3 results about "Semi supervised clustering" patented technology

The application provides a charging facility fault early warning model construction method based on multi-source data perception, and belongs to the technical field of charging facility fault early warning.The application forms a multi-source time series data set by collecting charging pile operation state data and environmental data and performing time series alignment, expands fault samples by using an adversarial generative network and a semi-supervised clustering algorithm to form a balanced sample data set, constructs a fault early warning model containing a time series memory pool encoding layer and a frequency domain convolution feature extraction layer, cooperatively optimizes early warning accuracy and response time delay by using a double-layer game optimization framework to obtain an optimal parameter combination, deploys the optimized model to an edge computing module to realize real-time fault early warning, establishes an online incremental learning mechanism based on sliding time window detection data distribution offset, and uses an elastic weight consolidation technology to update model parameters and retain historical knowledge, thereby solving the problem of early warning performance degradation of charging facility fault early warning in a data distribution evolution environment.

The application discloses a tobacco slitting method based on multi-view weight learning and a storage medium, obtains a hyperspectral image of target batch tobacco and performs division; an effective area of the tobacco is obtained by using a threshold segmentation method, the tobacco is divided into a set number of subareas with the same longitudinal length, characteristic spectra of the subareas corresponding to the tobacco are calculated, and a tobacco spectrum database is constructed; spectrum data in the tobacco spectrum database is subjected to band division, different band combinations are obtained, and a multi-view tobacco spectrum database is constructed; a semi-supervised clustering algorithm model is constructed based on multi-view weight and similarity learning, the semi-supervised clustering algorithm model is trained through the multi-view tobacco spectrum database, and a semi-supervised clustering algorithm model with an optimized target function is obtained; and a tobacco segmentation result is obtained through the semi-supervised clustering algorithm model. Through construction of the multi-view of the tobacco, weights are allocated to each view, differences between different tobaccos are accurately quantified, and the tobacco slitting effect is ensured.

This invention discloses a semi-supervised clustering modeling method and system for reservoir prediction. Addressing the challenges of large seismic attribute data volume, high redundancy, scarce labels, and low signal-to-noise ratio, this invention embeds a modified K-means framework simultaneously with pairwise constraint guidance and sparse feature weighting. It iteratively optimizes cluster assignment and attribute weights, achieving a balance between maximizing inter-class differences and minimizing intra-class differences. Differential privacy noise is introduced to ensure data security without significantly reducing accuracy. It supports downsampling acceleration and multi-layer 3D label alignment, enabling efficient processing of millions of data points. Compared to conventional K-means and waveform clustering, this invention significantly improves the accuracy of blind well testing, clearly characterizing micro-structures such as channels and riverbeds, providing a high-resolution, highly interpretable integrated solution for reservoir distribution, thickness, hydrocarbon content, and sedimentary facies analysis.