38456 results about "Prediction methods" patented technology

The present application relates to the technical field of load prediction, and provides an electrical load prediction method and device based on spatial-temporal correlation. The method comprises: performing spatial-temporal modeling on the basis of historical electrical load data and historical environment data to obtain a multi-layer spatial-temporal model, wherein each layer model is a directed graph and represents spatial causality in the fixed time, and a directed edge between the layer models represents temporal causality; on the basis of an interpretable spatial-temporal attention transformer, performing feature extraction and priori knowledge fusion on the multi-layer spatial-temporal model to obtain spatial-temporal characteristics of an electrical load, wherein the interpretable spatial-temporal attention transformer comprises a spatial causal attention network, a temporal attention network, and a spatial dependence comparison module; and on the basis of the spatial-temporal characteristics of the electrical load, and the real-time electrical load data and the real-time environment data of a target electrical node, predicting by using a trained STEF-DHNet model to obtain the predicted electrical load data of the target electrical node. The present application can improve the prediction accuracy and the model applicability.

The invention relates to the technical field of data processing, and discloses a multi-source heterogeneous marine data intelligent fusion and marine disaster prediction method and platform, and the method comprises the steps: carrying out the hierarchical deployment and network topology construction of marine environment monitoring equipment, communication relay equipment and data processing equipment, and obtaining a sea-air-shore integrated monitoring network; collecting marine environment data with a space-time identifier; carrying out ocean element modeling and dynamic feature extraction to obtain an ocean environment digital model; performing feature fusion and correlation analysis on the multi-source heterogeneous ocean data to obtain spatio-temporal feature data; and training and multi-model integration are carried out on the typhoon path prediction model, the storm surge prediction model and the red tide prediction model by adopting a transfer learning method to obtain a marine disaster prediction model set, so that the prediction precision and timeliness are improved, the allocation and scheduling of computing resources are optimized, and the real-time requirement of mass data processing is met.

The invention relates to the technical field of equipment fault diagnosis, and discloses an equipment fault diagnosis and prediction method based on deep learning, and the method comprises the following steps: S1, collecting multi-modal data in real time through a plurality of sensors installed on equipment; s2, preprocessing the collected data; s3, constructing a hybrid deep learning model; s4, dynamic weighted fusion is performed on the features of different modal data by using an attention mechanism, and comprehensive feature representation is generated; s5, using the marked fault data and normal data to supervise and train the model; s6, inputting equipment operation data acquired in real time into the trained model, and judging the state of the equipment; and S7, generating a potential fault early warning signal based on a prediction result of the model. A piezoelectric vibration sensor and a thermal infrared imager are arranged on a motor bearing through vibration, temperature and sound sensors, vibration waveforms, thermal imaging slices and time-frequency diagrams are synchronously captured, and composite state characteristics such as mechanical wear and temperature anomaly of equipment are comprehensively reflected.

The invention provides a coal mill fault diagnosis and prediction method and system based on big data analysis, and relates to the technical field of big data analysis, and the method comprises the steps: collecting a vibration signal of a coal mill bearing through a vibration sensor, carrying out the multi-scale analysis through wavelet decomposition, extracting features, constructing a deep convolutional neural network model, and carrying out the fault diagnosis and prediction of a coal mill bearing. A maximum mean value difference adaptive layer is introduced to carry out feature alignment on data of a source domain and a target domain, identification of fault types and degrees is realized, a health state evaluation index including a fault severity coefficient and a development trend coefficient is established, and the remaining service life is calculated and early warning is carried out. According to the invention, the accuracy and generalization ability of fault diagnosis are improved, accurate prediction and timely early warning of the bearing fault of the coal mill are realized, and the service life of equipment is prolonged.

