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  4. Vol 6, Issue 2, 2022

Rational predictive analysis of fire rescue problems based on optimal path algorithm

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DOI: 10.23977/acss.2022.060211 | Downloads: 5 | Views: 671

Author(s)

Guodong Zhang 1, Yuxuan Sun 2

Affiliation(s)

1 School of Chemical Engineering, China University of Mining and Technology, Jiangsu, Xuzhou, 221116, China
2 College of Education Science, Jiangsu Normal University, Jiangsu, Xuzhou, 221116, China

Corresponding Author

Guodong Zhang

ABSTRACT

In this paper, based on the relevant data of fire rescue in five years in a certain place, through the establishment of mathematical model, aiming at the prediction of the number of fire officers and the reasonable planning of the fire station. And use Matlab and other software to establish the model for solving analysis. First of all, on the basis of the data in the first four years of fire rescue, in the month, using the classic grey prediction model to establish the forecast model of the fire rescue response times, and in the fifth year of data validation data set as a model, the accuracy and stability of the evaluation model, on this basis, for the future predict fire rescue response times of the year; Finally, according to the existing data, Dijkstra algorithm in the optimal path algorithm is used to solve the optimal distance of linear programming. By establishing the linear programming mathematical model to calculate the total cost forecast of fire station, the reasonable sequence of fire station construction area in the next 9 years is obtained. The results can provide reference for the construction planning of fire station.

KEYWORDS

Fire rescue, Gray prediction model, Optimal path algorithm, Linear regression

CITE THIS PAPER

Guodong Zhang, Yuxuan Sun, Rational predictive analysis of fire rescue problems based on optimal path algorithm. Advances in Computer, Signals and Systems (2022) Vol. 6: 56-64. DOI: http://dx.doi.org/10.23977/acss.2022.060211.

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[3] Rao Qingqiang, Lu Tiejun. Prediction of the scale of foreign contracted projects in China based on GM(1,1) and regression model [C].//2016 China Fire Protection Association Annual Meeting of Science and Technology. 2016:113-116.
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[5] Li Qiang, Lei Xiaojun, Hu Liangrong, et al. Application of linear programming layout Model in grape Planting [J]. Nanfang Agriculture Industry, 2020, 14 (9):183-185.
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