摘要
受限于传统的程式固定的视窗界面人机交互方式,大型分布式工业过程SCADA系统主要运营于中控机房,配置专业人员维持运行,系统建设和运营维护成本很高,因此探索人机自然交互接口,引导系统自适应服务意义重大。以一种面向多种专业领域的分布式SCADA系统为背景,从实际运营的角度分析人机自然交互的核心需求。按照自然语言指令的复杂程度,推荐不同的语义解析算法。首先对指令采取词性标注,确定指令是否包含子指令。对于基本自然语言指令,采用TF-IDF关键词提取算法并结合余弦相似度进行结构化抽取,将其解析为SCADA操控中间语言后经形式化转换为实际操控指令。对于复杂自然语言指令,采用基于依存句法分析的结构化指令解析算法,实现实时操控接口。实验结果表明,所提出的自然语言接口能较好地解决SCADA系统的人机自然语言交互问题,指令解析方面的平均精确率、召回率以及F值分别达到了89.27%,89.28%以及89.27%,平均响应时间为1.593s,特别是为工农业信息化管控提供了更为便捷的交互手段。
Due to the traditional program fixed window interface human-computer interaction,large distributed industrial process SCADA systems are mainly operated in the central control room and maintained by professional staff,so the system construction and operation and maintenance costs are very high,and it is significant to explore the natural human-computer interaction interface and guide the system adaptive services.Taking a distributed SCADA system for various professional fields as the background,this paper analyzes the core requirements of natural human-computer interaction from the perspective of actual operation.Different semantic parsing algorithms are recommended according to the complexity of natural language instructions.For basic natural language instructions,TF-IDF keyword extraction algorithm is used and combined with cosine similarity for structured extraction,which is parsed into SCADA manipulation intermediate language and converted into actual manipulation instructions by formalization.For complex natural language instructions,a structured instruction parsing algorithm based on dependency syntax analysis is used to realize the real-time control interface.Experimental results show that the proposed natural language interface can better solve the human-computer natural language interaction problem of SCADA system.The average accuracy,recall and F-value of instruction parsing is 89.27%,89.28%and 89.27%,respectively.The average response time is 1.593s,which provides a more convenient means of interaction,especially for industrial and agricultural information control.
作者
王涛
郭武士
邓健
陈亮
WANG Tao;GUO Wushi;DENG Jian;CHEN Liang(School of Electrical Engineering,Southwest Jiaotong University,Chengdu 610031,China;School of Mechanical Engineering,Southwest Jiaotong University,Chengdu 610031,China;School of Information and Software Engineering,University of Electronic Science and Technology,Chengdu 610054,China;Robotics Engineering Laboratory for Sichuan Equipment Manufacturing Industry,Deyang,Sichuan 618000,China;China Resources Power Investment Company Limited,Qingdao,Shandong 266071,China)
出处
《计算机科学》
CSCD
北大核心
2023年第S01期74-82,共9页
Computer Science
基金
德阳市重点科技计划项目(2018SZY066)
四川工程职业技术学院规划科研项目(KJGH2020G08)
德阳市科技计划项目(成果转化)(2022KCZ158)
四川省科技计划项目(2022YFG0224)。
关键词
自然语言接口
神经网络语言模型
依存句法分析
SCADA系统
语义解析
Natural language interface
Neural network language model
Dependent syntactic analysis
SCADA system
Semantic parsing