The invention belongs to the technical field of coal mine disaster early warning, and discloses a coal mine fire and gas disaster risk prediction method and system, and the method comprises the steps: collecting multi-source heterogeneous data collected at a mine site, and carrying out the data preprocessing and conversion; for the preprocessed image and video data, a Video Swin Transform model is adopted to extract spatial and temporal features, and a cross-modal association fusion Transform model is adopted to realize multi-modal feature unified fusion; mapping the fused multi-modal feature representation to a disaster-causing factor index space, calculating a risk assessment index containing a secondary interaction item, and dividing risk levels; and generating a disaster early warning strategy according to risk level analysis, issuing an early warning notice, performing regular maintenance and updating, and outputting a decision report. According to the method, unified processing and fusion representation of multi-source heterogeneous data can be realized, complex association among disaster-causing factors is accurately captured, the risk index with physical significance and engineering reference value is calculated, and high-quality decision support is provided for prevention and control of coal mine fire and gas disasters.

The invention discloses a water quality prediction method based on graph neural network and spatial-temporal feature fusion, and the method comprises the steps: obtaining hydrological data and water quality data of a plurality of monitoring stations in a preset drainage basin in a preset time period, representing the monitoring stations as nodes of a graph, and representing the adjacency relation between the monitoring stations as edges of the graph, a graph topology network mapping multiple monitoring station pollutant data is constructed, a spatial-temporal feature fusion module is adopted to perform feature extraction and fusion on the graph topology network to obtain water pollution spatial-temporal feature data, and the spatial-temporal feature fusion module is obtained by training a graph convolutional network, a gating circulation unit and a self-attention mechanism. And performing fusion processing on the extracted feature data to form a time sequence data set of a graph topology structure, and finally predicting the data set through a graph time sequence prediction model to obtain water quality prediction results of the plurality of monitoring stations in a target time period. The method can effectively deal with the spatial and temporal change process of water pollution, reduce the water quality prediction cost and improve the water quality prediction precision.

The invention relates to the technical field of meteorological data analysis, and provides an aerosol prediction method and system based on artificial intelligence, which are used for accurately identifying an abnormal aerosol gathering mode in an extreme meteorological event. The method comprises the steps that an original multi-mode meteorological observation data set is acquired, and the original multi-mode meteorological observation data set comprises an earth surface aerosol concentration sequence, three-dimensional atmosphere state variables and boundary layer dynamic parameters; performing space-time heterogeneity correction on the original multi-modal meteorological observation data set to generate a space-time aligned target multi-modal meteorological observation data set; inputting the target multi-modal meteorological observation data set into a spatial-temporal feature fusion network to extract multi-scale spatial distribution features and cross-modal time sequence correlation features, and generating a spatial-temporal feature matrix based on the multi-scale spatial distribution features and the cross-modal time sequence correlation features; and performing multi-stage recursive optimization on the spatial-temporal characteristic matrix through a hierarchical time domain aggregation algorithm to generate an aerosol concentration prediction sequence of the target region.

The invention discloses a crop growth prediction method based on multi-source data fusion analysis, and the method comprises the steps: carrying out the standardization processing and missing value filling processing of multi-source data, constructing a time window, calculating the correlation between data sources, and generating a current data credibility vector based on historical credibility evaluation data; extracting features by adopting multi-scale decomposition, detecting abnormal points and analyzing abnormal degrees, and constructing an abnormal conduction network to evaluate conduction influence; performing feature importance evaluation and feature fusion; initial weight distribution is calculated, a weight prediction model is constructed, and consistency optimization is carried out on a weight matrix; performing hierarchical prediction by adopting a multi-level prediction model, and performing collaborative integration based on a prediction result and an optimized weight matrix; and calculating a prediction result deviation and carrying out optimization adjustment, and outputting a final prediction result. According to the invention, accurate prediction of the crop growth state is realized, and the reliability and practicability of the prediction result are improved.

