TY - JOUR
TI - Research on management strategy of coordination behavior of task conflicts in in-service thermal power unit operation based on big data modeling
AU - Liu Ying
AU - Hu Long Ying
JN - Thermal Science
PY - 2019
VL - 23
IS - 5
SP - 2703
EP - 2711
PT - Article
AB - With the development of the internet and information technology, the in-service thermal power unit is facing more challenges, and the innovation of the operation and management mode of the in-service thermal power unit is urgent and necessary. From the perspective of work conflict, this paper constructs a multi-objective genetic algorithm, which introduces big data modelling technology into the management innovation of in-service thermal power units. The algorithm solves the relationship between various operating entities in active thermal power units through functions. In order to get the optimal solution for vehicle distribution. Firstly, the contingency theory is introduced into the innovative design scheme of the in-service thermal pow¬er unit information system to optimize the management decision-making distribution path in the big data environment, design the multi-objective genetic algorithm steps, construct the non-dominated set, and combine the target cross-variation operations. The genetic sub-categories are jointly derived, and then the relationship between the parties in the management and decision-making innovation management activities of the in-service thermal power units is solved. The experimental results show that the shortest running time of the algorithm during the experimental operation is 0.56 seconds, and the longest running time is 2.48 seconds. The average running time in the whole process is less than 1 second, which meets the actual demand. The genetic algorithm can help the in-service thermal power unit. Reasonable arrangements for managing the delivery route of the decision-making fleet. The research in this paper has implications for the management innovation of in-service thermal power units in the information environment, and further expands the application field of big data modelling, which has practical significance.