Airport vehicle operation plan generation method, computer device, readable storage medium, and program product

Abstract

An airport vehicle operation plan generation method, an apparatus, a computer device, a readable storage medium, and a program product. Task assignment and operation simulation are performed on airport vehicles by means of a simulation model corresponding to an airport and on the basis of a task queue, configuration information of the airport vehicles, and assigned tasks of the airport vehicles; corresponding operation plans are outputted; and target operation plans corresponding to the airport vehicles are determined on the basis of the operation plans.

Description

Airport vehicle operation plan generation method, computer equipment, readable storage media and program products

[0001] Related applications

[0002] This application claims priority to Chinese patent application filed on February 21, 2025, with application number 2025101957564, entitled "Method for Generating Airport Vehicle Operation Plans, Computer Equipment, Readable Storage Medium and Program Product", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of logistics and transportation technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for generating airport vehicle operation plans. Background Technology

[0004] With the development of the logistics and transportation industry, airports are now a reliable means of transporting large volumes of goods. Therefore, to ensure efficient cargo transport, smooth ground operations of aircraft at airports are essential. In turn, smooth ground operations require the efficient operation of airport vehicles, necessitating planning for their operation.

[0005] In related technologies, the operation plans for airport vehicles are typically formulated manually based on experience. However, manually formulating operation plans for airport vehicles based on experience requires a considerable amount of time, leading to reduced efficiency in plan generation. Therefore, the airport vehicle operation plan generation methods in related technologies suffer from low efficiency. Summary of the Invention

[0006] According to various embodiments of this application, an airport vehicle operation plan generation method, apparatus, computer equipment, computer-readable storage medium, and computer program product are provided.

[0007] In a first aspect, this application provides a method for generating airport vehicle operation plans, including:

[0008] Obtain flight information and airport vehicle configuration information at the airport;

[0009] The flight information and configuration information are input into the simulation model corresponding to the airport; the simulation model is used to determine the task queue based on the flight information, and to perform task allocation and operation simulation for each airport vehicle based on the task queue, the configuration information and the assigned tasks corresponding to each airport vehicle, and output each operation plan corresponding to each airport vehicle.

[0010] Based on each of the aforementioned work plans, the target work plan corresponding to each of the aforementioned airport vehicles is determined.

[0011] Secondly, this application also provides an airport vehicle operation plan generation device, comprising:

[0012] The acquisition module is used to acquire flight information and configuration information from the airport;

[0013] The generation module is used to input the flight information and the configuration information into the simulation model corresponding to the airport; the simulation model is used to determine the task queue according to the flight information, and to perform task allocation and operation simulation for each airport vehicle according to the task queue, the configuration information and the assigned tasks corresponding to each airport vehicle, and output each operation plan corresponding to each airport vehicle.

[0014] The determination module is used to determine the target operation plan corresponding to each of the airport vehicles based on each of the operation plans.

[0015] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0016] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0017] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0018] Details of one or more embodiments of this application are set forth in the following drawings and description. Other features, objects, and advantages of this application will become apparent from the specification, drawings, and claims. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the disclosed drawings without creative effort. The additional details or examples used to describe the drawings should not be considered as a limitation on the scope of any of the disclosed invention, the currently described embodiments and / or examples, and the best mode of these inventions as currently understood.

[0020] Figure 1 is a flowchart illustrating an airport vehicle operation plan generation method according to one or more embodiments.

[0021] Figure 2 is a schematic diagram of the interface simulated according to one or more embodiments.

[0022] Figure 3 is a flowchart illustrating an airport vehicle operation plan generation method according to one or more other embodiments.

[0023] Figure 4 is a structural block diagram of an airport vehicle operation plan generation device according to one or more embodiments.

[0024] Figure 5 is an internal structural diagram of a computer device according to one or more embodiments. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] Reasonable scheduling of airport vehicles is one of the key factors in ensuring smooth ground operations of aircraft. How to schedule these vehicles scientifically and efficiently to improve airport operational efficiency has become an important issue.

[0027] In related technologies, tasks are often arranged based on human experience and relatively simple rules. However, when there are many flights, unpredictable takeoff and landing times, and short takeoff connection times, pre-scheduling operations requires consideration of multiple factors, making it quite challenging. Manually creating operational plans takes a considerable amount of time and is prone to problems such as unavailability of aircraft, inefficient task connections, and continuous cross-regional operations, leading to delays in aircraft pushback and consequently affecting flight takeoffs, which in turn negatively impacts the pushback and takeoff of other flights. Furthermore, the effectiveness of manual pre-scheduling is difficult to predict and analyze in advance to develop better strategies.

[0028] Based on this, the embodiments of this application adopt simulation and digital twin technology, which not only integrates the business rules of airport vehicle operation plan to realize one-click output of reasonable pre-scheduling scheme and improve the efficiency of airport vehicle operation plan generation; but also can simulate and pre-run the implementation effect of pre-scheduling scheme with high precision, and has intuitive and effective visual analysis function, which can more comprehensively evaluate and optimize scheduling strategy to provide a more scientific solution.

[0029] In one embodiment, as shown in Figure 1, an airport vehicle operation plan generation method is provided. This embodiment illustrates the method by applying it to a terminal. The method can also be applied to a server, and further to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. The method includes the following steps S202 to S206. Wherein:

[0030] Step S202: Obtain flight information and airport vehicle configuration information from the airport.

[0031] The aforementioned terminals can be computers or other equipment used to manage vehicles within the airport. An airport can deploy multiple aircraft and airport vehicles. Since the takeoff and landing times of each aircraft differ, the airport vehicles can include towing vehicles that guide aircraft to their designated locations within the airport. To account for various variables and factors at the airport, such as the number of flights, takeoff and landing times, parking positions, and runways, the terminals need to provide accurate data analysis and simulation, quickly generate safe and efficient pre-scheduling plans for airport vehicles, optimize resource utilization, reduce pushback waiting times, lower flight delay risks, and improve operational efficiency. The terminals must combine various information to pre-generate operational plans for airport vehicles, providing a basis for actual airport vehicle operations.

