226 results about "Dynamic assessment" patented technology

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 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 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 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 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 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.

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 relates to the technical field of geological engineering, and discloses a geological safety risk dynamic assessment method based on multi-source data fusion, which comprises the following specific steps: step 1, collecting and standardizing multi-source geological data; 2, performing semantic fusion and conflict resolution on the geologic features; 3, constructing a dynamic risk assessment model; 4, risk situation real-time updating and early warning are carried out; the satellite remote sensing system in the first step adopts the synthetic aperture radar interference measurement technology, the spatial resolution is better than 3 meters, the revisit period is shorter than 7 days, and the earth surface deformation monitoring precision reaches the millimeter level. Through standardized processing and semantic fusion of the multi-source geological data, the problem of heterogeneous data integration is effectively solved, the data basic quality of risk assessment is improved, a space-time coupling neural network model is adopted, nonlinear features and space-time correlation characteristics of geological risk evolution are accurately captured, and prediction precision and timeliness are improved.

The invention discloses an individual health risk dynamic assessment system based on multi-modal biological feature fusion, and relates to the technical field of medical health information processing. The method comprises the following steps: constructing a high / low-dimension data combination rule; dynamically adjusting the feature fusion weight according to the clinical feature importance weight and the medical efficiency value calculated by the detection time interval; a cost difference value formula is introduced to quantify the economical efficiency of combined evaluation and independent evaluation; calculating a health risk comprehensive coefficient of the monitoring scene by combining the health data credibility score and the index type / amplitude / duration weighted risk value; the risk level is rechecked through expert assessment or a secondary model, so that the assessment accuracy is ensured; updating the individual credibility score according to the evaluation result; and for the result exceeding the confidence interval, classifying and optimizing a feature fusion algorithm, a model parameter or a data combination strategy. The system realizes accurate, economic and real-time individual health risk dynamic assessment, and is suitable for scenes of chronic disease management, health intervention and the like.

The invention discloses a power distribution network photovoltaic openable capacity dynamic evaluation method fusing voltage stability margin and neural network optimization, and aims to solve the problems that a traditional method does not fully consider dynamic stability and is low in calculation efficiency. Firstly, a prediction method combining kernel density estimation and quantile regression is adopted to accurately quantify the uncertainty of distributed photovoltaic output. The core innovation of the method is that on the basis of traditional security constraints, a static voltage stability margin (VDSM) is introduced as a key constraint condition, and a double-layer interval analysis model capable of guaranteeing the dynamic stability of a power grid is constructed. Secondly, in order to efficiently solve, the invention provides a framework of'neural network pre-screening + parallel optimization ': after a model is decomposed into optimistic sub-problems and pessimistic sub-problems through an interval decoupling technology, massive candidate solutions are quickly screened by utilizing a neural network model, so that a feasible solution space is greatly reduced, and the solution efficiency is improved; and carrying out parallel optimization solution on the sub-models in combination with an improved particle swarm optimization algorithm.

The invention provides a disaster situation dynamic assessment method and system based on multi-source data fusion, and relates to the technical field of disaster assessment, and the method comprises the steps: collecting disaster multi-source parameters according to a plurality of data sources, and obtaining a first disaster data set; performing time scale unification by adopting a time alignment algorithm to obtain a second disaster data set; mapping the second disaster data set to a grid space framework by adopting a spatial interpolation algorithm to obtain a third disaster data set; performing multi-source data correction fusion to obtain a disaster fusion data set; inputting the disaster fusion data set into a disaster situation dynamic evaluation model to obtain a disaster situation dynamic evaluation result; and constructing a disaster situation map. According to the disaster situation assessment method and device, the technical problem that the accuracy of disaster situation assessment is poor due to the fact that different types of data are different and multi-source heterogeneous data are difficult to integrate is solved, and the accuracy and timeliness of disaster situation assessment are improved by fusing the multi-source data and data correction.

The invention discloses a water body health assessment system based on big data, relates to the technical field of water body health assessment, and realizes time synchronization, space mapping and quality assessment and restoration of multi-source data, calculation of water quality semantic indexes, data fusion, health level output and sampling work order generation. According to the method, deep fusion and standardized processing of multi-source heterogeneous monitoring data are realized, data islands are broken through, through high-precision space-time alignment and intelligent quality control, data reliability is ensured, human activity events are creatively fused for causal analysis, result interpretation is enhanced, multi-scale dynamic evaluation and adaptive threshold determination are supported, accuracy is improved, and the method is suitable for popularization and application. A closed-loop mechanism of sampling verification is evaluated, the unmanned ship is used for actively checking a suspicious area, secondary judgment is triggered, the response speed and result reliability of emergencies are remarkably improved, the whole-process compliance recording and standardized interface guarantee process is traceable, results are easy to share, and key technical support is provided for fine management of a water body.