The invention relates to a water quality change trend rapid prediction method based on multi-source data fusion and physical constraint, and the method specifically comprises the following steps: 1, synchronously collecting spectral information, DO, COD, temperature, pH and other data at a key monitoring station, constructing a hydrodynamic water quality coupling equation, simulating the spatial-temporal dynamic distribution of water quality parameters, and calculating the water quality change trend; 2, outputting a water quality sensitive area through a hydrodynamic force-water quality model, screening sensor layout point positions in combination with information entropy evaluation and spatial clustering, realizing low-cost water quality sensor network deployment through a multi-objective optimization algorithm, and calculating a water body global water quality distribution diagram by adopting a spatial interpolation method, 3, synchronously collecting spectral information according to key monitoring sites, analyzing main pollution sources, and adopting a principal component analysis and attention mechanism neural network; 4, based on real-time optical characteristic value-DO data, in combination with a spatial topology network, a water quality gradient and a cross-regional covariance, capturing water quality parameter spatial correlation among different sites, and determining the water quality parameter spatial correlation among different sites; an optical characteristic value-DO-COD dynamic prediction model is constructed; a COD predicted value is corrected by combining pollution traceability and spectral characteristics, and multi-source data is assimilated by adopting ensemble Kalman filtering, so that the model precision is improved.

The invention relates to the technical field of marine environment prediction, in particular to a marine environment multi-modal fusion prediction method and system based on digital twinning. The method comprises the following steps: capturing time-space correlation characteristics of multi-modal data of a marine environment based on a dynamic multi-modal graph neural network; performing multi-scale feature fusion on the space-time correlation features by using a multi-scale gating unit to obtain comprehensive feature representation; predicting the comprehensive feature representation by using a mixed time sequence prediction framework to obtain preliminary marine environment prediction data, including short-term dynamic modeling and long-term trend modeling; and performing noise fitting on the preliminary marine environment prediction data by using a generative adversarial network to generate final marine environment prediction data. According to the invention, through innovative combination of dynamic multi-modal data fusion, a mixed time sequence prediction framework and a multi-scale gating Tanh unit, the problems of insufficient data fusion, limited prediction precision and poor dynamic response capability in the traditional marine environment monitoring and prediction technology are effectively solved.

The invention relates to the technical field of geological exploration, and discloses an advanced geological forecasting method and system based on tunnel geological exploration. The method comprises the following steps: performing data denoising on a multi-source data set pre-collected in a tunnel construction area to obtain an original data set; performing preprocessing and multi-stage data fusion on the original data set to obtain a fused data set; performing three-dimensional space modeling and feature extraction on the fused data set to obtain an intelligent geological prediction result; performing multi-factor risk assessment on the intelligent geological prediction result to obtain a geological risk distribution map in front of the tunnel; performing construction scheme optimization on the geological risk distribution map to obtain a construction guidance scheme; and carrying out comprehensive analysis and dynamic updating on the real-time monitoring data, the construction guidance scheme, the intelligent geological prediction result and the geological risk distribution map to obtain an advanced geological prediction result. According to the method and the system, the defects of insufficient prediction precision, reliability and accuracy of a traditional geological prediction method are overcome, and the safety risk is reduced.

The invention discloses a power load prediction method based on a two-channel cross attention network, and belongs to the technical field of time sequence prediction in deep learning. The method comprises the following steps of obtaining a power load data set, performing preprocessing on the power load data set, decomposing power load data into a plurality of local components by applying VMD, iteratively optimizing VMD parameters through an APSO algorithm, and screening a subsequence with the lowest complexity to be combined with original features to serve as model input; and then constructing a dual-channel cross attention network model, processing time sequence data by using a parallel mechanism of a BiLSTM channel and a Transform channel, enhancing the feature expression ability through an improved cross attention layer, and performing power load prediction by using an MLP fused KAN network. According to the method, the time sequence prediction of the power load is carried out by constructing the two-channel cross attention network, the prediction precision and robustness are high, and the accuracy of load time sequence prediction is remarkably improved.

The invention provides a crop yield prediction method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining continuous historical meteorological data, soil data, crop planting data and remote sensing image data of a designated region for many years, and carrying out the alignment according to a time sequence, and constructing a multi-dimensional spatio-temporal data cube; a fusion prediction model is trained, the fusion prediction model is used for extracting growth feature vectors in the crop growth and development process and carrying out yield prediction, and model parameters of the fusion prediction model are dynamically adjusted through an adaptive learning rate optimization algorithm; the method comprises the following steps: acquiring real-time meteorological data, soil data and remote sensing image data of a specified area, inputting the data into a pre-constructed fusion prediction model, introducing an agricultural knowledge graph into the fusion prediction model to restrain and correct a prediction result of the fusion prediction model, and reasoning to obtain crop growth and estimated yield of each grid of the specified area. And performing spatial interpolation on the estimated yield of each grid to generate a yield distribution diagram of the specified area.