[0032] To this end, the terminal can obtain flight information for each flight in the airport, as well as configuration information for airport vehicles. The flight information can be related to each flight in the airport, such as flight information for a single day, and may include, but is not limited to, departure time, runway threshold, and physical attributes of the flight. The physical attributes of the flight may include, but are not limited to, fuselage length, aircraft speed, and safe distance. The configuration information for airport vehicles can be specifically for the configuration of towing vehicles within the airport. The configuration information for airport vehicles can include various parameters.

[0033] In one embodiment, the step of obtaining the configuration information of airport vehicles may include: obtaining the service area information corresponding to each airport vehicle in the aforementioned airport based on the parking space allocation information corresponding to the airport; and obtaining the configuration information of the aforementioned airport vehicles based on the service area information.

[0034] In this embodiment, the configuration information of the airport vehicles may include, but is not limited to, the service area information corresponding to each airport vehicle. The service area information represents the service area of ​​the airport vehicle within the airport; that is, the terminal divides the service area according to the parking positions of flights, and each service area is configured with a different number of airport vehicles. The terminal can obtain the service area information corresponding to each airport vehicle in the airport based on the parking position division information corresponding to the airport, and obtain the configuration information of the airport vehicles based on the service area information. The parking position division information represents the division of the parking positions for each flight in the airport. In some embodiments, the configuration information of the airport vehicles may also include other information. Taking the airport vehicle as a tractor as an example, the terminal can also obtain information such as the number of tractors in the airport, the vehicle service area, and vehicle parameters as the configuration information of the airport vehicles. The vehicle parameters may include, but are not limited to, vehicle length, speed, and vehicle safety distance. Therefore, it is possible to combine multiple factors to consider the operation plan of airport vehicles, improving the comprehensiveness of the generated operation plan.

[0035] Step S204: Input the aforementioned flight information and airport vehicle configuration information into the simulation model corresponding to the aforementioned airport. The simulation model is used to determine the task queue based on the aforementioned flight information, and to perform task allocation and operation simulation for each of the aforementioned airport vehicles based on the aforementioned task queue, the aforementioned airport vehicle configuration information, and the assigned tasks corresponding to each of the aforementioned airport vehicles, and output the respective operation plans corresponding to each of the aforementioned airport vehicles.

[0036] The terminal can generate a simulation model of the airport by combining various parameters and structures within the airport. This simulation model can then generate work plans by incorporating flight information and airport vehicle configuration information. For example, the terminal can input the aforementioned flight information and airport vehicle configuration information into the airport's simulation model; the simulation model then determines a task queue based on the flight information. This task queue can include multiple towing tasks corresponding to various flights, with each task including flight information such as departure time and flight location.

[0037] The terminal, through a simulation model, allocates tasks and simulates operations for each of the aforementioned airport vehicles based on the task queue, the configuration information of the airport vehicles, and the assigned tasks for each vehicle. It then outputs corresponding operation plans for each of the airport vehicles. Task allocation refers to assigning towing tasks to each airport vehicle that meet operational efficiency requirements. Operation simulation involves simulating the tasks performed by the airport vehicles within the simulation model to determine their execution performance and whether adjustments are needed. The simulation model can generate multiple operation plans simultaneously. Each operation plan can include a complete task allocation and execution scheme for each airport vehicle.

[0038] The configuration information for airport vehicles can be adjusted according to actual conditions. For example, the terminal can use the configuration information that was frequently used when generating historical work plans as the default configuration information for airport vehicles in the simulation model. Furthermore, for each period, such as daily flight information, where there are significant differences between weekdays and non-weekdays, the terminal can also support user-defined modifications to the configuration information, including the number of partitions, partition range, and the number of airport vehicles, to meet the needs of different scenarios.

[0039] Furthermore, due to differences in flight schedules and application scenarios each cycle, the priority requirements for airport vehicle dispatching rules will also vary. To better address these variations, the terminal allows users to configure vehicle dispatching priorities as needed, and can differentiate between tasks within and across zones, setting dispatching time intervals. This enables customized forecasting and optimization for weekdays and weekends based on actual daily production conditions, and supports personalized configurations such as partitioning and priority, adapting to various scenarios and improving resource utilization. Moreover, based on distributed processing technology, multiple simulation experiments and scenario analyses can be conducted simultaneously, helping decision-makers better understand the factors affecting scheduling and provide more scientific and reasonable work plans.

[0040] Step S206: Based on each of the above-mentioned work plans, determine the target work plan corresponding to each of the above-mentioned airport vehicles.

[0041] The simulation model described above can output multiple work plans for airport vehicles. The terminal can then determine the target work plan for each airport vehicle from these plans, and execute the actual work plan for each vehicle within that target plan to meet the efficiency requirements of airport vehicles in performing their duties and to satisfy the needs of flights using airport vehicles. The terminal can determine the target work plan by displaying each work plan and responding with confirmation messages. The terminal can also visually simulate the process and execution status of airport vehicles executing work plans within the airport.

[0042] Airport vehicles can be towing vehicles, and the main entities involved in generating work plans include aircraft and towing vehicles. Based on this, the terminal can modularly cut the simulation model, focus on business needs, improve simulation speed, achieve minute-level output of scheduling results, and support visual viewing of the entire process of flight landing, taxiing, positioning, pushback, and takeoff, as well as the operation process of towing vehicle scheduling, driving, and task execution.

[0043] Figure 2 shows a simulation interface diagram in one embodiment. The terminal utilizes digital twin technology to perform a 3D visualization preview of the entire scenario for each tractor operation plan, allowing users to intuitively and quickly obtain information on vehicle utilization efficiency and the impact on flight takeoffs. The displayed information may include, but is not limited to, tractor-related information, the tractor's task list, and the tractor's task execution nodes. Each task in the task list may include, but is not limited to, task start time, end time, task duration, and task mileage.

