Abstract
For satisfying the network trend and intelligent demand of biopharmaceutical, we proposed the energy optimization consumption and management scheme of the drug green crowd data in the biological pharmaceutical cloud environment. First, the biopharmaceutical process are mapped to the cloud platform, which can not only adapt to the revolutionary changes in the way of biopharmaceutical research and but also build a network management platform for pharmaceutical research and development. Secondly, based on the green crowd, we reconstruct the organization structure of the cloud platform, production process, and value chain-driven portfolio, etc. Then, we divide the core of the cloud platform architecture into five substages. The green screening, reorganization, and crowd data processing will be completed by the cooperation of these stages. Finally, the drug green crowd architecture is embedded into the time domain conversion interface and the state transition interface. In addition, the state energy consumption model of the biological pharmaceutical cloud platform is constructed. The experimental results show that compared with the traditional task-driven energy consumption management mechanism, the proposed management mechanism can ensure higher throughput, higher effective flow rate, and higher effective energy consumption ratio.
Highlights
With the rapid development of computer network and parallel computing [1], how to apply them to the biopharmaceutical industry [2, 3] has become a research focus in improving the production efficiency of the biopharmaceutical industry [4] and reducing the cost of research and development
A method of production scheduling oriented to energy consumption optimization for process industry was proposed in article [14], which is based on selfadaptive differential evolution algorithm
We proposed the energy consumption optimization management mechanism with drug green crowd data for biological pharmaceutical cloud environment
Summary
With the rapid development of computer network and parallel computing [1], how to apply them to the biopharmaceutical industry [2, 3] has become a research focus in improving the production efficiency of the biopharmaceutical industry [4] and reducing the cost of research and development. In article [16], the optimization of energy consumption and environmental impacts of chickpea production was conducted using data envelopment analysis and multi-objective genetic algorithm techniques Based on these findings, we proposed the energy consumption optimization management mechanism with drug green crowd data for biological pharmaceutical cloud environment. (2)Experimental platform of biopharmaceutical technology (3)The intelligent management of biological pharmaceutical cloud platform (4)Cloud platform for the virtual environment of various diseases of experimental drug performance, such as cancer, genetic disease, cardiovascular disease, infection, and immunity. (5)Network cloud platform for all kinds of drugs and pharmaceutical process. 2.2 Drug green crowd architecture There are P servers in the biopharmaceutical cloud platform. According to Eq (4), stages S3 and S5 complete the crowd data processing
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