1556 results about "Risk prediction models" patented technology

The invention discloses a construction site safety risk intelligent early warning system and method based on BIM and big data analysis, relates to the technical field of building engineering construction safety, and solves the problem that it is difficult to transmit construction site multi-source data which is collected and preprocessed in real time in real time and carry out space mapping with a BIM model. A rule engine is difficult to carry out initial early warning; a machine learning model is difficult to analyze time series data, predict collapse risks and identify dangerous behaviors; a risk prediction model is difficult to construct and is difficult to integrate into a BIM model; and pushing and closed-loop management are difficult to carry out on the risk early warning information. According to the method, the multi-source data is collected at the construction site, the digital twinborn scene is constructed by mapping the multi-source data to the BIM model by means of space-time alignment, the multi-source data is analyzed and processed by applying technologies such as a rule engine and a machine learning algorithm, and the result is integrated to the BIM model, so that visual risk monitoring and early warning are realized.

The invention discloses a partial discharge detection method and device, and the method comprises the steps: outputting a multi-mode signal matrix after noise reduction through employing a self-adaptive noise reduction algorithm according to a multi-mode sensing signal during the operation of a generator; based on the multi-modal signal matrix, a space-time convolutional neural network is adopted to extract discharge features, meanwhile, a topological relation between signal time-frequency features and sensor space is captured, and a multi-modal feature fusion tensor is output; according to the multi-modal feature fusion tensor, a model is generated through a dynamic map, manifold learning and a particle swarm optimization algorithm are combined, and a dynamic fault map containing discharge intensity, phase and frequency point distribution characteristics is output; and based on the dynamic fault map, calculating a real-time discharge danger coefficient by using a risk prediction model, and outputting a discharge grading early warning instruction and a maintenance priority sequence. According to the embodiment of the invention, high-precision and traceable discharge detection and graded early warning can be realized.

The invention discloses an intelligent psychological intervention system based on multi-modal fusion, which is characterized in that a three-dimensional evaluation system is constructed by integrating speech sentiment analysis, keyboard dynamics monitoring and physiological signal acquisition, and time sequence alignment and feature weighted fusion of multi-source data are realized by adopting a cross-modal Transform model. The core of the system comprises an adaptive intervention engine which defines a multi-dimensional state space based on a hierarchical reinforcement learning architecture, optimizes an intervention strategy through a PPO algorithm, and realizes dynamic emotion interaction in AR and VR scenes in combination with a digital twin training module; according to the clinical decision support system, physiological behavior characteristics and psychological assessment trends are integrated by using a multi-time scale risk prediction model, and a personalized early warning threshold system is constructed, so that the psychological state recognition accuracy is improved, the intervention intensity self-adaptive adjustment response time is shortened, and the high-risk signal early warning timeliness reaches the minute level; and the problems of evaluation hysteresis and strategy stiffness of traditional psychological intervention are obviously improved.

The invention relates to the technical field of coal mine safety monitoring, and discloses a coal mine safety data comprehensive analysis and early warning system which comprises a data integration module, a three-dimensional visualization module, a risk assessment module, a linkage control module, a model training module and a central processing unit and can further comprise a decision support module, a storage cluster and a communication gateway. The data integration module constructs a multi-source heterogeneous data acquisition channel and performs dynamic topology modeling; the three-dimensional visualization module dynamically renders the monitoring data based on the space-time reference axis; the risk assessment module generates a danger situation map through space-time correlation analysis; the linkage control module establishes a multi-level response mechanism; the model training module optimizes the risk prediction model; and the central processing unit schedules each module to operate. According to the system, integrated analysis, dynamic visualization, risk prediction and cross-system linkage disposal of coal mine safety data are achieved, and the intelligent level and emergency capacity of coal mine safety monitoring are improved.

