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Academic Journal of Engineering and Technology Science, 2021, 4(4); doi: 10.25236/AJETS.2021.040401.

Analysis of Ignition Points Based on Annealing Algorithm

Author(s)

Shuqi Chen1, Ruifeng Zhang2, Shengkai Zhao2, Chenxi Du2

Corresponding Author:
Chenxi Du
Affiliation(s)

1School of Statistics, Qufu Normal University, Jining, Shandong, 273165, China

2School of Mathematics and Science, Qufu Normal University, Jining, Shandong, 273165, China

Abstract

We summarize the distribution range of large-scale fires in Victoria over the years, and predict and explain that this model has good adaptability to the changes of fires. This paper proposes a model that predicts the cost of drones, based on screening all possible ignition points. In order to ensure economy and security, we use cluster analysis to divide the selected ignition points into 9 categories, introduce 0-1 variables, use model annealing algorithm to find the shortest path, iterative search, and obtain the global optimal solution of the optimization problem. We need to send a total SSA 162 UAVs, and SSA UAV equipment increased by 141, equipment costs increased by $1410000.

Keywords

ignition point, UAV, annealing algorithm, annealing algorithm

Cite This Paper

Shuqi Chen, Ruifeng Zhang, Shengkai Zhao, Chenxi Du. Analysis of Ignition Points Based on Annealing Algorithm. Academic Journal of Engineering and Technology Science (2021) Vol. 4, Issue 4: 1-5. https://doi.org/10.25236/AJETS.2021.040401.

References

[1] Australian Meteorological Agency: http://www.bom.gov.au/. 

[2] NASA: https://www.nasa.gov/.VIIRS 375 m Data from satellite instruments.

[3] Hu Yunquan, Guo Yaohuang. Course in Operational Research [M].]1 Version 5. Beijing: Tsinghua 

University Press, 2018: 201-213.

[4] Wang Xuemin. Application of Multivariate Statistical Analysis [M].]3 Shanghai: Shanghai University of Finance and Economics Press, 2017.8: 161-162.