6results about How to "Guaranteed richness" patented technology

This application discloses an adaptive voxelization attribute assignment and dual-model collaborative mineral exploration method and system based on multi-source data, relating to the fields of artificial intelligence and mineral exploration prediction. The method includes: dividing the target area into three-dimensional voxel grids using an adaptive grid function; assigning attribute values ​​of multi-source mineralization-prospecting information to the three-dimensional voxel grids of the target area and known mining areas, and assigning attribute values ​​of ore grade to the three-dimensional voxel grids of known mining areas; calculating contribution weights using a logistic regression model, and obtaining the first mineralization score of the target area's three-dimensional voxel grids through linear combination calculation and mapping; training a deep learning model using multi-source mineralization-prospecting information as input and ore grade as a label, and determining the second mineralization score of the target area's three-dimensional voxel grids; determining the comprehensive mineralization score of each three-dimensional voxel grid in the target area using a comprehensive scoring function, and delineating the mineral exploration target area based on the comprehensive mineralization score. This application improves the accuracy and interpretability of mineral exploration in the target area.

The invention relates to a GNSS-R and GNSS-S radar combined sea surface high wind speed inversion method and a satellite-borne receiving system, and the method comprises the following steps: S1, obtaining a reflection signal and a scattering signal in a time space under a high wind speed condition, and carrying out the full-link sampling of the reflection signal and the scattering signal; s2, performing delay Doppler mapping processing on the reflected signal to generate a sea surface delay Doppler map; performing double-station SAR imaging processing on the scattered signals to generate Q GNSS-S radar images with different double-station angles; s3, extracting statistical feature vectors of the sea surface delay Doppler map and the Q GNSS-S radar images with different bistatic angles, and forming a joint feature X; and S4, inputting the joint feature X into a pre-trained deep neural network, and outputting a sea surface wind speed result. According to the method, the characteristics of GNSS reflection signals and scattering signals can be fully utilized, and the precision of sea surface high-wind-speed inversion is effectively improved.

This invention discloses a method, system, and medium for predicting and controlling the normalized microstructure of oriented electrical steel based on a multilayer perceptron deep learning network and cellular automata. It takes two-dimensional normalized process parameters as input and ODF slice images as output. A cellular automata model driven by physical mechanisms generates virtual labeled data of "(t,T)-ODF" in batches within a preset process design space, constructing a dataset. Then, a deep learning surrogate model is used to achieve an end-to-end nonlinear mapping from the two-dimensional normalized process parameters to the ODF slice images, thereby achieving rapid prediction and visualization of the normalized microstructure without repeatedly calling the cellular automata model. This invention solves the key challenge of balancing prediction speed, structural accuracy, and inversion design capabilities within an extremely short process window.

This invention provides an intelligent prediction method for low-resistivity oil reservoirs based on hierarchical ensemble learning. This method uses small-layer data as a benchmark, integrates heterogeneous data from multiple sources such as well logging, well logging, and production data, and constructs a labeled dataset after standardized preprocessing. Multi-dimensional features are automatically extracted using the Tsfresh framework, and key feature subsets are selected using random forest. Subsequently, generative adversarial networks that integrate noise filtering, adaptive clustering, and residual connections are used to optimize sample distribution, addressing data imbalance and insufficient sample problems. A model cluster containing traditional machine learning models and convolutional hybrid neural networks is constructed, adapting to static and temporal feature learning respectively. A hierarchical stacking ensemble strategy is adopted, using the unbiased prediction results of five types of base learners as meta-features, and integrating the advantages of each model through meta-learners. Furthermore, this invention also provides an intelligent prediction device for low-resistivity oil reservoirs based on hierarchical ensemble learning. The technical solution provided by this invention can improve the prediction accuracy and generalization ability of low-resistivity oil reservoirs under complex geological conditions, thereby increasing the recovery rate.

The present application belongs to the field of computer three-dimensional scene generation, and more specifically, relates to a three-dimensional scene generation method based on entity space relationship reasoning. This method combines the reasoning ability of a large language model with the learning ability of a specific task to reason and summarize the entity and space position relationship in the text description. In this way, the scene described in the text can be converted into a specific three-dimensional scene and rendered and displayed in a three-dimensional engine. This method can be widely used in virtual reality, game development, film and television production and other fields, and has important value for improving work efficiency and innovation ability.

The application discloses a multi-agent task cooperative processing system and method for voice interaction. The system comprises: a voice input module for receiving and preprocessing original audio signals from a user; a voice processing module for generating a structured task representation based on the audio signals using a deep neural network model; a task coordination module for assigning tasks to agents in a task processing module based on the structured task representation; the task processing module contains multiple agents, all connected to the task coordination module, each configured to process tasks in a specific field; a response generation module for integrating the processing results from the agents into a coherent response and converting it into a voice signal for output. The application integrates voice understanding, task scheduling and cooperative execution to provide a robust, efficient and intelligent voice-driven solution that can complete complex voice interaction tasks.