Application of Big Data Analysis in Cost Control of Marine Fishery Breeding

Abstract

In order to improve the stability of the cost control of marine fishery culture, a method of controlling the cost of marine fishery culture based on big data analysis algorithm was proposed. We establish the cost analysis model of marine fishery, use the big data correlation analysis method to conduct distributed mining on the cost characteristics of marine fishery, deeply grasp the relevance of data characteristics, use the information fusion method to build the constraint parameter analysis model of aquaculture cost operation, limit the amount of calculation, and use the adaptive neural network weighted training method to adaptively optimize the cost control to avoid falling into local optimization. The objective model of aquaculture cost control is established, and the cost constraint is carried out by the parameter optimization method. The statistical feature quantity of marine fishery cost control is obtained, and the cost control of marine fishery is realized by the feature recombination method. The simulation results show that this method is more stable in the cost control of marine fishery culture and improves the adaptability of the cost control of marine fishery culture.