Gas type identification method and system based on multi-sensor rapid learning

The invention provides a gas type identification method based on multi-sensor rapid learning. The method comprises the following steps: constructing a basic gas identification model library; calibrating and adjusting a classification coordinate system model; correcting the adjustment model; and repeatedly adjusting the coordinate system, and enabling the coordinate system generated by using the LDA algorithm to effectively classify the gas. The time complexity and the space complexity of model training are reduced, the operand is reduced, and the hardware processing capacity required by modeloperation is reduced; the production efficiency is improved, and the problem that all equipment needs to be calibrated for multiple times to obtain a large amount of model data is avoided; the accuracy of gas identification is improved, and the model correction capability is improved. 本发明提出一种基于多传感器快速学习的气体种类识别方法，构建基础气体识别模型库；标定调整分类坐标系模型；矫正调整模型；通过对坐标系进行反复调整，使得利用LDA算法生成的坐标系可以对气体进行有效分类。本发明减低了模型训练的时间复杂度和空间复杂度，降低了运算量，降低了模型运算时所需要