Digital airport apron traffic operation control and simulation deduction method and system

Abstract

The invention relates to a digital airport apron traffic operation control and simulation deduction method and system. The method comprises the following steps: acquiring road network state information including the number of aircrafts or vehicles on each road section, the length of the road section and the maximum capacity of the road section; calculating the real-time traffic weight of each road section based on the road network state information; based on the real-time traffic weight, constructing an airport road network directed weighted graph; generating an executable path set based on the airport road network directed weighted graph; screening an optimal executable path from the executable path set and converting the optimal executable path into an aircraft and vehicle guiding instruction sequence; obtaining the position and speed of the physical carrying tool, and updating the predicted space-time trajectory of the physical carrying tool in a rolling manner; performing real-time conflict detection based on the predicted spatial-temporal trajectory; and if the space-time conflict between the paths is detected, carrying out dynamic re-planning on the mobile resources corresponding to the paths with lower priorities so as to eliminate the conflict. Compared with the prior art, the method has the advantage of high system interactivity.

Description

Technical Field

[0001] This invention relates to the field of airport ground operation simulation and intelligent control technology, and in particular to a digital airport apron traffic operation control and simulation method and system. Background Technology

[0002] Airport apron operation management is a core issue for civil aviation transportation efficiency and safety, involving complex topologies, strict operating rules, and highly dynamic collaborative scheduling logic. With the advancement of smart airport construction, how to build a high-confidence simulation environment to verify the logical completeness of apron operation rules, the parameter sensitivity of scheduling strategies, deduce emergency response plans, and train professional dispatchers has become a key technical problem that urgently needs to be solved in the field of aeronautical engineering.

[0003] However, existing related technologies suffer from significant polarization in practical applications: On the one hand, mature airport apron scheduling systems (such as A-CDM systems or tower control systems) are only designed for real airport operations. These systems rely on highly complex real-time data chains (such as radar, ADS-B, and ACARS data) and expensive server architectures, and involve extremely high security and confidentiality requirements and operational access restrictions. Due to the zero-fault-tolerance nature of real-world operating environments, real-world scheduling systems are difficult to deploy in lightweight form in university laboratories or research institutions, making it difficult to complete closed-loop verification of new control strategies in a low-cost environment.

[0004] On the other hand, existing airport sand tables for laboratory use often remain at the stage of "static display" or "mechanical demonstration," lacking the precise scheduling capabilities to address the complex operational processes of the airside apron and the micro-level conflicts between aircraft and vehicles. Traditional physical sand tables can typically only display the spatial layout of the airport or control the model's movement based on a pre-set, fixed script. This "step-by-step" demonstration method cannot reflect the changing logical constraints (such as right-of-way priorities and dynamic conflicts) in real apron operations, cannot simulate the impact of weather changes on operational efficiency, and lacks real-time status interaction between physical entities and digital logic. Users facing such sand tables can only observe the established processes as bystanders, lacking the interactivity and operability to intervene in the system to inject parameters, handle unexpected event interference, and iteratively optimize strategies.

[0005] The invention disclosed in CN119180457A presents a multi-agent simulation-based airport ground vehicle scheduling system and method. Given that efficient airport ground service is a key factor in improving flight turnaround time, this invention comprehensively considers the ground service needs throughout the entire process from aircraft landing to takeoff, and realizes the scheduling and control of ground vehicles based on multi-agent simulation. The method includes the following steps: design of agents and their interaction rules, input of basic information, construction of an airport ground vehicle scheduling and control model, and visualization output of results. Compared with existing technologies, this invention can realistically depict the service characteristics of airport ground vehicles, and the system can compare the effects of various ground vehicle scheduling and control strategies, providing a scientific basis for optimizing airport ground vehicle scheduling. However, the interactivity and operability of the system designed in this scheme are relatively poor.

[0006] Therefore, there is an urgent need to develop a simulation and deduction system that can deeply integrate the realistic experience of a physical sand table, the full-element mapping of digital twins, the complex logical rules of airport operations, and environmental interference factors, in order to solve the problem that existing technologies cannot achieve realistic restoration of apron operation scenarios, algorithm closed-loop verification, and high-degree-of-freedom deduction in low-risk environments. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a digital airport apron traffic operation management and simulation method and system.

[0008] The objective of this invention can be achieved through the following technical solutions: A digital airport apron traffic operation management and simulation method includes: Obtain road network status information including the number of aircraft or vehicles on each road segment, road segment length, and maximum capacity of each road segment; calculate the real-time traffic weight of each road segment based on the road network status information; and construct a directed weighted graph of the airport road network based on the real-time traffic weight. Based on the directed weighted graph of the airport road network, an executable path set is generated; from the executable path set, the optimal executable path for the target vehicle is selected according to the target location of the movement request; the optimal executable path is converted into a corresponding atomic guidance instruction sequence to guide the target vehicle to glide or travel along the optimal executable path to the target work point; The system acquires the position and speed feedback from the physical vehicle in real time and updates the predicted spatiotemporal trajectory of the physical vehicle on a rolling basis; it performs real-time conflict detection on the optimal executable path based on the predicted spatiotemporal trajectory; if a spatiotemporal conflict is detected between paths, it dynamically replans the movement resources corresponding to the path with lower priority to resolve the conflict.

[0009] Furthermore, based on the road network status information, the real-time traffic weight of each road segment is calculated, and the corresponding calculation formula is as follows: in, For real-time access weights, Based on the basic passage time, For the length of the road segment, For rated speed, The congestion penalty coefficient, The maximum capacity of the road segment. This represents the number of aircraft or vehicles currently present on this road segment. For the turning geometry penalty term, For environmental safety distance, This refers to the length of the vehicle.

[0010] Furthermore, the dynamic reprogramming process specifically includes: To address conflicts between different types of vehicles, the system compares preset priorities and avoids conflicts by keeping the paths of high-priority objects unchanged and performing path reselection or timing adjustment for low-priority objects. In the event of a conflict between similar vehicles, priority is determined based on the principle of first-come, first-served, and the lower-priority objects are controlled to perform the aforementioned path reselection or timing adjustment. After removing paths involving conflicting nodes from the set of executable paths, the total passage weight of the new path is calculated; based on the total passage weight, the detour cost is calculated; based on the position and speed fed back by the physical vehicle, the waiting cost is calculated; the detour cost and the waiting cost are compared, and if the detour cost is less than the waiting cost, the path is reselected for the low-priority object; if the detour cost is greater than the waiting cost, the timing adjustment is performed on the low-priority object.

[0011] Furthermore, based on the sum of the passage weights, the detour cost is calculated, and the corresponding calculation formula is as follows: in, In order to take a detour, This represents the sum of the travel weights for the new path. The weight of the remaining segment of the original path. This is the path switching penalty constant; Based on the position and speed feedback from the physical vehicle, the waiting cost is calculated using the following formula: in, The waiting time required for the vehicle to pass. The cost of waiting This is the time sensitivity coefficient.

