161 results about "Spatiotemporal Analysis" patented technology

A federated distributed computational system enables secure biological data analysis and genomic medicine with enhanced oncological therapy capabilities. The system implements patient-specific tumor-on-a-chip analysis through microfluidic control systems and cellular heterogeneity preservation, while integrating fluorescence-enhanced diagnostics using CRISPR-LNP targeting and robotic surgical navigation. The architecture coordinates spatiotemporal analysis of gene therapy delivery through molecular imaging and immune response tracking, and implements bridge RNA integration with multi-target synchronization. Treatment selection is optimized through multi-criteria scoring and patient-specific simulation modeling. Each federated node contains a local processing unit for biological data analysis, privacy preservation protocols, and a hierarchical knowledge graph structure. The system implements cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration, enabling research institutions to collaborate on complex, large-scale biological analyses while maintaining strict data privacy controls.

The invention provides an underground water pollution source tracing and evaluation method for a riverside water source in a karst area, and the method comprises the following steps: collecting and preprocessing multi-source information, and obtaining a multi-source data set; a water quality sample is collected and analyzed, the surface water-underground water interaction process and pollution characteristics are comprehensively analyzed, and a pollution source type is obtained; performing space-time analysis on the multi-source data set to obtain a pollution activity time period and a potential pollution source area; constructing a karst area hydrological model, simulating the hydrological characteristics of the karst area, constructing a pollutant migration model, and simulating the migration process of pollutants; and constructing a traceability algorithm, tracking pollutants, confirming a pollution source, carrying out risk partitioning and risk assessment, and carrying out visual display of a result. According to the invention, interaction between surface water and underground water can be effectively assessed, accurate traceability of pollutants and multi-angle accurate identification of pollution sources can be realized, and management efficiency can be optimized through reliable risk assessment prediction.

The invention provides an edge computing traffic light intelligent decision-making method integrated with AI video analysis, and the method comprises the steps: collecting a real-time traffic video stream of a target intersection through an edge computing device, and extracting a dynamic traffic feature set in the real-time traffic video stream, calling a pre-trained space-time analysis model to carry out multi-modal fusion processing on the dynamic traffic characteristic set, generating a traffic state vector of the target intersection, matching a candidate control strategy in a preset decision rule base based on the traffic state vector, and carrying out parameter adjustment on the candidate control strategy through a strategy optimization model to obtain a target traffic state vector; generating a target signal lamp control parameter; and issuing the target signal lamp control parameters to a traffic signal control terminal of the target intersection, and monitoring traffic state change data of the target intersection in real time to update weight parameters of the space-time analysis model. According to the invention, the traffic efficiency, pedestrian safety and traffic management intelligence degree of urban intersections can be improved.

A federated distributed computational system enables secure, multi-institutional biological data analysis and genomic medicine through interconnected, decentralized nodes in a federated distributed graph architecture. A federation manager coordinates computational resource allocation, control and data flows, establishes privacy and security boundaries, implements multi-scale spatiotemporal analysis and simulation modeling, models cross-species or intrapopulation elements, and maintains cross-institutional knowledge relationships. Each node includes a local processing unit for biological data analysis, including multiomics and gene editing, privacy-preserving protocols for secure multi-party computation, a hierarchical knowledge graph for managing multi-domain biological relationships across spatial and temporal scales, and encrypted network connections. The system implements cross-species genetic analysis via phylogenetic integration, environmental response modeling through spatiotemporal tracking, and multi-scale tensor-based data integration with adaptive dimensionality control. This architecture enables research institutions to collaborate on complex biological analyses and genomic medicine applications while maintaining strict data privacy and security controls.

The invention discloses a digital production plan scheduling method and system, and belongs to the technical field of optimal scheduling, and the method comprises the steps: constructing a distributed storage architecture based on edge computing nodes; a central coordinator is adopted to realize cross-node data synchronization through an improved Raft consensus algorithm, multi-version concurrency control is realized based on a vector clock, and a global consistent data view is established; a visual scheduling platform is built based on a Vue3 framework, and man-machine interaction is realized by adopting a Canvas and WebGL collaborative rendering framework; establishing a dynamic coordinate conversion model based on bilinear interpolation, designing a space mapping function containing distortion compensation, establishing a multi-thread coordinate service based on WebWorker, and realizing submillimeter-level bidirectional mapping of pixel coordinates and physical coordinates; constructing a three-dimensional space-time analysis model fused with the multi-dimensional features; and all the units are subjected to feature fusion through residual connection, and finally a scheduling scheme with a confidence coefficient weight is output. The method and the device have the effect of meeting various scheduling requirements.

