7 results about "Association mapping" patented technology

This invention discloses a method and system for identifying waste materials based on multimodal fusion, belonging to the field of intelligent waste material identification technology. The method involves acquiring visible light image data, depth structure data, and near-infrared spectral response data of the waste materials to be identified; obtaining candidate regions from the visible light image data and extracting a set of morphological features based on the depth structure data; extracting material reflection curves from the near-infrared spectral response data; extracting texture distribution features and color stability features from the visible light image data and generating a set of structural consistency features based on the morphological features; constructing a fusion feature vector by performing cross-modal association mapping on multiple features and inputting it into a waste material identification model to obtain candidate identification results; identifying key feature subsets through local sensitivity analysis and reconstructing the weights of the fusion features; and performing classification again to output the final identification result. This invention can effectively improve the accuracy and stability of waste material identification in complex recycling scenarios.

This invention relates to a real-time simulation and optimization control platform for integrated power generation, grid, load, and storage systems based on digital twins, belonging to the field of power system automation technology. The method includes: a twin model construction and self-calibration unit integrating multi-physics parameters, equipment, and grid characteristics to construct a model; a dual-algorithm collaborative optimization control unit constructing a bidirectional optimization framework through model predictive control and multi-agent reinforcement learning, coupled with a multi-dimensional adaptive reward mechanism to output control commands; a multi-source data fusion and planning linkage unit completing semantic annotation and association mapping of multiple types of data, adjusting production plans and decomposing loads based on simulation results; and a green electricity trading and scheduling coordination unit incorporating core elements of green electricity trading into the optimization objective to generate a trading and scheduling coordination scheme. This method achieves accurate fusion of multi-source data from the power generation, grid, load, and storage system, effectively solving the defects of unscientific planning adjustments and insufficient coordination in existing technologies, and improving the safety and economy of power grid operation.

This invention relates to the field of hole coaxiality measurement technology, specifically to a method and system for measuring the coaxiality of machined holes based on a laser beam. It includes identifying defect points and associated defect points during the re-inspection process, and then re-locating the defect points based on a defect-association mapping set. A precise re-inspection adjustment path is constructed based on the secondary location distribution of the defect points, and a fixed-point re-inspection is performed according to this path. This solution obtains the positional relationship between defect points and associated defect points, constructs a defect-association mapping set, and re-locates the defect points based on the re-inspection process, the associated defect points, and the defect-association mapping set. A precise re-inspection adjustment path is constructed based on the secondary location distribution of the defect points, and a fixed-point re-inspection is performed according to this path. This allows for targeted measurement path planning for the coaxiality testing equipment and location detection of each defect point after re-inspection, improving the efficiency of the re-inspection process.

The application discloses a river ecological abnormal change identification method based on machine learning, relates to the technical field of machine learning, and comprises the following steps: collecting multi-section ecological monitoring data, preprocessing, and constructing a time-space sequence dataset; a river directed topological structure is constructed, a topological adjacency matrix is generated, and the association mapping of time-space data and nodes is completed; the time-space characteristics and the adjacency matrix are inputted, the diffusion law is learned by improving DCRNN, and a dynamic ecological baseline sequence is outputted; a stable ecological sample set is constructed, a stable subspace is generated by mapping, the baseline is corrected, and the structural deviation is calculated; an ecological energy transmission interval matrix is constructed, bidirectional propagation consistency discrimination is performed, and a propagation consistency index is obtained; the baseline residual error is calculated, the structural deviation is combined, and an abnormal identification result is outputted. The application realizes accurate identification and early warning of gradual ecological abnormal changes in the river by constructing a topological constraint dynamic ecological baseline and fusing stable ecological structure and propagation consistency discrimination.

In accordance with aspects of the present disclosure, sequencing systems and methodologies described herein may be utilized in various contexts for determining associations between features of a single-cell data set (e.g., perturbations, accessibility, genotype) and gene expression. As discussed herein, various embodiments of the present technique comprise a computational pipeline that accurately maps such associations in a computationally rapid and efficient manner. By way of example, a single-cell data sample comprising hundreds of thousands of cells or more may be processed in less than a day.

This application provides a multi-dimensional flue gas desulfurization status monitoring method and system, relating to the field of intelligent monitoring technology. The method includes acquiring historical process parameters and corresponding fault type labels under historical fault conditions of the desulfurization system; establishing a process fault diagnosis model through multi-parameter joint feature extraction and association training; and continuously collecting status monitoring signals from the desulfurization target equipment. Multi-type diagnostic maps are generated through map analysis and matched with an equipment fault feature knowledge base to identify equipment faults. When a process or equipment fault exists, the method combines a process-equipment fault association mapping table to retrieve corresponding expected abnormal features within the same time window for verification and matching. Finally, the fault level and corresponding adjustment plan are determined based on the matching results. This method can achieve comprehensive identification and predictive control of desulfurization system faults.

This invention relates to the field of medical electro-digital data processing, and discloses a decision-making system for detecting melting curves of gene polymorphisms in hypertension medication. It collects fluorescence melting curve data of multiple gene targets; maps the melting temperature-fluorescence intensity derivative curve as graph structure nodes, and maps the melting feature associations between different gene loci as edges, constructing a multidimensional melting curve topology graph; extracts the co-fluctuation features of node peak temperature offset and edge weights and inputs them into a graph convolutional network, outputting a polymorphic combination feature vector; maps the polymorphic combination feature vector to a hypertension drug metabolism network graph model, calculates the joint decay coefficient of drug metabolic enzyme activity and receptor binding rate, and determines the type and dosage adjustment parameters for hypertension medication based on the joint decay coefficient. This invention overcomes the deficiency of single-peak independent alignment in separating overlapping peaks, achieves quantitative analysis of co-fluctuation associations of multi-gene polymorphisms, and eliminates medication bias caused by isolated qualitative matching.