Improved Particle Swarm Optimization in Quantity Monitoring Model of Microbial Information

Monitoring microbiology is a new discipline that combines microbiology, mathematics and statistics. Its development direction is to monitor the growth and development of microorganisms under different environmental conditions by establishing some mathematical models. At present, scientists at home and abroad have been able to use some very mature monitoring methods, through computer man-machine communication, monitoring the growth of microorganisms under various environmental conditions. Monitoring microbial technology has been widely studied abroad. This paper studies the construction of improved particle swarm optimization algorithm in the quantity monitoring model of microbial information, which improves the quality and effect of quantity monitoring of microbial information. The number of colonies from day 1 to day 5 was 2,6,9,8,12 (CFU/cm2), the improved particle swarm optimization algorithm could effectively and accurately monitor the number of colonies. Through data testing, the improved particle swarm optimization algorithm in the quantity monitoring model of microbial information is highly efficient in the field of microbial information monitoring.