3results about How to "Avoid information loss" patented technology

The application discloses a kind of remote sensing time series data analysis method and system considering the spatio-temporal correlation of geographical object, belong to data analysis technical field.The method includes: obtaining multi-source multi-temporal remote sensing image and auxiliary data and pre-processing, generate consistent time series data stack;In three-dimensional space-time domain, image is divided into voxel and is adjacent tracking, identify geographical process object with evolution behavior and record its attribute;Determine the spatio-temporal topological relationship between geographical process object, generate unified spatio-temporal relationship table by composite reasoning;With geographical process object as node, spatio-temporal relationship as edge constructs geographical process object spatio-temporal graph model, extracts and standardizes the attribute characteristics of node and edge;Importance index of each node is calculated by using graph convolution network and topological dynamic mechanism joint modeling.The application realizes closed-loop analysis from data processing, relationship modeling, importance evaluation to decision support, improves the interpretability and decision effectiveness of remote sensing time series change detection.

The invention discloses a map matching and forward and reverse projection wide remote sensing image segmentation and sample automatic generation method. The method comprises the following steps: step 1, taking an original image, metadata and coastline vector data as an input triple; data cleaning and normalization are carried out through an image preprocessing module; entering a geometric correction and registration module to generate a corrected image with an Alpha channel; 2, performing spatial indexing and matching on the coastline vector data and the corrected image; using a mask generation algorithm to automatically align and rasterize the vector data, and constructing a final true value mask of pixel-level alignment; 3, initializing a reverse mapping grid through a coordinate transformation processing module; calculating a nonlinear mapping relation from geographic coordinates to original sensor pixel coordinates by using a coordinate inverse transformation calculation module; and through resampling processing, accurately backfilling the semantic tag to an original image space, and outputting a final inverse transformation mask. According to the invention, high precision is ensured, and meanwhile, the order of magnitude of processing speed is improved.

The invention provides a cross-modal fusion time sequence prediction method, device and equipment based on a large language model, and relates to the technical field of time sequence prediction. The method comprises the following steps: acquiring numerical time sequence data in a historical time window and associated text information; respectively processing the numerical time sequence data and the text information through parallel processing modal preprocessing branches to obtain a time sequence feature representation, a semantic reasoning prediction result generated by a large language model and a text semantic feature representation; performing hierarchical interactive fusion on the time sequence feature representation, the semantic reasoning prediction result and the text semantic feature representation through a multi-stage cross-modal attention fusion network to obtain enhanced time sequence representation; generating an adaptive fusion weight for each time step in the prediction time window based on the enhanced time sequence representation; and carrying out weighted fusion on a numerical prediction result generated based on the time sequence feature representation and a semantic reasoning prediction result by using the adaptive fusion weight to obtain a final time sequence prediction result.