3 results about "Triangular decomposition" patented technology

The application discloses a text reasoning method, product, equipment and storage medium, and relates to the technical field of computers, and comprises the following steps: through the orthogonal triangular decomposition mode and in combination with the judgment mechanism whether the pre-trained language model has been trained on the fine-tuning data set, if the pre-trained language model has been trained on the fine-tuning data set, the orthogonal triangular decomposition is performed on the to-be-fine-tuned parameter matrix, the triangular matrix obtained after the decomposition is fine-tuned and trained on the fine-tuning data set, and the fine-tuned language model is obtained; if the language model has not been trained on the fine-tuning data set, the orthogonal fine-tuning is performed on the language model starting from a preset unit matrix, the fine-tuned language model is obtained, and the text reasoning is performed by using the fine-tuned language model, through the above mode, the robustness of the language model to different data sets is improved, and the accuracy of the text reasoning result is improved, and meanwhile, the problem that the robustness of the traditional pre-trained language model parameter after fine-tuning is poor and the performance is reduced is solved.

This invention relates to the field of mine safety monitoring technology, specifically disclosing a method for deploying mine fire monitoring sensors. The method includes: establishing a three-dimensional model; acquiring fire smoke propagation data through numerical simulation to construct an original spatiotemporal concentration matrix; extracting threshold response time feature vectors and maximum time-varying gradient feature vectors from the original spatiotemporal concentration matrix; performing nonlinear logarithmic mapping and centering on the original spatiotemporal concentration matrix, and constructing a spatial feature basis matrix through singular value decomposition; concatenating the spatial feature basis matrix, threshold response time feature vectors, and maximum time-varying gradient feature vectors, introducing feature weight penalty coefficients, and constructing an augmented feature matrix; performing orthogonal triangular decomposition with column pivot selection on the transpose of the augmented feature matrix, and determining the optimal monitoring point based on the row index of the permutation matrix, thereby achieving effective monitoring and early warning of the initial stage of a fire. This improves the ability to capture low-concentration signals in the initial stage of a fire and the accuracy of reconstructing the overall concentration field.

This invention discloses a multi-core computing method and apparatus for QR decomposition that integrates column-block TSQR and tile update. The method includes: for a large-scale matrix to be decomposed, horizontally dividing it into blocks based on the number of cores in the multi-core processor, and further dividing the large-scale matrix into multiple layers according to the tile update method; constructing a multi-stage pipeline with a binary tree structure, performing parallel triangular decomposition on the initial layers of the large-scale matrix through the multi-core processor to obtain the corresponding upper triangular matrix and orthogonal matrix; replacing the original layer block matrix with the upper triangular matrix, performing conjugate transpose on the orthogonal matrix to obtain the update matrix, and implementing parallel updates for the remaining layers through the multi-core processor; based on the reflection vector implicitly stored by the multi-core processor during the parallel triangular decomposition process, solving for the overall orthogonal matrix corresponding to the large-scale matrix, and simultaneously outputting the overall upper triangular matrix obtained after layer update. This invention can realize QR decomposition of large-scale matrices.