Method for intelligent scheduling and risk prevention and control of apron unpowered equipment based on situation awareness

Abstract

The present application relates to the technical field of equipment scheduling and risk prevention and control, in particular to a method for intelligent scheduling and risk prevention and control of apron non-powered equipment based on situation awareness, which obtains the position, speed and heading of non-powered equipment, combines the task period and aircraft weather data update state, analyzes the abnormal area formed by the equipment trajectory deviation, superimposes the aircraft activity to identify the conflict risk, screens high-risk equipment, and generates apron scheduling path instructions according to the environment and task position. The present application dynamically depicts the running state by obtaining the equipment coordinates, speed and heading, combining the task type and support period, identifies the abnormal area through trajectory deviation and direction change, identifies the cross space-time range through the superposition of path and aircraft trajectory, screens the key equipment through the task influence degree, adjusts the driving route by combining the task position and environmental elements, and reduces the path conflict probability.

Description

Technical Field

[0001] This invention relates to the field of equipment scheduling and risk control technology, and in particular to a method for intelligent scheduling and risk control of non-powered equipment on aprons based on situational awareness. Background Technology

[0002] The field of equipment scheduling and risk control technology mainly involves core aspects such as task allocation, path planning, status monitoring, and safety management of operating equipment, and is widely used in high-density operation scenarios such as airports and ports. This field achieves optimized scheduling and risk prediction of equipment operation through multi-source information collection and environmental perception. The system includes data acquisition, situation assessment, decision control, and risk intervention, aiming to improve operational efficiency and reduce potential safety hazards.

[0003] Traditional intelligent scheduling and risk control methods for non-powered apron equipment refer to task scheduling and risk management approaches for equipment without self-driving capabilities, such as baggage trolleys, within airports. These methods primarily rely on manual command, fixed route planning, or static rule-based scheduling systems. Task priorities are typically determined manually, path planning often uses static graph models to calculate the shortest path, equipment status is obtained through manual inspections or basic sensors, and risk control measures include setting vehicle spacing, delineating warning zones, and manual intervention. These methods struggle to achieve real-time response to the dynamic apron environment and collaborative scheduling among multiple devices, exhibiting problems such as scheduling lag and insufficient risk identification, thus limiting the overall intelligence and safety improvements in operation.

[0004] Existing methods rely on manual judgment of task prioritization, lacking dynamic adjustment criteria and resulting in untimely equipment operation scheduling responses. Path planning is based on static graph models, failing to consider environmental disturbances and making the planned paths insufficiently adaptable to unforeseen circumstances. Status monitoring depends on manual inspections, resulting in low sensing frequency and difficulty in detecting dynamic issues such as trajectory deviations. Risk control only defines fixed areas and vehicle spacing rules, lacking real-time intervention capabilities and exhibiting delayed risk identification. In scenarios with multiple devices operating densely, path overlaps and spatial conflicts are easily overlooked, causing scheduling imbalances and operational interference. Summary of the Invention

[0005] To address the technical problems existing in the prior art, embodiments of the present invention provide a method for intelligent scheduling and risk prevention of unpowered apron equipment based on situational awareness. The technical solution is as follows: A situational awareness-based intelligent scheduling and risk prevention method for non-powered apron equipment includes the following steps: S1: Obtain the position, speed and heading angle of the non-powered equipment on the apron, combine the equipment task type and flight support plan time period, collect aircraft trajectory and meteorological data, update the equipment spatial coordinates, speed and heading changes, combine the apron environmental monitoring status to ensure accurate reflection of the motion status, and form a set of equipment motion status; S2: Call the concentrated trajectory information of the device's motion state, identify the device's offset and directional changes within a time period, filter out trajectory deviations based on task requirements, analyze the position and direction changes of the trajectory, identify abnormal areas, and form a set of abnormal trajectory areas for the device. S3: Call the concentrated trajectory of the abnormal trajectory area of ​​the equipment, combine it with the aircraft trajectory, overlap the detection of the equipment path and the aircraft activity area, identify the intersection of the two, analyze the risks existing in the intersection time period and space, mark the conflict area, and form a set of equipment conflict risk area identifiers; S4: Based on the equipment number and trajectory in the equipment conflict risk area identifier set, examine the risk performance of the equipment in the conflict area, combine the importance and priority of the task, identify the equipment that affects the operation, extract high-risk equipment, and form a high-risk equipment identifier set; S5: Based on the high-risk equipment identification group equipment number, obtain the current location of the equipment and the target task location, combine the equipment travel path and apron environment, adjust the equipment travel route, guide the equipment through a suitable path, confirm the path selection, and output the apron scheduling path instruction set.

[0006] As a further embodiment of the present invention, the equipment motion state set includes equipment spatial location information, speed change data, heading angle change parameters, and status awareness tags; the equipment abnormal trajectory area set includes trajectory deviation segments, direction abnormal segments, abnormal area coordinates, and abnormal type markers; the equipment conflict risk area identifier set includes the intersection area of ​​equipment and aircraft paths, conflict occurrence time period, conflict spatial range, and risk level identifiers; the high-risk equipment identifier set includes equipment risk level, mission impact degree, priority handling flag, and equipment identification code; and the apron scheduling path instruction set includes path adjustment schemes, driving path codes, guidance instruction information, and path confirmation parameters.