The invention discloses a smart road driving time prediction method based on multi-source data fusion and deep learning, and the method comprises the steps: constructing a multi-source data collection and fusion platform, developing a spatial-temporal feature deep learning model, deploying a real-time data processing and anomaly detection system, building a self-adaptive prediction optimization mechanism, and constructing a smart traffic application service platform. Comprehensive acquisition, deep analysis, real-time processing, accurate prediction and intelligent service of traffic data are realized. The method specifically comprises the following steps: deploying an Internet of Things sensor network to collect multi-source data, and designing an adaptive weighted fusion algorithm to construct a multi-dimensional dynamic traffic database; constructing an LSTM-GNN hybrid neural network architecture to extract spatio-temporal features; real-time data processing and anomaly detection are carried out by adopting a streaming computing framework and various technologies; designing a multi-objective optimization algorithm and a reinforcement learning model to realize adaptive prediction optimization; and developing a visual decision support system and a personalized navigation service. The system effectively improves the traffic management efficiency and the public travel experience.

The invention relates to the technical field of power systems, provides an association rule analysis-based power load prediction method and system, and aims to solve the problem of hidden fault response lag caused by lack of an equipment health state and load fluctuation dynamic coupling mechanism in the prior art. And the problem of load prediction compensation deviation caused by insufficient weight quantization of the fault propagation path is solved. The method comprises the following steps: generating equipment state data according to a vibration spectrum and an insulation aging index of power equipment; performing fusion analysis, generating an equipment health degree evaluation index, and establishing a dynamic influence model of the equipment abnormal event on the power grid load fluctuation according to the association rule; identifying potential abnormal equipment, performing logic mapping, and generating a fault propagation path weight; and dynamically adjusting according to the weight to obtain an adjusted power load predicted value. According to the technical scheme provided by the invention, equipment vibration and insulation aging data are fused, a health assessment and fault propagation model is constructed, and load prediction is dynamically corrected to prevent and control power grid risks.

The invention relates to the technical field of intelligent traffic prediction, in particular to a time-space correlation traffic flow prediction method based on deep learning, which comprises the following steps: S1, collecting traffic flow data and related external environment data in real time, and establishing an initial traffic network topology structure; s2, calculating the dynamic weight of each node; s3, dynamically adjusting the weight of each edge in the traffic network according to the node weight change in the S2 so as to update the topological structure of the traffic network; s4, performing feature extraction on the spatial dependency relationship among the traffic nodes, and extracting time evolution features of the traffic flow data at the same time; s5, constructing an adaptive space-time fusion model; s6, traffic flow prediction is carried out, and adaptive space-time fusion model parameters are optimized; according to the method, through dynamic fusion of the spatial-temporal characteristics and adaptive adjustment of the model parameters, the precision and real-time performance of traffic flow prediction are effectively improved, and the adaptability and generalization ability of the model in a complex dynamic traffic scene are enhanced.

The invention provides a mine ecological risk prediction method and system based on artificial intelligence, and the method comprises the steps: collecting multi-source ecological monitoring data through a distributed sensor network disposed in a mine region, carrying out the time-space alignment processing to generate a time-space correlation feature set, calling a pre-trained ecological risk prediction model to predict the features, and carrying out the prediction of the features. Generating a risk conduction mode set including a risk propagation path, an initial node, a propagation direction and strength, constructing an ecological risk evolution network based on the risk conduction mode set, and displaying a topological structure and a time sequence dependency relationship of risk nodes, and according to the ecological risk evolution network, generating a risk early warning instruction set containing a risk grade division result and an ecological restoration strategy, and sending the risk early warning instruction set to an ecological management platform, thereby comprehensively and accurately predicting the ecological risk of the mine, providing an effective risk early warning and ecological restoration strategy, and ensuring the ecological safety of the mine.