[0044] The terminal can also run simulations of multiple versions of work plans simultaneously, comparing the execution effects of different work plans. Through data mining and analysis, the terminal automatically recommends that users pay attention to waiting tractors and tractors with the longest travel distances, demonstrating the support process with a single click. By referring to task information such as connection distance, connection time, and travel path, the terminal helps users determine whether adjustments to the tractor pre-scheduling plan are needed, thereby improving the availability of the work plan.

[0045] In some embodiments, the terminal can also pre-arrange and real-time schedule the operation plans of other vehicles in the airport, thereby fully considering the mutual influencing factors and outputting a special vehicle overall operation plan scheme that can achieve better comprehensive timeliness, thereby improving the efficiency of the coordination of various special vehicle tasks and achieving better comprehensive flight support timeliness.

[0046] In the aforementioned airport vehicle operation plan generation method, flight information and airport vehicle configuration information are input into the airport's corresponding simulation model. The simulation model determines the task queue based on the flight information, and then performs task allocation and operation simulation for each airport vehicle based on the task queue, airport vehicle configuration information, and assigned tasks. It outputs corresponding operation plans and determines the target operation plan for each airport vehicle based on these plans. Compared to traditional methods relying on manual experience, this solution combines airport flight information, airport vehicle configuration information, and assigned tasks within the airport's simulation model to perform task allocation and operation simulation for each airport vehicle, thereby determining the operation plan for each vehicle. Furthermore, it uses digital twin technology to preview the effect of the operation plans. By generating operation plans based on task allocation and simulated operation methods within the simulation model, it improves the efficiency of airport vehicle operation plan generation, optimizes vehicle resource utilization, reduces flight pushback waiting time, and thus reduces the risk of delays.

[0047] In one embodiment, determining a task queue based on the flight information, and then assigning tasks and simulating operations for each airport vehicle based on the task queue, the configuration information, and the assigned tasks for each airport vehicle, and outputting operation plans for each airport vehicle, may include: determining the departure time based on the flight information; determining a task queue based on the departure times; assigning tasks and simulating operations for each airport vehicle based on the departure times in the task queue, the service area information in the configuration information, and the assigned tasks for each airport vehicle, and outputting operation plans for each airport vehicle.

[0048] In this embodiment, the aforementioned airport vehicles can be vehicles that tow aircraft before takeoff, and the task allocation of these airport vehicles can be based on the flight's departure time. Therefore, the terminal can determine the departure time based on the flight information obtained through a simulation model. The departure time can be the aircraft's departure time. Each aircraft's departure time can be different. The terminal can use the simulation model to allocate tasks and simulate operations for each airport vehicle based on the departure times in the task queue, the service area information in the configuration information, and the assigned tasks corresponding to each airport vehicle, thereby obtaining and outputting the corresponding operation plans for each airport vehicle. During task allocation, the simulation model can allocate tasks one by one according to the task list. The assigned tasks can be those already assigned to airport vehicles. To avoid task conflicts, the terminal needs to determine the operation plan by combining the simulation model with the assigned tasks of the airport vehicles. Furthermore, the terminal uses the simulation model to determine the airport vehicles performing towing tasks for each flight from suitable service areas based on the departure time, improving the efficiency of operation plan generation.

[0049] Through this embodiment, the terminal can perform task allocation and operation simulation for vehicles at various airports in the simulation model by combining flight information, service area information and assigned tasks. The simulation model can take into account multiple factors to generate operation plans, thereby improving the efficiency of operation plan generation.

[0050] In related technologies, the method of generating airport vehicle operation plans based on human experience cannot effectively resolve potential conflicts during operations, and the time required to resolve potential conflicts is long, thus reducing the efficiency of operation plan generation. Therefore, this application's embodiment uses a simulation model to detect potential conflict points and problems, and to make timely adjustments and optimizations.

[0051] In one embodiment, based on the departure times in the task queue, the service area information in the configuration information, and the assigned tasks corresponding to each airport vehicle, task allocation and operation simulation are performed on each airport vehicle, and operation plans corresponding to each airport vehicle are output. This may include: performing a first task path allocation based on the departure times, the service area information corresponding to each airport vehicle, and the assigned tasks corresponding to each airport vehicle to obtain first operation plans corresponding to each airport vehicle; performing operation simulation on each airport vehicle in the simulation model based on the first operation plans; obtaining real-time road congestion information corresponding to the operation simulation; performing a second task path allocation on each airport vehicle based on the real-time road congestion information to obtain second operation plans corresponding to each airport vehicle to avoid road congestion; and outputting operation plans corresponding to each airport vehicle based on the second operation plans corresponding to each airport vehicle.

[0052] In this embodiment, the simulation model's process of generating work plans may include multiple allocation processes. Specifically, the terminal can perform a first task path allocation within the simulation model based on each takeoff time, the service area information corresponding to the airport vehicle, and the assigned tasks for each airport vehicle, thereby obtaining each first work plan for each airport vehicle. The first task path allocation includes assigning tasks to airport vehicles based on takeoff time and the service area corresponding to the airport vehicle, and assigning corresponding travel paths to airport vehicles based on the assigned tasks. The terminal can then simulate the execution of each airport vehicle according to the aforementioned first work plans within the simulation model.

[0053] During the operation simulation, the terminal obtains real-time road congestion information for each airport vehicle through the simulation model. This real-time road congestion information includes the congestion conditions of the roads traversed by the airport vehicles during the simulated operation. Therefore, the terminal can use the simulation model to allocate secondary task routes to each airport vehicle based on the real-time road congestion information, and obtain corresponding secondary operation plans for each airport vehicle. This secondary task route allocation can be a process of real-time route adjustment for airport vehicles during the simulated operation. Thus, the terminal avoids road congestion by adjusting the routes of airport vehicles in real time based on real-time road congestion information within the simulation model.