[0007] As a further aspect of the present invention, the step of obtaining the device motion state set is as follows: S101: Acquire the real-time spatial coordinates, movement speed and heading angle of the non-powered equipment on the apron, collect the scheduling information of the equipment task type and the corresponding time period in the flight support plan, collect the aircraft operation trajectory and the wind speed, wind direction and temperature changes in the weather, and align various types of data according to a unified time to obtain equipment operation related information. S102: Based on the equipment operation association information, call the coordinates in the equipment movement trajectory and the flight support scheduling time point to extract the continuous movement path, calculate the speed change within the continuous time period, and calculate the movement direction change in combination with the angle between the heading angle and the wind direction. Based on the coordinate movement range and the degree of direction change, the path offset performance is obtained. S103: Based on the path offset performance, call the wind speed and direction sequence and the equipment speed change sequence in the meteorology, compare the change trends of the two in each time period, screen out the intervals where the wind speed change is significantly stronger than the equipment speed change, and combine the magnitude of the direction change in the corresponding time period to analyze the stability performance and obtain the equipment motion state set.

[0008] As a further aspect of the present invention, the step of obtaining the abnormal trajectory region set of the device is as follows: S201: Call the trajectory coordinates, moving speed and heading angle data of the device motion state set, extract continuous position segments and direction change sequences, identify unstable direction segments based on the difference in moving direction between adjacent positions and the continuous change state, and obtain the trajectory direction change performance. S202: Based on the trajectory direction change performance, call the equipment task type and shift time data, compare the direction change segment with the corresponding time period of the task requirement, screen out the equipment trajectory segments with trajectory abnormalities during task execution, and obtain the trajectory deviation interval sequence. S203: Based on the trajectory deviation interval sequence, call the spatial position continuity and movement speed change of the corresponding trajectory segment, identify the trajectory interruption or movement change area, and extract the distribution location of the trajectory abnormal part in combination with its direction change content to obtain the equipment abnormal trajectory area set.

[0009] As a further aspect of the present invention, the step of obtaining the equipment conflict risk area identifier set is as follows: S301: Call the coordinates and corresponding time period data of each trajectory segment in the abnormal trajectory area set of the device, obtain the spatial position and activity time period of the aircraft's operation trajectory, perform position range overlap detection on the two types of trajectories, extract the trajectory segments that have spatial intersection in the same time period, and obtain the set of trajectory overlap segments. S302: Based on the set of overlapping trajectory segments, call the continuous path changes of the equipment trajectory and the aircraft trajectory within the overlapping segment, calculate the movement direction, movement interval and spatial proximity, and extract trajectory segments that are close in both time and space to obtain conflict proximity performance data. S303: Based on the conflict proximity performance data, extract the directional consistency change, path intersection range and spatial proximity duration within each segment, determine whether it meets the spatial conflict state, and mark the corresponding positions of the spatial segments that meet the conflict characteristics to obtain the equipment conflict risk area identifier set.

[0010] As a further aspect of the present invention, the step of obtaining the high-risk device identifier set is as follows: S401: Based on the device number corresponding to the device conflict risk area identifier set, extract the trajectory sequence and time segment of each device in the conflict area, determine whether there are any abnormalities in the movement direction, spatial extension and heading continuity of the trajectory in the area, and obtain the trajectory risk performance characteristics. S402: Based on the trajectory risk performance characteristics, call the task number and execution level in the task information, compare the task type of the corresponding device in the trajectory, mark the device causing interference in the trajectory according to the priority of task level, and obtain the conflict impact level distinction result. S403: Based on the conflict impact level differentiation results, extract the device numbers with high task level, concentrated trajectory anomalies and wide trajectory impact range, accumulate the number of conflicts that occur in their spatial locations, identify high-frequency conflict behaviors in combination with trajectory dwell status, and obtain a set of high-risk device identifiers.

[0011] As a further aspect of the present invention, the step of obtaining the apron scheduling path instruction set is as follows: S501: Based on the device number in the high-risk device identifier set, extract the current location coordinates and target coordinates of the corresponding task location for each device, call the task number information of the device, determine the area and time requirements to be reached by the task, and perform spatial path retrieval by combining the starting position of the device and the task position to obtain the task path planning point set. S502: Based on the task path planning point set, call the passable area, prohibited area and currently occupied path data in the apron, continuously filter the feasible passage segments of the equipment, identify the usage status of each segment at the current time, determine whether the path has the conditions for passage, and obtain the path passable status combination. S503: Based on the combination of the path passability status, comprehensively compare the passability status, length order, and intersection risk of each path segment, identify the continuous path segment with the least impact on the equipment during its journey from the current location to the task point, and output the driving guidance path for each device to obtain the apron scheduling path instruction set.