The invention relates to the technical field of network security, in particular to a network security analysis method and system based on big data. Comprising the following steps: collecting related multi-source heterogeneous data of a network, and carrying out standardized processing such as cleaning and de-noising; network analysis is carried out based on the preprocessed data, network traffic is analyzed in real time by using machine learning and deep learning algorithms, and abnormal conditions are detected; constructing a risk prediction model according to a network analysis result and related information, and predicting a future network security risk level; if the risk level exceeds the threshold value, determining a security event source and a responsibility subject through data tracing; and finally, generating a safety response strategy according to risk prediction and data traceability results, and performing disposal. The corresponding system covers the modules of data acquisition, preprocessing, network analysis, risk prediction, data tracing, security response and disposal and the like, and all the modules work cooperatively to form a complete network security analysis and guarantee system, so that the stable operation of the network system is guaranteed.

The invention provides a deep learning-based avalanche early warning model construction method and system, and the method comprises the steps: firstly obtaining multi-source environment monitoring data, including meteorological time sequence, topographic space and accumulated snow layer physical data, of a target region, carrying out the time dimension alignment of the meteorological time sequence data to generate a feature sequence, carrying out the meshing of the topographic space data to generate a feature set, and carrying out the construction of an avalanche early warning model; the method comprises the following steps: extracting parameters from accumulated snow layer physical data to generate a state vector, inputting a deep learning network model containing time sequence attention, spatial convolution and cross-modal interaction units, generating a fusion feature vector, constructing a training set based on historical avalanche event annotation data, performing dynamic weight optimization on the fusion feature vector, and generating an avalanche risk prediction model. And finally, receiving current monitoring data in real time, outputting a risk level and an early warning trigger threshold value by the avalanche risk prediction model, and generating a multi-level early warning signal when a real-time risk value exceeds the threshold value, thereby realizing accurate avalanche early warning.

The invention discloses an intelligent mine supervision system based on 5G communication and a supervision method thereof, and the method comprises the following steps: the system collects environment parameters, equipment states and personnel behavior data through sensing nodes disposed in a mine region, and carries out the real-time evaluation of a high-risk region through the calculation of a pressure factor; and dynamically adjusting the acquisition frequency, range and collaborative mode of the sensing network, generating a risk distribution diagram, predicting the change trend of a high-risk area by the system, allocating resources in real time based on gradient change, optimizing the task allocation and coverage range of sensing nodes, and supporting dynamic setting of an early warning threshold by the system, and when a pressure factor reaches the threshold, performing early warning. The method comprises the following steps of: receiving an emergency instruction, triggering an emergency response, generating an equipment operation mode adjustment, personnel evacuation path optimization and task redistribution instruction, distributing the emergency instruction in real time through a 5G network, continuously optimizing node distribution and a risk prediction model in combination with feedback data and historical data, and realizing intelligence, real-time performance and high efficiency of mine supervision.

The invention provides a division method and system for a forest pest control area, and relates to the technical field of data processing, and the method comprises the steps: building a forest pest control database through collecting pest data and environment data of a control area; and training a pest risk prediction model based on historical data to generate a risk distribution map. And then, in combination with a GIS technology and a deep learning image segmentation algorithm, intelligent division is performed on the target area, and it is ensured that the unmanned aerial vehicle efficiently covers the prevention and control unit. According to the method, the optimal spraying route can be planned by adopting a path optimization algorithm based on the flight capability of the unmanned aerial vehicle, the pesticide carrying capacity and the environmental conditions, spraying parameters are dynamically adjusted by monitoring the wind speed, obstacle information and the like in real time, precise pesticide application is ensured, the pest control efficiency can be improved, repeated spraying and spraying blind areas are reduced, the pesticide use amount is reduced, and the pesticide application cost is reduced. Intelligent and precise forest pest control is achieved, and the method is suitable for large-scale unmanned aerial vehicle autonomous operation scenes.

