2results about How to "Reasonable value" patented technology

The application provides a similarity feature constraint-based inverse synthesis prediction method, medium and equipment; wherein the method is: converting a to-be-predicted product into a corresponding product SMILES sequence; using a graph neural network to extract graph structure features corresponding to all characters in the product SMILES sequence; fusing the graph structure features and word vector representations to obtain a fusion vector; inputting the fusion vector matrix into a Transformer encoder and decoder to obtain a reactant set SMILES sequence, and converting the reactant set SMILES sequence to obtain corresponding reactants. The method integrates the graph structure features of the product molecule into the prediction features, improves the prediction accuracy effect, learns the commonality between the reactant and product sets through feature constraint, uses the commonality features to better help the Transformer model to perform inverse synthesis prediction, and further improves the prediction accuracy of the model.

The application discloses a network protocol fuzzy test seed evaluation method and device based on multi-standard combination weighting. In order to realize multi-standard combination weighting, first, evaluation indexes for measuring the value of a network protocol fuzzy test seed are selected, including basic evaluation indexes, future potential indexes and network protocol related indexes. Then, pre-fuzzy testing is performed to obtain item evaluation index values of sample seeds. Based on the evaluation index values, an entropy weight method is used to determine first weights of all evaluation indexes, and a maximum deviation method is used to determine second weights of all evaluation indexes. The evaluation weights of the evaluation indexes are determined by comprehensively considering the first weights and the second weights. According to the evaluation weights, all evaluation index values of the sample seeds are integrated to obtain evaluation values of the sample seeds. The application can improve the network protocol information utilization rate, thereby ensuring the stability of the seed selection process, improving the overall quality of the selected seeds, and further improving the efficiency of the fuzzy test and the vulnerability discovery capability.