[0012] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: In this invention, the spatial coordinates, speed, and heading angle of the equipment are continuously acquired, and combined with the flight support period and mission attributes, to dynamically characterize the equipment's operating status; the correlation analysis of trajectory offset and direction change forms the basis for determining abnormal activity areas; the spatial superposition of the equipment path and the aircraft's activity trajectory identifies the spatiotemporal range with overlapping relationships; the level of equipment concern is distinguished by the degree of mission impact, focusing on key equipment; and the probability of path intersections and operational conflicts is reduced by adjusting the driving route in combination with mission location and environmental factors. Attached Figure Description

[0013] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a flowchart illustrating the process of obtaining the motion state set of the device according to the present invention. Figure 3 This is a flowchart illustrating the process of obtaining the abnormal trajectory region set of the device according to the present invention. Figure 4 This is a flowchart illustrating the process of obtaining the device conflict risk area identifier set according to the present invention. Figure 5 This is a flowchart illustrating the process of obtaining the high-risk device identifier set in this invention. Figure 6 This is a flowchart illustrating the process of obtaining the apron scheduling path instruction set of the present invention. Detailed Implementation

[0014] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0015] refer to Figures 1 to 6 A situational awareness-based intelligent scheduling and risk prevention method for non-powered apron equipment includes the following steps: S1: Acquire the location information, speed and heading angle of the non-powered equipment on the apron, combine the equipment task type and time period in the flight support plan, collect aircraft trajectory and meteorological data, update the spatial coordinates, speed and heading changes of the equipment in real time, monitor the equipment status based on the environmental information in the apron, ensure the accurate reflection of the equipment movement status, and form a set of equipment movement status. S2: Extract the trajectory information of the device from the device motion status collection, identify the device's offset and directional changes within a time period, filter out devices with trajectory deviations based on task requirements, examine abnormal behavior in trajectory segments, analyze the deviation of the device trajectory, identify the areas of trajectory abnormality, and form a set of abnormal trajectory areas of the device. S3: Extract the trajectory from the abnormal trajectory area of ​​the equipment, combine it with the aircraft trajectory, overlap the equipment path and the aircraft activity area, identify the intersection of the two, analyze whether there are potential risks in the intersection time period and space, mark the conflict area, and form a set of equipment conflict risk area identifiers; S4: Based on the equipment numbers and related trajectories in the equipment conflict risk area identification set, examine the potential risks of equipment in the conflict area, identify the equipment that causes the impact according to the importance and priority of the equipment tasks, identify high-risk equipment, and form a high-risk equipment identification set; S5: Based on the equipment number in the high-risk equipment identification set, obtain the current location of the equipment and the target task location, combine the equipment's travel path and apron environment, adjust the equipment's travel route, guide the equipment through a suitable path, confirm the path selection, and output the apron scheduling path instruction set.

[0016] The equipment motion status set includes equipment spatial location information, speed change data, heading angle change parameters, and status awareness tags. The equipment abnormal trajectory area set includes trajectory deviation segments, directional abnormal segments, abnormal area coordinates, and abnormal type markers. The equipment conflict risk area identifier set includes the intersection area of ​​equipment and aircraft paths, conflict occurrence time period, conflict spatial range, and risk level identifiers. The high-risk equipment identifier set includes equipment risk level, mission impact degree, priority handling flag, and equipment identification code. The apron dispatch path instruction set includes path adjustment schemes, driving path codes, guidance instruction information, and path confirmation parameters.

[0017] Please see Figure 2 The steps for obtaining the device motion state set are as follows: S101: Acquire the real-time spatial coordinates, movement speed and heading angle of the non-powered equipment on the apron, collect the scheduling information of the equipment task type and the corresponding time period in the flight support plan, collect the aircraft operation trajectory and the wind speed, wind direction and temperature changes in the weather, and align various types of data according to a unified time to obtain equipment operation related information. Based on a pre-defined communication protocol, the connection channels with various sensors and databases are initialized. Utilizing a dual-frequency RTK-GNSS positioning terminal deployed on top of unpowered apron equipment (such as the baggage tractor numbered Dev-08), the latitude and longitude information of the equipment in the WGS84 coordinate system is captured in real time at a high sampling frequency of 5Hz. This information is then instantly converted into spatial coordinates in the local Cartesian coordinate system of the apron. Simultaneously, a high-sensitivity six-axis IMU inertial measurement unit onboard the device reads instantaneous longitudinal acceleration, lateral acceleration, three-axis angular velocity, and heading angle data to construct a complete digital twin mapping of the equipment's physical motion. Meanwhile, the data processing center accesses the airport AODB operation database and ADS-B aircraft surveillance data stream in parallel via a high-bandwidth fiber optic network, locks onto the current monitoring time window, retrieves all task orders associated with the equipment ID within that time period from the AODB, and parses out fine-grained business attributes, including task type, etc. The system provides service flight numbers, planned execution time windows, and target parking position numbers. It also demodulates the ICAO addresses, real-time 3D coordinates, ground speed vectors, track angles, and flight call sign information of all aircraft within the apron area from the ADS-B data stream. Simultaneously, the meteorological data interface obtains local wind speed, wind direction, temperature, and visibility parameters from the apron automatic weather station at a frequency of 1Hz. Given the significant differences in sampling frequencies between multi-source heterogeneous data (such as the 200-millisecond interval of GNSS and the 1-second interval of meteorological data), the system uses a high-frequency GNSS timestamp as a unified master clock reference. It performs linear interpolation or zero-order alignment-preserving processing based on time gradients on low-frequency meteorological environmental data and operational status data. The system encapsulates the equipment physical motion vectors, associated task operational attributes, and external meteorological environmental parameters at the same microsecond moment into structured multi-dimensional data frames, thereby obtaining equipment operation association information that includes the equipment's own status, task background, and environmental constraints.