The invention relates to the technical field of electric power government affair management, and provides a cross-department government affair big data business co-processing method based on a computing power platform and data fusion, comprising the following steps: S1, deploying a heterogeneous computing power cluster, and establishing a computing power resource dynamic scheduling data processing platform comprising a CPU, a GPU, an FPGA and an edge computing node; s2, acquiring multi-dimensional data of power grid equipment data, power grid load data, industry power consumption data and government policy data; and S3, designing a cross-department data access system based on the zero-trust architecture, and developing a multi-dimensional model of a power grid risk prediction model, an industry energy consumption analysis model and a carbon emission accounting model in power management. The space-time joint probability prediction and conflict priority ranking algorithm is applied to power-government affair emergency disposal, intellectualization and precision of cross-department business collaboration are achieved, power faults with serious consequences are rapidly handled according to the priority level, and the harmfulness and unpredictability of the power faults can be reduced.

The invention relates to an intelligent management and control platform and method based on base station management. The intelligent management and control platform and method are applied to operation state monitoring and regulation and control optimization of a plurality of communication base stations. The method comprises the following steps: S10, acquiring operation data of a plurality of communication base stations in a target area, and performing structured processing on the data to generate operation data input in a standard format; s20, performing fusion analysis on the structured operation data, and calculating an operation state score value and a corresponding state label of each base station; s30, based on the score value and the state label, constructing a risk prediction model, identifying a potential fault trend of each base station, and outputting a risk level and early warning information; s40, matching a regulation and control strategy template from a strategy library according to the risk level and the early warning information, and automatically executing corresponding scheduling operation; and S50, collecting a feedback result of the regulation and control operation, and calculating an operation performance difference before and after regulation and control to optimize a parameter weight in the regulation and control strategy library.

The invention discloses a permanent magnet motor demagnetization fault diagnosis method based on deep learning, and relates to the technical field of permanent magnet motor fault diagnosis, and the method comprises the steps: collecting the operation state data of a permanent magnet synchronous motor and a fan in real time through a sensor network, and generating a basic variable; multi-modal features in the basic variables are extracted, a health index sequence is constructed, and a demagnetization risk score is generated; setting a self-adaptive diagnosis opportunity control strategy, and determining the opportunity of next diagnosis detection according to the current demagnetization risk score; establishing a demagnetization risk prediction model, predicting a demagnetization risk score at the next diagnosis time, generating a dynamic risk threshold value, and judging whether a demagnetization early warning decision is triggered or not; allocating a maintenance task priority according to the current demagnetization risk score and the historical mode; comprehensively calculating the overall health index of the fan system, and predicting the residual life of the fan permanent magnet motor. According to the method, the problem of resource waste caused by unreasonable diagnosis time in the demagnetization fault diagnosis of the permanent magnet motor is solved.

The invention relates to the technical field of medical monitoring, in particular to a multi-mode intelligent management system for monitoring and preventing stress injury. The system comprises a multi-modal data acquisition and preprocessing module, a deep fusion risk prediction model module, an intelligent path planning and resource allocation module, a personalized intelligent intervention module and a data security module, so as to acquire time sequence physiological data and generate a body pressure thermodynamic diagram in real time, and realize standardization through normalization; a risk prediction model is generated through modeling, the contribution degree of each modal feature is calculated, and a risk level is determined according to a preset early warning grading mechanism; an optimal path is generated through intelligent planning, a nursing scheme is dynamically optimized and generated according to the risk level, and the nursing scheme is encrypted and transmitted to a cloud platform through HTTPS to be regularly subjected to security vulnerability scanning and repairing. According to the invention, the skin condition, the body position change and the pressure distribution of the patient can be accurately monitored, so that the closed-loop management of the pressure injury is realized.

