CAJ | 학술논문

农业温室采集温湿度数据时，有线网络传输数据存在布线复杂、节点移动困难等缺点，采用Zigbee无线传感器网络传输提取的数据信息可以有效解决上述问题。受电磁干扰、传输信道拥塞等不利因素影响，采用Zigbee无线传感器网络监测的数据会出现不准确的情况，所以剔除误差数据成为关键。该文在温室数据采集系统的计算机软件里加入了针对误差数据剔除的改进肖维涅算法。改进肖维涅算法将误差数据剔除掉，把准确的数据存入数据库，方便系统分析温室环境现状。该文对改进肖维涅算法系统的误差数据处理时间进行了分析，与传统肖维涅算法相比，从主机发布采集命令到显示1组数据的处理结果，改进算法节省了5 s的时间。现场试验结果表明，改进肖维涅算法对采集到的数据进行处理后，数据分布更集中，得到的温室环境数据更加准确快速。
농업온실채집온습도수거시，유선망락전수수거존재포선복잡、절점이동곤난등결점，채용Zigbee무선전감기망락전수제취적수거신식가이유효해결상술문제。수전자간우、전수신도옹새등불리인소영향，채용Zigbee무선전감기망락감측적수거회출현불준학적정황，소이척제오차수거성위관건。해문재온실수거채집계통적계산궤연건리가입료침대오차수거척제적개진초유열산법。개진초유열산법장오차수거척제도，파준학적수거존입수거고，방편계통분석온실배경현상。해문대개진초유열산법계통적오차수거처리시간진행료분석，여전통초유열산법상비，종주궤발포채집명령도현시1조수거적처리결과，개진산법절성료5 s적시간。현장시험결과표명，개진초유열산법대채집도적수거진행처리후，수거분포경집중，득도적온실배경수거경가준학쾌속。
When agricultural greenhouse collects temperature and humidity data, cable network transmit data lead to wiring complexity and difficulties of moving nodes. Zigbee wireless sensor network transmit data can effectively solve the above problems. Zigbee has the virtue of ad hoc network, low power consumption, equipment layout flexibility, good stability and so on. The core components in greenhouse are consisted of terminal nodes, route nodes and coordinator node. Terminal nodes placed in greenhouse continuously send out request signals to join the network. All the terminal nodes receiving a response signal from the coordinator node can join the network. Terminal nodes collect temperature and humidity data and transmit these data to coordinator node. Coordinator node is the core of Zigbee and is placed in the geometric center of the greenhouse. It is responsible for the establishment of network and can communicate with computer in the control center. Coordinator node sends commands to terminal nodes and sends temperature and humidity data monitored by terminal nodes to the control center. Affected by unfavorable factors such as electromagnetic interference and transmission channel congestion, the collected data might include error data. This paper applied an improved Chauvenet’s Criterion to eliminate the error data in greenhouse data collection system software. Traditional Chauvenet’s Criterion eliminates one error data per cycle of operation, which leads to low convergence rate. Amount of error data cost a long time to process error data, which result in that data information cannot be obtained in a timely manner. Improved Chauvenet’s Criterion is designed to solve this problem by applying the interquartile deviation method before data began cycle operation. According to the distribution of data, the data is sorted from smallest to largest so that improved Chauvenet’s Criterion can find the upper quartile, median and lower quartile quickly. Quantile is used to determine a numerical interval. Large deviation error data will be eliminated. Improved Chauvenet’s Criterion reduce the number of data participated in each cycle operation in order to save computer memory. Using the standard deviation to determine condition, each cycle of operation can eliminate multiple error data. This can improve data convergence rate, save processing time and meet requirements for real-time tracking data in greenhouse. Comparative analysis of the time complexity of traditional Chauvenet’s Criterion and improved Chauvenet’s Criterion, it fully proved that the improved Chauvenet’s Criterion saves computing time. Greenhouse environment data collection system was tested in the Northeast Agricultural University Horticulture Station. The tomato plants were growing in order, greenhouse is convenient for arrangement of monitoring points. Monitoring nodes extracted a data per second. A group of 32 data was transmitted to the data processing software. From the system sending out commands to system display data, traditional Chauvenet’s Criterion use 40s, improved Chauvenet’s Criterion use 35s, and improved Chauvenet’s Criterion saves 5s. It was found that data distribution area where data is processed by the improved Chauvenet’s Criterion is more concentrated. And it is convenient for system and experts to make a decision and take the necessary control measures according to data distribution.