[0018] S102: Based on the equipment operation association information, call the coordinates in the equipment movement trajectory and the flight support schedule time point to extract the continuous movement path, calculate the speed change within the continuous period, and calculate the movement direction change in combination with the angle between the heading angle and the wind direction. Based on the coordinate movement range and the degree of direction change, the path offset performance is obtained. The system retrieves a sequence of continuous data frames within a specific time period from the equipment's operational information. Based on the equipment's current instantaneous velocity scalar and heading angle, a refined vehicle kinematic model is constructed. A differential recursive algorithm is used to deduce the theoretically predicted coordinates of the equipment at the next sampling moment. These theoretically predicted coordinates are then compared with the Euclidean distance of the actual physical coordinates collected by the GNSS terminal to quantify the path deviation. In strong crosswind conditions, the system finds a significant lateral deviation between the actual trajectory and the theoretically predicted trajectory; for example, calculations show a deviation of 0.206 meters. The system synchronizes... By combining high-resolution meteorological environmental data, the system calculates the relative angle between the equipment's heading angle vector and the real-time wind direction vector, accurately identifying whether the equipment is currently in a specific aerodynamic state affected by crosswinds. For example, it determines whether the angle is in the strong crosswind range of 60 to 120 degrees. If the calculated path offset exceeds the preset environmental interference judgment benchmark value, the system will further combine the angular velocity change rate of the equipment's heading and sideslip angle data during that period for weighted evaluation, comprehensively quantifying the stability of the equipment's trajectory under specific wind field conditions, and generating a path offset performance that includes offset magnitude, directional characteristics, and environmental correlation factors.

[0019] S103: Based on the path offset performance, call the wind speed and direction sequence and the equipment speed change sequence in the meteorology, compare the change trends of the two in each time period, screen out the intervals where the wind speed change is significantly stronger than the equipment speed change, and combine the magnitude of the direction change in the corresponding time period to analyze the stability performance and obtain the equipment motion state set.

[0020] Based on path deviation performance, the system delves into the causal coupling between environmental factors and equipment motion states. By constructing a fixed-length sliding time window (e.g., a 5-second window containing 25 sampling points), it extracts the environmental wind speed time series and equipment speed change time series within the window. Mathematical statistics are used to calculate the standard deviation of wind speed fluctuations and the standard deviation of equipment speed stability within the window to identify sudden gust disturbances and uninstructed equipment speed fluctuations. When the system detects an average wind speed exceeding 8 m / s accompanied by significant gust fluctuations, and the standard deviation of equipment speed exceeds 0.5 m / s, the system further performs lag correlation analysis of the multidimensional time series to detect the synchronicity or lag response characteristics of the wind speed change moment and the equipment speed fluctuation moment on the time axis, and calculates the Pearson correlation coefficient between the two sets of sequences. If and only if the correlation between the two in time is significant and the mean rate of change of the equipment heading angle exceeds 5 degrees per second, the system determines that the time interval is a wind-induced instability interval, and packages and marks the corresponding trajectory coordinate sequence, velocity vector set, and meteorological environmental parameters as an unstable sample with typical environmental disturbance characteristics, thereby obtaining the equipment motion state set.

[0021] Please see Figure 3The steps for obtaining the set of abnormal trajectory regions of the equipment are as follows: S201: Call the trajectory coordinates, speed and heading angle data of the device motion state set, extract continuous position segments and direction change sequences, identify unstable direction segments based on the difference in movement direction between adjacent positions and the continuous change state, and obtain the trajectory direction change performance. The system uses sample data from the device's motion state set to perform micro-geometric analysis on the trajectory morphology, extracts a high-density trajectory coordinate sequence, and uses a differential geometric algorithm to calculate the actual trajectory tangential angle formed by adjacent trajectory points. This angle is then compared point by point with the heading angle recorded by the device's IMU to calculate the angular deviation value between the two to assess the degree of vehicle sideslip. Based on a set 10-degree directional stability threshold, the deviation sequence is continuously scanned for detection. For example, in a crosswind interference event, the system detects through geometric calculation that although the device is trying to maintain a due east driving posture, the strong crosswind pushes its actual trajectory tangential angle to drift significantly to the southeast, with a deviation angle of 15 degrees. This deviation state exists continuously on the time axis for more than 0.6 seconds (i.e., more than 3 consecutive sampling points). The system then uses a sliding window to lock the start and end timestamps of this continuous deviation segment and calculates the average and maximum deviation angles within this segment, thereby obtaining the trajectory direction change performance characterizing the device's driving posture instability.