The invention relates to the technical field of geological disaster monitoring and early warning, and discloses a Beidou-based geological disaster intelligent monitoring and early warning method and system. Beidou high-precision monitoring equipment is deployed by selecting a geological disaster prone area, earth surface displacement, settlement and inclination deformation data are collected in real time, and a multi-modal database is constructed in combination with environmental parameters. And performing alignment and noise correction on the spatio-temporal data by adopting Kalman filtering and a weighted evidence theory, extracting short-term and long-term deformation characteristics by utilizing a DBSCAN spatial clustering algorithm, and realizing multi-scale abnormal change pattern recognition in combination with a GeoHash grid index. Dimensional differences are eliminated through Z-score standardization processing, a geological stability index and change rate model is established, a causal reasoning framework is further constructed based on a Bayesian network, and a risk prediction model is trained in combination with a space-time neural network. The system can dynamically adjust a monitoring period threshold value and automatically trigger graded early warning, and supports hidden danger rectification whole-process tracing and multi-level gridding management. According to the scheme, the limitation of traditional single-source monitoring is broken through, the full-chain prevention and control of geological disasters from deformation feature extraction, causal relationship modeling to dynamic risk prediction is realized, and the early warning timeliness and accuracy are remarkably improved.

The invention relates to the technical field of hazardous chemical substance safety management and control, and particularly provides a hazardous chemical substance storage accident risk analysis method, which comprises the following steps: collecting and preprocessing time sequence data and spatial topological data of a storage area; inputting the time sequence data into a time dynamic risk prediction model, and extracting time sensitive features to output a risk level time sequence curve with a risk storage unit label; labeling a high-risk period window in the curve; constructing a spatial diffusion risk assessment model based on the spatial topological data, extracting risk diffusion features of a target storage unit, associating adjacent units, and outputting a dynamic risk spatial distribution result; performing space-time coupling analysis on the high-risk time period and the spatial distribution, and outputting early warning information after correction; and finally, calling the management and control rule base to generate a risk management and control decision instruction. According to the method, time-space two dimensions are covered, dynamic risk assessment, early warning and management and control are realized, the comprehensiveness and management and control accuracy of hazardous chemical substance storage risk analysis are effectively improved, and a scientific support is provided for safety management of a storage area.

The invention relates to the technical field of food traceability, and discloses a food safety knowledge graph system, which comprises an acquisition module used for acquiring multi-modal data of food in stages to form traceability data; the risk portrait module is used for constructing a multi-dimensional risk portrait of the food; the feature fusion module is used for fusing the features of the traceability data to generate feature representation; the block chain evidence storage module is used for storing risk portraits and traceability data; the risk prediction module outputs a prediction result according to the risk prediction model; and the AI decision center module is used for optimizing a prediction result of the risk prediction model and outputting a food safety knowledge graph. According to the method, multi-modal data of links such as production, transportation and storage are collected, the feature fusion module fuses features of the traceability data based on a weighted multi-head attention mechanism, feature representation is generated, and analyzability of traceability information is enhanced. And multiple data sources are weighted and fused, so that the system can capture risks more accurately, and the omission ratio is reduced.

The embodiment of the invention relates to a processing method and device of an Alzheimer disease risk prediction model. The method comprises the steps that DT I-ALPS feature information of dementia crowds and healthy crowds of a specified age group is obtained in a big data collection and volunteer recruitment mode, and a corresponding model data set is constructed and recorded as a first data set; constructing a three-classification prediction model for predicting the risk of the Alzheimer's disease as an Alzheimer's disease risk prediction model corresponding to a specified age group; training an Alzheimer disease risk prediction model based on the first data set; after model training is finished, DT I-ALPS feature information, input by the user, of any tested person of the specified age group is input into the Alzheimer disease risk prediction model for prediction, and a corresponding classification probability vector is obtained and fed back to the current user. According to the invention, prediction accuracy and prediction stability can be improved.

The invention relates to the technical field of risk prediction, in particular to a wireless sensor-based high-altitude operation online monitoring and early warning system and a wireless sensor-based high-altitude operation online monitoring and early warning method. The method comprises the following steps: deploying multiple types of wireless sensor arrays for the aerial work platform to collect structural stress data, environment temperature and humidity and personnel state data, and constructing a multi-source standard data set; performing space-time alignment on the multi-source standard data set to generate a synchronized high-altitude data set; constructing a three-dimensional feature space by using the synchronized high-altitude data set, and generating a high-altitude operation fusion feature matrix; generating a high-altitude operation risk prediction model based on the high-altitude operation fusion feature matrix; therefore, through fusion of multi-source data, alignment of spatial-temporal characteristics, dynamic simulation of risk evolution and construction of a multi-level response mechanism, the risk prediction precision and real-time response capability in high-altitude operation are improved, and a comprehensive, systematic and efficient safety management and control system is finally formed.

