###### Abstract  
Aiming at the problems of the imprecise safety distance standards for low-altitude UAVs within complex low-altitude environments and the difficulty of managing heterogeneous vehicles, a UAV safety interval calibration method based on random heading is proposed. Firstly, a UAV clustering model based on K-Means++ was established for the performance characteristics of UAVs, using evaluation indexes such as contour coefficient, sum of squares of errors, Davidson-Bourdain index, etc. Then, combining this with the characteristics of UAVs with random heading, a UAV safety interval calibration model based on random heading was constructed, and the conflict probability and airspace utilization rate were determined and metered for UAV safety interval calibration. The experimental results showed that the profile coefficient, sum of squares of errors, and Davidson-Berding index of the iteratively optimized UAV clustering were optimized by 53.9%, 55.6%, and 46.6%, respectively, compared with the initial clustering results, and that the safe intervals calibrated in the experimental environment of a single category of UAVs were also applicable to the fusion airspace environment after validation. The research results can provide a theoretical basis and methodological support for the safety interval calibration of UAVs in low-altitude fusion operations. air traffic management; buckling; low altitude airspace; security interval; unmanned aerial vehicle 20251575010 trend indicates that the drone industry is developing rapidly and has broad application prospects. However, as the scale of drone operations expands, ensuring operational safety has become the main obstacle to the industry's further development. Therefore, studying drone operational safety, especially in the context of the rapid growth of the low-altitude economy, is of great practical significance. Currently, many scholars have carried out work to solve the operational safety issues of drones in terms of airspace management and drone safety. In airspace management, many scholars have conducted in-depth research on airspace discretization and digital expression of airspace. For instance, [PERSON] et al. [2] deeply analyzed the control issues of urban drones from the perspective of digital airspace and proposed effective solutions to key technologies. [PERSON] et al. [3] used the GeoSOT grid partitioning method to grid airspace information for easy representation of airspace data. [PERSON] et al. [4] proposed an airspace plane grid model and airspace spatial grid model to address the high density and heterogeneity of air traffic, providing scientific guidance for the sustainable development of airspace gridization and digital theory and applications. [PERSON] [5] used a discretization method to classify airspace hierarchically and quantitatively evaluated airspace spatiotemporal efficiency indicators. [PERSON] et al. [6] created a digital low-altitude airspace gridding representation measurement method to address the complex and difficult-to-represent nature of low-altitude flight environments. [PERSON] [7] proposed a detection method for conflicts between drone planning paths based on airspace grids. [PERSON] [8] proposed a new low-altitude flight conflict detection algorithm to address the strong timeliness and high computational complexity of conflict monitoring, transforming traditional trajectory traversal multi-variable conflict calculations into distributed grid database conflict state queries. In the field of drone safety, scholars from both domestic and international backgrounds have conducted research on drone conflicts and safety intervals. [PERSON] et al. [9] proposed a drone flight conflict model based on dynamic recognition of conflict occurrence to address the safety issues brought by the large-scale application and intensive flight of drones in the future. [PERSON] et al.