Research On The Prediction Of Spare Parts Consumption Based On Gauss Process

Air conditioning aftermarket repair and maintenance requires manufacturers to provide sufficient spare parts storage, but at the same time, too much equipment storage will be denied in the process of oxidation loss and waste unnecessary storage costs. Therefore, it should be based on the use of spare parts in the history of the best in the purchase of a number of storage, to use the best cost-effective to ensure the normal operation of the air conditioning. At this time with a scientific algorithm to accurately predict the air-conditioning spare parts consumption is the key to the crux of the problem.The predictive control of spare parts shows great advantage and advance in the industrial control. Therefore, the predictive control is introduced in the industrial process control. The control algorithm should be developed in accordance with the forecast model,so the construction of the predictive model largely determines the time and space complexity of the predictive control algorithm. Today, the new moon in science and technology benefit, the form of industrial process is increasingly rich, the content is also more full of diversification, the conventional mode of construction has been unable to meet the demand for spare parts forecast. Because of machine learning method constantly enrich and develop, spare parts prediction modeling method is also getting more and more advanced and intelligent, with respect to the commonly used modeling form of early,today's modeling form better performance can adapt to the industrial system of diversification and complexity, therefore, the modern industrial gave great expectations in the prediction of spare parts based on machine learning.At the beginning of this paper, describes the common model for forecasting of spare parts and related algorithm theory, focusing on the theory and the mode of modeling of Gaussian process and application specific, based on the commonness of these two aspects,the integration of the spare parts prediction control with Gaussian process modeling and the use of typical spare parts prediction problem, the python language will be practical algorithms and test, accuracy rate is as high as more than 80 percent, and a variety of other methods compared prediction effect is very good. Furthermore, the Gaussian process have robust regression and fitting function, therefore the prediction curve with the original data by curve fitting effect is very good. Then, this paper using Taylor function will be Gaussian process prediction function decomposition, calculated by the likelihood of treatment of expressions, thus infer more specific analytical forecasting of spare parts of the basic law and calculate, the efficiency of the algorithm has been greatly improved. Atthe end of this paper, the successful integration of the Gauss process and the spare parts prediction, based on the Gauss process of the spare parts prediction algorithm, from the specific experiment shows that the algorithm has excellent performance.