The invention relates to the technical field of safety monitoring, in particular to a building construction safety monitoring method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-source heterogeneous data of a construction site, carrying out the distributed feature extraction of the multi-source heterogeneous data through employing a federal learning framework, and generating time-space correlated construction site state representation data; based on a preset dynamic risk prediction model, risk prediction is carried out by using the construction site state representation data, a multi-level risk prediction result is output, and the preset dynamic risk prediction model is constructed based on a construction safety knowledge graph and a space-time diagram neural network; and triggering an adaptive feedback mechanism according to the risk level corresponding to the prediction result, generating visual early warning information and an equipment control instruction, and linking a construction site control system to execute emergency response operation. The problems that a traditional monitoring method is tedious in data processing, insufficient in real-time performance, high in cost, lack of prediction capacity and the like are solved.

The invention relates to the technical field of medical health information, in particular to a heart failure risk prediction method and system based on multi-source heterogeneous data fusion, and the prediction system comprises a data collection module, a data management module, a multi-modal feature extraction module and a dynamic risk prediction model module. An intervention strategy recommendation module; and a visualization and iterative optimization module. The prediction method is applied to the prediction system, patient health data is collected and obtained, a multi-source heterogeneous health database is constructed, physiological time sequence features, traditional Chinese medicine dialectical features and western medicine clinical features are extracted respectively to be subjected to structured coding processing, a multi-modal feature set is formed, the multi-modal feature set is divided into a training set, a verification set and a test set, and the training set, the verification set and the test set are combined. A Bayesian attention mechanism is introduced, dynamic prediction of the heart failure risk is achieved through the constructed depth time sequence model, an intervention scheme can be adjusted and formulated in the whole course of heart failure management, excessive medical treatment is reduced, meanwhile, disease progress is effectively restrained or delayed, and the life quality of a patient is improved.

The invention discloses a depression risk screening optimization method based on large and small model linkage. The method comprises the following steps: selecting depression related indexes, obtaining interviewee questionnaire data, and preprocessing the data to obtain a scale source database; a depression risk prediction model is constructed, and PHQ-9 measurement results are compared for model training and verification; training a dialogue strategy module of a semantic analysis enhanced fine-tuning training large language model, constructing answer mapping through dynamic question generation and dialogue flow control, and converting a natural language of a user into standardized data required by a small model; training a man-machine interaction reinforcement learning model, generating a depression risk screening result based on a small model, inviting a user to carry out recognition degree evaluation, and dividing feedback into two types of recognition and question; for different feedbacks, strengthening or correcting the current interaction strategy and prediction logic, and storing the audited data as high-quality data to a training database by the system for subsequent large model fine tuning; and outputting a result and performing result interpretation and suggestion by using the semantic analysis reinforced fine-tuning large language model. Hierarchical early screening of depression risks is carried out based on large and small model linkage.

The invention discloses a gestational diabetes auxiliary analysis system and method based on placenta ultrasonic textures, and the system comprises a multi-modal database which is used for storing placenta two-dimensional ultrasonic image data and clinical comprehensive data; the preprocessing module is used for screening the clinical comprehensive data and carrying out standardization processing and labeling on the image data; the image feature extraction module is used for constructing an image segmentation model based on a CNN network, performing image processing on the input placenta two-dimensional ultrasonic image through the image segmentation model, and obtaining an image feature data set; the risk prediction module is used for constructing a GDM risk prediction model; and the judgment module is used for carrying out risk classification on the GDM risk of the current pregnant woman by utilizing the image segmentation model and the GDM risk prediction model. By introducing a deep learning technology, intelligent analysis is performed on placenta ultrasonic images, clinical comprehensive data and other multi-modal data, and a reliable tool is provided for auxiliary analysis of gestational diabetes mellitus.