[0012] Furthermore, the optimal executable path includes the optimal executable coasting path and the optimal executable driving path; The process of generating the optimal executable taxiway path specifically includes: firstly, extracting edges containing only runway and taxiway attributes from the directed weighted graph of the airport road network to construct a taxiway subgraph; and then, based on the road network state information and the taxiway subgraph, calculating a set of executable paths containing the optimal taxiway path and the suboptimal taxiway path, and selecting the optimal executable taxiway path from it. The process of generating the optimal executable driving path specifically includes: firstly, extracting edges containing service lane attributes from the directed weighted graph of the airport road network to construct a service lane subgraph; calculating the set of executable paths from the current location to the target work point based on the service lane subgraph; and selecting the optimal executable driving path from the set of executable paths.

[0013] Furthermore, based on the aforementioned position and velocity, the predicted spatiotemporal trajectory of the physical vehicle is calculated, and the corresponding calculation formula is as follows: in, For transport vehicles In the future The predicted location, For transport vehicles Current coordinates To predict speed.

[0014] This invention also provides a system for digital airport apron traffic operation management and simulation method as described above, comprising: The physical experiment sandbox system is used to construct a realistic airside environment of an airport, including airport runway and taxiway structures, movable physical aircraft models, miniature intelligent connected ground support vehicles, and environmental simulation devices. The digital twin simulation subsystem is used to construct a virtual three-dimensional scene that is consistent with the physical experimental sandbox system, including a digital map model, virtual aircraft and ground support vehicle models, and to perform virtual-real synchronous rendering based on the position and operating status of the physical vehicles to achieve virtual-real synchronous display. The multi-source model fusion scheduling engine, as the control center of the system, communicates and connects with the physical experimental sandbox system and the digital twin simulation subsystem respectively, and is used to run the rule model, the executable path generation module, the aircraft operation control module, the task chain state machine and the environmental state model. The aircraft operation control module is used to generate aircraft taxiing requests, the task chain state machine is used to generate ground support vehicle movement requests, and respectively call the executable path generation module to generate corresponding paths; The simulation interactive control terminal provides a human-computer interaction interface, has a pre-built emergency event library, and allows users to import or configure simulated flight plans, insert emergencies, adjust operating parameters, or trigger environmental changes. It also triggers a joint response from the physical and digital sides through the multi-source model fusion scheduling engine to achieve standardized process demonstrations or emergency drills.

[0015] Furthermore, the mobile physical aircraft model and the miniature intelligent connected ground support vehicle, as physical transportation tools, are both equipped with embedded processing units and positioning modules. By executing atomic instructions from the multi-source model fusion scheduling engine and providing real-time feedback on physical location and operating status, they physically verify the scheduling strategy on the digital side.

[0016] Furthermore, the executable path generation module is used to generate a set of executable paths that satisfy the rules defined in the rule model; the generation process of the executable path set specifically includes: First, a unified directed weighted graph of the airport road network is constructed based on the topology of the digital map model. Then, a specific passage subgraph is extracted from the directed weighted graph of the airport road network according to the type attribute of the vehicle. Finally, the set of executable paths is generated by filtering according to the specific requests of the aircraft operation control module and the task chain state machine.

[0017] Furthermore, the operation rule model is used to store the topological constraints of airport apron operations, the right-of-way priority of aircraft and ground support vehicles, the speed limit of ground support vehicles, the standard service duration distribution of various ground support tasks, the safe isolation waiting time, and the safe distance threshold for ground operations. The operational rule model receives the position and speed feedback from the physical vehicle in real time, and combines the travel time weight of each segment in the executable path set with the current operating status of the vehicle to continuously update the expected spatiotemporal trajectory of the aircraft and ground support vehicles. When it is detected that the predicted spatiotemporal trajectories of any two vehicles occupy the same spatial node within the same time window or that their spatial distance is less than a preset safety interval threshold, a path conflict is determined, and dynamic replanning is performed.

[0018] Compared with the prior art, the present invention has the following advantages: (1) This invention no longer relies on fixed road segment attributes or historical statistical data, but instead constructs an accurate profile of the current traffic situation based on the real-time collection of aircraft / vehicle numbers and physical constraints, providing real and objective underlying data support for decision-makers; the generation of the executable path set ensures that all candidate paths conform to the airport physical structure, operating rules and aircraft kinematic constraints; the selection of the optimal path seeks to maximize operational efficiency while satisfying the feasibility; further, the optimal path is transformed into a specific aircraft guidance instruction sequence, thus realizing a seamless connection from macro-path planning to micro-motion control, ensuring that instructions can be directly issued and executed.

[0019] Based on real-time positioning data and motion status, this invention employs a rolling time-domain prediction method to continuously update the future spatiotemporal trajectory of each vehicle. Compared to traditional point-based location reporting, this method can detect motion trends in advance, providing a high-confidence predictive basis for conflict detection. Compared to traditional proximity alarms or post-event handling, this predictive detection mechanism provides sufficient reaction time for dynamic adjustments, significantly improving the system's proactive safety protection capabilities.

[0020] By constructing a closed-loop management mechanism encompassing state awareness, dynamic weighting, global planning, real-time tracking, and conflict resolution, this invention achieves efficient, safe, and intelligent management of complex apron traffic environments. Compared to traditional management methods that rely on fixed rules, static paths, or manual intervention, this invention integrates the typically separate processes of operation planning, operation monitoring, and operation adjustment into a single digital process, eliminating information silos and realizing integrated and intelligent operation of apron traffic control.

[0021] (2) This invention quantifies the impact of congestion and weather on travel costs by introducing the concepts of maximum road segment capacity and safe distance; this is the theoretical maximum carrying capacity calculated based on physical space constraints: each vehicle needs to occupy not only its own length during movement, but also a dynamic safety interval; when the road segment length is fixed, the maximum road segment capacity is... The larger the value, the greater the safety distance, and the higher the sensitivity of the road segment; the maximum capacity of the road segment The smaller the distance, the smaller the safety distance, which indicates a lower sensitivity of the road segment. This allows the present invention to adjust the traffic weight of each road segment in real time according to the road congestion and weather conditions. Furthermore, it enables the paths in the constructed executable path set to accurately represent the current travel cost of each route, so that the optimal executable path selected in the end is the best choice under the current environment and can effectively alleviate the current road congestion. This method does not rely on static path distances or fixed priorities. Instead, it uses the congestion level of the road network, remaining carrying capacity, and environmental conditions as core variables to construct a directed weighted graph of the airport road network in real time. This achieves the adaptability of the road network model, accurately reflects the instantaneous congestion of apron traffic, and avoids the problem of seemingly short planned paths that are actually congested due to static planning. It provides a foundational data with real-time traffic guidance for subsequent path planning, significantly improving the foresight and rationality of path planning.

[0022] (3) Based on the dynamic weighted graph, this invention utilizes graph theory search and optimization algorithms to select the optimal executable path with the minimum overall cost from the generated set of executable paths. Furthermore, the optimal solution here is the globally optimal solution calculated based on the current real-time traffic weights, rather than a local optimum, thus achieving global dynamic optimization of aircraft taxiing paths. This effectively avoids excessive concentration in apron traffic hotspots, balances road network load, and reduces aircraft taxiing waiting time and fuel consumption. Simultaneously, the concept of "executable path" ensures that the generated path conforms to the airport's physical structure and operating rules, improving the feasibility and safety of instructions.

