7 results about "Semantic grid" patented technology

The application discloses an indoor distribution cable path intelligent generation method combined with building environment characteristics, and comprises the following steps: constructing a semantic grid map capable of distinguishing different building function areas by performing layer identification, semantic annotation and space discretization on an input building plan design map; based on the semantic grid map, different basic passing generation values are given to different types of areas, and a dynamic engineering constraint function comprising a corner turning penalty and a wall sticking reward rule is defined; path search of an improved A* algorithm is performed, in an engineering constraint cost field fused with the basic passing generation value and the dynamic engineering constraint function, a heuristic search algorithm with a dynamic penalty term is used to calculate and generate a candidate cable path meeting engineering specifications; virtual convergence points are determined through node clustering, the path search algorithm is applied hierarchically, a backbone path limited in a public area is generated, branch paths entering rooms are generated, and a complete indoor distribution system cable path topology is generated by merging.

This invention relates to the fields of deep space exploration engineering planning and artificial intelligence technology, specifically disclosing a method for selecting a lunar south pole research station based on a multimodal large model. The method includes: constructing a site selection index toolkit containing various evaluation tools; acquiring and fusing multi-source data from the lunar south pole to construct a unified multimodal semantic grid and comprehensive cost model; encoding environmental features and mission objectives into cue vectors using an encoder; calculating the similarity between the cue vectors and tool capabilities based on the multimodal large model, dynamically filtering matching toolchains and generating adaptive weight parameters; iteratively optimizing the site selection evaluation results through a closed-loop optimization mechanism of "evaluation-feedback-adjustment" until convergence conditions are met; and finally outputting the optimal or near-optimal site selection scheme, supporting dynamic relocation under data and mission updates. This invention achieves intelligent fusion of multi-source data and multi-objective adaptive decision-making in complex environments, significantly improving the scientific rigor, objectivity, and efficiency of lunar research station site selection.

The present disclosure provides a point cloud noise identification method, device and unmanned vehicle, comprising: performing feature identification on the point cloud data of the current frame to obtain semantic point cloud; performing rasterization processing on the semantic point cloud, and removing the grid unit representing the dynamic target category according to the category to which each point in the semantic point cloud belongs, to determine the target semantic grid data of the current frame; fusing the target semantic grid data of the current frame with the target semantic grid data of the historical frame to generate terrain data; and checking the point cloud data of the current frame according to the terrain data to determine the noise of the point cloud data of the current frame.

This application relates to a semantic grid map construction method, system, and medium with automatic obstacle removal function, belonging to the field of lawnmower technology. The semantic grid map construction method includes: acquiring original point cloud data and original image data of the lawn according to the target projection resolution, and converting them to the same view plane; temporally synchronizing the original point cloud data and original image data to obtain a three-dimensional point cloud and a two-dimensional image, and transmitting them to a data queue; spatially transforming the three-dimensional point cloud, and constructing fused image data by combining object semantic labels with the two-dimensional image in a layered fusion mechanism; generating a lawn grid based on the fused image data, and marking obstacle nodes using a ray tracing mechanism to obtain a lawn grid map; accurately distinguishing obstacle types (static / dynamic, passable / impassable) through category information; and dynamically clearing grids in obstacle-free areas through a ray tracing mechanism, reducing false positive obstacles caused by limited detection field of view.

This invention discloses a method and system for analyzing the navigation behavior of ships in waterways based on big data, relating to the field of ship technology. This invention collects dynamic ship data and static waterway data, constructs an environmental semantic grid, and maps the dynamic data to generate semantic trajectory sequences. Based on the sequences, it calculates the basic spatiotemporal correlation value, extracts trajectory direction entropy using a local minimum spanning tree, and constructs a ship behavior feature vector. Based on physical limits, it constructs a dynamic pseudo-label dataset and uses an evolutionary algorithm based on a weighted ROC convex hull guidance strategy to iteratively optimize the parameters of the nonlinear classification decision function. Using the optimal parameter set, it constructs a decision function to identify abnormal ship behavior, calculates risk potential energy, and generates chain reaction warnings based on the risk transmission coefficient. This invention effectively integrates environmental semantics and entropy features, solving the problems of scarce abnormal samples and complex nonlinear feature identification without manual annotation, and achieving proactive and precise prevention and control of waterway collision risks.

The application discloses a transformer partial discharge positioning method based on a partial discharge wave speed adaptive compensation. The method comprises the following steps: converting a three-dimensional semantic grid model of a transformer into a three-dimensional semantic voxel matrix; determining a target sound speed tensor matrix corresponding to the three-dimensional semantic voxel matrix; determining a time-of-arrival strategy template of each voxel in the target sound speed tensor matrix according to a semantic label identified by the three-dimensional semantic voxel matrix; generating a plurality of three-dimensional time field matrices according to the respective corresponding time-of-arrival strategy templates based on the target sound speed tensor matrix; and in the case of detecting a partial discharge signal of the transformer, determining a partial discharge positioning result of the transformer based on the plurality of three-dimensional time field matrices and observed time difference data. The application solves the technical problem of low transformer partial discharge positioning accuracy in related technologies due to the neglect of the anisotropic propagation characteristics of sound waves of the transformer winding structure.

A point grid network is a neural network that can model graph-structured data. The point grid network receives a graph-structured data sample, which may be a graph representation of an object. The point grid network uses an assignment matrix to transform the graph representation into a grid representation of the object. The assignment matrix defines whether graph nodes in the graph representation is to be assigned to grid elements in the grid structure. The grid representation is a tensor that can be processed through convolutional operations or other types of tensor operations. The point grid network can perform convolution on the grid representation and one or more filters to generate a grid-structured feature map. Values in the filter (s) and values in the assignment matrix are determined through training the point grid network. The point grid network may further determine a condition of the object based on the grid-structured feature map.