Data Asset Management and Visualization Based on Intelligent Algorithm: Taking Power Equipment Data as An Example

Abstract

Data asset management is adequate in solving the problem of data silence and data idleness for enterprises. Through intelligent algorithms such as neural network, in-depth learning and block chain, and guided by business needs, it extracts, analyzes and visualizes the existing business precipitation data, and forms scattered and disordered data into valuable information to support the development of the company, so as to activate data assets. Taking the management data of electric power equipment as an example, this paper proposes a method of fusion of multiple intelligent control algorithms. The specific modules include the fusion of heterogeneous data; feature extraction of equipment asset management data based on machine learning; intelligent control of multi-objective optimization environment based on energy consumption data; BIM data visualization based on data classification-energy extraction-neural network (SVM-CART-SAE-DNN) algorithm fusion. The algorithm can effectively improve the efficiency of equipment management and enhance the security and economy of power infrastructure through intelligent control of equipment management.

Auto-Alignment Non-Contact Optical Measurement Method for Quantifying Wobble Error of a Theodolite on a Vehicle-Mounted Platform

“…Electro-optical theodolites are mainly used to record the parameters of the target trajectory, flight attitude and radiation characteristics [1,2]. In recent years, electro-optical theodolites have made considerable progress in application fields, tracking control algorithm and observation capabilities [3][4][5][6][7][8][9][10][11][12][13]. At the same time, the measurement method has also undergone important changes, gradually changing from a fixed foundation measurement mode into a vehicle-mounted measurement mode.…”