[0054] In the first task path allocation, the terminal can combine multiple factors in the simulation model to allocate the first task path for airport vehicles.

[0055] In one embodiment, a first task path allocation is performed based on the departure times, the service area information corresponding to the airport vehicles, and the assigned tasks corresponding to the airport vehicles to obtain the first work plans corresponding to each of the airport vehicles. This may include: for each flight in the task queue, finding the first airport vehicles with the same service area as the parking position of the flight based on the service area information; determining the first departure time corresponding to the latest assigned task of the first airport vehicles based on the assigned tasks of the first airport vehicles; and performing a first task path allocation on the first target airport vehicles based on the parking position of the flight and the service area information of the first target airport vehicles to obtain the first work plan corresponding to the first airport vehicles. The difference between the first departure time of the first target airport vehicles and the departure time of the flight is greater than a first time threshold, and the difference is the maximum value among the differences between the first departure times and the departure times of the flights.

[0056] In this embodiment, the terminal allocates the first task path using a simulation model, prioritizing airport vehicles serving the same area as the flight's parking position, and assigns tasks to these airport vehicles. For example, for each flight in the task queue, the terminal uses the simulation model to find all first airport vehicles serving the same area as the flight's parking position based on the airport vehicle's service area information. The simulation model then determines the first departure time corresponding to the latest assigned task of each first airport vehicle based on its assigned tasks, and calculates the difference between this first departure time and the departure time of the flight to be assigned.

[0057] If the difference between the first departure time and the flight's departure time is greater than a first time threshold, and this difference is the largest among all differences within the same region, the terminal, through a simulation model, allocates a path from the first airport vehicle to the flight's parking position using a shortest path algorithm based on the flight's parking stand and the service area information of the first airport vehicle. This achieves the first task path allocation for the first airport vehicle, resulting in a first work plan corresponding to the first airport vehicle. If the difference is less than or equal to the first time threshold, the terminal can further determine the outcome through the simulation model.

[0058] In one embodiment, after determining the first departure time corresponding to the latest assigned task of the first airport vehicle based on the assigned tasks of the first airport vehicle, the method may further include: if the difference between the first departure time and the departure time of the flight is less than or equal to the first time threshold, then determining the departure time corresponding to the latest assigned task of the airport vehicle based on the assigned tasks of the airport vehicle; allocating a first task path to the second target airport vehicle based on the parking position of the flight and the service area information of the second target airport vehicle to obtain a first work plan corresponding to the airport vehicle; the difference between the departure time of the second target airport vehicle and the departure time of the flight is greater than the second time threshold, and the difference is the maximum value among the differences between each departure time and the departure time of the flight; the second time threshold is greater than the first time threshold.

[0059] In this embodiment, the terminal obtains the difference between the first takeoff time and the flight's takeoff time through a simulation model. If the terminal determines through the simulation model that the difference is less than or equal to a first time threshold, the terminal further judges using the simulation model. This further judgment can be performed on all airport vehicles within the entire airport area. The terminal determines the takeoff time corresponding to the latest assigned task of each airport vehicle based on its assigned tasks using the simulation model. The terminal can also judge the difference between the takeoff time of the second target airport vehicle and the flight's takeoff time through the simulation model. If the difference is greater than the second time threshold, and this difference is the maximum value among all the differences corresponding to each airport vehicle within the airport, the terminal uses the simulation model to allocate the path from the second target airport vehicle to the flight's parking position using a shortest path algorithm based on the flight's parking position and the service area information of the second target airport vehicle. This achieves the first task path allocation for the second target airport vehicle, resulting in a first work plan corresponding to the airport vehicle. The second time threshold is greater than the first time threshold, meaning that a more generous time allowance is needed for airport vehicles across the entire airport area. In cases where the difference is less than or equal to the second time threshold, the terminal can make further judgments through a simulation model.

[0060] In one embodiment, after determining the departure time corresponding to the latest assigned task of the airport vehicle based on its assigned tasks, the process may further include: if the difference between the departure time corresponding to the latest assigned task of each airport vehicle and the departure time of the flight is less than or equal to the second time threshold, then a first task path is assigned to the third target airport vehicle to obtain a first work plan corresponding to the airport vehicle; where the difference between the departure time corresponding to the latest assigned task of the third target airport vehicle and the departure time of the flight is the largest.

[0061] In this embodiment, when the terminal determines through a simulation model that the difference between the departure time of the latest assigned task for each airport vehicle within the airport and the departure time of the aforementioned flight is less than or equal to a second time threshold, the terminal can determine a third target airport vehicle through the simulation model. The third target airport vehicle can be the airport vehicle with the largest difference between the departure time of the latest assigned task and the departure time of the aforementioned flight. Therefore, the terminal performs a first task path allocation for the third target airport vehicle through the simulation model, obtaining a first work plan corresponding to the aforementioned airport vehicle.

[0062] The terminal can construct discrete events based on a task list using a simulation model, and then simulate the scheduling of airport vehicles within the discrete timeframe of a flight. Flight events may include, but are not limited to, simulating aircraft landing, taxiing, parking, pushback, taxiing, and takeoff; airport vehicle events may include, but are not limited to, simulating vehicle departure, vehicle movement, acceleration / deceleration, aircraft avoidance, conflict resolution, intersection passage, curve driving, and lane changes. When generating and simulating work plans using the simulation model, the terminal needs to consider all the above event factors to achieve path allocation and simulation.

[0063] The aforementioned tasks of aircraft taxiing and airport vehicle execution can be planned using a shortest path algorithm. The first task path allocation can be a statically planned path, while the second task path allocation can be a dynamically planned path. Specifically, the terminal incorporates road congestion weights into a simulation model. For congested road segments, the terminal uses the simulation model to reduce the probability of airport vehicles choosing those segments. During the simulated driving process, the terminal dynamically replans the path based on road congestion conditions using the simulation model, thus achieving the second task path allocation.