The invention discloses a deep learning-based deep fusion network production line fault prediction method, which comprises the following steps of: firstly, acquiring SCADA (Supervisory Control And Data Acquisition) system or sensor data of an automatic production line, and carrying out data preprocessing, feature extraction and fault prediction label construction; then a deep fusion network 1D-CNN-LSTM-Attention-DCE model combining a one-dimensional convolutional network, a long short-term memory neural network, a fusion attention mechanism and a dynamic cross entropy loss function DCE is established, the preprocessed data is input into the constructed model for training, and finally the trained model is used for fault prediction to obtain a fault prediction result. According to the method, a 1D-CNN-LSTM-Attention-DCE fault prediction model is provided, local features are extracted from time sequence data by using the 1D-CNN, then a long-time dependency relationship is captured through LSTM, and finally weight distribution of key features is enhanced through an attention mechanism, so that the processing capability of the fault prediction model on complex time sequence data is comprehensively improved, and the fault prediction efficiency is improved. And the fault of the production line can be accurately predicted within a certain time length range.

The invention relates to a power equipment fault prediction method based on multi-source heterogeneous data and a twinborn model, and the method comprises the following steps: S1, obtaining multi-modal data related to the operation of power equipment, constructing a joint feature encoder through an attention mechanism in deep learning, and carrying out the joint modeling of multi-modal information, and obtaining a joint feature representation; s2, based on joint feature representation, adopting a dynamic twin model of self-supervised learning and fusion reinforcement learning, and dynamically updating the equipment health state; s3, constructing a complete-cycle prediction framework based on Transform, capturing long-term dependency and dynamic change characteristics in equipment operation, and identifying trigger factors of faults in combination with causal relationship analysis; and S4, based on the equipment health state and the fault prediction result, optimizing equipment operation parameters by adopting an intelligent operation and maintenance optimization engine, and dynamically generating an optimal operation and maintenance plan in combination with a genetic algorithm. The reliability and the operation efficiency of the power equipment are remarkably improved.

The invention discloses a pulmonary infectious disease prediction system based on multi-modal data fusion, and aims to improve the accuracy and reliability of disease prediction through comprehensive analysis of multi-dimensional data. The system comprises a data acquisition module, a feature extraction module, a dynamic fusion module and a prediction modeling module. The data acquisition module is used for acquiring clinical and medical images and environmental factor data of a patient in real time; the feature extraction module extracts feature vectors of all data sources; the dynamic fusion module performs adaptive weight distribution on the multi-modal features and fuses the multi-modal features into multi-modal feature representation; the prediction modeling module is used for carrying out dynamic change trend modeling on the multi-modal feature representation through time sequence analysis so as to generate a probability prediction value of suffering from pulmonary infectious diseases and a disease trend prediction result of the patient; the invention provides a comprehensive and efficient disease prediction method by utilizing the collaborative advantage of multi-modal data and combining an attention mechanism and time sequence analysis, and can be widely applied to the fields of medical diagnosis and public health management.

This application relates to the field of geological disaster early warning technology, and discloses a method for monitoring, predicting and warning of geological disasters based on artificial intelligence, including the following steps: data collection: collecting geological disaster monitoring data; data preprocessing: time alignment and missing completion of the collected data; spatiotemporal correlation matrix construction: analyzing the preprocessed monitoring data; feature extraction: extracting the nonlinear spatiotemporal characteristics of geological disaster monitoring data; time series prediction: outputting the geological disaster risk level in the future period; feedback optimization: based on the error between the prediction result and the actual observation data; warning release: generating geological disaster warning information based on the predicted risk level. Through data collection, preprocessing, feature extraction, prediction optimization and warning release, a complete geological disaster monitoring and prediction method is constructed, which integrates multi-source data, extracts key features and dynamically predicts risks, so as to ensure the accuracy of the model and provide a scientific basis for disaster prevention and control.

