Real-time sensor networks based on genetic algorithms application in the analysis of innovative data in cultural industry management

Abstract

Against the backdrop of sustained economic development and steady improvement of China's overall national strength, the cultural industry is in a good stage of development, but it also faces problems such as data complexity and difficulty in organizing. Based on this, we propose a management innovation data analysis system based on genetic algorithm and real-time technology of the Internet of Things, and apply this method to cultural industry management to analyze relevant management data. It was found that based on the improved genetic algorithm, it can achieve better optimization results with less computational complexity. Meanwhile, genetic algorithms based on penalty functions adopt a non-uniform mutation operator, which can accelerate convergence. Compared with existing genetic algorithms, the new genetic algorithm has lower computational complexity and lower system average overhead, greatly improving work efficiency and making its work cost lower. On this basis, combined with theoretical and empirical research results, provide theoretical and practical support for the sustainable development of the industry.

Keywords

• Genetic algorithm
• Real time internet of things
• Cultural industry management
• Innovative data