###### Abstract
[10] proposed a safety risk assessment and three-dimensional path planning method for low-altitude drones in complex urban environments, where flight safety and efficiency need significant improvement. [PERSON] [11] proposed a new operational framework for four-dimensional (4D) trajectory conflict management in the context of free-flight airspace. [PERSON] et al. [12] established a collision risk assessment model between drones and transport aircraft throughout the full drone system. [PERSON] [13] used geometric methods to determine the collision area of drones and established a dynamic collision area mathematical model. [PERSON] [14] improved the Event model with ADS-B and studied the regulation intervals of drones flying in different airspaces. Figure 1: Drone industry development trend chart. [PERSON] [15] set collision templates targeting manned aircraft and established a safety flight risk assessment model for drones, to avoid intruding into manned aircraft's isolated airspace. [PERSON] [16] established a drone flight risk assessment model based on fuzzy cognitive maps. [PERSON] [17] established a cylindrical drone collision risk model in non-isolated airspace by referring to the civil aviation passenger aircraft conflict zone theory. [PERSON] [18] analyzed the collision risk between drones and manned aircraft in shared airspace by improving the existing civil aviation transport aircraft collision risk model. [PERSON] [19] constructed a collision model for large drones based on four-dimensional coordinates, analyzing the flight collision probability of large drones by collecting and analyzing the three-dimensional coordinates, velocity, and other information of large drones and intruder aircraft. [PERSON] [20] proposed a framework and method for non-isolated airspace design and planning based on conflict risks, along with research methods for drone collision risks in non-isolated airspace. [PERSON] [21] established a cylindrical static protection zone model for drones to detect flight conflict trends and near-air collisions between drones and intruders, and proposed a three-dimensional dynamic collision avoidance area modeling method based on the emergency collision avoidance trajectories of drones. [PERSON] [22] established a minimum interval algorithm for the phased ballistic deviations of combat drones, which can deduce the minimum interval between different aircraft types according to side wind effects and drone characteristics at different flight stages. At present, research in the field of drone operations encounters several significant challenges. Firstly, the lack of a unified definition for drone categories both domestically and internationally has resulted in varied classification standards. These standards are predominantly based on factors such as application scenarios and drone weight, but there remains a considerable gap in classifications related to the flight safety performance of drones. Secondly, the demand for effective low-altitude airspace management is on the rise, necessitating adaptations for the joint operation of various types of drones. However, existing research has predominantly focused on maintaining safety intervals among drones within the same category, while insufficient attention has been paid to the safety intervals between different drone categories. This oversight limits the efficiency of integrated operations in low-altitude airspace, where diverse drone types are increasingly utilized. To address these issues, this paper highlights the inadequacies of current drone classification standards in low-altitude environments and the lack of universal safety intervals. To this end, we employed the K-Means++ algorithm to cluster drones operating in low-altitude airspace based on four key feature indicators: maximum takeoff weight, maximum flight altitude, maximum vertical speed, and maximum horizontal speed. This clustering approach facilitated the development of a classification model specifically tailored for low-altitude drones.