[0064] The terminal can simulate the execution of tasks assigned to airport vehicles within a simulation model. For example, the aforementioned airport vehicle could be a towing vehicle. After receiving a task, the towing vehicle travels to the task location to simulate towing a flight. Upon reaching the parking position, it begins simulating the towing of the aircraft from the parking position to the designated pushback point. After completing the task, the towing vehicle travels to a designated parking area, is placed into the vehicle resource pool, and its parking area information is updated, awaiting the next dispatch. The vehicle resource pool can be a collection of airport vehicles capable of task assignment.

[0065] When the terminal allocates vehicle tasks through the simulation model, it can do so according to the corresponding scheduling rules. Taking airport vehicles as an example, the task queue includes both regular and emergency tasks. The terminal retrieves the tasks to be allocated from the task queue through the simulation model, first scheduling from tractors in the same area as the flight (the first airport vehicle). Specifically, when scheduling tractors in the same area, the one with the largest difference between the scheduled departure time of the current flight and the latest flight of the current tractor being greater than a first time threshold (e.g., 15 minutes) is selected. If multiple tractors simultaneously meet this condition, the one with the smallest distance is selected.

[0066] If all towing vehicles in the same area fail to meet the planned takeoff time difference exceeding the first time threshold, the terminal performs a global search using a simulation model, selecting the vehicle with the largest difference between its planned takeoff time and the latest scheduled flight of the current towing vehicle, which is greater than or equal to the second time threshold (e.g., 25 minutes). If multiple towing vehicles simultaneously meet this threshold, the one with the smallest distance is selected as the vehicle for the second target airport. If no vehicle globally meets the second time threshold difference, the terminal selects the vehicle with the largest difference time as the vehicle for the third target airport using a simulation model. When a towing vehicle triggers an emergency task, it enters the priority scheduling task queue.

[0067] Through the above embodiments, the terminal uses a simulation model, combined with information such as region and task time, to comprehensively consider the task allocation of airport vehicles and achieve reasonable task allocation for airport vehicles. Employing simulation and digital twin technology, it provides a brand-new means of pre-scheduling work plans to effectively and quickly resolve adjustments to tractor work plans caused by changes in flight schedules that evening. Furthermore, through a full-view analysis method, it can predict the impact of the plan on flight takeoff in advance, thereby better avoiding risks such as delays and conflicts and improving the practicality of the generated work plans.

[0068] In one embodiment, the airport vehicle operation plan generation method of this application may further include: obtaining road network information corresponding to the airport; and generating a simulation model corresponding to the airport based on the road network information.

[0069] In this embodiment, the terminal can pre-build a simulation model of the aforementioned airport. The terminal can generate the simulation model by combining information such as the airport's structure and traffic configuration. For example, the terminal can obtain the road network information corresponding to the airport and generate a simulation model based on this information. The road network information includes various types of data. The terminal can acquire business data, build a simulation data platform, and form a simulation model. Specifically, the terminal can acquire airport tractor scheduling zone data, parking location data, vehicle quantity data, flight schedule data, etc., and initialize a 3D road network to obtain the aforementioned road network information. This road network information may include, but is not limited to, the physical locations of parking lots and sorting centers, parking spaces, latitude and longitude, road connections, road attributes, road directions, and road speed limits, allowing the terminal to generate a simulation model using this road network information.

[0070] Through this embodiment, the terminal can use the airport's road network information to build a simulation model, thereby generating work plans in the airport through the simulation model, which improves the efficiency of work plan generation.

[0071] In one embodiment, determining the target operation plan corresponding to each of the aforementioned airport vehicles based on the aforementioned operation plans includes: obtaining the execution information of each task corresponding to each of the aforementioned operation plans; outputting each of the aforementioned operation plans and the corresponding execution information of each of the aforementioned tasks; and determining the target operation plan corresponding to each of the aforementioned airport vehicles based on the confirmation information of the target operation plan in each of the aforementioned operation plans.

[0072] In this embodiment, the simulation model can output multiple work plans. The terminal determines the target work plan from these plans for execution by various airport vehicles. The terminal can obtain task execution information corresponding to each work plan. This task execution information may include the execution information of the airport vehicles working according to the work plan, and may include, but is not limited to, task duration and task mileage. The terminal can output each work plan and its corresponding task execution information. The user can confirm each work plan, i.e., select the optimal work plan as the target work plan. After receiving confirmation information for the target work plan, the terminal can determine the target work plan corresponding to each airport vehicle.

[0073] The terminal can output various work plans through simulation and visualization. Users can select a favorable work plan as the target work plan for export, so that airport vehicles can operate according to the target work plan within the period corresponding to the above flight information.

[0074] Through this embodiment, the terminal can determine the target job plan from multiple job plans for actual execution based on the user's confirmation information, thereby improving the flexibility of job plan generation.

[0075] In one exemplary embodiment, as shown in Figure 3, which is a flowchart illustrating an airport vehicle operation plan generation method in another embodiment, the terminal can pre-generate a simulation model and configure simulation parameters, taking airport vehicles as tractor units as an example. The simulation parameters of the model may include, but are not limited to, airport runway capacity, number of tractor units, zoning, and scheduling rules. Zoning refers to the terminal dividing the airport into different service areas based on the various parking positions, allocating a different number of tractor units to each service area. The terminal can upload the simulation parameters to the simulation model and run it to execute discrete events, including but not limited to takeoff and landing, parking, taxiing, and pushback; tractor unit scheduling, path planning, driving, and task execution. The terminal can also view, analyze, and export results from the simulation model. This includes the entire process of flight landing, pushback, and takeoff; the entire tractor unit operation process; the tractor unit's operation plan, driving distance, scheduling waiting time, and task interval time.

