Forest fire is an ecosystem regulating factor and affects the stability, renewal, and succession of forest ecosystems. However, uncontrolled forest fires can be harmful to the forest ecosystem and to the public at large. Although Yunnan, China is regarded as a global hotspot for forest fires, a general lack of understanding prevails there regarding the mechanisms and interactions that cause forest fires. A logistic regression model based on fire points in Yunnan detected by satellite in 2005–2019 was used to estimate how environmental factors in local areas affect forest fire events. The results show that meteorology is the dominant cause of the frequent forest fires in the area. Other factors of secondary importance are the daily minimum relative humidity and the daily maximum temperature. When using the logistic regression model based on the data of fire points in Yunnan over the period 2005–2019, the key threshold for the daily minimum relative humidity is 28.07% ± 11.85% and the daily maximum temperature is 21.23 ± 11.15 °C for a forest fire probability of 50%. In annual and monthly dynamic trends, the daily minimum relative humidity also plays a dominant role in which combustible substance load remains relatively stable from January to March, and the impact on forest fire becomes greater in April, May, and June, which plays a secondary role compared with the interannual climate. The maximum daily temperature ranks third in importance for forest fires. At the county level, minimum relative humidity and maximum temperature are the top two factors influencing forest fires, respectively. Meanwhile, the differences in forest fire points between counties correspond to the pathways of the two monsoons. This study applies quantitative expressions to reveal the important environmental factors and mechanisms that cause forest fires. The results provide a reference for monitoring and predicting forest fires.

Climate change is becoming increasingly severe. Today, several studies have found that climate change substantially influences the increasing number of forest fires. Regional climate models (RCMs) are currently a vital tool for climate forecasting in researching how to combat forest fires. As China’s forest fire area, Yunnan province has frequent forest fires that generate significant losses, so it is a crucial area for forest fire prevention in China. Therefore, this study uses meteorological observational data from 25 stations in Yunnan over the period 2004–2018 to compares and evaluates the Regional Climate Forecast Model (RegCM) and Weather Research and Forecasting model (WRF) in multiple dimensions. The optimal RCM is then determined for the forest area of Yunnan. The results show that the deviations of RegCM predictions from the spatial mean of the real temperature are less than 3°C, whereas the deviations of WRF are all greater than 3°C. In addition, the RegCM correlation coefficient exceeds 0.8, whereas the WRF correlation coefficient exceeds 0.75. In terms of precipitation, the deviation of RegCM predictions for the whole territory is less than 2 mm, whereas the overall deviation of WRF predictions is great. The correlation coefficient for RegCM and WRF are both less than 0.5, but the RegCM correlation coefficient exceeds that of the WRF. We thus conclude that RegCM is more suitable for predicting the climate of the forest area of Yunnan. This study also provides references for related climate forecasting and research into forest fire dynamics in general.

Under the influence of factors such as climate and land use changes, it is highly useful to investigate the spatio-temporal occurrence characteristics of forest fires using remote sensing data. This study utilized long-term remote sensing data on Active Fire Spots (AFSs), Burned Areas (BA), and Land Cover Types (LCT) in Yunnan Province. Through pixelization of AFSs, spatial extraction, and spatio-temporal clustering, 39,101 Forest Fire Events (FFEs) were identified. The results indicate that FFEs in Yunnan Province exhibit spatio-temporal clustering, with an overall annual fluctuating decline trend. The clustering is more pronounced in spring and winter, with a delayed temporal span. Over 88% of FFEs are concentrated in southern Yunnan, and the frequent occurrence areas have shifted eastward in recent years. This study deepens the understanding of the spatio-temporal dynamics of forest fires and provides a basis for regional forest fire management to promote sustainable development in related fields.

Aiming at the problems of leakage alarms and false alarms in fire detection systems, this paper explores the commonality and characteristics of its development process through in-depth analysis of typical fire detection and fire alarm modes at home and abroad and effectively summarizes the process of building fire alarms. In the case of fully grasping the principles of fire alarm and related policy protection, find out the appropriate development mode of fire alarm. At the same time, it can also study the perspectives of the government, the market, and the public, according to the needs of the market, it can be driven by industry and technological innovation can be used as a strong support for the development of fire alarm. Finally, the results of example analysis show that: using blockchain, this paper can effectively obtain high-precision fire detection and fire alarm target analysis results, and meet the needs of real-time analysis of fire detection and fire alarm analysis. Experiments show that the system can detect fires correctly and quickly, and identify interference sources such as fluorescent lamps and alcohol lamps. It has strong interference and good economic benefits.

In nature, plants have unique microstructures that have been adapted for engineering applications. In this study, we propose a simple and fast 3D printed anisotropic mobile robot based on the foxtail grass structure. Employing liquid bridge printing method, angled standing microfibers function as robot legs in a controllable manner of the velocity. Under alternative magnetic fields, the robot body vibrates to induce unidirectional locomotion. Experiments confirm the relation of the frequency of the magnetic fields with the velocity of the robot and identify an optimal tilting angle of legs for faster locomotion. In addition, LuGre friction model is applied to analyze the locomotion process and to simulate the behavior of the anisotropic structure of the legs, which demonstrates the significance of the locomotive mechanism of our bioinspired robot. Under alternative magnetic fields, our proposed multipede robot is remotely manipulated in enclosed and unstructured environments, which exhibits a significant advantage of rapid locomotion.

In recent years, the rise in global warming has significantly increased forest fires, affecting the environment and economy. Predicting forest fire dynamics under climate change is now a crucial research field. To address this need, this study focuses on the impact of climate change on forest fires, with a particular focus on the fire dynamics in Yunnan Province. This study utilizes the RegCM regional climate model and the Canadian Fire Weather Index (FWI) to simulate and analyze forest fire dynamics in Yunnan Province from 2019 to 2033 under three climate scenarios: RCP2.6, RCP4.5, and RCP8.5. Findings indicate climate change will increase temperatures, alter humidity and wind speed, and reduce precipitation in Yunnan, extending the fire danger period, especially under RCP8.5 scenarios. The FWI values rise across Yunnan, particularly in the west under RCP2.6 and RCP8.5. The study concludes that future carbon emissions correlate with these changes, leading to more frequent, longer, and severe forest fires. This research is vital for managing and preventing forest fires in Yunnan, a region prone to such disasters.