5 results about "Old-growth forest" patented technology

The invention relates to a forest fire danger notification semantic analysis method based on a knowledge graph, and belongs to the technical field of natural language processing. The method comprises the following steps: collecting an original forest fire danger notification text, and constructing a forest fire danger notification data set; constructing a forest fire danger notification semantic analysis model which comprises a knowledge sub-graph construction and coding module, a deep fusion representation generation module, a graph convolution calculation module and a multi-task output module; training and optimizing the forest fire danger notification semantic analysis model through a multi-task collaborative loss function to obtain a trained forest fire danger notification semantic analysis model; and inputting the new forest fire danger notification text into the trained forest fire danger notification semantic analysis model to obtain a forest fire danger notification semantic analysis result. According to the method, refined modeling of evolutionary logic in the professional field of forest fire danger can be realized, and the interpretability of a result is enhanced.

The present invention relates to the field of microbial control technology, and specifically to a strain of Streptomyces mycarofaciens DNW2 and an application thereof. The Streptomyces of the present invention is named Streptomyces mycarofaciens DNW2 and was deposited in the General Microbiology Center of the China Microorganism Culture Collection Administration Committee on September 2, 2022, with a deposit number of CGMCC No. 25639. The Streptomyces mycarofaciens DNW2 of the present invention is isolated from the rhizosphere soil of the virgin forest in Shunchang County, Nanping City, Fujian Province. It has strong growth adaptability, high acid-base tolerance, and can be harmoniously compatible with the soil ecology. The biocontrol agent prepared by the strain can effectively prevent and control various plant diseases such as honey pomelo black spot disease, honey pomelo anthracnose, honey pomelo black spot disease, pepper wilt, tomato wilt, banana wilt, banyan anthracnose, succulent black spot disease, tomato leaf mold, orchid stem base rot, corn stalk rot, strawberry root rot, etc., especially for honey pomelo black spot disease. The control effect is significant, and it has the advantages of being pollution-free and promoting growth.

The application relates to the technical field of point cloud data processing, and discloses a point cloud tree segmentation method and system based on collaborative attention and density adaptive voxelization, a terminal and a storage medium.The method comprises the following steps: performing density adaptive voxelization on original forest point cloud to construct a non-uniform voxel grid; then inputting the non-uniform voxel grid into a sparse convolutional neural network to perform feature extraction by using a collaborative spatial channel attention module; and finally performing model training by using a boundary perception composite loss function, so that the segmentation result of each tree in the forest point cloud can be extracted.Under the premise of ensuring accuracy, the application optimizes the allocation of computing resources through adaptive voxelization, improves the efficiency of processing large-scale forest point cloud, and finally generates a segmentation result with more accurate geometric morphology and clearer contour, thereby providing a higher-quality data basis for subsequent forestry parameter extraction.

The invention relates to the technical field of point cloud data processing, and discloses a point cloud individual tree segmentation method and system based on collaborative attention and density adaptive voxelization, a terminal and a storage medium, and the method comprises the steps: carrying out the density adaptive voxelization of an original forest point cloud to construct a non-uniform voxel grid, and then inputting a sparse convolutional neural network, performing feature extraction by using a collaborative space channel attention module, performing model training through a boundary perception composite loss function, and finally extracting a segmentation result of each individual tree in the forest point cloud. According to the method, on the premise that the precision is guaranteed, distribution of computing resources is optimized through self-adaptive voxelization, the efficiency of processing large-scale forest point cloud is improved, the finally generated segmentation result has a more accurate geometric shape and a clearer contour, and a higher-quality data basis is provided for subsequent forestry parameter extraction.

The present application relates to a kind of forest fire danger based on knowledge graph's semantic analysis method of report, belong to natural language processing technical field.It includes the following steps: collection original forest fire danger report text, constructs forest fire danger report data set;Forest fire danger report semantic analysis model is constructed, including knowledge subgraph construction and coding module, deep fusion representation generation module, graph convolution calculation module and multi-task output module;Through multi-task collaborative loss function, forest fire danger report semantic analysis model is trained and optimized, and the trained forest fire danger report semantic analysis model is obtained;New forest fire danger report text is input to the trained forest fire danger report semantic analysis model, and the forest fire danger report semantic analysis result is obtained.The present application can realize the fine modeling of the evolution logic of forest fire danger professional field, and enhance the explainability of result.