12 results about "Spatiotemporal Analysis" patented technology

The application discloses a kind of war destruction global evaluation method based on multi-source remote sensing data, can realize the spatio-temporal continuous monitoring and evaluation of large-scale regional war destruction.It includes the following steps: obtaining and preprocessing land use, NPP-VIIRS night light, multispectral image and surface temperature and other multi-source remote sensing data;Using local optimal threshold method to extract built-up area boundary;Based on total value of night light and light ratio index, using spatio-temporal cumulative calculation method to represent the spatio-temporal evolution characteristics of armed conflict in built-up area;Through principal component analysis method, build non-built-up area war damage index, and reveal the evolution trend and spatial differentiation of non-built-up area armed conflict based on spatio-temporal analysis method.The method of the application can effectively depict the spatio-temporal characteristics of war destruction, has the advantages of global coverage, time sequence integrity, high resolution, and can provide scientific decision support for humanitarian aid and post-war reconstruction planning in conflict area.

This application relates to a dynamic monitoring method and system for urban ground collapse disasters based on multi-source sensor information fusion. It addresses the problems of existing monitoring methods, such as single monitoring dimensions and isolated data, leading to insufficient detection of collapse precursors and low accuracy and timeliness of early warnings. The method includes: collecting multi-source data on pressure, displacement, vibration, and remote sensing; standardizing and spatiotemporally aligning the data; and then performing noise suppression and error compensation. A deep learning model is then used to automatically extract deep features from the multi-source data and perform fusion analysis. The generated feature vectors are input into a risk discrimination model, outputting risk distribution and deformation prediction. Finally, a dynamic risk map is constructed for interactive display, and graded early warnings and emergency response are automatically executed based on the risk level. This application has the following effects: achieving deep fusion and spatiotemporal analysis of multi-source heterogeneous data, improving the accuracy of early collapse risk identification and dynamic early warning capabilities.

This application provides a method and apparatus for identifying cloned vehicles. Through an innovative spatiotemporal analysis model, it achieves accurate screening of unreachable vehicles via distance calculation and speed verification. A feature recognition system is constructed, combining image segmentation and tag extraction to establish a reliable mechanism for identifying cloned vehicles. Predictive deployment is introduced, using trajectory analysis and route prediction to ensure the accuracy of strikes. This method effectively addresses the shortcomings of traditional technologies in spatiotemporal analysis, feature recognition, and predictive deployment, providing technical support for the control of cloned vehicles.

This invention discloses a method for simulating the spatial evolution of commercial districts based on spatiotemporal analysis, relating to the field of smart city technology. The method includes: collecting multi-source spatiotemporal data to construct an initial spatiotemporal knowledge graph, and using the initial spatiotemporal knowledge graph to obtain a baseline simulation model; collecting real-time multi-source data streams and incrementally updating the initial spatiotemporal knowledge graph based on the real-time multi-source data streams to form a dynamic spatiotemporal knowledge graph; detecting state changes in the dynamic spatiotemporal knowledge graph, and when any rate of change of key entities or key relationships exceeds a predetermined change threshold, triggering and executing incremental simulation of a local area to obtain a local predicted state graph, and updating the local predicted state graph to the dynamic spatiotemporal knowledge graph to obtain a fused predicted knowledge graph. This invention achieves accurate capture and rapid response to micro-level disturbances.

This invention provides an interpretable spatiotemporal analysis method for traffic congestion prediction, extracting key features that trigger congestion events and deep connections between roads from the interpretation. Traditional data mining methods often explore the correlations between traffic spatiotemporal data from a statistical perspective, failing to fully reveal the deep connections and key factors of traffic congestion. Therefore, this invention proposes a spatiotemporal interpretation generation model based on STGCN, leveraging the ability of neural networks to discover hidden features and using deep learning interpretability techniques to extract key input features of interest to the neural network. The model uses a perturbation-based interpretation method to generate a mask and a gradient-based interpretation method to generate the gradient mapping of the mask; furthermore, considering the problem of coarse granularity and poor targeting of spatial masks, a step-by-step masking method is proposed to reduce the interpretation granularity. This increases the effective extraction of hidden information, thereby obtaining more accurate and comprehensive key congestion information.

This invention, based on spatiotemporal analysis and deep learning, proposes a joint downscaling method for multiple oceanic elements, belonging to the field of meteorological and climate prediction and data processing technology. It includes: S1, data preparation and preprocessing; S2, multivariate empirical mode decomposition (MEOF); ​​S3, constructing an SRGAN model capable of learning spatial mode mapping relationships at different resolutions; and S4, generation and reconstruction of high-resolution ocean fields. This invention obtains joint spatial modes and time series through multivariate empirical mode decomposition. The model's learning of the joint spatial modes maintains the inherent spatiotemporal coupling relationships between multiple variables. It significantly improves the detail restoration capability of the downscaling results. In adversarial training, the model can generate more realistic and detailed high-resolution spatial structures, effectively capturing complex nonlinear processes in the climate system. It is highly efficient and flexible; after model training, it can quickly downscale long-term, multi-scenario low-resolution data, greatly saving computational resources.