[0022] S202: Based on the trajectory direction change performance, call the equipment task type and shift time data, compare the direction change segment with the corresponding time period of the task requirement, screen out the equipment trajectory segments with trajectory abnormalities during task execution, and obtain the trajectory deviation interval sequence. The system performs time-domain mapping and logical comparison between the identified trajectory direction changes and the associated task information of the equipment. Based on airport apron operation specifications, it determines the specific task execution stage of the equipment at the time of the anomaly (such as the aircraft docking service stage with extremely high precision requirements, the long-distance travel stage between aircraft stands, or the waiting stage). It then calls the deviation tolerance parameter corresponding to that stage for differentiated judgment to distinguish between normal maneuvering and non-compliant anomalies. In the current example, the system identifies that the equipment is in the empty travel stage before the task. The maximum allowable directional deviation threshold for this stage is 15 degrees, and the measured trajectory deviation value has reached this critical value. If such a deviation occurs during the aircraft docking stage, it is considered a serious Level 1 safety anomaly. Based on the comparison between the measured deviation value and the tolerance threshold of the current task stage, the system calculates the deviation ratio, filters out all trajectory segments that exceed the safety tolerance range, and records in detail their duration, deviation magnitude, and associated task number, thereby obtaining a trajectory deviation interval sequence that includes business attribute context.

[0023] S203: Based on the trajectory deviation interval sequence, call the spatial position continuity and movement speed change of the corresponding trajectory segment to identify the trajectory interruption or movement change area, and extract the distribution location of the trajectory abnormal part by combining its direction change content to obtain the equipment abnormal trajectory area set.

[0024] Based on the trajectory deviation interval sequence, the system further performs spatial continuity and integrity analysis on the trajectory data within the abnormal interval, calculates the Euclidean distance between adjacent GNSS coordinate points, and calculates the theoretical maximum single-step displacement based on the physical maximum speed limit of the equipment and the sampling time interval. If the distance between adjacent points is detected to be abnormally greater than the theoretical limit (e.g., more than 3 meters, far exceeding the physical limit displacement), it is determined that the data point has a position jump or positioning drift anomaly. If the timestamp is continuous but the spatial coordinate data is missing, it is determined that the trajectory signal is interrupted. The system comprehensively considers the direction deviation points, position change points, and signal interruption points, and uses a density-based clustering algorithm (such as DBSCAN) to set a neighborhood search radius of 5 meters to spatially aggregate the abnormal points, associate discrete abnormal points into connected abnormal event clusters, and calculate the convex hull contour of the point set to generate a closed polygonal region description. At the same time, it labels the specific anomaly type (such as sideslip, drift, or signal loss) to obtain an accurate set of abnormal trajectory regions of the equipment that describes the abnormal geographical range.

[0025] Please see Figure 4 The steps for obtaining the equipment conflict risk area identifier set are as follows: S301: Call the coordinates and corresponding time period data of each trajectory segment in the abnormal trajectory area set of the device to obtain the spatial position and activity time period of the aircraft's operation trajectory, perform position range overlap detection on the two types of trajectories, extract the trajectory segments that have spatial intersection in the same time period, and obtain the set of trajectory overlap segments. The system retrieves polygonal spatial range data from the abnormal trajectory region set of the equipment, loads the high-precision operating trajectory of the target aircraft obtained by the ADS-B monitoring system in real time, and constructs a dynamic bounding box for the target aircraft based on the aircraft size database, including wingspan, length, and forward and backward safety margins (e.g., a rectangular protection zone with a width of 60 meters and a length that dynamically extends with speed). The system then uses the Weiler-Atherton polygon clipping algorithm in computational geometry to detect whether there is spatial overlap between the polygons of the abnormal equipment region and the dynamic bounding box of the aircraft within the same time window. When the system calculates that there is a non-zero overlap area, it determines that the abnormal trajectory of the equipment has physically intruded into the aircraft's taxiing safety clearance range. The system then extracts the geometric vertex coordinates of the overlapping area and the corresponding time segment to clarify the specific location and time period of the conflict, thereby obtaining a set of trajectory overlap segments representing the spatiotemporal intersection.

[0026] S302: Based on the set of overlapping trajectory segments, call the continuous path changes of the equipment trajectory and the aircraft trajectory within the overlapping segments, calculate the direction of movement, movement interval and spatial proximity, and extract trajectory segments that are close in both time and space to obtain conflict proximity performance data. For sets of overlapping trajectory segments, the system delves into the micro-dynamics level, using the velocity vectors, acceleration vectors, and position coordinates of the equipment and aircraft at corresponding moments to perform high-frequency collision calculations. It accurately measures the real-time Euclidean distance and relative velocity vector between the two, and further estimates the estimated time to collision (TTC) based on the relative motion trend and current distance. For example, in a typical collision scenario, the system calculates that the current physical distance between the two is only 16.55 meters, far below the 20-meter safety warning threshold, and the estimated collision time calculated based on the projection of their relative velocity vectors is only 5.65 seconds, below the 10-second emergency avoidance threshold. This calculation indicates that the equipment has not only spatially intruded into the aircraft's protected area but also possesses extremely high collision urgency in the temporal dimension. Without intervention, a scraping collision is highly likely. The system integrates these specific measurements, relative motion parameters, and judgment conclusions to obtain conflict proximity performance data that quantifies the degree of risk. S303: Based on the conflict proximity performance data, extract the directional consistency change, path intersection range and spatial proximity duration within each segment, determine whether it meets the spatial conflict state, and mark the corresponding positions of the spatial segments that meet the conflict characteristics to obtain the equipment conflict risk area identifier set.

