6results about How to "Resolve ambiguity" patented technology

The application discloses a multi-modal-based digital human interaction control method and device, and relates to the technical field of artificial intelligence digital human, and specifically comprises the following steps: collecting multi-source heterogeneous multi-modal data of user interaction with a digital human in real time; constructing a dynamic adaptive multi-modal space-time hypergraph Transformer network, deeply fusing the multi-source heterogeneous multi-modal data, and obtaining a fused interaction feature vector; generating a multi-dimensional interaction situation tensor based on the fused interaction feature vector; generating a digital human response action sequence, voice content, expression posture and interaction strategy according to the multi-dimensional interaction situation tensor, and driving the digital human to execute; acquiring feedback data of the digital human after executing the response in real time, and optimizing parameters of the dynamic adaptive multi-modal space-time hypergraph Transformer network based on the feedback data; and effectively improving the naturalness and response accuracy of digital human interaction, and reducing the interaction misunderstanding rate and user cognitive load.

This invention discloses a Monte Carlo-based mineral assemblage analysis method, belonging to the field of mineral analysis. The invention includes the following steps: multi-source data fusion and preprocessing; physical model construction and initial solution generation; multi-scale feature fusion and extraction; Monte Carlo random sampling and solution space exploration; physical-data dual-driven evaluation and screening; iterative update and model optimization; convergence judgment and result output. This invention effectively solves the problem of multiple solutions in mineral assemblage identification by combining multi-source data fusion, a physical-data dual-driven model, and the Monte Carlo method with multi-scale feature fusion and iterative optimization. This method can integrate geophysical, geochemical, remote sensing, borehole core, and mineral spectral data, and through iterative model updates, gradually converges to the most probable mineral assemblage, providing uncertainty assessment, thereby improving the accuracy and reliability of mineral identification.

The present application belongs to the technical field of data processing of land-sea interface zone, and particularly relates to a joint processing method of comprehensive geophysical data of land-sea interface zone, which is used for processing and interpreting data of geophysical prospecting methods at the land-sea interface zone, finding out geological structure features and distribution of adverse geological bodies in the study area, including data processing and comprehensive interpretation of different methods, wherein the method comprises A: conical field source transient electromagnetic method, B: geological radar data processing method, C: high-density resistivity method, and D: micro-motion data processing method, and the specific steps of the conical field source transient electromagnetic method are A1: data arrangement and preprocessing are performed on original measurement data, and the arranged data are converted into.TEM format, so that the data become editable data traces. The present application has higher sensitivity to faults and fracture zones, has strong adaptability to urban environment, and to some extent, overcomes the problem of multi-solution, and has high efficiency, low cost, and higher accuracy and reliability.

This invention provides a method for predicting mapping relationships of medical data fields based on world model learning, belonging to the field of model prediction technology. The method includes acquiring a medical data object containing a source field set and a target field set, performing causal state configuration based on a generative world model to generate intermediate world states, performing alignment inference, and combining consistency policy constraints to generate mapping relationship prediction results. Simultaneously, it utilizes backfilling feedback information for closed-loop optimization. This invention improves the accuracy and interpretability of medical data field mapping and reduces the mapping error rate.

The invention discloses a coal field earthquake interpretation multi-mode hidden geological disaster information fusion and dynamic modeling method. The method comprises the following steps: acquiring multi-source heterogeneous geological data of a target coal field area; according to the method, the multi-modal feature tensor fusion model is constructed, so that the problem of multiplicity of solutions of a single data source is effectively solved, and the integrity and consistency of hidden geological disaster features are remarkably improved; conventional seismic attributes are extracted, high-order statistical characteristics of a logging curve and geologic body structure parameters are introduced, lithologic sudden change and tectonic deformation are described from multiple dimensions, and the recognition capacity of a micro-scale hidden disaster body is enhanced; a hybrid neural network combining CNN and LSTM is adopted, historical stratum stress field data and geologic features are fused, a space-time prediction module is constructed, the limitation of traditional static snapshot type interpretation is broken through, a geologic structure stability probability body of a future time sequence can be output, and dynamic prediction of a disaster evolution trend is achieved.

This application discloses a learning method, apparatus, device, and medium for testing a large language model. Test data is input into a large language model with low-rank adaptation parameters to obtain the prediction score of lexical units in the vocabulary at each generation step. Based on the prediction scores, the lexical-level evidence quality of the generation step is extracted, and the lexical-level cognitive uncertainty of the generation step is calculated. The lexical-level cognitive uncertainty is smoothed, and a stable region of the generation step is determined based on the smoothed uncertainty. A mask for the model update window is constructed. Based on the mask, the mask entropy loss is calculated, and the low-rank adaptation parameters are updated through backpropagation. This application can solve the problem of adaptive drift in model updates in related technologies.