为解决水产养殖池环境因子的人工监测存在的随机性大、难以控制的问题,设计了一种基于物联网的智能测控系统.重点对水产养殖智能测控系统中的若干关键技术进行了研究.采用多跳树型无线网络拓扑结构作为系统底层数据采集传输的网络结构.通过ZigBee技术和GPRS技术,对水环境温度、溶解氧、pH等因子进行实时采集,对增氧机、投饲机和水泵的状态实现远程控制.用户使用网页或手机APP,不仅可以查看系统运行中各项参数的历史轨迹,还可以实时监测与控制现场的各项参数.对监测节点等软硬件设计进行了详细阐述,采用卡尔曼滤波算法对模拟量数据进行处理以提高准确度.测试结果表明,模拟量数据精度和数据传输误码率均在1%以内;远程设置控制参数,网络延迟在10 s以内.该系统稳定可靠、数据检测准确、易于扩展,可在水产养殖中推广使用.%To handle the randomness and difficulty to control in manual monitoring of aquaculture environment,an intelligent monitoring system based on internet of things has been designed. Several key technologies of this monitoring system are researched. The multi-hop tree shaped wireless network topological structure is used as the network structure for bottom layer data acquisition transmission. Based on ZigBee and GPRS techniques,the environmental parameters,such as temperature,dissolved oxygen,pH,etc., are collected in real time; and the aerators,feeders and pumps are controlled remotely. Therefore,users are able to query the historical traces of parameters and monitor and control remotely all kinds of on-spot parameters through websites as well as mobile phone apps. The design of corresponding software and hardware of each monitoring node is described in detail. Due to Kalman filtering algorithm is used to process analog data,the accuracy is improved. The test results show that the accuracy of the analog data and the data transfer error rate are both within 1%; and the network latency is within 10 s for remote setting. The system is stable, reliable,and expandable,which can be promoted in aquaculture.
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