[0027] Based on the data on the proximity of the conflict, the system extracts the characteristics of the consistency of the motion direction and the duration of the high-risk state within the conflict segment. It determines the type of conflict by calculating the angle between the equipment's heading vector and the aircraft's heading vector (e.g., an angle less than 30 degrees indicates a risk of a same-direction rear-end collision, and an angle greater than 150 degrees indicates a risk of a head-on collision). It also verifies whether the duration of the state that meets the high-risk threshold (both distance and time limits exceed the limit) on the time axis exceeds the validity threshold (e.g., 3 seconds). After confirming that the conflict state is real and valid and is not instantaneous sensor noise, the system marks the apron electronic map grid area where the conflict occurred as a high-risk blockade zone and maps the associated equipment number, aircraft call sign, and conflict type attributes to the grid data structure. It outputs a set of equipment conflict risk area identifiers containing grid coordinate indexes, associated object IDs, and conflict semantic descriptions.

[0028] Please see Figure 5 The steps for obtaining the high-risk equipment identifier set are as follows: S401: Based on the equipment number corresponding to the equipment conflict risk area identifier set, extract the trajectory sequence and time segment of each equipment in the conflict area, determine whether there are any abnormalities in the movement direction, spatial extension and heading continuity of the trajectory in the area, and obtain the trajectory risk performance characteristics. Based on the equipment conflict risk area identifier set, specific equipment involved in the incident is identified, and its complete trajectory sequence before and after the conflict area is extracted. The dispersion of trajectory points in the direction perpendicular to the main motion is calculated using statistical methods to assess the vehicle's driving stability. At the same time, the continuous rate of change of heading angle and longitudinal acceleration characteristics are examined. For example, the system analysis found that the trajectory dispersion of the equipment in the conflict area was within the normal range, but its heading was always continuously deviated towards the aircraft side. Moreover, in an emergency situation with a very short expected collision time, the acceleration data did not show a significant braking deceleration trend. Based on this, the system comprehensively judged that the equipment had unintentional yawing behavior and lacked active avoidance operation, thereby extracting trajectory risk performance characteristics including key behavioral features such as "no deceleration trend", "approaching in the same direction", and "continuously narrowing lateral distance".

[0029] S402: Based on the trajectory risk performance characteristics, call the task number and execution level in the task information, compare the task type of the corresponding device in the trajectory, mark the device causing interference in the trajectory according to the priority of task level, and obtain the result of the conflict impact level distinction. The system acquires trajectory risk characteristics and conducts in-depth business correlation analysis on high-risk behavioral features such as "no deceleration trend" and "continuous lateral approach." It calls upon the device's task order number in AODB and the preset conflict severity judgment matrix. In the current example, the system detects that the device is performing a "baggage handling task" (task level-2), which has a decisive impact on flight departure punctuality. Furthermore, the analysis results of S401 clearly indicate that the device is in an uncontrolled approach state with "no intention to avoid collisions," a stark contrast to the emergency braking behavior of ordinary pilots during minor violations. The system jointly weights the "high-risk behavioral characteristics" (unconscious approach) extracted by S401 with the high task level of Level-2. Based on the judgment logic of "uncontrolled behavior and critical task," it confirms that the conflict is not a simple space occupation but a major accident symptom leading to aircraft damage and flight delays. Therefore, the system directly upgrades the device's conflict impact level from the conventional warning level to "Level 1 Severe" and marks it as the highest priority intervention target, thus obtaining a conflict impact level distinction result that differentiates the handling priorities.

[0030] S403: Based on the results of the conflict impact level classification, extract the device numbers with high task level, concentrated trajectory anomalies and wide trajectory impact range, accumulate the number of conflicts in their spatial location, and identify high-frequency conflict behavior in combination with trajectory dwell status to obtain a set of high-risk device identifiers.

[0031] Based on the results of the conflict impact level classification, the system performs long-term historical behavior accumulation analysis on the equipment, retrieves and counts the frequency of the equipment triggering similar conflict warnings and the number of times it illegally stayed in non-mission areas within the past hour; for example, if the system detects that the equipment has triggered three crosswind yaw warnings in a short period of time and has an illegal stay record of more than 45 seconds, based on the preset high-risk judgment logic (such as triggering the circuit breaker mechanism when the number of conflicts reaches three or the number of illegal stays reaches two), the system determines that the equipment is in a high-risk state of being out of control, malfunctioning, or improperly operated by the driver, and then adds its equipment number to the key monitoring and dispatch circuit breaker list, along with risk characteristic descriptions such as "high frequency crosswind sensitivity" and "frequent yaw", and obtains a set of high-risk equipment identifiers.

[0032] Please see Figure 6 The steps for obtaining the apron scheduling route instruction set are as follows: S501: Based on the device number in the high-risk device identifier set, extract the current location coordinates and the target coordinates of the corresponding task location for each device, call the task number information of the device, determine the area and time requirements to be reached by the task, and perform spatial path retrieval by combining the starting position of the device and the task position to obtain the task path planning point set. The scheduling processor initiates an emergency route replanning program for target equipment in the high-risk equipment identification set. It extracts the current real-time location coordinates as the starting point and the target aircraft position coordinates in the task work order as the ending point. Combined with the topology map of the apron road network, it performs a full-domain spatial search and initially generates multiple geometric alternative paths connecting the starting point and the ending point. For example, the system calculates three physically feasible path schemes through a graph search algorithm, including a straight shortcut through the currently marked high-risk conflict zone, a long-distance path around the outer service lane, and a three-dimensional traffic path through the underpass of the terminal building. The system outputs a task path planning point set containing the coordinate sequence of all key nodes and road segment attributes of these three paths.

