智能环境下基于边缘设备规则推理的数据预部署研究

汪成亮; 赵凯; 刘嘉敏

doi:10.12263/DZXB.20200891

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智能环境下基于边缘设备规则推理的数据预部署研究

学术论文 | 更新时间：2025-12-08

- 智能环境下基于边缘设备规则推理的数据预部署研究
- Study on Data Pre-Deployment Based on Inference and Computing of Edge Devices in Smart Environment
- 电子学报 2022年50卷第10期页码：2347-2360
- 作者机构：
  
  1.重庆大学计算机学院，重庆 400044
  2.重庆大学光电工程学院，重庆 400044
- 作者简介：
  
  [ "汪成亮男，1975年5月出生，四川资阳人.博士.现为重庆大学计算机学院教授，博士生导师.主要研究方向为复杂系统智能控制、无线网络及RFID研究与应用等.E-mail: wangcl@cqu.edu.cn" ]
  [ "赵凯男，1995年10月出生，湖北潜江人.重庆大学在读硕士研究生.主要研究方向为智能环境、物联网.E-mail: 20144732@cqu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(61672115);重庆市技术创新与应用发展专项重大主题专项(cstc2019jscx-zdztzxX0037)
- DOI：10.12263/DZXB.20200891
  中图分类号： TP181;
- 收稿：2020-08-15，
  
  修回：2021-10-08，
  
  纸质出版：2022-10-25
- 稿件说明：
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汪成亮,赵凯,刘嘉敏.智能环境下基于边缘设备规则推理的数据预部署研究[J].电子学报,2022,50(10):2347-2360.

WANG Cheng-liang,ZHAO Kai,LIU Jia-min.Study on Data Pre-Deployment Based on Inference and Computing of Edge Devices in Smart Environment[J].ACTA ELECTRONICA SINICA,2022,50(10):2347-2360.
汪成亮,赵凯,刘嘉敏.智能环境下基于边缘设备规则推理的数据预部署研究[J].电子学报,2022,50(10):2347-2360. DOI： 10.12263/DZXB.20200891.

WANG Cheng-liang,ZHAO Kai,LIU Jia-min.Study on Data Pre-Deployment Based on Inference and Computing of Edge Devices in Smart Environment[J].ACTA ELECTRONICA SINICA,2022,50(10):2347-2360. DOI： 10.12263/DZXB.20200891.

摘要

在现有的规则推理机制下，大量的传感器数据导致的过大规则匹配期间的实时特征计算量降低了推理实时性，同时边缘设备受限的内存资源难以应对如此庞大的数据量.为此，本文设计了数据预部署方案（Data Pre-Deployment Scheme，DPDS）.利用规则解析与预处理模块解析规则集得到的规则网络和轻量级特征表（Light-weight Characteristic Table，LCT），该方案无需进行实时特征计算，使推理效率和实时性得到显著提高，并大大降低了规则匹配期间的内存占用量.实验表明，即使在规则、数据规模很大的情况下，DPDS仍然具有较高的时间效率和空间效率.

Abstract

Under the existing rule inference mechanism

the amount of real-time feature calculation during rule matching caused by a large amount of sensor data reduces the inference real-time performance. At the same time

the limited memory resources of edge devices cannot cope with such a huge amount of data. For this reason

this thesis designs the Data Pre-Deployment Scheme(DPDS). By utilizing the rule network and Light-weight Characteristic Table(LCT) obtained by the rule analysis and preprocessing module

this scheme enables the rule network to directly reference the characteristic values in the LCT during inference without real-time feature calculations

which significantly improves efficiency and real-time and greatly reduces the memory usage of the inference process. The experimental results show that even in the case of a large amount of data and rules

DPDS still has high time efficiency and space efficiency.

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