[0076] The main entities involved in the generation of the work plan are aircraft and towing vehicles. Based on this, the terminal can modularly cut the simulation model, focus on business needs, improve simulation speed, and achieve minute-level output of scheduling results. It also supports visual viewing of the entire process of flight landing, taxiing, positioning, pushback, and takeoff, as well as the operation process of towing vehicle scheduling, driving, and task execution.

[0077] The terminal can acquire data such as airport towing vehicle dispatch service zoning data, parking location data, vehicle quantity data, and flight schedule data to initialize a 3D road network, thus obtaining the aforementioned road network information. This road network information may include, but is not limited to, the physical locations of parking lots and sorting centers, parking spaces, latitude and longitude, road connections, road attributes, road directions, and road speed limits. The terminal can then use this road network information to generate a simulation model.

[0078] The terminal can also acquire information such as takeoff time, runway headings, and flight physical attributes. These physical attributes may include, but are not limited to, aircraft length, speed, and safe distance. It can also acquire information such as the number of towing vehicles, their service areas, and vehicle parameters as airport vehicle configuration information. These vehicle parameters may include, but are not limited to, vehicle length, speed, and safe distance.

[0079] The terminal can construct discrete events based on a task list using a simulation model, and then simulate the scheduling of airport vehicles within the discrete time frame of a flight. Flight events may include, but are not limited to, simulating aircraft landing, taxiing, parking, pushback, taxiing, and takeoff; airport vehicle events may include, but are not limited to, simulating vehicle departure, vehicle movement, acceleration / deceleration, aircraft avoidance, vehicle conflict resolution, intersection passage, curve driving, and lane changing.

[0080] The aforementioned tasks of aircraft taxiing and airport vehicle execution can be performed using a shortest path algorithm for path planning. The first task path allocation can be a statically planned path, while the second task path allocation can be a dynamically planned path. Specifically, the terminal incorporates road congestion weights into a simulation model. For congested road segments, the terminal uses the simulation model to reduce the probability of airport vehicles choosing that segment. During the simulated driving process, the terminal dynamically replans the path based on road congestion conditions using the simulation model, thus achieving the second task path allocation.

[0081] The terminal can simulate the execution of tasks assigned to airport vehicles in the simulation model. For example, after receiving a task, the towing vehicle drives to the task location to simulate towing a flight. After arriving at the parking position, it begins to simulate towing the aircraft from the parking position to the designated pushback point. After completing the task, the towing vehicle drives to the designated parking area, puts it into the vehicle resource pool, updates the towing vehicle's parking area information, and waits for the next dispatch.

[0082] When the terminal allocates vehicle tasks through the simulation model, it can do so according to the corresponding scheduling rules. The task queue includes both regular and emergency tasks. The terminal retrieves the tasks to be allocated from the task queue using the simulation model, first scheduling them from tractors (vehicles from the first airport) located in the same area as the flight carrying the task. Specifically, when scheduling tractors in the same area, the one with the largest difference between the scheduled departure time of the current flight and the latest flight carrying the tractor's current task, exceeding a first time threshold (e.g., 15 minutes), is selected. If multiple tractors simultaneously meet this requirement, the one with the smallest distance is chosen.

[0083] If all towing vehicles in the same area fail to meet the planned takeoff time difference exceeding the first time threshold, the terminal performs a global search using a simulation model, selecting the vehicle with the largest difference between its planned takeoff time and the latest scheduled flight of the current towing vehicle, which is greater than or equal to the second time threshold (e.g., 25 minutes). If multiple towing vehicles simultaneously meet this threshold, the one with the smallest distance is selected as the vehicle for the second target airport. If no vehicle globally meets the second time threshold difference, the terminal selects the vehicle with the largest difference time as the vehicle for the third target airport using a simulation model. When a towing vehicle triggers an emergency task, it enters the priority scheduling task queue.

[0084] The terminal can use frequently used configuration information from historical job plan generation as the default configuration information for airport vehicles in the simulation model. Furthermore, for each period, such as daily flight information, and in scenarios where there are significant differences between weekdays and non-weekdays, the terminal also allows users to customize configuration information, including the number of partitions, partition ranges, and the number of airport vehicles, thus meeting the usage needs of different scenarios.

[0085] Furthermore, due to differences in flight schedules and application scenarios each cycle, the priority requirements for airport vehicle dispatching rules will also vary. To better address these variations, the terminal allows users to configure vehicle dispatching priorities as needed, and can differentiate between tasks within the same area and across areas, setting dispatching time intervals. This enables customized forecasting and optimization for weekdays and weekends based on actual daily production conditions, and supports personalized configurations such as partitioning and priority to adapt to various scenarios and improve resource utilization. Moreover, based on distributed processing technology, multiple simulation experiments and scenario analyses are conducted simultaneously to help decision-makers better understand the factors affecting scheduling and provide more scientific and reasonable work plans.

[0086] The terminal can also utilize digital twin technology to create a 3D visualization of the entire scenario for each tractor operation plan, allowing users to intuitively and quickly obtain information on vehicle utilization efficiency and the impact on flight takeoffs. The displayed information may include, but is not limited to, tractor-related information, the tractor's task list, and the tractor's task execution nodes. Each task in the task list may include, but is not limited to, task start time, end time, task duration, and task mileage.

[0087] The terminal can also run simulations of multiple versions of work plans simultaneously, comparing the execution effects of different work plans. Through data mining and analysis, the terminal automatically recommends that users focus on waiting tractors and those with the longest travel distances, simulating the entire support process with a single click. By referencing task information such as connection distance, connection time, and travel path, the terminal helps users determine whether adjustments to the tractor pre-scheduling plan are necessary, thereby improving the usability of the work plan.

[0088] The terminal can output various work plans through simulation and visualization. Users can select a favorable work plan as the target work plan for export, so that airport vehicles can operate according to the target work plan within the period corresponding to the above flight information.