[0033] S502: Based on the task path planning point set, call the passable area, prohibited area and currently occupied path data in the apron, continuously filter the feasible passage segments of the equipment, identify the usage status of each segment at the current time, determine whether the path has the conditions for passage, and obtain the path passable status combination. Based on the task path planning point set, the system overlays real-time apron traffic monitoring data, area closure orders, and physical restriction data to verify the traffic capacity of each alternative path, accurately identifying the actual traffic status of each road segment at the current moment. For example, if the system detects that a straight path crosses a conflict grid area marked as high-risk in previous steps, its status is forcibly set to "No Entry". Although the underpass path meets the height restriction requirements of the equipment, the roadside sensors show that there is a disabled vehicle occupying the entrance, so its status is set to "Blocked / Temporarily Unavailable". Although the outer lane path has the longest physical distance, there is no risk of aircraft passing through the entire route and the average vehicle speed monitoring data shows that the traffic is smooth, so its status is set to "Passable". Based on this, the system outputs a combination of path passable statuses that includes the real-time status attributes of each path.

[0034] S503: Based on the combination of path passability status, comprehensively compare the passability status, length order, and intersection risk of each path segment, identify the continuous path segment with the least impact on the equipment during its journey from the current location to the task point, and output the driving guidance path for each device to obtain the apron scheduling path instruction set.

[0035] Based on the combination of route passability status, a comprehensive cost evaluation function is constructed, which includes travel time cost and safety risk cost. Weights are set for efficiency and safety dimensions. Considering the equipment is currently in a high-risk state, the system dynamically increases the safety weight to 0.6 and sets the efficiency weight to 0.4, quantitatively scoring each alternative route. The system calculates that the straight route is directly eliminated due to its extremely high risk coefficient caused by crossing a conflict zone. The underpass route suffers from a surge in total travel time and congestion variables due to waiting for a disabled vehicle to be cleared, resulting in a comprehensive cost as high as 136.2. While the outer lane route has the longest physical distance, its comprehensive cost is only 108.6 due to smooth road conditions and the lowest risk coefficient. After comprehensive comparison, the system selects the outer lane route with the lowest cost as the optimal scheduling scheme, generates a guidance data packet containing specific turning point coordinates and driving instructions, and sends it to the equipment terminal via wireless network to obtain the apron scheduling route instruction set.

[0036] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for intelligent scheduling and risk prevention of non-powered equipment on aprons based on situational awareness, characterized in that, Includes the following steps: S1: Obtain the position, speed and heading angle of the non-powered equipment on the apron, combine the equipment task type and flight support plan time period, collect aircraft trajectory and meteorological data, update the equipment spatial coordinates, speed and heading changes, combine the apron environmental monitoring status to ensure accurate reflection of the motion status, and form a set of equipment motion status; S2: Call the concentrated trajectory information of the device's motion state, identify the device's offset and directional changes within a time period, filter out trajectory deviations based on task requirements, analyze the position and direction changes of the trajectory, identify abnormal areas, and form a set of abnormal trajectory areas for the device. S3: Call the concentrated trajectory of the abnormal trajectory area of ​​the equipment, combine it with the aircraft trajectory, overlap the detection of the equipment path and the aircraft activity area, identify the intersection of the two, analyze the risks existing in the intersection time period and space, mark the conflict area, and form a set of equipment conflict risk area identifiers; S4: Based on the equipment number and trajectory in the equipment conflict risk area identifier set, examine the risk performance of the equipment in the conflict area, combine the importance and priority of the task, identify the equipment that affects the operation, extract high-risk equipment, and form a high-risk equipment identifier set; S5: Based on the high-risk equipment identification group equipment number, obtain the current location of the equipment and the target task location, combine the equipment travel path and apron environment, adjust the equipment travel route, guide the equipment through a suitable path, confirm the path selection, and output the apron scheduling path instruction set.

2. The intelligent scheduling and risk prevention method for non-powered apron equipment based on situational awareness as described in claim 1, characterized in that: The set of equipment motion states includes equipment spatial location information, speed change data, heading angle change parameters, and status awareness tags. The set of abnormal equipment trajectory regions includes trajectory deviation segments, directional abnormal segments, abnormal region coordinates, and abnormal type markers. The set of equipment conflict risk area identifiers includes the intersection area of ​​the equipment and aircraft paths, the time period of the conflict, the spatial range of the conflict, and the risk level identifier. The set of high-risk equipment identifiers includes the equipment risk level, the degree of impact on the mission, the priority handling flag, and the equipment identification code.

