Data Analysis and Cloud Computing of Power Grid Infrastructure Projects under the Background of Informatization

With the continuous development of the economy, the scale of the power grid continues to expand, the social power consumption continues to increase, and the amount of power grid investment continues to increase. The reasonable allocation of power grid investment has also become an important issue. In the context of informatization, scientific and accurate analysis of power grid infrastructure project (PGIP) investment data is conducive to optimizing PGIP management investment and decision-making. The purpose of this article is to study the analysis of the investment data of PGIP under the background of informatization. Starting from the background of informatization, this article takes PGIP investment as the research object, and analyzes the difficulties and shortcomings of traditional PGIP investment methods from the perspective of data analysis. This article combines the existing problems of PGIP investment, and proposes corresponding evaluation and management methods for the improvement of PGIP investment benefits. In this paper, a regression tree prediction model is established to predict the investment amount of PGIP, and through experimental research. The experimental results show that the investment forecasts of the five subsidiaries' infrastructure projects in 2019 are 0.1707, 0.1642, 0.0821, 0.0243, and 0.0462 respectively; the true values are 0.1485, 0.1474, 0.0719, 0.0238, and 0.0445. It can be seen that the regression tree prediction model proposed in this study has high prediction accuracy.