The invention discloses an infection risk grading early warning method and system based on clinical parameter dynamic fusion. The method comprises the following steps: acquiring patient data from a plurality of medical systems to obtain initial data; performing regularization preprocessing on the initial data to obtain a preprocessing result; quantifying the importance of the preprocessing result in combination with clinical expert opinions and a machine learning model, and generating a final risk assessment weight through dynamic weighted fusion; based on the final risk assessment weight and the knowledge graph, factors influencing the risk and weights of the factors are determined, and a dynamic assessment model used for calculating a risk score is established; and performing continuous monitoring by using the dynamic evaluation model, and automatically triggering an alarm when the risk score exceeds a threshold value. By implementing the method provided by the invention, the accuracy and reliability of infection judgment can be effectively improved, false alarms and missing alarms are reduced, and more timely and accurate infection risk early warning is realized.

The invention discloses a drag suction dredger sailing collision risk dynamic assessment and collision avoidance decision-making method, and relates to the technical field of sailing collision risk assessment. According to the method, a precise evaluation foundation is laid through all-dimensional basic data standardization processing, a potential risk area is dynamically determined in combination with ship manipulation and environment characteristics, and the collision risk degree and the response level are quantified to improve the timeliness and the accuracy of risk early warning; a personalized operation strategy library is constructed based on an LSTM model, so that a collision avoidance instruction fits an artificial habit, the man-machine adaptation cost is reduced, and the anti-interference capability and the real-time performance of the instruction are enhanced through feedforward-feedback dual-path control and parallel DQN dynamic optimization weight. The problems that in the prior art, due to the fact that multi-source data are disordered and free of standards, evaluation bases are not unified, potential collision risk area recognition is not combined with ship handling characteristics and lacks dynamics, collision risk quantization is fuzzy, collision avoidance instructions break away from manual operation habits, and due to the fact that traditional control is poor in anti-interference capacity and fixed in weight, robustness is insufficient are solved.

The invention relates to the technical field of government affair services, in particular to a government affair service policy effect intelligent evaluation system based on multi-source data fusion. The system comprises a data acquisition module, a data fusion module, an evaluation model module and a result output module. The data acquisition module acquires policy-related data from a plurality of heterogeneous data sources; the data fusion module performs cleaning, normalization and semantic fusion processing on the multi-source data; the evaluation model module analyzes fusion data by using an intelligent evaluation model based on a causal inference framework, and quantitatively evaluates multiple dimensions of a policy effect; and the result output module generates a structured evaluation report. According to the method, the causal relationship between policy intervention and effects is accurately identified through a causal inference technology, multi-source data semantic fusion is realized by adopting the knowledge graph, the method has dynamic assessment and feedback calibration capabilities, and the accuracy and practicability of policy effect assessment are effectively improved.

The invention discloses an urban underground space water level risk dynamic assessment method and device, and relates to the technical field of water level risk assessment, and the method comprises the steps: collecting underground space utilization characteristics of a preset city; collecting underground space geologic structure information of the preset city; the method comprises the following steps: collecting underground water level and rainfall information in real time through a laid sensor network, performing risk grading evaluation, and generating a water level risk grade; and performing underground water level risk prediction according to the water level risk level. According to the method, the technical problem that the underground water level risk management scientificity is insufficient due to the lack of dynamics and accuracy in urban underground space water level risk assessment in the prior art is solved, and the dynamic assessment and prediction of the urban underground space water level risk are realized; and the scientificity and accuracy of underground water level risk management are improved.

