The use of artificial intelligence technology to optimize the coordination ability of drone group actions is essential to improve the efficiency of military reconnaissance missions. The following are some specific methods： 1. **Intelligent task assignment** -**Target priority evaluation**: Use artificial intelligence algorithms to evaluate the priority of targets in the reconnaissance area. For example, by analyzing the strategic value of the target, the degree of threat, the frequency of activities and other factors, each target is assigned a corresponding priority weight. -**Mission matching**: According to the performance characteristics, load capacity and current location of the uav, the target is intelligently matched with the appropriate UAV. For example, for important targets with high-resolution reconnaissance requirements, drones with high-definition cameras and easy flight routes are assigned to approach the target. 2. **Path planning optimization** -**Environmental perception and modeling**: With the help of artificial intelligence environmental perception technologies, such as deep learning target detection and recognition algorithms, drones can construct environmental models of reconnaissance areas in real time, including topography, landforms, obstacle distribution and other information. -**Dynamic path planning**: Combining environmental models and mission objectives, plan the optimal flight path for each UAV. For example, when encountering sudden obstacles, the drone can quickly re-route, avoid obstacles and continue to perform tasks, while ensuring that the overall reconnaissance efficiency is not greatly affected. 3. **Communication and collaboration** -**Self-organizing network communication**: Self-organizing network communication between drones is realized through artificial intelligence technology. Drones can automatically establish stable communication links based on each other's location and signal strength to ensure real-time transmission and sharing of information. -**Collaborative decision-making**: During the execution of tasks, collaborative decision-making can be made between drones based on artificial intelligence algorithms. For example, when a certain drone finds an important target, it can automatically notify other nearby drones to adjust their flight routes, and cooperate in more comprehensive reconnaissance to avoid blind spots in reconnaissance. 4. **Group behavior control** -**Simulation bird group algorithm**: Drawing on the behavior patterns of birds, fish and other groups in nature, artificial intelligence is used to simulate group behavior algorithms. Enable drone groups to flexibly adjust their formation like a flock of birds to adapt to different reconnaissance scenarios and mission needs. -**Situational awareness and collaborative adjustment**: Each drone has a certain situational awareness ability and can monitor the location and status of surrounding drones in real time. Through artificial intelligence algorithms, drone groups can automatically adjust the flight speed, spacing and other parameters according to the overall situation to maintain the best group action state. 5. **Real-time data analysis and processing** - * *Intelligent analysis of images and data**: Use artificial intelligence technologies such as deep learning to analyze and process images and data collected by drones in real time. For example, quickly identify targets in images, extract key information, classify and label them, and provide accurate and timely intelligence support for command decision-making. -**Anomaly detection and early warning**: Through the learning and analysis of a large amount of historical data and real-time data, an abnormal behavior model is established. When there is an abnormal situation in the operation of a drone group, such as a drone deviating from a predetermined route, communication interruption, etc., an early warning can be issued in a timely manner so that the operator can take appropriate measures.