The invention relates to the technical field of digital factories, and discloses an equipment real-time monitoring and fault prediction method and system based on a digital factory. The method comprises the following steps: carrying out iterative filtering interpolation processing on equipment operation data collected by a multi-modal sensor in a digital factory to obtain smooth time sequence data, and constructing a multi-modal feature matrix based on the smooth time sequence data; inputting the multi-modal feature matrix into a joint training generalized Siamese-like wide learning network for model training to obtain an equipment state monitoring model; executing system-level state evaluation and subsystem-level fault prediction calculation based on the equipment state monitoring model, and generating an equipment operation and maintenance strategy; according to the equipment operation and maintenance strategy, a fault risk heat map is generated, a digital twin visualization system is constructed, and an equipment maintenance scheduling scheme is output, the production efficiency and equipment health management are balanced, and collaborative optimization of a maintenance plan and a production plan is achieved.

The invention relates to the technical field of operation and maintenance scenes, in particular to a fault diagnosis prediction method and system based on an operation and maintenance scene, and the method comprises the steps: collecting equipment data, and constructing a scene fault diagnosis model; performing nonlinear fusion on the collected operation and maintenance data and historical maintenance records to obtain a real-time data fusion result; constructing an adaptive prediction algorithm based on interaction of a scenarized fault diagnosis model and real-time data fusion; and establishing a multi-level warning system according to a prediction result. According to the method, through multi-dimensional data acquisition, nonlinear data fusion, adaptive algorithm prediction and a multi-level warning system, accurate prediction and effective prevention of equipment faults are realized. Experimental results show that compared with a traditional method and a general machine learning method, the method has remarkable advantages in the aspects of early warning accuracy, false alarm rate, early warning time in advance and the like, meanwhile, the maintenance cost is effectively reduced, the downtime of equipment is shortened, and the method has good application prospects.

The invention provides a technical condition prediction method for a tunnel civil engineering structure, and relates to the technical field of traffic control monitoring systems.A biological acoustic composite sensing system comprising a distributed optical fiber acoustic sensing system and microorganism sample collection and analysis is constructed, and acoustic depth mode characteristics are mined by applying a deep learning model; the microbial dynamic biomarker is analyzed and identified by using bioinformatics; multi-modal information, a domain knowledge graph and a causal inference algorithm are fused, and a dynamic causal network model for revealing an internal driving relation in the degradation process is constructed; further, training and applying an adaptive neural network prediction model based on causal driving and fusing physical and biochemical mechanism constraints, and carrying out probabilistic prediction on future technical conditions of the tunnel; and executing anti-fact reasoning by using the causal network and the prediction model, and generating and optimizing an intervention strategy of cooperation of physical measures and biochemical measures. According to the method, the accuracy and advance of tunnel structure state prediction can be remarkably improved.

The invention discloses a power load prediction method based on multi-source heterogeneous data feature fusion, system equipment and a medium, and relates to the technical field of power system load prediction, and the method comprises the steps: obtaining a power load prediction data set, and carrying out the preprocessing; performing input feature processing on the static data, the graph data and the time series data based on a multi-layer perceptron, a graph convolutional network and a gating loop unit network, and fusing the processed features based on a feature splicing method to obtain comprehensive feature information; based on full-connection neural network construction, taking the comprehensive features as input, inputting training set data into a model, and performing iterative optimization according to a model effect to adjust model parameters and hyper-parameters; and based on the parameter optimization adjustment model, using the evaluated optimal model to predict the power load in the future time period. The method is superior to a traditional method in precision and stability, can better adapt to complex and changeable load prediction scenes, and has wide application prospects and remarkable actual value.