The invention discloses an earthquake disaster loss dynamic assessment method and system based on multi-source disaster situation data, and relates to the technical field of earthquake disaster loss assessment, and the method comprises the following steps: S1, obtaining the multi-source original data of an earthquake disaster, and carrying out the preprocessing, the multi-source original data comprises seismic oscillation parameter data and multi-source disaster data; and S2, based on the preprocessed multi-source data, constructing a coupling driving model of the earthquake influence field and the multi-source disaster situation data, and generating a regional disaster damage thermodynamic map. According to the earthquake disaster loss dynamic evaluation method and system based on the multi-source disaster situation data, the earthquake multi-source disaster situation data is acquired, the coupling driving model of the earthquake influence field and the multi-source disaster situation data is constructed, and the influence of various factors on the earthquake disaster loss is comprehensively considered, so that the limitation that a traditional method depends on a single data source is overcome; the real situation of earthquake disaster loss can be reflected more comprehensively and accurately, and a more reliable basis is provided for rescue decision making.

The invention relates to the technical field of fire safety monitoring and early warning, in particular to a fire risk dynamic assessment and early warning method based on deep reinforcement learning, comprising the following steps: acquiring multi-source sensing data of a target area; a dynamically updated fire risk assessment state space is constructed based on multi-source sensing data, and a real-time state in the state space is processed and a risk assessment decision is generated through a deep reinforcement learning agent optimized based on an accumulated award function weighted by an accuracy award, a false alarm punishment and a missing alarm punishment; the deep reinforcement learning agent adopts a deep deterministic strategy gradient network structure; mapping the continuous output of the risk assessment decision into a discrete dynamic fire risk level; and triggering an early warning signal matched with the dynamic fire risk level according to the dynamic fire risk level. Through multi-source data standardization, dynamic state space, reinforcement learning optimization and grading early warning binding, accurate dynamic evaluation, reliable early warning and efficient emergency response are realized.

The invention discloses a steel structure construction safety risk dynamic assessment method and system based on artificial intelligence. The method comprises the steps of obtaining a multi-dimensional data set in real time; based on the multi-dimensional data set, a deep learning algorithm is adopted to fuse an image and a sensor signal, a structure defect is accurately identified, and a defect feature vector is extracted; when the defect feature vector exceeds a preset threshold value, a machine learning classifier is introduced to construct a risk assessment model, classification quantification of defects is realized, and an initial risk level is output; for the initial risk level, analyzing the space-time fusion data by using a probability state transition model, simulating the dynamic evolution trend of the risk and judging the stability of the risk; a mapping model is constructed based on a virtual simulation technology, dynamic simulation deduction is carried out on risk changes, and a visual visualization report and a graded early warning signal level are generated. According to the invention, full-chain intelligent closed loop from defect perception to risk prospective early warning is realized, and the safety management and control level and accident prevention capability of a construction site are greatly improved.

The invention relates to the technical field of machine learning models, in particular to a geological disaster risk dynamic assessment method based on multi-source data fusion, which comprises the following steps: constructing a basic geographic information database; dividing geological disaster risk areas by adopting a machine learning algorithm; calculating the contribution degree of each environment factor to the geological disaster through an information amount model; fusing the dynamic rainfall data, carrying out weighted fusion on the dynamic rainfall data and the static geological disaster factors, and calculating a comprehensive risk value; dividing risk levels according to the risk values, and generating a geological disaster risk zoning map; compared with the prior art which mainly depends on single static geological data for analysis and has the problems of incomplete evaluation dimensions and poor timeliness, the scheme realizes multi-dimensional fusion analysis of geological conditions and real-time meteorological factors by constructing the geographic information database integrating the multi-source environmental factors and the dynamic rainfall data; and the comprehensiveness and the momentality of risk assessment are obviously improved.

The invention discloses a community fire risk dynamic assessment method and system based on AI prediction, and belongs to the technical field of fire prevention and control, and the method comprises the steps: S1, collecting and preprocessing multi-source data of a community, and constructing a comprehensive risk factor database; s2, constructing a risk prediction model, taking the preprocessed multi-source data as input, identifying a key risk factor, and outputting a fire risk probability; s3, calculating the dynamic weight of each index by fusing the static expert experience and the real-time risk contribution degree; s4, based on the dynamic weight and the risk probability, calculating a comprehensive risk value and dividing risk levels; s5, constructing a prevention and control strategy library based on the risk level and the key risk factor; according to the community fire risk dynamic assessment method and system based on AI prediction, the change rule of the fire risk factors in the community can be captured in real time, potential fire hazards can be predicted in advance, and a targeted prevention and control strategy can be automatically generated.

