Sunlight greenhouse wireless sensor multi-data fusion method

Abstract

The invention discloses a sunlight greenhouse wireless sensor multi-data fusion method. Greenhouse data are acquired in real time through a wireless sensor; a Grubbs judging criterion is used for data pretreatment; an adaptive weighted average fusion algorithm is then used, a weighted factor is derived for fusion calculation on the data, real-time north sunlight greenhouse data fusion is realized, and the real-time data precision is improved. An experimental result shows that the Grubbs judging criterion can effectively eliminate gross errors, and the data precision is improved by 8%; and the adaptive weighted average data fusion can obviously improve the data precision, the data precision is improved by 6% after fusion, the fusion result can describe the real-time greenhouse environment accurately, and more accurate basic data can be provided for control on the greenhouse environment.

Description

technical field [0001] The invention relates to the field of sensor data processing, in particular to a multi-data fusion method of a solar greenhouse wireless sensor. Background technique [0002] The winter in northern my country is long, and the time to meet the needs of crop growth is very short. However, the use of solar greenhouses can create a good environment for crop growth, solve the long-term problem of vegetable supply in winter in the north, and bring convenience for farmers to efficiently produce low-cost crops. With the continuous development of solar greenhouse technology in the north, monitoring and controlling the greenhouse environment has become inevitable. Arranging various sensors in the greenhouse for monitoring can improve the accuracy and management level of environmental control, obtain high-quality greenhouse products and ultimately improve the economic benefits of the greenhouse. play a pivotal role. However, due to the natural characteristics of...

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Problems solved by technology

[0003] In recent years, many researchers have studied the data fusion problem of how to improve the data accuracy in solar greenhouses. For example, the method of data fusion based on the idea of ​​Huffman tree needs to scan the data twice and then store the data in two states. , the processing process is very complicated; using the support function of the gray system theory combined with the triple exponential smoothing method for greenhouse data fusion calculations can obtain high-precision fusion results, but this method requires system flexibility and takes up less resource space when processing data. There is a large problem of optimization space; the batch trajectory method that includes the average value is used to fuse the greenhouse environmental data, and only the measurement data collected by the sensor can be provided by the system to perform data fusion calculations, but this method has high requirements for sensor grouping , if the grouping is improper, the final fusion result will be biased

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