Abstract
In order to cope with the problems of high frequency and multiple causes of mountain fires, it is very important to adopt appropriate technologies to monitor and warn mountain fires through a few surface parameters. At the same time, the existing mobile terminal equipment is insufficient in image processing and storage capacity, and the energy consumption is high in the data transmission process, which requires calculation unloading. For this circumstance, first, a hierarchical discriminant analysis algorithm based on image feature extraction is introduced, and the image acquisition software in the mobile edge computing environment in the android system is designed and installed. Based on the remote sensing data, the land surface parameters of mountain fire are obtained, and the application of image recognition optimization algorithm in the mobile edge computing (MEC) environment is realized to solve the problem of transmission delay caused by traditional mobile cloud computing (MCC). Then, according to the forest fire sensitivity index, a forest fire early warning model based on MEC is designed. Finally, the image recognition response time and bandwidth consumption of the algorithm are studied, and the occurrence probability of mountain fire in Muli county, Liangshan prefecture, Sichuan is predicted. The results show that, compared with the MCC architecture, the algorithm presented in this study has shorter recognition and response time to different images in WiFi network environment; compared with MCC, MEC architecture can identify close users and transmit less data, which can effectively reduce the bandwidth pressure of the network. In most areas of Muli county, Liangshan prefecture, the probability of mountain fire is relatively low, the probability of mountain fire caused by non-surface environment is about 8 times that of the surface environment, and the influence of non-surface environment in the period of high incidence of mountain fire is lower than that in the period of low incidence. In conclusion, the surface parameters of MEC can be used to effectively predict the mountain fire and provide preventive measures in time.
Highlights
China’s mountainous area is wide with complex landform and rich resources, including forests, minerals, hydropower, tourism resources, etc., with significant productivity value
Combined with the HDA algorithm, the mountain fire warning model based on mobile edge computing (MEC) is designed to realize effective monitoring and early warning of mountain fire
In order to verify the performance of the hierarchical discriminant analysis algorithm, the hierarchical discriminant analysis is compared with other representative feature extraction algorithms MFA [21], LDNE [22], and DAG-DNE [23]
Summary
China’s mountainous area is wide with complex landform and rich resources, including forests, minerals, hydropower, tourism resources, etc., with significant productivity value. Adoption of image surface parameters in the construction of mountain fire warning method mountains are covered with vegetation and defoliated leaves. The occurrence frequency of mountain fires is high, and there is a certain concealment, there are certain limitations through ground monitoring and early warning. It is very important to adopt appropriate technology to monitor and early warning mountain fires [2]. In order to protect forest resources and people’s property, China has adopted a variety of means to monitor and warn mountain fires and achieved certain results [3]. How to monitor and warn mountain fires through a small number of surface parameters is a hot topic in current research [4]
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