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Human-Computer natural language interaction is helpful to reduce the operation and maintenance cost of the SCADA system, and it is necessary to solve the complex natural language interface problem that supports data query and real-time control. According to the complexity of natural language instructions, a hierarchical classification of semantic parsing algorithm is adopted. Firstly, the KWECS method is used to classify the intent of natural language instruction, then the TF-IDF keyword extraction algorithm combined with the cosine similarity is used to structure the key-value of the classified natural language instructions which was used into SCADA control intermediate language and then formally converted into actual control or query instruction. If the analysis fails, the complex control and query instruction analysis are carried out according to the classification results, structuring instruction parsing based on dependency parsing and SQL natural language parsing based on deep learning are adopted respectively to implement real-time control interface and database query interface. Our experimental results show that the proposed hierarchical classification of natural language comprehensive query and control interface can better solve the problem of human-computer natural language interaction in the SCADA system, and the accuracy of intent recognition reaches 96.5%. In more detail, the accuracy, precision, recall, and F-score of instruction parsing reach 88.47%, 90.21%, 89.48%, and 89.72% respectively. Especially, it provides more convenient interactive means for industrial and agricultural information management and control.INDEX TERMS Natural language interface, intent classification, semantic parsing, scada systems, humancomputer interaction.

Hyperspectral images are rich in both spectral information and spatial dependence information between pixels; however, hyperspectral images are characterized by the high dimensionality of small data sets and the spectral variance. Facing these problems, spatial dependence information as supplementary information is a relatively effective means to solve them. And the label dependence characteristic of hyperspectral images is excellent spatial dependence information. Therefore, to address the above issues, based on residual network and spatial information extractor(RAS), which is based on a residual network, pixel embedding(PE), and a spatial information extractor(SIE). At the stage of mining spectral information, we use the residual network to mine spectral features; At the stage of mining spatial information, we utilize the label dependency characteristic to feed the set of pixels containing the target pixels into PE. Then, a pixel vector with location information and self-defined dimensionality is obtained. Next, this vector is fed into our proposed SIE to mine the spatial dependency information. In multi-group ablation experiments, our proposed model achieves overall accuracy (OA) scores of 79.16% on the 5% Indian Pines test set, 90.82% on the 1% Pavia University test set, and 92.17% on the 1% Salinas test set. Especially, the experimental results demonstrate that the joint spectral-spatial approach is effective in improving the accuracy of hyperspectral image classification. INDEX TERMSDeep learning, hyperspectral image, image classification, pattern recognition.

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