The invention discloses an intestinal cancer metastasis prediction method and device based on spatial omics, a medium and equipment, and the method comprises the steps: collecting original multi-omics data, and carrying out modal alignment and quality control processing to obtain pre-processed multi-omics data comprising second spatial transcriptome data, second single-cell RNA sequencing data and second pathological image data; performing cross-modal semantic embedding on the second spatial transcriptome data based on the second single-cell RNA sequencing data to generate a spatial enhanced expression profile; performing multi-scale graph construction on the second spatial transcriptome data and the second pathological image data, and extracting spatial heterogeneity features; inputting the spatial enhancement expression spectrum and the spatial heterogeneity features into a pre-trained metastasis risk prediction model, and outputting a liver metastasis probability spatial heat map and a key driving feature list; and finally generating a clinical prediction report containing high-risk area positioning. According to the method, through dynamic optimization of spatial resolution and multi-scale feature collaborative modeling, the sensitivity of early transfer detection is remarkably improved.

The invention discloses an electric power operation risk early warning method and system based on knowledge enhancement and multi-modal fusion. The method comprises the steps that video monitoring data, sensor monitoring data and service system data are collected in real time through multi-source sensing equipment deployed on an electric power operation site; the method comprises the following steps of: extracting entities and relationships from unstructured texts such as regulation documents and job logs by utilizing a natural language processing technology based on deep learning, extracting behavior characteristics from video streams by adopting a computer vision algorithm, and constructing an electric power security knowledge graph with dynamic updating capability; designing a multi-modal feature fusion algorithm based on an attention mechanism, and effectively integrating visual features, text features and sensor data; a graph neural network is adopted to train a dynamic risk prediction model to carry out risk prediction, intelligent research and judgment of electric power operation risks are realized, accurate management and control of the risks are realized through a grading early warning mechanism, and closed-loop management from risk perception to early warning treatment is formed.

The invention discloses a shallow lake water quality pollution treatment system and method, and relates to the technical field of water pollution, and the method comprises the steps: S1, building a disturbance intensity index DIX and ammonia nitrogen concentration change curve, achieving the quantitative recognition of bottom mud disturbance and pollution release behaviors, training a rebound risk prediction model according to the quantitative recognition, and outputting a rebound risk prediction coefficient Ft; and the capability of pre-judging the rebound trend of the ammonia nitrogen pollution is effectively improved. In the step S2, the adjusted aeration response frequency BAF is dynamically generated based on the rebound risk prediction coefficient Ft value, so that the aeration behavior is converted into a risk-driven system from a traditional timing system, the energy consumption and disturbance intensity are further reduced, and the treatment efficiency is improved. And S3, further introducing a governance residual coefficient GRC, constructing a complete feedback closed loop, and realizing accurate evaluation and secondary strategy optimization of the governance effect of each round, thereby avoiding the problem of effect back-off or excessive disturbance after governance.

The invention provides an ocean red tide early warning method based on remote sensing inversion and a time-space map convolutional network. The ocean red tide early warning method comprises the following steps: collecting hyperspectral data and multispectral data; respectively carrying out data quality inspection on the collected hyperspectral data and multispectral data; preprocessing the hyperspectral data and the multispectral data after the quality inspection is qualified to respectively obtain a hyperspectral result image and a digital orthoimage; analyzing the hyperspectral result image and the digital orthoimage to obtain remote sensing inversion data of water temperature, salinity, total nitrogen, total phosphorus and chlorophyll a, and generating a gradient distribution thematic map of each index; according to water temperature, salinity, total nitrogen, total phosphorus, chlorophyll a and historical monitoring data, in combination with a change trend of key environmental parameters, introducing a space-time diagram convolutional network model, simulating a red tide diffusion situation, setting a risk threshold, and performing risk prediction on red tide occurrence through a red tide occurrence risk prediction model; and updating the red tide occurrence risk prediction model based on buoy monitoring and field monitoring data.