[0023] (4) This invention combines real-time positioning and trajectory prediction technologies to continuously update the predicted spatiotemporal trajectory of each vehicle. Subsequently, based on this high-precision trajectory, conflict detection is performed within a sliding time window. Once a potential conflict is detected in the future spatiotemporal domain, a local dynamic replanning algorithm is immediately triggered to fine-tune the path or control the speed of lower-priority mobile resources. This achieves closed-loop management from static path planning to dynamic cooperative collision avoidance, ensuring the continuity and smoothness of apron traffic.

[0024] (5) This invention constructs an airport apron traffic operation management and simulation system based on a three-element coupling architecture of digital-entity-logic: a physical experimental sand table as the entity layer, providing an operation verification environment under real physical space constraints; a digital twin simulation as the digital layer, providing full-element visualization monitoring and data enhancement; and a multi-source model fusion scheduling engine as the logic layer, serving as the control center of the system and connecting digital and entity. The three work together through a virtual-real consistency synchronization mechanism and an environmental parameter mapping mechanism to realize the full-process simulation and closed-loop management of complex traffic flow on the airport apron, realize the virtual-real closed-loop control and restoration of the apron operation scenario, effectively support the verification of complex operation rules, emergency drills for sudden events and demonstration of standardized processes, and can be widely applied to multiple fields such as universities and research institutions.

[0025] (6) This invention constructs a dynamic conflict management system based on multi-source model fusion, builds a traffic subgraph that distinguishes between aircraft and ground support vehicles based on a directed weighted graph, and implements priority-based right-of-way control in conjunction with an operational rule model. It automatically triggers detour or timing adjustment instructions for conflicts. It simulates the stringent rules of vehicles yielding to aircraft in real airports, providing an industry-standard verification platform for studying ground support vehicle scheduling under high-density flight flows.

[0026] (7) The present invention also provides a simulation interactive management terminal that supports the injection of emergencies, allowing high-risk interference factors such as equipment failure, illegal intrusion, and runway closure to be dynamically injected during operation. It can not only display standard operating procedures, but also deduce emergency response and system recovery logic under emergencies, providing a low-cost, zero-risk, highly realistic comprehensive training environment. Attached Figure Description

[0027] Figure 1 This is a flowchart of a digital airport apron traffic operation management and simulation method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the system framework of a digital airport apron traffic operation control and simulation system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of dynamic conflict detection and replanning of a digital airport apron traffic operation control and simulation system provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the ground vehicle task chain state machine of a digital airport apron traffic operation control and simulation system provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of a simulation interaction control terminal for a digital airport apron traffic operation control and simulation simulation system provided in an embodiment of the present invention; Figure 6 This is a flowchart illustrating the complete working process of a digital airport apron traffic operation control and simulation system provided in this embodiment of the invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0029] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0030] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0031] Definitions: Ultra-wideband (UWB) positioning is a wireless communication technology that uses narrow, non-sinusoidal pulses in the nanosecond to picosecond range for data transmission. Its core characteristics are extremely high bandwidth (typically exceeding 500MHz) and extremely low transmission power. In positioning applications, by measuring the precise time of flight (ToF) or time difference of arrival (TDoA) of the UWB signal between devices, high-precision distance or position calculations at the centimeter or even millimeter level can be achieved. This technology boasts advantages such as strong anti-interference capabilities, high time resolution, low power consumption, and good penetration, and is widely used in scenarios such as indoor precision positioning, industrial IoT, autonomous driving, smart warehousing, and personnel and equipment tracking.

[0032] Visual fusion positioning technology is a method that combines data from visual sensors (such as cameras) with information from other positioning sources to achieve high-precision and robust position and attitude estimation. Its core idea is to utilize complementary fusion of visual information (image features, depth, motion estimation) and data from auxiliary sensors (such as IMU, GNSS, LiDAR, UWB, etc.): vision can provide absolute or relative positioning references in textured, static scenes, but it is prone to failure in dynamic environments, under changing lighting conditions, or with rapid movement; while IMU can provide high-frequency short-term motion predictions, GNSS can provide absolute position but is susceptible to occlusion interference, and UWB can provide stable anchor point constraints. Through filtering algorithms (such as Kalman filtering) or optimization algorithms (such as factor graph optimization), spatiotemporal synchronization and fusion of multi-source data are achieved, ultimately resulting in stable, continuous, and significantly improved positioning performance in complex environments. This technology is widely used in autonomous driving, robot navigation, augmented reality (AR), and autonomous flight of unmanned aerial vehicles (UAVs).

[0033] Cubic spline interpolation is a classic piecewise polynomial interpolation method used to construct a smooth curve passing through a given set of data points (nodes). Its core idea is to connect every two adjacent nodes using an independent cubic polynomial, forcing that not only are the function values ​​continuous at all connection points (nodes), but also the first derivative (slope) and second derivative (curvature) are continuous. This constraint ensures global continuity of the entire curve (i.e., the curve appears very smooth, without abrupt angles or curvature jumps).

[0034] Example 1 like Figure 1 As shown in the figure, this embodiment provides a digital airport apron traffic operation management and simulation method, which includes the following steps: S1: Obtain road network status information including the number of aircraft or vehicles on each road segment, road segment length, and maximum capacity of each road segment; calculate the real-time traffic weight of each road segment based on the road network status information; construct a directed weighted graph of the airport road network based on the real-time traffic weight. Specifically, The process of generating the optimal executable path for an aircraft specifically includes: firstly, extracting edges containing only runway and taxiway attributes from a pre-constructed directed weighted graph of the airport road network to construct a taxiway subgraph; and then, based on the arrival and departure information of the flight plan and the pre-set road network weights, calculating a set of executable paths containing the optimal taxiway path and the second-best taxiway path, and selecting the optimal executable path from it. The process of generating the optimal executable route for ground support vehicles specifically includes: firstly, extracting edges containing service lanes and allowed intersection attributes from the directed weighted graph of the airport road network, and constructing a service lane subgraph; based on the service lane subgraph and the road network weights, calculating a set of executable routes containing the optimal and suboptimal driving routes, and then selecting the optimal executable route from it.

[0035] S2: Based on the directed weighted graph of the airport road network, generate a set of executable paths; from the set of executable paths, select the optimal executable taxiing path for the target aircraft from the runway exit to the designated parking position; convert the optimal executable taxiing path into a sequence of aircraft guidance instructions to guide the target aircraft to taxi along the optimal executable taxiing path; S3: Real-time acquisition of the position and speed feedback from the physical vehicle, and rolling updates of the predicted spatiotemporal trajectory of the physical vehicle; real-time conflict detection of the optimal executable taxiing path based on the predicted spatiotemporal trajectory; if a spatiotemporal conflict is detected between paths, dynamic replanning of the movement resources corresponding to the path with lower priority is performed to resolve the conflict.

[0036] Specifically, The dynamic reprogramming process specifically includes: To address conflicts between different types of vehicles, the system compares preset priorities and avoids conflicts by keeping the paths of high-priority objects unchanged and performing path reselection or timing adjustment for low-priority objects. Path reselection specifically includes removing paths involving conflicting nodes from the set of executable paths and selecting the path with the lowest travel cost to replace them. Timing adjustment specifically includes causing vehicles to perform waiting actions or adjusting the speed of vehicles. For conflicts involving similar vehicles, priority is determined based on the first-come, first-served principle, and the path reselection or timing adjustment is controlled for low-priority objects.