The invention discloses an urban water supply management data trend analysis method based on space-time analysis, and relates to the field of data processing, and the method comprises the steps: collecting data in real time through an urban water supply pipe network sensor network, building a space-time unified coordinate system, building a space-time Kriging interpolation model based on pipe network topology, and achieving the space-time alignment of multi-source data; dividing an adaptive space-time grid by using a Voronoi diagram and a sliding window mechanism, and calculating multi-dimensional features; constructing a dynamic space-time diagram by taking a grid as a node, performing multi-step prediction in combination with a space-time diagram convolution circulation network, fusing a Kriging interpolation result, and evaluating an abnormal probability and a confidence interval through a Bayesian neural network; a monitoring layer, a prediction layer and a risk layer are overlaid in a three-dimensional GIS, a dynamic thermodynamic diagram is generated, an early warning path is optimized based on a Dijkstra algorithm, and a minimum risk topology path is output. The method has the advantages that through space-time analysis and accurate prediction, the intelligence, stability and emergency response efficiency of urban water supply management are remarkably improved, and powerful support is provided for smart city construction.

The invention relates to the technical field of intelligent agricultural equipment, in particular to a tomato disease diagnosis method based on multi-modal data analysis, which comprises the following steps of: 1, synchronously acquiring and preprocessing multi-modal data, synchronously triggering a hyperspectral imaging device and a microscopic camera, respectively acquiring a plant canopy hyperspectral image and a stem microscopic image, and acquiring a plant canopy hyperspectral image and a stem microscopic image; meanwhile, temperature, conductivity and dissolved oxygen environment parameters are continuously collected in the root zone; 2, self-adaptive feature extraction and fusion in the growth stage are carried out, reflectivity correction and leaf segmentation are carried out on the hyperspectral image, and leaf surface spectrum curve features are extracted; step 3, hybrid model construction and space-time analysis: constructing a hybrid model comprising spectrum, microscopy and environment analysis networks, and dynamically adjusting each network weight through a gating network; and 4, generating a disease decision. The method can realize accurate, efficient and real-time tomato disease diagnosis, has high practical value, and can effectively improve the disease prevention and control capability in agricultural production.

The invention discloses an intelligent traffic flow dynamic regulation and control method and system based on multi-source data fusion, and relates to the technical field of data processing. The method comprises the following steps: executing multi-source data acquisition through a data acquisition interface to obtain a multi-source traffic data set; performing spatial fusion on the multi-source traffic data set to obtain a flow marking map; performing current traffic state analysis based on the flow marking map, identifying local and global congestion, and constructing a traffic situation map; and performing traffic flow state prediction in a preset future time zone based on the traffic situation map, performing regulation and control decision based on a prediction result, and generating a traffic flow regulation and control strategy. The technical problem that traffic flow prediction and regulation are not accurate enough in the prior art is solved, and the technical effects that intelligent traffic flow dynamic regulation is achieved through multi-source data fusion and space-time analysis, and the traffic management precision and the response speed are improved are achieved.

The invention provides a key component fault prediction method and system based on multi-source data fusion, and the method comprises the steps: S1, collecting key component data of a screen scarifier, constructing a multi-source heterogeneous sensor network, and completing the time-space alignment and quality optimization of multi-dimensional monitoring data; s2, a dynamic topological graph structure is constructed based on the physical connection relation of the components, the fault propagation path and strength are quantified, and multi-level feature representation covering the local state and the overall health degree is formed; s3, designing a hybrid prediction model fusing space-time analysis and memory modeling, and completing accurate description of an equipment degradation trend and early warning of potential faults through a self-adaptive feature integration mechanism; and S4, combining real-time prediction errors and historical operation and maintenance knowledge to dynamically optimize the hybrid prediction model, establishing a data-driven and knowledge-guided dual learning framework, and completing self-adaptive continuous learning along with equipment aging. The method breaks through the limitation of static state of a traditional prediction model, and the accuracy and reliability of a prediction result are remarkably improved.