The invention provides a prediction method and system for a rock stratum fracture disaster in tunnel engineering, and belongs to the field of tunnel engineering. Comprising the steps of multi-modal monitoring data acquisition and synchronization, data preprocessing and feature calculation, multi-modal information entropy evolution analysis, catastrophe point detection and precursor identification, risk assessment and early warning output and the like. Multiple types of sensors are arranged at key positions of a tunnel, an edge computing gateway is accessed through an industrial-grade field bus, and space-time reference unification of multi-source heterogeneous data is achieved; carrying out preprocessing and feature calculation on the collected original data; performing sliding window probability distribution modeling on each modal feature, calculating an information theory index, and analyzing non-linear relevance among the multi-modal features; judging whether a catastrophe precursor event exists or not by adopting multiple methods; and finally, based on a catastrophe point detection result, performing risk assessment on the tunnel region by using a multi-dimensional risk quantification model, and performing early warning output on high-risk and emergency states.

The invention discloses a sewage treatment dynamic process simulation prediction method and system based on digital twinning, and relates to the field of digital twinning sewage treatment. By introducing a digital twinborn technology and a simulation prediction method based on advanced data analysis, complex dynamic changes in the sewage treatment process can be reflected in real time, and the defects of a traditional simulation technology in the aspects of treatment of nonlinear disturbance, frequent fluctuation and sudden pollution sources are overcome. Through multi-source data acquisition and time sequence alignment, in combination with nonlinear frequency domain disturbance modeling, noise and disturbance in the data can be effectively suppressed, and the quality and reliability of the data are improved, so that a more accurate basis is provided for subsequent modeling and prediction.

The invention relates to the technical field of artificial intelligence and medical diagnosis, in particular to a disease diagnosis prediction method and system based on a graph neural network. The disease diagnosis prediction method based on the graph neural network comprises the five steps of heterogeneous medical knowledge graph construction, adaptive node embedding representation, hierarchical graph attention network modeling, incremental learning dynamic graph updating and multi-dimensional feature input and result output. The invention discloses a disease diagnosis and prediction system based on a graph neural network. The system comprises a multi-source data acquisition module, a heterogeneous graph construction module, a self-adaptive embedding module, a graph network calculation engine, a dynamic updating module, a disease prediction module and a feedback optimization module. According to the method, the multi-modal heterogeneous knowledge graph is constructed to integrate the multi-dimensional data of the patient, and the hierarchical graph attention network and the dynamic incremental learning are combined, so that the accurate prediction of the disease risk and the visual explanation of the pathological association path are realized.

The invention provides a construction scene prediction method and device fusing an image, a text and a BIM modal, and relates to the technical field of intelligent construction prediction management. According to the method, the BIM semantic graph is constructed by extracting the BIM semantic information of the BIM model, and the target detection recognition of the construction site video is carried out in combination with the YOLO model to obtain the target detection result; performing cross-modal alignment with the CLIP to realize deep fusion of the multi-modal data of the image, the text and the BIM to obtain a multi-modal heterogeneous graph; and inputting the multi-modal heterogeneous graph into a space-time sequence model for prediction, outputting prediction results of construction scenes at a plurality of moments in the future, and dynamically mapping the prediction results to a digital twinborn platform to realize risk early warning and visual display. The construction dynamic change can be captured in real time, the construction progress and risk can be accurately predicted, and the intelligent level of construction management is improved.

The invention relates to the technical field of wind power prediction. The invention provides a wind power prediction method and system. The method comprises the following steps: acquiring multi-dimensional meteorological time series data, three-dimensional elevation data and unit operation data of a target wind power plant; constructing a spatial-temporal feature fusion network, extracting time sequence dynamic features, and performing weighted fusion on the spatial correlation features and the time sequence dynamic features to obtain a fusion feature vector; establishing a hybrid prediction model, and taking the fusion feature vector as input to obtain a wind power initial prediction result; introducing a terrain correction factor, constructing a turbulence intensity compensation function, and performing micro-terrain disturbance correction on the wind power initial prediction result; and outputting a final power prediction curve and a confidence interval. The problems that in an existing wind power prediction method, a physical model is insufficient in complex terrain microclimate modeling precision, high in calculation complexity and difficult to meet the real-time requirement, a statistical learning method is limited in high-dimensional nonlinear time sequence feature expression capacity, and prediction errors are remarkably increased under the abnormal working condition are solved.