Fire Evacuation Sign Layout Optimization Based On Improved Genetic Algorithm

Due to the characteristics of fire disaster being sudden,changeable and rapid,China faces a severe challenge in fire safety with thousands of casualties and billions of direct economic losses due to fire disaster every year.In the process of fire evacuation,proper fire evacuation signs can effectively improve the efficiency of human evacuation and reduce casualties and loss of life and property.The existing research is often not objective and comprehensive enough in the evaluation of sign guidance effectiveness,and it is difficult to measure the guidance effect of sign layout accurately.In terms of algorithm optimization to solve the sign layout,existing algorithms including genetic algorithm often have the disadvantages of slow solution speed and low efficiency,which can hardly meet the requirements of efficient and accurate solution for evacuation sign layout optimization.Therefore,this paper focuses on the research of evacuation sign layout optimization based on improved genetic algorithm,completes the key technology breakthroughs such as sign guidance efficiency calculation based on sign information transmission model,improved genetic algorithm for fire evacuation sign layout optimization,develops the prototype system and carries out experimental analysis.The main research work and results of this paper are as follows.(1)A calculation method of layout guidance effectiveness based on the sign information transmission model is proposed.Firstly,the influence of factors such as view size,spacing size and obstacle blockage on the sign guidance process is analyzed;Then,the logo recognition probability is calculated taking into account the human eye characteristics;finally,the sign information transmission process is analyzed,the sign information transmission model is established,and the sign layout guidance effectiveness is calculated.(2)The calculation method of fire evacuation sign layout optimization with improved genetic algorithm is established.Firstly,a layout optimization model with sign guidance efficiency constraint is constructed;then the genetic algorithm is improved by adaptive crossover probability and variation probability to improve its solution performance;finally,the algorithm is speeded up by parallelization to achieve solution rapidly,so that the fire evacuation sign layout optimization results can be solved accurately,efficiently,and rapidly.(3)A prototype system was developed,and an experimental environment was built to conduct experimental analysis.The prototype system was developed based on Jet Brains Py Chram,and a subway platform model was built as the test environment to conduct case studies,including the experimental analysis of sign guidance efficiency improvement and the experimental analysis of speed improvement of improved genetic algorithm.The experimental results show that the speed of the sign guidance efficiency solution can be increased to 3 times of the speed before the optimization of the sign layout,and the guidance efficiency can be increased by 8.41%,which can effectively improve the guidance efficiency of the sign layout and provide a more efficient sign guidance layout,so that pedestrians can escape more rapidly.