The invention belongs to the technical field of cloud computing and communication resource management, and discloses a communication equipment resource dynamic allocation method and system based on cloud computing, and the method comprises the steps: obtaining biological migration tide data, communication network load historical data and environmental parameter data, constructing a migration trajectory prediction model and a resource demand prediction model, and carrying out the prediction of a migration trajectory; generating an elastic resource allocation scheme; base station group division is carried out on a communication network coverage area, resource demand space-time analysis is carried out based on a migration trajectory prediction result, and a resource utilization rate index is calculated; generating a resource scheduling instruction set based on the resource elastic allocation scheme, performing priority ranking, optimizing resource allocation in combination with a resource utilization rate index, and generating a base station parameter configuration instruction; adjusting the resource configuration of the base station according to the configuration instruction, monitoring the operation state, evaluating the resource efficiency, performing feedback tuning and applying to the resource elastic allocation scheme; and the equipment operation and maintenance complexity and the manual intervention requirement are effectively reduced.

The invention discloses an intelligent-based abnormal carbon emission data automatic identification early warning system, which belongs to the technical field of intellectualization and comprises a data acquisition preprocessing module, a high-precision space-time analysis module, an intelligent analysis module, an automatic abnormal identification module, a carbon footprint tracing module, a self-adaptive adjustment module and an intelligent emission prediction module. A carbon emission source and time-space distribution characteristics are accurately positioned through spatial positioning and time sequence analysis of the high-precision time-space analysis module, a spatial distribution diagram is more detailed and accurate through the optimized sensor position and an interpolation algorithm, and time sequence analysis is more accurate through timestamp correction and power spectrum density analysis. The method effectively evaluates the periodic intensity of the signal, analyzes a hidden mode and an association rule in the data through the intelligent analysis module integrating the spatial-temporal characteristics and the related information of the data acquisition and preprocessing module, analyzes the spatial-temporal association between variables through the calculation of a clustering center and the association intensity, and facilitates the discovery of a potential abnormal mode.

The invention discloses a surveying and mapping method based on remote sensing big data analysis, and relates to the technical field of remote sensing big data. During operation of the system, images and spectral data of a target water body area are collected through satellite remote sensing, unmanned aerial vehicle remote sensing or a ground sensor, the collected remote sensing data are preprocessed, and the target water body area is obtained through waveband combination or spectral line analysis; the method comprises the following steps: extracting characteristic parameters of a water body by utilizing spectral information in remote sensing data, carrying out multi-dimensional analysis on field data, calculating to obtain a water body turbidity coefficient Ct, an algal bloom coefficient Ca and an oil pollution coefficient Co, and analyzing the influence of potential pollution sources, agricultural runoff and industrial wastewater discharge on the water body by combining the remote sensing data and geographic information. And performing spatial-temporal change analysis on the water body pollution condition, identifying the trend of pollutant expansion or degradation, and based on the results of pollution assessment and spatial-temporal analysis, comparing the comprehensive water quality index WQI with a preset threshold to generate water quality level early warning information.

The invention relates to the field of remote sensing image processing, in particular to a karst landform remote sensing dynamic evolution analysis method and a karst landform remote sensing dynamic evolution analysis system, which can be applied to the fields of karst region resource management, environmental protection, geological disaster early warning and the like. According to the method, karst landform change information is obtained through multi-temporal remote sensing image data, a time-space analysis model composed of self-attention enhancement, a feature pyramid and a recurrent neural network is constructed, a constraint module is designed in combination with karst landform priori knowledge, and accurate prediction is achieved; multi-source data fusion improves the feature capture capability, and the observation advantage is obvious especially under complex meteorological conditions; the multi-module cascade design is used for accurately modeling space-time dynamic change, and the prediction accuracy is improved; priori knowledge constraint is introduced to enhance feature recognition, and misclassification is reduced; on the whole, the method effectively integrates multidisciplinary technologies, the precision and reliability of karst landform dynamic evolution analysis are improved, and a powerful tool is provided for research in related fields.

The invention provides a historical and cultural block spatial evolution simulation method and system based on GIS space-time analysis, and the method comprises the steps: firstly obtaining a target space-time data set of a target block, which comprises geographic space distribution, building form change, humanistic activity tracks and the like, and then carrying out the preprocessing of a space-time topological structure; generating a target spatio-temporal topological data set containing a spatio-temporal association block space unit sequence and a dynamic attribute tag, and then extracting a static structure, a dynamic evolution mode and a space interaction dependency feature of each space unit based on a preset spatio-temporal evolution feature extraction model; and calling a space-time evolution prediction model to obtain evolution probability distribution and evolution association strength, and finally constructing a space-time evolution path map according to the evolution probability distribution and the evolution association strength, thereby generating a spatial form simulation result and planning intervention decision parameters of a target block, and realizing effective simulation and planning decision support for spatial evolution of historical and cultural blocks.

