Big Data Analysis Technology for Artificial Intelligence Decision-Making Platform Construction and Application

With the development of the information age, it is the opportunity and challenge for enterprises to apply big data analysis technology to make decisions and better solve the major problems of global and sustainable development. Decision-making is crucial for enterprises, and a correct decision can improve the development potential and competitiveness of enterprises. However, traditional decision models have certain limitations, and it is difficult to handle the massive, polymorphic, and changing decision data. To address these problems, we propose a combination of temporal data anomaly detection and width learning big data analysis technology for building an intelligent decision platform to assist enterprises to better solve major decision problems. First, the goal of time series data anomaly detection is to correctly determine whether the data points in each moment of the time series are abnormal. The variation of time series data is affected by various factors, and the fluctuation of data caused by some nonanomalous factors can increase the difficulty of anomaly detection. To address the above problems, we propose an anomaly detection algorithm for time series data based on time series decomposition method. In this algorithm, the time series are decomposed by STL method and HP filtering method according to whether the time series is periodic or not, and then the components of the time series that are relevant to anomaly detection are retained and anomaly detection is performed on the processed time series using a cyclic model. Then, based on the call text and signaling data in enterprise decision-making, an improved width learning model called coding width learning is proposed. The coding width learning model is used to identify decision problems and make comprehensive decisions to improve the model training time and the accuracy of identification. At the same time, an integrated learning method with parallelized training is proposed for width learning in order to further improve the efficiency of coding width learning and prevent the potential memory explosion problem. Finally, the experimental results show that the proposed anomaly detection method effectively improves the anomaly detection performance of the model, and its performance is better than the existing time series anomaly detection algorithm based on variation self-encoder; combined with the improved width learning model, it can make fast decisions and analysis without using scenarios and targets, and help and guide the work of related personnel.