Study On Prediction Methods For Agricultural Machine Total Power Of Heilongjiang Province

The agriculture of Heilongjiang province is in a transition stage. The agricultural machinery is an important tool to promote agricultural development and the total power is simplified index. To analyze machinery total power of the complex and predictive value to process of the evolution of agricultural. This way can conducive to enhancing the level of agricultural machinery. Currently, the research on gross agricultural machinery mostly base on their own law of development to predict future trends, but the research is less the influence factors into forecast model. And there are lack of quantitative analysis when determining the number of factors that affect the gross agricultural machinery. Therefore, this paper apply to the chaos theory, Grey theory, the BP network model to effectively combine impact on the agricultural machinery total power analysis and prediction research.This paper has discussed the detail factors which affecting the development of Heilongjiang province agricultural machinery, and applies the Grey correlation method and correlation to analyze influence factors. It based on the GP algorithm of chaos theory to analyze the number of the quantitative influencing factors, to build the prediction model screening main factors influencing variables. It applied to the gray BP and forecast model to construct the influence factors of the agricultural machinery total power prediction model. It also apply to the Markov chain analysis method, to convert single prediction value into the interval value.In the view of agricultural machinery of Heilongjiang province abut the development level. The factors include four different angles such as the agricultural labor force, land scale of production, economic environment of agricultural machinery, agricultural machinery and equipment purchase quantity representative indexes. It has choosed 14 different factors which from the different aspects. Then, this paper to apply the Grey correlation method and the G-P algorithm choose seven main factors from 14 factors: farmers residential income, the district agricultural output, rural labor transfer rate, maximum proportion of crops, the rural collective economic fixed assets and put into production, agricultural labor force quality, the government financial input to agriculture. Finally, the application of the gray BP model to construct the seven factors variables prediction model and obtained gross agricultural machinery development target in the 5 years. The Markov computing will draw agricultural machinery total power conversion intointerval value, increase the reliability of the predictive results.