The invention relates to a tartary buckwheat pest and disease damage dynamic monitoring method, system and device and a medium, and belongs to the technical field of agricultural intelligent monitoring, the dynamic monitoring method comprises the following steps: periodically collecting environmental parameter data through fixed sensor nodes deployed in a farmland, and obtaining leaf vibration signals and multispectral image data at the same time; performing space-time alignment on the blade vibration signal and the multispectral image data, correcting radiation distortion in the multispectral image data, and outputting a registration data set; according to the registration data set, fusing to generate a multi-modal feature vector, inputting the multi-modal feature vector into a pre-trained space-time analysis model, and outputting a risk level distribution diagram with a geographic coordinate mark; generating a control instruction set according to the risk level distribution map, and triggering execution equipment to execute pest control operation; and optimizing weight parameters of the space-time analysis model through the generative adversarial network based on the execution log of the control instruction set and the historical multi-modal feature vector. The scientificity and timeliness of pest control can be improved.

The invention discloses a digital intelligent operation monitoring system for an expressway. The system comprises a data acquisition module, a feature extraction and fusion module, a space-time analysis and prediction module, an anomaly detection module and an intelligent decision module. Wherein the feature extraction and fusion module fuses multi-source heterogeneous data by using a collaborative mechanism of a graph neural network and an auto-encoder; the space-time analysis and prediction module generates a prediction result containing a congestion diffusion probability by using a Fourier neural operator, and provides reference data for anomaly detection; and the intelligent decision-making module inputs the prediction result as an environment model of a reinforcement learning agent, and dynamically adjusts a reward function according to the predicted congestion diffusion probability, so as to realize prospective autonomous decision-making. According to the invention, through deep cooperation and feedback closed loop among the modules, the traffic operation efficiency and the safety emergency capability are significantly improved.

The invention relates to the technical field of cadastral data dynamic management, and discloses a cadastral data dynamic updating and storage method, device and equipment and a storage medium. By receiving a land law enforcement task set of a target law enforcement vehicle, a law enforcement driving route map for a plurality of law enforcement tasks is constructed; issuing cadastral data of each law enforcement task to a cadastral data edge library at different driving road sections is taken as a decision variable, and the storage capacity of a target law enforcement vehicle, the maximum communication capability of each driving road section, the cadastral data advanced issuing characteristic and the cadastral data real-time performance are considered; and controlling cadastral data scheduling of the cadastral data edge library and the cadastral data core library, and assisting law enforcement officers in land law enforcement. Therefore, by adopting the task-oriented accurate data issuing and dynamic and the space-time analysis-based bandwidth prediction and optimization strategy, organic unification of the real-time performance, the security and the access efficiency of the cadastral data is realized, and efficient and reliable data support is provided for land law enforcement.

The invention discloses a variable polyadenylation dynamic analysis method and device. The method comprises the following steps: acquiring initial space transcriptome data and an initial space bright field image; performing first preprocessing on the initial space transcriptome data to obtain target space transcriptome data; performing second preprocessing on the initial space bright field image to obtain a target space bright field image; performing data analysis and integration processing to obtain spatial variable polyadenylation data, spatial gene expression data and spatial cell distribution dynamic data; constructing a gene expression regulation network; generating an integrated analysis result; performing space-time analysis to obtain a space-time analysis result; performing trajectory analysis to obtain a trajectory analysis result; performing disease process analysis to obtain a disease process analysis result; and generating a variable poly-adenosine acidification dynamic analysis result. According to the invention, dynamic analysis of variable polyadenylation is realized, the accuracy is improved, and multi-angle biological understanding is provided. The method can be applied to the technical field of gene analysis.