The invention discloses an intelligent fire-fighting risk prediction and dynamic early warning method and system based on multi-source data fusion, and relates to the technical field of intelligent fire-fighting risk prediction, and the method comprises the steps: collecting multi-source fire-fighting data, and constructing a fire-fighting safety state multi-dimensional data matrix; constructing a risk prediction model based on the fire safety state multi-dimensional data matrix to carry out real-time risk scoring; and executing dynamic early warning through a risk prediction result. According to the method, through the multi-source data acquisition and fusion module, multi-type fire-fighting data are comprehensively acquired and standardized, a structured and multi-dimensional fire-fighting safety state matrix is constructed, and the problems of data dispersion and information splitting are solved. In combination with a risk collaborative prediction and scoring module, the risk level of each region is dynamically output based on multi-factor analysis, and the accuracy and foresight of risk prediction are improved. Through a dynamic early warning generation and linkage module, real-time grading early warning and linkage of a fire fighting system are realized, and rapid response and intelligent prevention and control of a high-risk area are realized.

The invention discloses an artificial intelligence automobile authority safety control method and device, and relates to the technical field of automobile electronics. According to the technical scheme, physiological monitoring data, driving behavior data and environment sensing data in a current period are acquired; calculating a physiological anomaly index, an operation deviation degree and an environment danger coefficient of the current period; inputting the physiological anomaly index, the operation deviation degree and the environmental risk coefficient into a risk prediction model fused with an attention mechanism to obtain a dynamic risk value of a next period; correcting the dynamic risk value according to the risk aging decay factor, and matching a target risk level according to the corrected dynamic risk value; determining a dynamic permission adjustment strategy corresponding to the target risk level by using a risk-permission linkage rule; and performing real-time verification on the dynamic permission adjustment strategy based on the driving scene where the vehicle is located, generating a controllable function list, and executing permission authorization operation in the next period. The main purpose is to realize accurate permission adaptation of vehicle functions.

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

The invention discloses a photovoltaic output hybrid probability interval prediction method and system based on a parallel deep learning architecture. The method comprises the following steps: constructing a photovoltaic output multi-source driving factor set; constructing an original feature matrix based on the photovoltaic output multi-source driving factor set; performing spatial-temporal feature parallel decoupling on the original feature matrix, and inputting the decoupled time features and spatial features into a spatial-temporal feature complementary enhancement module for fusion; inputting the photovoltaic output spatial-temporal feature matrix after feature enhancement into a photovoltaic output reference type prediction model to obtain a photovoltaic output reference type prediction result and a corresponding error; inputting the photovoltaic output reference type prediction result errors into the risk type prediction model, calculating prediction error risk interval boundary values, and superposing the prediction error risk interval boundary values to the reference type prediction result to obtain respective photovoltaic output risk type prediction results; and constructing a photovoltaic output hybrid risk type prediction framework, inputting two risk type prediction model results for coupling and optimization, and obtaining a photovoltaic output hybrid risk type prediction result.

The invention discloses a coronary intervention postoperative risk prediction model construction method and system, and belongs to the technical field of medical care information and health monitoring. A coronary intervention postoperative risk prediction model construction method comprises the steps of constructing an attending doctor experience scoring system and a nursing personnel ability scoring system, and performing weighted fusion on results of a doctor experience model and a nursing personnel model to form a final additional model for output. According to the coronary intervention postoperative risk prediction model construction method and system provided by the invention, the main model is combined with the additional model, the main model is comprehensively modeled through the regression model and the time sequence model based on the static and dynamic characteristic data of the patient, and the individual health state change of the patient is captured; the additional model evaluates the influence of medical teams and relatives on the postoperative risk by quantifying the experience of doctors and the ability of caregivers, and overcomes the defect that a traditional model only depends on patient data.