[0037] Preferred, The environmental parameters are specifically configured by the user, including rainfall, snowfall, light intensity, and visibility parameters; the operational parameters include: the speed limit and braking threshold of the vehicle, the safe distance between vehicles, the road network weight, and the road segment toll.

[0038] Example 2 like Figure 2 As shown, this embodiment provides a system for digital airport apron traffic operation control and simulation method as described in Embodiment 1, comprising: The physical experiment sandbox system is used to construct a realistic airside environment of an airport. It includes a scaled-down airport runway and taxiway structure, a movable physical aircraft model, a miniature intelligent connected ground support vehicle, and an environmental simulation device. The movable physical aircraft model and the miniature intelligent connected ground support vehicle serve as physical transport tools. Both are equipped with embedded processing units and positioning modules. By executing atomic instructions from the multi-source model fusion scheduling engine and providing real-time feedback on physical position and operating status, the scheduling strategy on the digital side is physically verified. The digital twin simulation subsystem is used to construct a virtual 3D scene that is consistent with the physical experimental sandbox system, including digital map models, virtual aircraft and ground support vehicle models, and performs virtual-real synchronous rendering based on the position and operating status of physical vehicles to achieve millisecond-level virtual-real synchronous display. The multi-source model fusion scheduling engine, serving as the system's control center, communicates with both the physical experiment sandbox system and the digital twin simulation subsystem for operation. Operational rule model: Used to store rules such as topological constraints, dynamic conflict detection and replanning, right-of-way priority of aircraft and ground support vehicles, speed limit of ground support vehicles, standard service duration distribution of various ground support tasks, safe isolation waiting time and ground operation safe distance threshold, and use them as mandatory constraints for behavioral decisions. Executable path generation module: Based on the digital map model, it generates a set of executable paths that conform to the constraints of the operation rule model in response to the movement requests of aircraft or ground support vehicles. Aircraft Operation Control Module: Based on the simulated flight plan and its service time window configured in the simulation interactive control terminal, the executable path generation module is called to generate the aircraft taxiing path, which is then converted into atomic instructions to drive the movable physical aircraft model to simulate the arrival and departure process on the physical sandbox, and the ground service request time window is issued to the task chain state machine according to the aircraft status. Task chain state machine: In response to service demand time windows, it generates ground support task chains, calls the executable path generation module to plan ground support vehicle routes, and decomposes the task chains into atomic instructions to drive the movement of miniature intelligent connected ground support vehicles. The task chain state machine executes ground support vehicle state transitions based on the constraints of the running rule model. Environmental state model: By controlling the environmental simulation device to adjust the lighting, visibility, etc. of the physical sand table, and injecting environmental parameters, the environmental parameters are mapped to the behavioral constraint parameters of vehicles and aircraft, and input into the operation rule model to dynamically update the behavioral logic of aircraft and ground support vehicles. The virtual-real consistency synchronization mechanism is used to achieve synchronization and consistency between the physical and digital sides at the level of position, attitude and operation logic based on timestamp alignment, interpolation operation and data cleaning strategies. The simulation interactive control terminal provides a human-computer interaction interface, allowing users to import or configure simulated flight plans, insert emergencies, adjust operating parameters, or trigger environmental changes. It also triggers joint responses from the physical and digital sides through a multi-source model fusion scheduling engine to achieve standardized process demonstrations or emergency drills.

[0039] Preferred, The executable path generation module's executable path calculation is characterized by using high-precision geometric data of a digital map model as the calculation benchmark, and introducing a geometric constraint verification and dynamic occupancy cost mechanism. Specific steps include: (1) Constructing a digital road network with physical attributes: Analyze the three-dimensional spatial data of the digital map model, project its topology into a two-dimensional directed weighted graph, establish nodes and edges that strictly correspond to the physical reality, and pre-set physical constraint attributes: Node attributes: Record the node's 3D coordinates in the digital map, the angles between the incoming and outgoing edges of all connected edges, and the maximum aircraft wingspan class that the node is allowed to pass through; Edge attribute: Records the physical length of the road segment projected onto the digital map. Rated speed as defined by regulations .

[0040] (2) Define the passage cost function based on spatiotemporal state: for each edge in the road network (from node) arrive ), calculate its real-time passage weight : in, Based on the basic travel time, determined by the length of the road segment. Divide by the rated speed The conclusion is that ; The number of aircraft or vehicles currently existing on this road segment (based on real-time location information fed back by the physical experiment sandbox system and real-time statistics from the multi-source model fusion scheduling engine). The maximum capacity of this road segment (aircraft or vehicle capacity), expressed as the length of the road segment. For molecules, the length of the delivery vehicle Safe distance from the environment The sum is used as the denominator for calculation. ,in This is an equivalent safety interval distance based on feedback from the environmental state model and adapted to the scale of the sand table. Indicates rounding down; The congestion penalty coefficient is based on saturation. ( Piecewise nonlinear adjustment strategy: when When < 0.5 (free flow), Take the smaller base value (e.g., 0.2); when 0.5 < When < 0.9 (dense flow), Take a moderate weighting value (e.g., 1.5); when When >0.9 (saturated flow), By setting a high blocking value (such as 10.0), the system can proactively avoid high-congestion-risk road sections. when near At that time, the weight increases significantly, prompting the algorithm to automatically avoid congested road sections; This is a turning geometry penalty term. The system retrieves nodes based on the type of aircraft or vehicle (e.g., Category E mainframes). The geometric angle formed by the incoming and outgoing edges is compared with the physical minimum turning radius of the currently planned object. If the geometric angle in the digital map is less than the physical turning radius limit, it is determined to be physically unreachable. Set it to infinity (i.e., a logically broken circuit); if the constraint is met, then... It is 0.

[0041] (3) Generate a set of executable paths: In response to movement requests from aircraft or ground support vehicles, the system executes an improved Dijkstra's or A* algorithm: Geometric initial screening: The algorithm traverses the digital road network nodes and reads the data from step (2) based on the vehicle type. The decision logic directly eliminates all geometrically unreachable turning nodes at the graph search level, generating a subgraph that contains only physically feasible paths. Dynamic optimization: based on subgraph The weighted calculation minimizes the cumulative cost of the main path, and retains the suboptimal paths whose cost is within a certain range (e.g., 10%) of the main path, together forming the set of executable paths.

[0042] Command conversion: The sequence of path coordinate points generated in the digital map is parsed into a sequence of atomic commands containing the target position, heading angle and desired speed, and then sent to the physical vehicle for execution through the communication interface.

[0043] Preferred, The multi-source model fusion scheduling engine includes an executable path generation module. Based on the topology of the digital map model, it constructs a unified directed weighted graph of the airport road network. Spatial nodes represent aircraft stands or intersections, and edges represent runways, taxiways, or service lanes. The edge weights map to segment lengths or travel time costs. According to the type and attributes of the vehicle, it extracts specific travel subgraphs from the directed weighted graph of the airport road network. Responding to requests from the aircraft operation control module and the task chain state machine, it generates a set of executable paths that satisfy the operation rule model through rule constraints. For aircraft: Extract edges containing only runway and taxiway attributes to construct a taxiway subgraph. Based on the arrival and departure information of the flight plan, calculate the optimal path and multiple suboptimal paths from the runway exit to the assigned parking position, or from the parking position to the runway entrance, based on road network weights, to form an executable path set. For ground support vehicles: extract edges containing service lanes and allowed intersection attributes to construct a service lane subgraph. Based on the target location in the task chain, calculate the optimal path and multiple suboptimal paths from the current location to the target work point based on road network weights, forming an executable path set.