The invention discloses an online anomaly monitoring method and system for a linear movement cutting ore pulp sampler, and relates to the technical field of industrial automation, and the method comprises the steps: executing frequency band energy separation and sliding window statistical analysis on a working condition data set of the ore pulp sampler, and obtaining a multi-dimensional feature matrix; performing weight distribution and dynamic weighted aggregation on the multi-dimensional feature matrix to form a space-time analysis data packet, performing collaborative analysis on the space-time analysis data packet, and outputting a trend collaborative interaction matrix; and performing risk quantification and contribution degree distribution on the trend collaborative interaction matrix by using an entropy weight method to generate an abnormal quantification parameter, and performing confidence coefficient weighted calculation on the abnormal quantification parameter to form an abnormal probability value. According to the method, the working condition data set of the ore pulp sampler is fully fused through the sliding window statistical analysis and the entropy weight method, and meanwhile, deep feature mining and spatial relation fusion are performed through the dynamic causal atlas and the space-time convolutional neural network model, so that the reliability of anomaly monitoring is improved.

The invention provides a tobacco marketing hotspot event real-time analysis method based on big data and AI, and relates to the technical field of big data and artificial intelligence, and the method comprises the steps: collecting multi-source heterogeneous data through a distributed crawler, and achieving the structured processing of unstructured data through the semantic analysis and multi-modal fusion technology; hot event identification and early warning are carried out by combining deep learning and a propagation dynamics model; further fusing the knowledge graph, NLP and space-time analysis to generate brand specification popularity ranking and trend prediction; and finally, an evaluation model is constructed based on historical and real-time data, new product research and development, brand promotion and supply chain optimization strategies are output, and full-process intelligent decision support is realized.

The invention provides an unmanned aerial vehicle cluster forest fire scene three-dimensional situation construction and updating method and system, and the method comprises the steps: receiving real-time fire scene data from an unmanned aerial vehicle cluster, and building an initial fire scene three-dimensional situation model based on a UNet network and a digital elevation model; the fire scene three-dimensional situation model comprises a fire scene three-dimensional situation map and a pyramid hierarchical structure; when a preset updating period is reached, constructing a pyramid hierarchical structure of the key updating area based on the real-time fire scene data; according to the pyramid hierarchical structure of the key updating area, the pyramid hierarchical structure in the to-be-updated fire scene three-dimensional situation model is compressed based on space-time analysis driving of information entropy, then robust registration fusion is carried out, and the fire scene three-dimensional situation model is updated.

The invention discloses a network loss data cleaning method, system and device based on multi-dimensional space-time analysis and a medium, and belongs to the technical field of data processing, and the method comprises the steps: collecting power grid operation data and topological structure information, and constructing a time sequence data set; identifying nodes with abnormal features in the time series data set as edge nodes based on an anomaly identification mechanism of topology constraints, and constructing edge data packets for different topology types; performing anomaly judgment according to the feature change of each type of data packets, and outputting a corresponding topological structure anomaly result; performing dynamic cleaning according to a topological structure abnormal result; and after the dynamic cleaning is completed, executing load flow calculation, and carrying out typed statistics and display on a network loss result in combination with a topology category. According to the method, a network loss data cleaning mechanism fusing a topological structure, a time sequence and an operation state is constructed, so that abnormal nodes and abnormal parameters are accurately identified, and data cleaning and statistical optimization facing network loss calculation requirements are realized.

The invention relates to the field of medical image processing and artificial intelligence, in particular to a pulmonary nodule treatment effect AI evaluation system which comprises an image acquisition module, an image registration module, a feature representation module, a multi-scale analysis module, a trajectory analysis module, a response prediction module and a decision support module. According to the system, accurate alignment of CT images before and after treatment is realized through a 4D registration technology, manifold representation of a pulmonary nodule state is constructed based on a differential geometry theory, and nodule features are mapped into points on a high-dimensional manifold; extracting features of different time and space scales by adopting multi-scale space-time analysis, and constructing a manifold trajectory representing a treatment response process; geodesic prediction is realized by using a Riemann geometric framework, and long-term curative effect is predicted from early treatment response; the system not only evaluates the current treatment effect, but also can provide personalized treatment suggestions and optimal follow-up visit plans.

The invention relates to the technical field of geographic information systems, and discloses a geographic information data processing method and system.The geographic information data processing method comprises the steps that geographic data are packaged into self-activation data nodes; monitoring a network state in real time through a network environment perception function; constructing an activation threshold function based on a neuron activation principle to determine data transmission; carrying out multi-scale analysis on the data stream by applying wavelet transform to realize self-adaptive compression; predicting hotspot distribution dynamic scheduling resources through space-time analysis; an edge-fog-cloud three-level cooperative processing architecture is constructed to realize distributed processing; according to the method, the technical problem of geographic information data transmission and processing in an unstable network environment is solved, the overall performance and the adaptive capacity of the system are improved through a multi-level adaptive mechanism, and the method has wide application value.