This invention, entitled "A Multi-Source Fusion Groundwater Level Prediction System and Method Based on Dual-Model Machine Learning," belongs to the field of groundwater level prediction technology. The technical problem it aims to solve is the disconnect between spatiotemporal analysis and time prediction in existing groundwater level prediction methods, insufficient multi-source data adaptation and processing capabilities, and a lack of specificity in predictions that deviate from hydrological patterns. The key technical solution is as follows: the system comprises six functional modules. First, multi-dimensional, multi-source data is collected and standardized preprocessed. Then, a feature set is constructed and optimized, and a random forest temporal dimension prediction model and a Kriging spatial dimension optimization model are trained. Initial predictions generate preliminary values, which are then validated. Next, spatial weighted fusion optimization is performed, and finally, after verifying and correcting outliers, predicted data, charts, and reports are output. The method simultaneously implements this entire process.

This invention relates to the field of data processing technology, specifically to a GIS 3D dynamic modeling and analysis system and method integrating big data and cloud platform networks. It includes: a multi-source geographic data acquisition unit; a distributed data processing unit; a cloud platform support unit; a 3D dynamic modeling unit for constructing a 3D dynamic model of a geographic scene, achieving real-time updates of the 3D dynamic model based on multi-source data fusion technology; an intelligent analysis unit for performing multi-dimensional geospatial analysis of the 3D model, outputting pattern mining and trend prediction results through improved spatiotemporal analysis algorithms; and a visualization and interaction unit. This invention constructs a dynamic access channel and automated fusion mechanism for multi-source heterogeneous geographic data, integrates a big data distributed parallel processing framework and the elastic resource scheduling capabilities of a cloud platform, breaking the closed-loop limitations of traditional BIM+GIS static data integration, and achieving efficient incremental iteration of large-scale geographic scene 3D models.

This invention discloses a method and related apparatus for identifying the spatiotemporal evolution characteristics of watershed water resources. The method includes: acquiring an overview of watershed zoning and water resource quantity information; using a point conversion tool to convert the surface features of the zoning into point features at their geometric centers; using a spatiotemporal cube construction tool to create a spatiotemporal cube of water resources quantity in the watershed; using the Mann-Kendall statistical method to determine the trend of water resource quantity change in each zoning; using Sen's Slope Estimator to quantify the rate of change of water resources quantity in each zoning; and finally, visualizing the evolution characteristics. This invention, by constructing a spatiotemporal cube and embedding the time dimension into a spatial analysis framework, achieves joint analysis and dynamic presentation of the spatiotemporal evolution of water resources. It can quickly and accurately identify the evolution trend of watershed water resource patterns and has advantages such as comprehensive spatiotemporal analysis, high visualization, and dynamic expression.

This invention relates to the field of adaptive water level prediction, and discloses an adaptive inland river water level prediction method based on a deep learning prediction model. The method includes the following steps: collecting inland river correlation data and performing spatiotemporal characteristic statistical analysis of the data; constructing a matrix for the spatiotemporal analysis of the data to build a model for real-time water level prediction; and finally, performing closed-loop learning and updating of the model to control the model to achieve adaptive inland river water level prediction. This invention can adaptively achieve high-precision inland river water level prediction through a multi-source data-driven approach, improving prediction efficiency and accuracy.

This invention discloses a method and system for monitoring the insulation components of a primary and secondary integrated ring main unit (RMU) with multi-parameter joint protection, relating to the field of RMU insulation monitoring technology. The method includes: establishing a monitoring scenario parameter mapping list; connecting to multi-source monitoring devices to acquire multi-source monitoring data of the insulation components and matching it with the monitoring scenario parameter mapping list; performing coupled analysis on the multi-source monitoring data to generate the monitoring status of the insulation components; establishing an insulation monitoring spatiotemporal grid; and performing spatiotemporal analysis and control isolation analysis based on the insulation monitoring spatiotemporal grid to generate monitoring feedback results. This invention solves the technical problems of existing technologies regarding single monitoring parameters, inaccurate fault location, and untimely isolation control in primary and secondary integrated RMU insulation monitoring. It achieves multi-parameter coupled monitoring, accurate spatiotemporal fault location, and intelligent isolation control of insulation components in primary and secondary integrated RMUs, improving the accuracy of insulation fault identification and the speed of operation and maintenance response.

The application discloses a farmland non-grain monitoring method, device, equipment and storage medium. The method comprises the following steps: determining the non-grain level of a target area according to the non-grain crop sowing area and the total crop sowing area of the target area; performing spatio-temporal transition analysis of non-grain by using an exploratory spatio-temporal analysis method according to the non-grain level, and determining the transition mode of the target area; obtaining the population non-agricultural rate of the target area, and predicting the future non-grain level and population non-agricultural rate according to the historical statistical data of the non-grain level and the population non-agricultural rate; and determining the non-grain level safety index according to the transition mode of the target area and the predicted non-grain level and population non-agricultural rate, so as to monitor the non-grain safety situation of the target area. The application can effectively improve the accuracy of farmland non-grain monitoring.