## 3 Methods
### Fire Point Identification and Control  
The UAV path planning algorithm can be utilized to quickly locate a mountain forest fire point. However, to systematically detect forest fires, it is necessary to first locate the mountain forest fire point, then identify the fire severity and make control measures. Therefore, a mechanism for identifying and controlling mountain forest fire points is required. The artificial bee colony algorithm is a global optimization algorithm based on swarm intelligence that was proposed by Turkish scholar [PERSON] in 2005 [35]. The algorithm is inspired by the honey-harvesting behavior of bees in nature and finds the optimal solution to the problem by simulating the division of labor, cooperation, and information-sharing mechanisms of bees. In the ABC algorithm, bees are divided into three categories: hired bees, observation bees, and scout bees. The hired bees are responsible for searching for new honey sources in the solution space and updating their positions according to the quality of the honey sources. The observer bees choose between the honey sources found by the hired bees to decide whether to follow a certain honey source. When the scout bees encounter a food source, they will abandon current food sources and explore new food sources. The specific implementation steps of the ABC algorithm are as follows: Firstly, initialize each nectar source and set the relevant parameters, including the number of nectar sources, iterations, and so on. Secondly, assign a hired bee to each nectar source, search to generate new nectar sources according to Equation (11), and calculate the fitness. Thirdly, observation bees choose the nectar source according to the greedy strategy and calculate the probability of choosing the nectar source according to Equation (12). Fourthly, when all the hired bees and observers have searched the entire search space, the scout bees search for new possible solutions using Equation (13). Lastly, loop the above operations until the termination condition is met and output the optimal solution. \[v_{id}=x_{id}+\varphi\Big{(}x_{id}-x_{jd}\Big{)} \tag{11}\]  
\[pr_{i}=fit_{i}/\sum_{i=1}^{NP}fit_{i} \tag{12}\]  
\[v_{id}^{\prime}=x_{id}^{min}+r*\Big{(}x_{id}^{max}-x_{id}^{min}\Big{)} \tag{13}\]  
where \(v_{id}\) and \(v_{id}^{\prime}\) represent the new nectar sources, \(pr_{i}\) represents the probability of choosing the nectar source, \(pr_{i}\) represents the probability that the observation bee follows the nectar  
Figure 4: Procedure for fire point location. source, \(fit_{i}\) represents the fitness, \(NP\) represents the number of nectar sources, \(x^{max}_{id}\) and \(x^{min}_{id}\) represent the maximum and minimum values of nectar source locations, \(\varphi\) is a random number of [\(-\)1, 1] on the interval, and \(r\) is a random number of [0, 1] on the interval. A mechanism for mountain forest fire point identification and control can be set up based on the ABC algorithm. When arriving at the fire point location, the cluster head UAV can be defined as the hired bee, and the other member UAVs can be defined as the observation bees. UAVs carry relevant sensors to detect the fire through image processing methods. The cluster head UAV (hired bee) quickly reaches the center of the fire, and the other member UAVs (observation bees) detect the overall fire situation. The severity of the fire corresponds to the fitness of the nectar source in the ABC algorithm. Based on the image recognition algorithms, the situation of the fire (the fitness of the nectar source) can be identified. A mountain forest fire control mechanism can be set up to assign how many UAVs to fight fires according to the fire situation. The fire control mechanism corresponds to the observation of bees following the nectar source according to the probability provided by Equation (12) in the ABC algorithm. When the mechanism of fire identification and control is set up, the cluster head UAV turns to the scout bee to detect the next fire point.