A time-lapse image classification device and method is disclosed that uses a diffractive optical network to classify an optical input, significantly advancing classification accuracy and generalization performance on complex input objects by using the lateral movements of the input objects and / or the diffractive optical network relative to each other. The design space and performance limits of time-lapse diffractive optical networks were numerically tested, revealing a blind testing accuracy of 62.03% on the optical classification of objects from the CIFAR-10 dataset. This constitutes the highest inference accuracy achieved so far using a single diffractive optical network on the CIFAR-10 dataset. Time-lapse diffractive optical networks will be broadly useful for the spatio-temporal analysis of input signals using all-optical processors.

The invention relates to a time-space analysis-based charging demand prediction method and system for a charging station. The method comprises the steps of obtaining historical data of road conditions, charging station positions and charging demands in a target area; based on the historical data of the charging station positions and the charging demands, establishing a charging station relation graph of the charging demands of all the charging stations in the target area; constructing a road relation graph based on historical data of road conditions, charging station positions and charging demands; a pre-trained charging demand model carries out space-time modeling on charging demand dependence according to a node feature sequence of charging station relation graph nodes and a charging station adjacent matrix formed by charging station relation graph edges, a node feature sequence of road relation graph nodes and a road adjacent matrix formed by road relation graph edges, and a charging station road adjacent matrix. And predicting the charging demand of the charging station in the target area.

Generating a semantic synopsis is provided. A dataset comprising structured data is received and semantic analysis of the dataset is performed to determine categories of information included in the datasets. A spatio-temporal analysis of the dataset is also performed to determine time periods and locations to which the dataset applies. A static synopsis of the dataset is then generated in natural language sentences that describes semantic information in the dataset and time periods and locations covered by the dataset. The synopsis can be customized according to a user role and search query data.

The invention discloses an electric energy quality assessment method and system based on dynamic visual data, and relates to the technical field of power distribution network electric energy assessment, and the method comprises the steps: collecting electrical operation data, environmental parameters and load characteristic data through a multi-modal sensor network, and generating a dynamic visual data set through space-time alignment; the method comprises the following steps: extracting voltage sag characteristics by using wavelet transform, mining harmonic time sequence characteristics in combination with LSTM, capturing environmental space distribution through CNN, and constructing a multi-dimensional characteristic matrix; and then, inputting a TCN-GAT model, fusing the spatial-temporal characteristics, calculating node voltage sensitivity, harmonic propagation weight and risk index, and outputting a comprehensive score of power quality. And triggering visual analysis and mapping knowledge domain matching according to the comprehensive score of the power quality to realize abnormality diagnosis and strategy generation. According to the method, through multi-dimensional feature fusion and space-time analysis, the evaluation accuracy, real-time performance and interpretability are improved, and an intelligent scheme is provided for power distribution network dispatching and fault prevention and control.

The invention discloses an EEG intelligent agent automatic analysis method based on a large language model, which takes the large language model as a strategy engine, autonomously understands user analysis intentions and intelligently decomposes tasks, and further dynamically schedules and collaboratively integrates analysis resources including traditional feature engineering, diversified deep learning models and an external knowledge base. According to the method, end-to-end automatic coordination and execution of complex EEG analysis tasks such as signal preprocessing, feature extraction, event positioning, classification diagnosis, emotion recognition, sleep staging and the like can be realized, the limitation of a single detection or classification task is broken through, and through context perception and flexible space-time analysis capability, the accuracy of the EEG analysis is improved. And multitask and continuous deep reasoning and interpretation can be carried out on the complex EEG data. According to the method, the general planning and reasoning capability of a large language model is deeply fused with a special analysis model in the EEG field, so that the automation level, flexibility and clinical application potential of EEG analysis are remarkably improved.

Generating a semantic synopsis is provided. A dataset comprising structured data is received and semantic analysis of the dataset is performed to determine categories of information included in the datasets. A spatio-temporal analysis of the dataset is also performed to determine time periods and locations to which the dataset applies. A static synopsis of the dataset is then generated in natural language sentences that describes semantic information in the dataset and time periods and locations covered by the dataset. The synopsis can be customized according to a user role and search query data.