5results about How to "Efficient and accurate analysis" patented technology

The invention discloses an RGB-T semantic segmentation method and system based on multi-attention guidance and hierarchical fusion, and relates to the technical field of image processing. The system is composed of a double-flow encoder, a discriminative local texture perception unit, a semantic-driven cross-modal fusion unit, a semantic enhancement unit and a multi-scale layered refinement decoder, and efficient fusion and analysis of multi-modal features in a complex traffic scene are achieved. According to the discriminative local texture perception method, saliency features are learned through multi-attention guidance and a self-adaptive gating mechanism, accurate modeling of shallow texture information is focused, and the distinguishing ability of a region of interest and a target edge is improved; according to the semantic-driven cross-modal feature fusion method, efficient aggregation of global contexts is realized through high-level semantic guidance and cross-modal feature interaction, and feature complementarity is enhanced, so that the semantic-driven cross-modal feature fusion method has significant advantages in analysis of small targets, long-distance targets and boundary regions. The decoder adopts a progressive fusion mode, an additional edge detection module does not need to be added, and the overall segmentation precision is improved.

This invention relates to the technical field of agricultural production management and discloses a collaborative management method and system for agricultural production processes based on multiple intelligent agents. The method intelligently and accurately selects the agricultural production guidance agents needed for user-side agricultural production based on user-side agricultural production stage location data, user-side agricultural production process analysis data, artificial intelligence algorithms, and standard agricultural production stage information for different agricultural production guidance agents. Furthermore, it intelligently formulates guidance suggestions for user-side agricultural production problems based on agricultural production problem characteristic data, user-side agricultural production stage location data, user-side agricultural production process analysis data, user-side agricultural production guidance agent characteristic data, and in conjunction with agricultural management platforms and internet platforms. This enables the comprehensive and reliable formulation of highly scenario-based and operable agricultural production guidance suggestion data based on multiple agricultural production guidance agents, achieving intelligent and convenient agricultural production management for users.

The application discloses a training method, a simulation method, equipment and a medium of a simulation model of combustion fracturing crack propagation. The training method of the simulation model of combustion fracturing crack propagation based on a graph neural network comprises the following steps: obtaining combustion fracturing crack propagation simulation parameters; generating a triangular mesh geometric model according to the geometric model parameters; performing numerical simulation of combustion fracturing crack propagation according to the generated triangular mesh geometric model and the combustion fracturing engineering parameters to obtain displacement data and crack state data of the triangular mesh geometric model at multiple time steps; constructing a graph structure and determining features according to the displacement data and the crack state data of the triangular mesh geometric model at the multiple time steps; taking graph data of the graph structure as a training data set according to the constructed graph structure; and training a first graph neural network model constructed in advance according to the training data set.

The invention provides a route generation method and device, electronic equipment, a storage medium and a computer program product, and relates to the technical field of artificial intelligence, and the method comprises the following steps: in the embodiment of the invention, performing data extraction on acquired tourism data by using a large model, and determining a plurality of routes; and determining a target route based on the similarity between the related data corresponding to the plurality of routes and the demand data corresponding to the current user. Through the scheme in the embodiment of the invention, a personalized target route can be provided.

Systems and methods for determining the concentration of a plant component in a developed food product prior to or during the manufacture of the food product are disclosed. The method may include: receiving, by a computing system, protein data of a plant component; retrieving at least one model, the at least one model having been trained to generate an output indicative of a desired concentration of a plant component to be used for developing the food product, the at least one model comprising at least one of a pH model, a temperature model, and a protein content model; providing the received protein data as input to the model; receiving an output from the model; and generating instructions for developing the food product, the instructions including a desired concentration range of the plant ingredient.