改进的Apriori算法在智能温室大棚系统中的应用

Application of an Improved Apriori Algorithm in Intelligence Greenhouse System

针对智能温室大棚系统内局部传感器故障造成其不能及时有效向上推送准确数据的问题,提出将关联规则中的Apriori算法应用于故障传感器数据的预测.以温度传感器发生故障为例,首先将关联规则中传统的Apriori算法进行优化,然后将其运用到故障传感器参数的预测当中去.实验仿真表明,改进的Apriori算法能够快速的发现温室各参数之间的关联规则,从而估计出故障传感器的参数的范围,有一定的应用价值.

To solve the problem that the accure data can＇t be pushed by the failure of local sensor in intelligence greenhouse system, it was presented that the Apriori algorithm which was based on association rule applied in the prediction of sensor fault data. Forcasting the greenhouse environment temperature is provided as an example in this paper, firstly, the classic Apriori algorithm is modified. Then it was used in the prediction of fault sensor data. The experimental results show that the improved Apriori algorithem could quickly find the association rule between the parameters in Greenhouse, thus estimated the range of parameters of the fault sensor and the method could be proved to be feasible.