[0044] Preferred, The multi-source model fusion scheduling engine includes an executable path generation module. Based on the topology of the digital map model, it constructs a unified directed weighted graph of the airport road network. Spatial nodes represent aircraft stands or intersections, and edges represent runways, taxiways, or service lanes. The edge weights map to segment lengths or travel time costs. According to the type and attributes of the vehicle, it extracts specific travel subgraphs from the directed weighted graph of the airport road network. Responding to requests from the aircraft operation control module and the task chain state machine, it generates a set of executable paths that satisfy the operation rule model through rule constraints. For aircraft: Extract edges containing only runway and taxiway attributes to construct a taxiway subgraph. Based on the arrival and departure information of the flight plan, calculate the optimal path and multiple suboptimal paths from the runway exit to the assigned parking position, or from the parking position to the runway entrance, based on road network weights, to form an executable path set. For ground support vehicles: extract edges containing service lanes and allowed intersection attributes to construct a service lane subgraph. Based on the target location in the task chain, calculate the optimal path and multiple suboptimal paths from the current location to the target work point based on road network weights, forming an executable path set.

[0045] Specifically, The operational rule model performs dynamic conflict detection and replanning based on a rolling time window. Its key feature is the introduction of a physically measured trajectory prediction mechanism and a cost-minimizing decision logic. Specific implementation steps include: (1) Constructing high-fidelity predicted spatiotemporal trajectories The system receives real-time feedback on the position and speed of physical vehicles, combines the travel time weights of each segment in the set of executable paths with the current operating status of the vehicles, and continuously updates the predicted spatiotemporal trajectories of aircraft and ground support vehicles; it also sets a forward-scrolling prediction time window. (e.g., the next 30 seconds), in time steps Calculate the vehicle in units of (e.g., 1 second). In the future Predicted location : in: A vehicle for transporting physical experiment sand tray systems with real-time feedback via UWB or visual positioning. Current precise coordinates are used to correct accumulated errors in the digital twin; To predict speed. If the vehicle is in normal operating condition, then Take the rated speed of the road section If the physical sand table indicates that the vehicle's current measured speed is reduced due to power or mechanical failure... Significantly lower than (If the deviation exceeds 10%), the measured speed will be used as the prediction benchmark, i.e. = This ensures that the predicted trajectory matches the actual performance of the physical entity.

[0046] (2) Perform spatiotemporal conflict detection operation For any two vehicles and In the prediction time window Within, calculate the Euclidean distance between the predicted positions of the two. ; The decision logic is as follows: If there exists at any time such that < If so, a conflict is determined to have occurred.

[0047] in The safe interval distance is dynamically generated based on environmental condition models (such as rain and snow simulations).

[0048] When it is detected that the predicted spatiotemporal trajectories of any two vehicles (including between aircraft, between ground support vehicles, or between an aircraft and a ground support vehicle) occupy the same spatial node within the same time window or that their spatial distance is less than a preset safety interval threshold, a path conflict is identified, and a local replanning strategy is triggered. In response to conflicts between different types of vehicles, the paths of high-priority objects are kept unchanged, while path reselection or timing adjustment is performed on low-priority objects. Path reselection is to reselect the alternative path with the lowest passage cost that meets the constraints from its set of executable paths. Timing adjustment includes inserting waiting actions or adjusting speed. In the event of a conflict between similar vehicles, priority is determined based on the "first-come, first-served" principle, and the execution path reselection or timing adjustment is controlled for low-priority objects.

[0049] When determining the vehicle and The execution path conflicts, and according to the priority rules... For high priority (such as aircraft) When the priority is low (e.g., ground support vehicles), the following hardware control instructions are executed based on a local replanning strategy, specifically including two avoidance strategies for the vehicle. Calculate the execution cost of the two avoidance strategies and select the one with the lowest cost to execute: Strategy A: Timing Adjustment (Wait in place / Slow down) Computational vehicle Wait for the aircraft carrier at the current location or a safe position before the point of conflict. By the required waiting time .

[0050] Cost calculation: in This is the time sensitivity coefficient.

[0051] Strategy B: Route reselection (detour) The executable path generation module is invoked to generate a vehicle in the subgraph after removing conflicting road segments. By replanning alternative routes, the total traffic weight of the new routes is obtained. Compare the weights of the remaining segments of the original path. .

[0052] Cost calculation: in This is a path switching penalty constant (to avoid frequent path changes that could cause instability in the physical vehicle's movement).

[0053] like < Generate detour instructions to drive the vehicle. Change the driving route; like > Generate pause or deceleration commands to drive the vehicle. Execution timing adjustment.

[0054] Specifically, The operational rule model adopts a priority-based right-of-way control mechanism: Aircraft are given higher priority than all ground support vehicles. When it is determined that the operating paths of the aircraft and ground support vehicles conflict at intersections or taxiway entrances, the following hardware control instructions are executed based on the local replanning strategy: if the ground support vehicle has a conflict-free alternative path, a detour instruction is sent to it to perform path reselection; if the ground support vehicle has no alternative path or the travel time / distance of the alternative path exceeds a preset threshold, a wait-in or deceleration instruction is sent to it to perform timing adjustment until the aircraft leaves the conflict area.

[0055] Preferred, The task chain state machine encapsulates the ground support process into a sequence of states containing the following order: Parking Ready Status: Ground support vehicles are waiting for dispatch instructions in the parking area; Approaching driving status: Ground support vehicles are driving towards the target aircraft position along the planned route; Safety isolation status: Ground support vehicles automatically stop when they reach a preset distance from the aircraft, simulating the safety confirmation process of waiting for the aircraft's engines to shut down or wheel chocks to be placed, until a signal indicating the end of the waiting time is received, at which point they enter the operational status; Operation status: The ground support vehicle model moves to the aircraft model docking position and remains stationary to simulate the execution of corresponding ground support service operations; Evacuation and return to position status: Ground support vehicles have finished their service and left the safe area of ​​the aircraft stand; Task completion status: Ground support vehicles release resource occupation and send out a task completion signal; The state machine actively triggers state transitions based on the logical judgment results of the operating rule model and generates corresponding atomic instructions to drive the physical vehicle to perform actions.

[0056] Preferred, The environmental state model is configured with an environment-behavior parameter mapping mechanism to control the environmental simulation device to adjust the light intensity and visibility simulation effect of the physical sandbox; it is configured with virtual weather scenarios such as rain and snow, and the corresponding weather scenarios are mapped to the behavioral constraint parameters of vehicles and aircraft. The behavioral constraint parameters include at least the speed limit, braking threshold, safe distance and state machine waiting time. Behavioral constraint parameters are input into the multi-source model fusion scheduling engine in real time, triggering the running rule model to update the safety interval rules and constraints, and driving the task chain state machine to generate atomic instructions with new constraints to control the physical vehicle to perform running state adjustments.

