Information retrieval-based question answering (IRQA) and knowledge-based question answering (KBQA) are the main forms of question answering (QA) systems. The answer generated by the IRQA system is extracted from the relevant text but has a certain degree of randomness, while the KBQA system retrieves the answer from structured data, and its accuracy is relatively high. In the field of policy and regulations such as household registration, the QA system requires precise and rigorous answers. Therefore, we design a QA system based on the household registration knowledge graph, aiming to provide rigorous and accurate answers for relevant household registration inquiries. The QA system uses a semantic analysis-based approach to simplify one question into a simple problem consisting of a single event entity and a single intention relationship, and quickly generates accurate answers by searching in the household registration knowledge graph. Due to the scarcity and imbalance of QA corpus data in the field of household registration, we use GPT3.5 to augment the collected questions dataset and explore the impact of data augmentation on the QA system. The experiment results show that the accuracy rate of the QA system using the augmented dataset reaches 93%, which is 6% higher than before.

Accurate large-scale regional wave height prediction is important for the safety of ocean sailing. A regional multi-step wave height prediction model (ConvGRU-RMWP) based on ConvGRU is designed for the two problems of difficult spatial feature resolution and low accuracy of multi-step prediction in ocean navigation wave height prediction. For multi-step prediction, a multi-input multi-output prediction strategy is used, and wave direction and wave period are used as exogenous variables, which are combined with historical wave height data to expand the sample space. For spatial features, a convolutional gated recurrent neural network with an Encoder-Forecaster structure is used to extract and resolve multi-level spatial information. In contrast to time series forecasting methods that consider only backward and forward dependencies in the time dimension and a single assessment of the properties of the predictor variables themselves, this paper additionally considers spatial correlations and implied correlations among the meteorological variables. This model uses the wave height information of the past 24 h to predict the wave height information for the next 12 h. The prediction results in both space and time show that the model can effectively extract spatial and temporal correlations and obtain good multi-step wave height prediction results. The proposed method has a lower prediction error than the other five prediction methods and verifies the applicability of this model for three selected sea areas along the global crude oil transportation route, all of which have a lower prediction error.