3. The intelligent scheduling and risk prevention method for non-powered apron equipment based on situational awareness as described in claim 1, characterized in that: The steps for obtaining the device motion state set are as follows: S101: Acquire the real-time spatial coordinates, movement speed and heading angle of the non-powered equipment on the apron, collect the scheduling information of the equipment task type and the corresponding time period in the flight support plan, collect the aircraft operation trajectory and the wind speed, wind direction and temperature changes in the weather, and align various types of data according to a unified time to obtain equipment operation related information. S102: Based on the equipment operation association information, call the coordinates in the equipment movement trajectory and the flight support scheduling time point to extract the continuous movement path, calculate the speed change within the continuous time period, and calculate the movement direction change in combination with the angle between the heading angle and the wind direction. Based on the coordinate movement range and the degree of direction change, the path offset performance is obtained. S103: Based on the path offset performance, call the wind speed and direction sequence and the equipment speed change sequence in the meteorology, compare the change trends of the two in each time period, screen out the intervals where the wind speed change is significantly stronger than the equipment speed change, and combine the magnitude of the direction change in the corresponding time period to analyze the stability performance and obtain the equipment motion state set.

4. The intelligent scheduling and risk prevention method for non-powered apron equipment based on situational awareness according to claim 1, characterized in that: The steps for obtaining the set of abnormal trajectory regions of the device are as follows: S201: Call the trajectory coordinates, moving speed and heading angle data of the device motion state set, extract continuous position segments and direction change sequences, identify unstable direction segments based on the difference in moving direction between adjacent positions and the continuous change state, and obtain the trajectory direction change performance. S202: Based on the trajectory direction change performance, call the equipment task type and shift time data, compare the direction change segment with the corresponding time period of the task requirement, screen out the equipment trajectory segments with trajectory abnormalities during task execution, and obtain the trajectory deviation interval sequence. S203: Based on the trajectory deviation interval sequence, call the spatial position continuity and movement speed change of the corresponding trajectory segment, identify the trajectory interruption or movement change area, and extract the distribution location of the trajectory abnormal part in combination with its direction change content to obtain the equipment abnormal trajectory area set.

5. The intelligent scheduling and risk prevention method for non-powered apron equipment based on situational awareness according to claim 1, characterized in that: The steps for obtaining the set of device conflict risk area identifiers are as follows: S301: Call the coordinates and corresponding time period data of each trajectory segment in the abnormal trajectory area set of the device, obtain the spatial position and activity time period of the aircraft's operation trajectory, perform position range overlap detection on the two types of trajectories, extract the trajectory segments that have spatial intersection in the same time period, and obtain the set of trajectory overlap segments. S302: Based on the set of overlapping trajectory segments, call the continuous path changes of the equipment trajectory and the aircraft trajectory within the overlapping segment, calculate the movement direction, movement interval and spatial proximity, and extract trajectory segments that are close in both time and space to obtain conflict proximity performance data. S303: Based on the conflict proximity performance data, extract the directional consistency change, path intersection range and spatial proximity duration within each segment, determine whether it meets the spatial conflict state, and mark the corresponding positions of the spatial segments that meet the conflict characteristics to obtain the equipment conflict risk area identifier set.

6. The intelligent scheduling and risk prevention method for non-powered apron equipment based on situational awareness according to claim 1, characterized in that: The steps for obtaining the high-risk device identifier set are as follows: S401: Based on the device number corresponding to the device conflict risk area identifier set, extract the trajectory sequence and time segment of each device in the conflict area, determine whether there are any abnormalities in the movement direction, spatial extension and heading continuity of the trajectory in the area, and obtain the trajectory risk performance characteristics. S402: Based on the trajectory risk performance characteristics, call the task number and execution level in the task information, compare the task type of the corresponding device in the trajectory, mark the device causing interference in the trajectory according to the priority of task level, and obtain the conflict impact level distinction result. S403: Based on the conflict impact level differentiation results, extract the device numbers with high task level, concentrated trajectory anomalies and wide trajectory impact range, accumulate the number of conflicts that occur in their spatial locations, identify high-frequency conflict behaviors in combination with trajectory dwell status, and obtain a set of high-risk device identifiers.

7. The intelligent scheduling and risk prevention method for non-powered apron equipment based on situational awareness according to claim 1, characterized in that, The method further includes step S5: S5: Based on the equipment number in the high-risk equipment identification set, obtain the current location of the equipment and the target task location, combine the equipment travel path and the apron environment, adjust the equipment travel route, guide the equipment through a suitable path, confirm the path selection, and output the apron scheduling path instruction set; The apron dispatch route instruction set includes route adjustment schemes, driving route codes, guidance instruction information, and route confirmation parameters.

8. The intelligent scheduling and risk prevention method for non-powered apron equipment based on situational awareness according to claim 7, characterized in that: The steps for obtaining the apron scheduling path instruction set are as follows: S501: Based on the device number in the high-risk device identifier set, extract the current location coordinates and target coordinates of the corresponding task location for each device, call the task number information of the device, determine the area and time requirements to be reached by the task, and perform spatial path retrieval by combining the starting position of the device and the task position to obtain the task path planning point set. S502: Based on the task path planning point set, call the passable area, prohibited area and currently occupied path data in the apron, continuously filter the feasible passage segments of the equipment, identify the usage status of each segment at the current time, determine whether the path has the conditions for passage, and obtain the path passable status combination. S503: Based on the combination of the path passability status, comprehensively compare the passability status, length order, and intersection risk of each path segment, identify the continuous path segment with the least impact on the equipment during its journey from the current location to the task point, and output the driving guidance path for each device to obtain the apron scheduling path instruction set.

Images