[0057] Preferred, The virtual-real consistency synchronization mechanism includes: The positioning module uses UWB ultra-wideband positioning or visual fusion positioning technology to collect high-frequency physical location data, and uploads the physical vehicle's operating data to the digital twin simulation subsystem in real time based on the TCP / IP communication protocol. The digital twin simulation subsystem timestamps the uploaded data and aligns it with the simulation clock of the digital twin scene. To address the discrete gaps caused by the data update interval, the digital twin simulation subsystem uses an interpolation algorithm to calculate the motion pose of intermediate frames, so as to smoothly restore the continuous motion of the physical vehicle in the digital map and achieve synchronous display between the physical and digital sides.

[0058] Preferred, The simulation-interactive control terminal has a pre-built emergency event library, allowing users to dynamically inject the following types of disturbances during system operation: Equipment failure type: Loss of positioning signal or power system failure of simulated physical vehicle; Unauthorized intrusion category: Simulating unauthorized vehicles entering the runway protected area; Resource unavailability category: simulated temporary closure of parking positions, insufficient support equipment, or temporary closure of the runway; Operational plan deviations: Simulating aircraft delays, early arrivals, or cancellations; In response to the injected event, the system triggers the multi-source model fusion scheduling engine to execute conflict detection and local replanning strategies, or regenerates the ground support task chain and atomic instructions of claim 1.

[0059] Preferred, The simulation interactive control terminal supports both standard operating mode and drill mode: In standard operating mode, the system executes a standardized process demonstration based on the operating rule model and locks in the preset optimal path logic; In drill mode, the system combines an emergency database to execute model-driven dynamic responses for emergency response simulations and training.

[0060] Example 3 This embodiment provides an airport apron traffic operation control and simulation system based on the digital-physical-logic ternary coupling, such as... Figure 2 As shown, it mainly consists of four parts: a physical experiment sandbox system, a digital twin simulation subsystem, a multi-source model fusion scheduling engine, and a simulation interaction control terminal. 1. A physical experiment sandbox system, which serves as the physical verification carrier for digital algorithms. In this embodiment, the sandbox is constructed at a 1:400 scale, depicting the airside of a large hub airport, including runways, taxiways, aprons, and terminal structures. Physical vehicles include movable physical aircraft models and miniature intelligent connected ground support vehicles. All vehicles integrate embedded processing units (such as STM32 or ARM architecture chips), motor drive modules, and UWB (Ultra Wideband) positioning tags. They receive atomic commands (such as "move forward at 3 cm / s" or "turn 30 degrees") via wireless networks (Wi-Fi or ZigBee) and use these commands to drive DC motors to perform actions, while simultaneously transmitting their high-precision coordinates back at a frequency of 20 Hz. The environmental simulation device includes an adjustable LED array (for simulating changes in lighting) mounted above the sandbox and an ultrasonic atomizer (for simulating low-visibility foggy environments).

[0061] 2. A digital twin simulation subsystem, built on a 3D rendering engine (such as Unity3D or Unreal Engine), connects to a physical sandbox through a virtual-real consistency synchronization mechanism. This subsystem is used to construct a virtual 3D scene consistent with the physical experimental sandbox system, including digital map models, virtual aircraft, and ground support vehicle models. The system receives coordinate streams uploaded by the physical vehicles via TCP / IP protocol. To address the jitter and packet loss issues in the physical positioning data, Kalman filtering is used for data cleaning, and interpolation algorithms (such as cubic spline interpolation) are employed to calculate the intermediate frame poses within the data update interval, thereby achieving synchronized display on the virtual large screen.

[0062] 3. The multi-source model fusion scheduling engine is the core control hub of the system, communicating with both the physical experiment sandbox system and the digital twin simulation subsystem, and running on a high-performance server. It integrates multiple functional modules: 3.1. Operational rule model: This model stores rules such as topological constraints for airport apron operations, dynamic conflict detection and replanning, right-of-way priorities for aircraft and ground support vehicles, speed limits for ground support vehicles, standard service duration distribution for various ground support tasks, safe isolation waiting time, and safe distance thresholds for ground operations. These rules serve as mandatory constraints for behavioral decisions. like Figure 3 As shown, dynamic conflict detection and replanning involves receiving real-time feedback on the position and speed of the physical vehicle, combining the travel time weights of each segment in the executable path set with the current operating status of the vehicle, and continuously updating the predicted spatiotemporal trajectories of the aircraft and ground support vehicles. Specifically, the system updates the predicted spatiotemporal trajectory by using the basic kinematic formula (time = distance / speed) based on the current real-time position of the physical vehicle, combined with the current maximum permissible speed and remaining segment length set by the environmental state model, and calculating and accumulating the predicted time for the vehicle to reach each subsequent key node in segments, thereby constructing a trajectory that includes both position and time information. When it is detected that the predicted spatiotemporal trajectories of any two vehicles (including between aircraft, between ground support vehicles, or between an aircraft and a ground support vehicle) occupy the same spatial node within the same time window or that their spatial distance is less than a preset safety interval threshold, a path conflict is identified, and a local replanning strategy is triggered. For conflicts between different types of vehicles, the paths of high-priority objects remain unchanged, while path reselection or timing adjustment is performed on low-priority objects. Path reselection involves removing paths involving conflicting nodes from the set of executable paths and selecting the alternative path with the lowest travel cost. Timing adjustment includes inserting waiting actions or adjusting speed. For conflicts between vehicles of the same type, priority is determined according to the "first-come, first-served" principle, and path reselection or timing adjustment is performed on low-priority objects.

[0063] The priority-based right-of-way control mechanism means that the system pre-sets the passage priority of aircraft to be higher than that of all ground support vehicles. When it is determined that there is a conflict between the operating paths of aircraft and ground support vehicles at intersections or taxiway entrances, the following hardware control instructions are executed based on the local replanning strategy: if there is a non-conflicting alternative path for the ground support vehicle, a detour instruction is sent to it to perform path reselection; if there is no alternative path for the ground support vehicle or the passage time / distance of the alternative path exceeds a preset threshold, a wait-in or deceleration instruction is sent to it to perform timing adjustment until the aircraft leaves the conflict area.

[0064] It should be noted that the operational rule models described in this embodiment (such as absolute aircraft priority, first-come, first-served, etc.) are the system's pre-set optimal scheduling strategies. To meet the needs of scientific research verification and algorithm comparison, the multi-source model fusion scheduling engine supports strategy configuration. Users can modify rule weights or load custom scheduling algorithm modules through the simulation interactive management terminal, thereby comparing and verifying the operation of different management strategies in the same physical sandbox environment.

[0065] 3.2. Executable Path Generation Module: Based on the topology of the digital map model, a unified directed weighted graph of the airport road network is constructed. Spatial nodes represent aircraft stands or intersections, and edges represent runways, taxiways, or service lanes. The edge weights map to segment lengths or travel time costs. According to the type and attributes of the transportation vehicle, specific travel subgraphs are extracted from the directed weighted graph of the airport road network. Responding to requests from the aircraft operation control module and the task chain state machine, a set of executable paths that satisfy the operation rule model is generated through rule constraints. For aircraft: Extract edges containing only runway and taxiway attributes to construct a taxiway subgraph. Based on the arrival and departure information of the flight plan, calculate the optimal path and multiple suboptimal paths from the runway exit to the assigned parking position, or from the parking position to the runway entrance, based on road network weights, to form an executable path set. For ground support vehicles: extract edges containing service lanes and allowed intersection attributes to construct a service lane subgraph. Based on the target location in the task chain, calculate the optimal path and multiple suboptimal paths from the current location to the target work point based on road network weights, forming an executable path set.