The invention discloses a big data analysis-based occupational health management system optimization method. The method comprises the following steps of 1, collecting and preprocessing multi-source health information data of employees; 2, mapping the multi-modal data set into a unified-dimension employee health feature tensor; 3, constructing a health risk isomerism map based on the employee health feature tensor; 4, inputting the health risk heterogeneous atlas into the improved GATv2 model to obtain a health risk embedded representation vector; 5, performing risk assessment on the health risk embedded representation vector, and identifying a high-risk employee node; step 6, constructing an intervention action candidate set for the high-risk employee nodes, and generating an optimal intervention strategy by adopting an MOPSO algorithm; and step 7, executing the optimal intervention strategy and generating a score of an intervention effect. According to the invention, the improved GATv2 model and the MOPSO algorithm are fused, and dynamic assessment and accurate intervention of occupational health are realized.

The invention discloses an underground safety level intelligent evaluation response method and system fusing fuzzy credibility weighting and Bayesian reasoning, and relates to the technical field of underground safety monitoring. According to the method, a multi-source heterogeneous sensor is deployed to collect data, a fuzzy membership matrix is obtained through fuzzy membership function normalization, a credibility factor matrix is constructed by combining time sequence stability, spatial neighborhood consistency and node long-term health weight, and the two are subjected to weighted fusion to obtain a fusion index vector. And inputting the Bayesian network fused with the credible nodes, reasoning to obtain security level posterior probability distribution, and triggering a linkage response. The system correspondingly comprises a plurality of modules for implementing the steps. According to the method, the dynamic evaluation and credibility quantification of the data quality of the underground multi-source sensor are realized, and an intelligent evaluation mechanism for deeply integrating the credibility of the data source into a reasoning structure is constructed, so that the risk evaluation robustness and accuracy are improved, the closed-loop management from evaluation to response is realized, and the underground safety is guaranteed.

The invention relates to the technical field of laboratory safety risk assessment, and discloses a laboratory safety risk dynamic assessment method based on machine learning, and the method comprises the steps: S1, collecting the multi-source real-time data of a laboratory environment and equipment; s2, carrying out preprocessing and time sequence synchronization on the multi-source real-time data; s3, extracting time sequence features from the time sequence data based on a sliding time window; s4, inputting the feature vector into a machine learning model with an online learning capability; s5, distributing according to the risk prediction result and the current data; and S6, when the risk level reaches a preset condition, triggering laboratory safety early warning and linkage control. Incremental updating is carried out on the machine learning model by inputting feature vectors in real time, adaptive iteration adjustment is carried out on a to-be-updated parameter set based on prediction error changes of a continuous time window, risk judgment is corrected in time along with environment changes, and the real-time performance, sensitivity and stability of model risk prediction are improved.

The invention provides a small hydropower station safety and ecological multi-source risk dynamic assessment method and system, and belongs to the technical field of hydropower station management. Constructing a multi-dimensional risk feature tensor, including preprocessing multi-source data to obtain standardized data, extracting features from the standardized data, organizing the features according to risk types, time, space and feature four-dimensional organizations to form a four-order tensor, and performing decomposition to extract a core risk mode; constructing a conditional risk probability field, including conditional probability density estimation, risk probability field dynamic evolution model establishment and multi-source information entropy weight fusion; constructing a risk degree assessment model, including risk influence degree matrix construction, matrix spectrum analysis and risk measurement generation; according to the method, the defects of single dimension, static evaluation, high subjectivity and the like in the prior art are overcome, and accurate quantification and dynamic evaluation of the safety and ecological risk of the small hydropower station are realized.

The invention discloses a chronic obstructive pulmonary disease high risk dynamic assessment model system based on an artificial intelligence algorithm, relates to the technical field of chronic obstructive pulmonary disease high risk assessment, and aims to solve the technical problem that the timeliness and accuracy of an assessment result of an existing assessment model are limited. The medical data acquisition module is used for acquiring medical data of a subject through a wearable device, an electronic medical record system or a manually input multi-source channel, generating standardized preprocessed medical data after performing format cleaning, missing value filling and abnormal value filtering on the data, and transmitting the standardized preprocessed medical data to the memory of the cloud database; and the artificial intelligence dynamic assessment model construction module is used for constructing an artificial intelligence assessment model fusing time sequence characteristics and a nonlinear relationship based on the preprocessed medical data, and generating an intermediate result and a final parameter of risk assessment through multi-level calculation. The method has the advantage that the high risk of chronic obstructive pulmonary disease can be dynamically evaluated in real time.