Version 1
: Received: 18 February 2023 / Approved: 20 February 2023 / Online: 20 February 2023 (07:25:25 CET)
How to cite:
Liang, G.; Li, Y.; Li, S. Building Intelligent Natural Interfaces for Distributed SCADA Systems Using a Semantic Parsing Framework. Preprints2023, 2023020325. https://doi.org/10.20944/preprints202302.0325.v1
Liang, G.; Li, Y.; Li, S. Building Intelligent Natural Interfaces for Distributed SCADA Systems Using a Semantic Parsing Framework. Preprints 2023, 2023020325. https://doi.org/10.20944/preprints202302.0325.v1
Liang, G.; Li, Y.; Li, S. Building Intelligent Natural Interfaces for Distributed SCADA Systems Using a Semantic Parsing Framework. Preprints2023, 2023020325. https://doi.org/10.20944/preprints202302.0325.v1
APA Style
Liang, G., Li, Y., & Li, S. (2023). Building Intelligent Natural Interfaces for Distributed SCADA Systems Using a Semantic Parsing Framework. Preprints. https://doi.org/10.20944/preprints202302.0325.v1
Chicago/Turabian Style
Liang, G., Yurong Li and Shijun Li. 2023 "Building Intelligent Natural Interfaces for Distributed SCADA Systems Using a Semantic Parsing Framework" Preprints. https://doi.org/10.20944/preprints202302.0325.v1
Abstract
Converting natural language into machine language that can be recognized by distributed systems is the core challenge of intelligent interactive interfaces and human-machine dialogue systems. The human-machine interface interaction of large distributed SCADA measurement and control system is tedious and the operation and maintenance cost is high, so it is significant to design an intelligent natural language interaction interface for distributed measurement and control system. In this paper, we design the intermediate language format of SCADA system, i.e., Key-value logic form, and then formulate the Text2SCADA framework and propose the TICS algorithm and SDPA algorithm, the former adopts the keyword extraction and cosine similarity optimization algorithm to complete the structured extraction of natural language for basic natural language instructions, and the latter adopts the keyword extraction and cosine similarity optimization algorithm to complete the structured extraction of natural language for relatively The latter one adopts the algorithm of dependent syntactic analysis for the structured representation of natural language instructions with relatively complex natural language instructions. Based on the above algorithms, a lightweight information extraction model based on DGCNN and probabilistic graph ideas is constructed, aiming to enhance the scientific generalization ability of the framework on unknown instruction sets. The experimental results show that the proposed intelligent natural language interface can better solve the human-machine interface interaction problem of distributed SCADA measurement and control system. The average accuracy, recall and F-value of instruction parsing reach 89.27\%, 89.28\% and 89.27\%, respectively. The average response time is 1.593 s. Especially, it provides a more convenient means of interaction for industrial and agricultural information control.
Keywords
Natural language interface; Neural network language model; Dependent syntactic analysis; SCADA system; Human-computer interaction
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