[0066] 3.3. Aircraft Operation Control Module: Based on the simulated flight plan and its service time window configured in the simulation interactive control terminal, the executable path generation module is called to generate the aircraft taxiing path, which is then converted into atomic instructions to drive the movable physical aircraft model to simulate the arrival and departure process on the physical sandbox, and the ground service request time window is issued to the task chain state machine according to the aircraft status. 3.4. Task chain state machine (e.g.) Figure 4 As shown): This module responds to service demand time windows, generates a ground support task chain, calls the executable path generation module to plan the ground support vehicle route, and decomposes the task chain into atomic instructions to drive the movement of miniature intelligent connected ground support vehicles. The task chain state machine executes the ground support vehicle state transitions based on the constraints of the running rule model; the task chain state machine encapsulates the ground support process into a state sequence containing the following order: Parking Ready Status: Ground support vehicles are waiting for dispatch instructions in the parking area; Approaching driving status: Ground support vehicles are driving towards the target aircraft position along the planned route; Safety Isolation Status: Ground support vehicles automatically stop when they reach a preset distance from the aircraft, simulating the safety confirmation process of waiting for the aircraft's engine to shut down or wheel chocks to be placed. A countdown based on the safety isolation waiting time in the operation rule model is executed until a signal indicating the end of the waiting time is received, after which the vehicle enters the operation status. Operation status: The ground support vehicle model moves to the aircraft model docking position and remains stationary to simulate the execution of the corresponding ground support service operation; the state machine executes a countdown according to the standard service duration of the aircraft type defined in the operation rule model to simulate the operation time. Evacuation and return to position status: Ground support vehicles have finished their service and left the safe area of ​​the aircraft stand; Task completion status: Ground support vehicles release resource occupation and send out a task completion signal; The state machine actively triggers state transitions based on the logical judgment results of the operating rule model and generates corresponding atomic instructions to drive the physical vehicle to perform actions.

[0067] 3.5. Environmental State Model: It is equipped with an environment-behavioral parameter mapping mechanism to control the environment simulation device to adjust the lighting intensity and visibility simulation effect of the physical sandbox; it is equipped with virtual weather scenarios such as rain and snowfall, and maps the corresponding weather scenarios to the behavioral constraint parameters of vehicles and aircraft. The behavioral constraint parameters include at least the speed limit, braking threshold, safe distance and state machine waiting time. Behavioral constraint parameters are input into the multi-source model fusion scheduling engine in real time, triggering the running rule model to update the safety interval rules and constraints, and driving the task chain state machine to generate atomic instructions with new constraints to control the physical vehicle to perform running state adjustments.

[0068] 4. A virtual-real consistency synchronization mechanism is used to achieve synchronization and consistency between the physical and digital sides at the level of position, attitude and operational logic based on timestamp alignment, interpolation operation and data cleaning strategies; The positioning module uses UWB ultra-wideband positioning or visual fusion positioning technology to collect high-frequency physical location data, and uploads the physical vehicle's operating data to the digital twin simulation subsystem in real time based on the TCP / IP communication protocol. The digital twin simulation subsystem timestamps the uploaded data and aligns it with the simulation clock of the digital twin scene. To address the discrete gaps caused by the data update interval, the digital twin simulation subsystem uses an interpolation algorithm to calculate the motion pose of intermediate frames, so as to smoothly restore the continuous motion of the physical vehicle in the digital map and achieve synchronous display between the physical and digital sides. Meanwhile, the atomic instructions generated by the multi-source model fusion scheduling engine are sent to the physical vehicle in real time via wireless network to drive the physical entity.

[0069] 5. Simulation interactive control terminal (e.g.) Figure 5 As shown, it is used to provide a human-computer interaction interface, allowing users to import or configure simulated flight plans, insert emergencies, adjust operating parameters or trigger environmental changes, and trigger joint responses from the physical and digital sides through a multi-source model fusion scheduling engine to achieve standardized process demonstrations or emergency drills.

[0070] The simulation-interactive control terminal has a pre-built emergency event library, allowing users to dynamically inject the following types of disturbances during system operation: Equipment failure type: Loss of positioning signal or power system failure of simulated physical vehicle; Unauthorized intrusion category: Simulating unauthorized vehicles entering the runway protected area; Resource unavailability category: simulated temporary closure of parking positions, insufficient support equipment, or temporary closure of the runway; Operational plan deviations: Simulating aircraft delays, early arrivals, or cancellations; In response to the injected event, the system triggers the multi-source model fusion scheduling engine to execute conflict detection and local replanning strategies, or regenerate ground support task chains and atomic instructions. The simulation interactive control terminal supports both standard operating mode and drill mode: In standard operating mode, the system executes a standardized process demonstration based on the operating rule model, locking in the preset optimal path logic; In drill mode, the system combines an emergency database to execute model-driven dynamic responses for emergency response simulations and training.

[0071] To more clearly illustrate the system's operational logic, the following example, "an inbound flight CA123 requests refueling service and encounters sudden rain," describes the system's complete workflow. Figure 6 As shown: Step S1 Initial Planning and Command Issuance: Based on the flight plan and its service time window, the aircraft operation control module calls the executable path generation module to calculate the taxiing path of CA123 from the runway exit to gate 205, converts it into a series of atomic commands (speed, turn angle), and sends them to the physical aircraft model via wireless network. The physical aircraft then begins to move on the sand table.

[0072] Step S2 Ground service task triggering: The system publishes the type of ground service required by the flight and the time window to the task chain state machine. The state machine generates a refueling task and dispatches the nearest available refueling truck to the task.

[0073] Step S3: Dynamic injection of environmental parameters: At this point, the user switches the weather to "moderate rain" on the simulation interactive control terminal. The LED lights on the sand table are dimmed, and the ultrasonic atomizer is activated to generate a thin fog in a localized area of ​​the sand table to simulate a low-visibility environment accompanied by rainfall. The environmental state model immediately updates the operating rules according to the moderate rain weather: reducing the maximum speed of the refueling truck, increasing the safety interval between vehicles, updating the road network weights, and increasing the passage cost of all road segments (e.g., increasing the passage time of road segments).

[0074] Step S4: Conflict Detection and Replanning: During its journey to gate 205, the refueling truck's projected spatiotemporal trajectory potentially collides with that of a shuttle bus crossing the intersection. Due to the "moderate rain" weather conditions, the safety distance threshold is automatically increased by the system. The operational rules model determines that while the current distance between the two vehicles will not physically collide, it is less than the "rainy weather safety distance," thus classifying it as a conflict. The system performs local replanning: the shuttle bus is closer to the intersection, thus the refueling truck is considered to have a lower priority, and a "decelerate and wait" command is sent to its physical model. The refueling truck noticeably slows down on the physical model and continues its journey only after the shuttle bus has completely passed.

