectively solved, and accurate, dynamic and differentiated evaluation and early warning of geological disaster risks are realized.