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作 者:肖雪朋 王明飞[1] 张馨[1] 王利春[2] 魏晓明 郑文刚[1] Xiao Xuepeng;Wang Mingfei;Zhang Xin;Wang Lichun;Wei Xiaoming;Zheng Wengang(Information Technology Research Center,Beijing Academy of Agriculture and Forestry Sciences,Beijing,100097,China;Intelligent Equipment Technology Research Center,Beijing Academy of Agriculture and Forestry Sciences,Beijing,100097,China)
机构地区:[1]北京市农林科学院信息技术研究中心,北京市100097 [2]北京市农林科学院智能装备技术研究中心,北京市100097
出 处:《中国农机化学报》2024年第8期100-106,共7页Journal of Chinese Agricultural Mechanization
基 金:国家食用菌产业技术体系(CARS—20);北京市食用菌创新团队(BAIC03—2022);农业物联网技术北京市工程实验室建设(PT2022—27);北京市科委项目(Z201100008020013)。
摘 要:无线传感器数据为智能环境调控提供决策依据,合理准确的数据是正确决策的前提,实时检测异常数据至关重要。针对传统的静态数据异常检测算法检测精度和效率低下、将数据上传至云计算中心分析增加带宽的传输压力和控制决策反馈时间等问题,提出一种基于边缘计算和数据融合的新方法。采用多模态感知融合算法进行异常数据检测,对实际发生的湿度、温度、光照等农业温室异常数据集进行仿真分析,使用滑动窗口方式处理数据流无限问题,计算单传感器和多传感器数据方差、多传感器数据间相关性系数,优化关键结构参数。结果表明,该模型能够检测出传感器的异常数据,单节点多传感器故障识别率为82.5%,多节点多传感器故障识别率为72.5%,汇聚数据上传可减少传输频率,单次节约30%数据流量,减轻服务器压力与数据传输延迟。对于解决温室传感器数据异常问题及边缘计算在温室环境设备中的应用提供有益参考。Wireless sensor data provides decision basis for intelligent environment regulation and control,reasonable and accurate data is the prerequisite for correct decision making,and real⁃time detection of abnormal data is crucial.In response to the problems of low detection accuracy and efficiency of traditional algorithms used for static data anomaly detection,uploading data to the cloud computing center for analysis,increasing the transmission pressure of bandwidth and control decision feedback time,a new method based on edge computing and data fusion is proposed.The multi⁃modal sensing fusion algorithm was used to detect anomaly data,simulate and analyze the actual abnormal data sets of agricultural greenhouse such as humidity,temperatureand light.The sliding window method was used to deal with the infinite data flow,and calculate the variance of single sensor and multi⁃sensor data,and the correlation coefficient between multi⁃sensor data,so as to optimize the key structural parameters.The results show that the model can detect the abnormal data of the sensor,the fault recognition rate of single⁃node multi⁃sensor is 82.5%,and the fault recognition rate of multi⁃node multi⁃sensor is 72.5%.The aggregated data uploads can reduce the frequency of transmission,saving 30%of data traffic in a single pass,reducing server pressure and data transmission latency.And the results of this paper can provide useful references for solving the problem of abnormal greenhouse sensor data and the application of edge computing in greenhouse environmental equipment.
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