The flight area is divided into multiple uniformly - sized grids in two - dimensional or three - dimensional space, with each grid corresponding to an actual geographical area.

The assessment elements of grid attributes are detailed, including terrain height (such as whether it is a peak or valley), building distribution (location and height of high - rise buildings), electromagnetic interference sources (high - voltage lines, signal towers), and meteorological conditions (strong wind areas, rainfall areas). These pieces of information are transformed into attribute values of the grids through mathematical models, such as risk coefficients and passage difficulties.

The characteristics of the selected large language model architecture are introduced. For example, models based on the Transformer have powerful capabilities in language understanding and generation, and can handle complex and unstructured environmental information.

It is explained how the large language model converts the grid - based environmental information (encoded in text form) into an understanding of the flight space, constructing a spatial map that includes safe flight areas, passable paths, and flight restriction conditions.

Through statistical data from multiple experiments, the performance of different algorithms in various indicators is compared in the form of charts. For example, bar charts show the average path length and flight time of different algorithms; line charts present the trend of energy consumption with flight distance; scatter plots analyze the relationship between the number of collisions and environmental complexity.

Combined with actual flight trajectory maps, the differences in path planning between this algorithm and other algorithms in complex environments are intuitively displayed, highlighting the advantages of this algorithm in avoiding obstacles and optimizing paths.

High Adaptability: It can quickly adapt to different complex environments, whether it's urban canyons, mountain forests, or electromagnetic interference areas, and accurately plan safe paths.

Efficiency: Leveraging the powerful reasoning ability of large language models, it reduces unnecessary search space. Compared to traditional algorithms, it significantly shortens calculation time and improves path planning efficiency.

Accuracy: Taking into account various environmental factors and flight restrictions, the generated paths precisely avoid obstacles and no - fly zones, ensuring flight safety and significantly increasing the probability of successful mission completion.