[0075] Step S5 Safety Operation and Feedback: Upon arrival at aircraft stand 205, the refueling truck automatically executes the "safety isolation" logic, stopping in the safety zone for 3 seconds (this duration is set by the operational rule model according to the task type, simulating the confirmation process), and then docks with the aircraft. After the operation is completed, the vehicle drives away, and the system interface displays "task completed."

[0076] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A digital airport apron traffic operation management and simulation method, characterized in that, include: Obtain road network status information including the number of aircraft or vehicles on each road segment, the length of the road segment, and the maximum capacity of the road segment; Based on the road network status information, the real-time traffic weight of each road segment is calculated; Based on the real-time traffic weights, a directed weighted graph of the airport road network is constructed; Based on the directed weighted graph of the airport road network, an executable path set is generated; from the executable path set, the optimal executable path for the target vehicle is selected according to the target location of the movement request; the optimal executable path is converted into a corresponding atomic guidance instruction sequence to guide the target vehicle to glide or travel along the optimal executable path to the target work point; The system acquires the position and speed feedback from the physical vehicle in real time and updates the predicted spatiotemporal trajectory of the physical vehicle on a rolling basis; it performs real-time conflict detection on the optimal executable path based on the predicted spatiotemporal trajectory; if a spatiotemporal conflict is detected between paths, it dynamically replans the movement resources corresponding to the path with lower priority to resolve the conflict.

2. The digital airport apron traffic operation control and simulation method according to claim 1, characterized in that, Based on the road network status information, the real-time traffic weight of each road segment is calculated, and the corresponding calculation formula is as follows: in, For real-time access weights, Based on the basic passage time, For the length of the road segment, For rated speed, The congestion penalty coefficient, The maximum capacity of the road segment. This represents the number of aircraft or vehicles currently present on this road segment. For the turning geometry penalty term, For environmental safety distance, This refers to the length of the vehicle.

3. The digital airport apron traffic operation control and simulation method according to claim 1, characterized in that, The dynamic reprogramming process specifically includes: To address conflicts between different types of vehicles, the system compares preset priorities and avoids conflicts by keeping the paths of high-priority objects unchanged and performing path reselection or timing adjustment for low-priority objects. In the event of a conflict between similar vehicles, priority is determined based on the principle of first-come, first-served, and the lower-priority objects are controlled to perform the aforementioned path reselection or timing adjustment. After removing paths involving conflicting nodes from the set of executable paths, the total passage weight of the new path is calculated; based on the total passage weight, the detour cost is calculated; based on the position and speed fed back by the physical vehicle, the waiting cost is calculated; the detour cost and the waiting cost are compared, and if the detour cost is less than the waiting cost, the path is reselected for the low-priority object; if the detour cost is greater than the waiting cost, the timing adjustment is performed on the low-priority object.

4. The digital airport apron traffic operation control and simulation method according to claim 3, characterized in that, Based on the sum of the passage weights, the detour cost is calculated using the following formula: in, In order to take a detour, This represents the sum of the travel weights for the new path. The weight of the remaining segment of the original path. This is the path switching penalty constant; Based on the position and speed feedback from the physical vehicle, the waiting cost is calculated using the following formula: in, The waiting time required for the vehicle to pass. The cost of waiting This is the time sensitivity coefficient.

5. The digital airport apron traffic operation control and simulation method according to claim 1, characterized in that, The optimal executable path includes the optimal executable coasting path and the optimal executable driving path; The process of generating the optimal executable taxiway path specifically includes: firstly, extracting edges containing only runway and taxiway attributes from the directed weighted graph of the airport road network to construct a taxiway subgraph; and then, based on the road network state information and the taxiway subgraph, calculating a set of executable paths containing the optimal taxiway path and the suboptimal taxiway path, and selecting the optimal executable taxiway path from it. The process of generating the optimal executable driving path specifically includes: firstly, extracting edges containing service lane attributes from the directed weighted graph of the airport road network to construct a service lane subgraph; calculating the set of executable paths from the current location to the target work point based on the service lane subgraph; and selecting the optimal executable driving path from the set of executable paths.

6. The digital airport apron traffic operation control and simulation method according to claim 1, characterized in that, Based on the aforementioned position and velocity, the predicted spatiotemporal trajectory of the physical vehicle is calculated using the following formula: in, For transport vehicles In the future The predicted location, For transport vehicles Current coordinates To predict speed.

7. A system for digital airport apron traffic operation control and simulation as described in any one of claims 1-6, characterized in that, include: The physical experiment sandbox system is used to construct a realistic airside environment of an airport, including airport runway and taxiway structures, movable physical aircraft models, miniature intelligent connected ground support vehicles, and environmental simulation devices. The digital twin simulation subsystem is used to construct a virtual three-dimensional scene that is consistent with the physical experimental sandbox system, including a digital map model, virtual aircraft and ground support vehicle models, and to perform virtual-real synchronous rendering based on the position and operating status of the physical vehicles to achieve virtual-real synchronous display. The multi-source model fusion scheduling engine, as the control center of the system, communicates and connects with the physical experimental sandbox system and the digital twin simulation subsystem respectively, and is used to run the rule model, the executable path generation module, the aircraft operation control module, the task chain state machine and the environmental state model. The aircraft operation control module is used to generate aircraft taxiing requests, the task chain state machine is used to generate ground support vehicle movement requests, and respectively call the executable path generation module to generate corresponding paths; The simulation interactive control terminal provides a human-computer interaction interface, has a pre-built emergency event library, and allows users to import or configure simulated flight plans, insert emergencies, adjust operating parameters, or trigger environmental changes. It also triggers a joint response from the physical and digital sides through the multi-source model fusion scheduling engine to achieve standardized process demonstrations or emergency drills.

8. The digital airport apron traffic operation control and simulation system according to claim 7, characterized in that, The mobile physical aircraft model and the miniature intelligent connected ground support vehicle serve as physical transport tools. Both are equipped with embedded processing units and positioning modules. By executing atomic instructions from the multi-source model fusion scheduling engine and providing real-time feedback on physical location and operating status, they physically verify the scheduling strategy on the digital side.

9. The digital airport apron traffic operation control and simulation system according to claim 7, characterized in that, The executable path generation module is used to generate a set of executable paths that satisfy the rules set in the rule model; The process of generating the executable path set specifically includes: First, a unified directed weighted graph of the airport road network is constructed based on the topology of the digital map model. Then, a specific passage subgraph is extracted from the directed weighted graph of the airport road network according to the type attribute of the vehicle. Finally, the set of executable paths is generated by filtering according to the specific requests of the aircraft operation control module and the task chain state machine.

10. The digital airport apron traffic operation control and simulation system according to claim 7, characterized in that, The operational rule model is used to store the topological constraints of airport apron operations, the right-of-way priority of aircraft and ground support vehicles, the speed limit of ground support vehicles, the standard service duration distribution of various ground support tasks, the safe isolation waiting time, and the ground operation safe distance threshold. The operational rule model receives the position and speed feedback from the physical vehicle in real time, and combines the travel time weight of each segment in the executable path set with the current operating status of the vehicle to continuously update the expected spatiotemporal trajectory of the aircraft and ground support vehicles. When it is detected that the predicted spatiotemporal trajectories of any two vehicles occupy the same spatial node within the same time window or that their spatial distance is less than a preset safety interval threshold, a path conflict is determined, and dynamic replanning is performed.

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