[0089] Through the above embodiments, the terminal, by combining airport flight information, airport vehicle configuration information, and assigned tasks to airport vehicles within an airport simulation model, performs task allocation and operational simulation for each airport vehicle, thereby determining the operational plan for each vehicle. By generating operational plans based on task allocation and simulated operational methods within the simulation model, the efficiency of airport vehicle operational plan generation is improved. Furthermore, the terminal can consider various variables and factors, such as constantly changing factors like the number of flights, takeoff and landing times, parking positions, and runways, providing accurate data analysis and simulation to quickly offer safe and efficient operational plan solutions, significantly improving efficiency compared to manual pre-scheduling.

[0090] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0091] Based on the same inventive concept, this application also provides an airport vehicle operation plan generation apparatus for implementing the airport vehicle operation plan generation method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more embodiments of the airport vehicle operation plan generation apparatus provided below can be found in the limitations of the airport vehicle operation plan generation method described above, and will not be repeated here.

[0092] In an exemplary embodiment, as shown in FIG4, an airport vehicle operation plan generation device is provided, comprising: an acquisition module 500, a generation module 502, and a determination module 504, wherein:

[0093] The acquisition module 500 is used to acquire flight information and configuration information from the airport.

[0094] The generation module 502 is used to input the above flight information and the above configuration information into the simulation model corresponding to the above airport; the simulation model is used to determine the task queue according to the above flight information, and to perform task allocation and operation simulation for each of the above airport vehicles according to the above task queue, the above configuration information and the assigned tasks corresponding to each airport vehicle, and output each operation plan corresponding to each of the above airport vehicles.

[0095] The determination module 504 is used to determine the target operation plan corresponding to each of the above-mentioned airport vehicles based on each of the above-mentioned operation plans.

[0096] In one embodiment, the acquisition module 500 is used to acquire service area information corresponding to each airport vehicle in the airport based on the parking space allocation information corresponding to the airport; and to obtain the configuration information of the airport vehicles based on the service area information.

[0097] In one embodiment, the generation module 502 is configured to determine the departure time based on the flight information; determine the task queue based on each departure time; and perform task allocation and operation simulation for each airport vehicle based on each departure time in the task queue, the service area information in the configuration information, and the assigned tasks corresponding to each airport vehicle, and output each operation plan corresponding to each airport vehicle.

[0098] In one embodiment, the generation module 502 is configured to: allocate a first task path based on the departure time, the service area information corresponding to the airport vehicle, and the assigned task corresponding to the airport vehicle, to obtain a first work plan for each airport vehicle; simulate the operation of each airport vehicle in the simulation model based on the first work plan; obtain real-time road congestion information corresponding to the operation simulation; allocate a second task path for each airport vehicle based on the real-time road congestion information, to obtain a second work plan for each airport vehicle to avoid road congestion; and output the work plan for each airport vehicle based on the second work plan.

[0099] In one embodiment, the generation module 502 is configured to, for each flight in the task queue, locate each first airport vehicle in the same service area as the parking stand of the flight based on the service area information; determine the first departure time corresponding to the latest assigned task of the first airport vehicle based on the assigned tasks of the first airport vehicle; allocate a first task path to the first target airport vehicle based on the parking stand of the flight and the service area information of the first target airport vehicle to obtain a first work plan corresponding to the first airport vehicle; the difference between the first departure time of the first target airport vehicle and the departure time of the flight is greater than a first time threshold, and the difference is the maximum value among the differences between the first departure time and the departure time of the flight.

[0100] In one embodiment, the generation module 502 is configured to: if the difference between the first takeoff time and the takeoff time of the flight is less than or equal to the first time threshold, determine the takeoff time corresponding to the latest assigned task of the airport vehicle based on the assigned tasks of the airport vehicle; allocate a first task path to the second target airport vehicle based on the parking position of the flight and the service area information of the second target airport vehicle, thereby obtaining a first work plan corresponding to the airport vehicle; the difference between the takeoff time of the second target airport vehicle and the takeoff time of the flight is greater than the second time threshold, and the difference is the maximum value among the differences between each takeoff time and the takeoff time of the flight; the second time threshold is greater than the first time threshold.

[0101] In one embodiment, the generation module 502 is configured to perform a first task path allocation on the third target airport vehicle if the difference between the departure time corresponding to the latest assigned task of each of the airport vehicles and the departure time of the flight is less than or equal to the second time threshold, thereby obtaining a first work plan corresponding to the airport vehicle; wherein the difference between the departure time corresponding to the latest assigned task of the third target airport vehicle and the departure time of the flight is the largest.

[0102] In one embodiment, the apparatus further includes: a construction module for acquiring road network information corresponding to the airport; and generating a simulation model corresponding to the airport based on the road network information.

[0103] In one embodiment, the determining module 504 is used to obtain the execution information of each task corresponding to each of the above-mentioned work plans; output each of the above-mentioned work plans and the corresponding execution information of each of the above-mentioned tasks; and determine the target work plan corresponding to each of the above-mentioned airport vehicles based on the confirmation information of the target work plan in each of the above-mentioned work plans.

[0104] The modules in the aforementioned airport vehicle operation plan generation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0105] In an exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram is shown in Figure 5. The computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for wired or wireless communication with external devices; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements an airport vehicle operation plan generation method. The display unit of the computer device is used to form a visually visible image and may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0106] Those skilled in the art will understand that the structure shown in Figure 5 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or may combine certain components, or may have different component arrangements.

[0107] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described airport vehicle operation plan generation method.

[0108] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described airport vehicle operation plan generation method.

[0109] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the airport vehicle operation plan generation method described above.

[0110] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0111] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0112] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0113] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for generating an airport vehicle operation plan, the method comprising: Obtain flight information and airport vehicle configuration information at the airport; Input the flight information and configuration information into the simulation model corresponding to the airport; The simulation model is used to determine the task queue based on the flight information, and to perform task allocation and operation simulation for each airport vehicle based on the task queue, the configuration information and the assigned tasks corresponding to each airport vehicle, and to output the operation plan corresponding to each airport vehicle. Based on each of the aforementioned work plans, the target work plan corresponding to each of the aforementioned airport vehicles is determined.

2. The method according to claim 1, the step of obtaining the configuration information of airport vehicles includes: Based on the parking space allocation information corresponding to the airport, obtain the service area information corresponding to each airport vehicle in the airport; Based on the service area information, the configuration information of the airport vehicles is obtained.

3. The method according to claim 2, wherein determining the task queue based on the flight information, allocating tasks and simulating operations for each airport vehicle based on the task queue, the configuration information, and the assigned tasks corresponding to each airport vehicle, and outputting each operation plan corresponding to each airport vehicle, includes: Determine the departure time based on the flight information; Determine the mission queue based on each of the stated takeoff times; Based on the departure times in the task queue, the service area information in the configuration information, and the assigned tasks for each airport vehicle, tasks are assigned and operations are simulated for each airport vehicle, and operation plans for each airport vehicle are output.

4. The method according to claim 3, wherein the step of allocating tasks and simulating operations for each airport vehicle based on the departure times in the task queue, the service area information in the configuration information, and the assigned tasks corresponding to each airport vehicle, and outputting operation plans corresponding to each airport vehicle, includes: Based on the departure times, the service area information corresponding to the airport vehicles, and the assigned tasks corresponding to the airport vehicles, a first task path is allocated to obtain the first operation plans corresponding to each airport vehicle. According to each of the first work plans, the operation simulation of each of the airport vehicles is carried out in the simulation model; Obtain real-time road congestion information corresponding to the operation simulation, and allocate second task paths to each of the airport vehicles based on the real-time road congestion information to obtain each second operation plan corresponding to each of the airport vehicles. Based on the second operation plan corresponding to each of the airport vehicles, output the operation plan corresponding to each of the airport vehicles.

5. The method according to claim 4, wherein the step of allocating a first task path based on each of the departure times, the service area information corresponding to the airport vehicle, and the assigned task corresponding to the airport vehicle, to obtain each of the first work plans corresponding to each of the airport vehicles, comprises: For each flight in the task queue, locate the first airport vehicles that are in the same service area as the parking position of the flight, based on the service area information. Based on the assigned tasks of the vehicles at the first airport, determine the first takeoff time corresponding to the latest assigned task of the vehicles at the first airport. Based on the parking position of the flight and the service area information of the vehicle at the first target airport, a first task path is assigned to the vehicle at the first target airport to obtain the first operation plan corresponding to the vehicle at the first airport. The difference between the first departure time of the vehicle at the first target airport and the departure time of the flight is greater than a first time threshold, and the difference is the maximum value among all the differences between the first departure time and the departure time of the flight.

6. The method according to claim 5, further comprising, after determining the first takeoff time corresponding to the latest assigned task of the first airport vehicle based on the assigned tasks of the first airport vehicle: If the difference between the first takeoff time and the takeoff time of the flight is less than or equal to the first time threshold, then the takeoff time corresponding to the latest assigned task of the airport vehicle is determined according to the assigned tasks of the airport vehicle. Based on the parking position of the flight and the service area information of the vehicles at the second target airport, a first task path is assigned to the vehicles at the second target airport to obtain a first operation plan corresponding to the airport vehicles. The difference between the departure time of the vehicle at the second target airport and the departure time of the flight is greater than a second time threshold, and the difference is the maximum value among all the differences between the departure time and the departure time of the flight; the second time threshold is greater than the first time threshold.

7. The method according to claim 6, further comprising, after determining the departure time corresponding to the latest assigned task of the airport vehicle based on the assigned tasks of the airport vehicle: If the difference between the departure time of the latest assigned task of each airport vehicle and the departure time of the flight is less than or equal to the second time threshold, then the first task path is assigned to the third target airport vehicle to obtain the first operation plan corresponding to the airport vehicle. The difference between the departure time of the vehicle at the third target airport and the departure time of the flight is the largest.

8. The method according to any one of claims 1 to 7, further comprising: Obtain the road network information corresponding to the airport; Based on the road network information, a simulation model corresponding to the airport is generated.

9. The method according to any one of claims 1 to 7, wherein determining the target operation plan corresponding to each of the airport vehicles based on each of the operation plans includes: Obtain the execution information of each task corresponding to each of the aforementioned work plans; Output each of the aforementioned work plans and the corresponding task execution information, and determine the target work plan corresponding to each of the airport vehicles based on the confirmation information of the target work plan in each of the aforementioned work plans.

10. An airport vehicle operation plan generation device, the device comprising: The acquisition module is used to acquire flight information and airport vehicle configuration information in the airport; The generation module is used to input the flight information and the configuration information into the simulation model corresponding to the airport; the simulation model is used to determine the task queue according to the flight information, and to perform task allocation and operation simulation for each airport vehicle according to the task queue, the configuration information and the assigned tasks corresponding to each airport vehicle, and output each operation plan corresponding to each airport vehicle. The determination module is used to determine the target operation plan corresponding to each of the airport vehicles based on each of the operation plans.

11. The apparatus according to claim 10, wherein the acquisition module is configured to acquire service area information corresponding to each airport vehicle in the airport based on the parking space allocation information corresponding to the airport; and to obtain configuration information of the airport vehicles based on the service area information.

12. The apparatus according to claim 11, wherein the generation module is configured to determine the departure time based on the flight information; determine a task queue based on each departure time; perform task allocation and operation simulation on each airport vehicle based on each departure time in the task queue, the service area information in the configuration information, and the assigned tasks corresponding to each airport vehicle, and output each operation plan corresponding to each airport vehicle.

13. A computer device comprising a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method according to any one of claims 1 to 9.

14. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 9.

15. A computer program product comprising a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1 to 9.

Images