Multi-agent collaborative intelligent airport surface dispatching system and method

Abstract

This invention discloses a multi-agent collaborative intelligent airport surface scheduling system and method, belonging to the field of intelligent surface scheduling technology. The system includes the following steps: comparing and analyzing arrival and departure times with scheduled times to calculate flight delays, and calculating additional fuel consumption based on flight delays and fuel consumption; obtaining the total taxiing distance and taxiing time of the aircraft, calculating taxiing speed, and constructing a taxiing consumption model based on taxiing speed and taxiing time to obtain taxiing consumption data; judging aircraft fuel consumption based on fuzzy reasoning, and reducing aircraft fuel consumption by shortening taxiing distances and reducing delays; optimizing passenger walking distances and aircraft fuel consumption based on the NSGA-II algorithm, and selecting an optimization scheme according to actual needs. This invention solves the problems of resource waste and scheduling conflicts, thereby improving the overall operational efficiency of airports, reducing flight delays, and optimizing resource utilization.

Description

Technical Field

[0001] This invention relates to the field of intelligent airport surface dispatching technology, and more specifically, to an intelligent airport surface dispatching system and method involving multi-entity collaboration. Background Technology

[0002] With the continuous growth of global air transport demand, the number of passengers and flights at airports is constantly increasing, and airport surface management (i.e., airport ground and surrounding areas) faces more complex scheduling tasks.

[0003] Shortcomings of existing technology:

[0004] Traditional airport surface dispatching methods mostly rely on manual dispatching or decision-making based on a single system. This approach is inefficient when dealing with complex and dynamic situations and is easily affected by human factors, leading to resource waste and dispatching conflicts, which may ultimately cause flight delays or airport operational bottlenecks. Therefore, there is an urgent need for a multi-stakeholder collaborative intelligent airport surface dispatching system to improve airport operational efficiency and reduce human intervention.

[0005] To address the above problems, this invention proposes a solution. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a multi-entity collaborative intelligent airport surface dispatching system and method, which optimizes the multi-entity collaborative intelligent airport surface dispatching system to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A multi-entity collaborative intelligent airport surface dispatching system includes an acquisition module, a construction module, an inference module, and an optimization module, with interconnections between the modules: The acquisition module acquires aircraft arrival and departure times, flight distances, and fuel consumption during flight. It compares and analyzes arrival and departure times with scheduled times to calculate flight delays and additional fuel consumption based on delay times and fuel consumption. The construction module acquires total taxiing distances and taxiing times for aircraft arrival and departure, calculates taxiing speeds, constructs a taxiing consumption model based on taxiing speeds and times, and obtains taxiing consumption data. The inference module uses fuzzy inference based on additional fuel consumption and taxiing consumption data to determine aircraft fuel consumption and reduces fuel consumption by shortening taxiing distances and reducing delay times.

[0009] The optimization module is used to obtain passenger walking distance based on Dijkstra's algorithm, optimize passenger walking distance and aircraft fuel consumption based on NSGA-II algorithm, and output Pareto solution set, and select optimization scheme according to actual needs.

[0010] In a preferred embodiment, the specific calculation process for the flight delay time is as follows: obtain the actual departure time and the scheduled departure time of the aircraft, and subtract the actual departure time from the scheduled departure time to obtain the takeoff delay time; obtain the actual arrival time and the scheduled arrival time of the aircraft, and subtract the actual arrival time from the scheduled arrival time to obtain the arrival delay time; add the takeoff delay time and the arrival delay time together to obtain the flight delay time.

[0011] In a preferred embodiment, the specific process for obtaining the flight delay time is as follows: The flight time is obtained based on the aircraft's arrival and departure times, i.e., the flight time is obtained by subtracting the actual arrival time from the actual departure time; the aircraft's detour distance is obtained based on the flight distance and the predetermined flight distance, and the flight speed is calculated based on the flight distance and flight time; the fuel consumption rate per unit distance is calculated based on the fuel consumption during the flight and the flight distance; and additional fuel consumption is calculated based on the flight delay time, flight time, and fuel consumption rate per unit distance.

[0012] In a preferred embodiment, the taxiing consumption data is acquired as follows: the aircraft's approach and departure taxiing distance and taxiing time are acquired; the approach and departure taxiing distance and taxiing time are added together to obtain the total taxiing distance and taxiing time; the aircraft's taxiing speed is calculated by dividing the total taxiing distance by the taxiing time; a taxiing consumption model is constructed based on the taxiing speed and taxiing time to obtain taxiing consumption data.

[0013] In a preferred embodiment, the specific steps for determining the aircraft fuel consumption based on fuzzy inference using additional fuel consumption and taxiing consumption data are as follows: defining the additional fuel consumption and taxiing consumption data as input variables and dividing them into different fuzzy sets; defining the aircraft fuel consumption as an output variable and dividing it into fuzzy sets; formulating fuzzy rules to describe the impact of additional fuel consumption and taxiing consumption data on the aircraft fuel consumption; and performing fuzzy inference based on the fuzzy rules to determine the aircraft fuel consumption.

[0014] In a preferred embodiment, the specific process of obtaining passenger walking distance based on Dijkstra's algorithm is as follows: A directed graph G = (V, E) represents the passenger walking network within the airport; in the graph, V is a set of nodes, each node represents an intersection, E is a set of edges, each edge represents a passenger road connection between two nodes, and the weight of the edge represents the passenger's walking distance on that road; d(u, v) is the distance from node u to node v, and the goal of path planning is to find the shortest path from the starting node s to the target node t, such that the sum of d(u, v) is minimized.

[0015] In a preferred embodiment, the steps for optimizing passenger walking distance and aircraft fuel consumption based on the NSGA-II algorithm are as follows: An initial solution set is randomly generated, where each solution represents a scheduling scheme, including passenger walking paths, aircraft taxiing paths, and aircraft paths; for each solution, the corresponding passenger walking distance and aircraft fuel consumption are calculated; the solution set is non-dominated and sorted according to the target value, dividing the solution set into different levels, and for solutions in each level, the congestion degree is calculated to ensure the diversity of the solution set; a new population is generated using selection, crossover, and mutation operations, and the optimal solution is selected to continue evolving into the next generation; the solution set is continuously optimized towards the Pareto front; after the maximum number of iterations is reached, the algorithm terminates and outputs the Pareto front solution set; after several generations of evolution, the NSGA-II algorithm outputs a set of Pareto optimal solutions, which represent the optimal trade-off between passenger walking distance and aircraft fuel consumption under given target constraints; each solution has different passenger walking distances and fuel consumption, and the most suitable scheme is selected according to actual needs.

[0016] A multi-agent collaborative intelligent airport surface scheduling method includes the following steps: acquiring aircraft arrival and departure times, flight distances, and fuel consumption during flight; comparing and analyzing arrival and departure times with scheduled times to calculate flight delays; and calculating additional fuel consumption based on flight delays and fuel consumption. The method also includes acquiring total taxiing distances and taxiing times for aircraft arrival and departure, calculating taxiing speeds, constructing taxiing consumption models based on taxiing speeds and times, and obtaining taxiing consumption data. Furthermore, it involves using fuzzy inference to determine aircraft fuel consumption based on additional fuel consumption and taxiing consumption data, and reducing aircraft fuel consumption by shortening taxiing distances and reducing delay times. Finally, it uses the Dijkstra algorithm to acquire passenger walking distances, and the NSGA-II algorithm to optimize passenger walking distances and aircraft fuel consumption, outputting a Pareto solution set, and selecting an optimization scheme based on actual needs.

[0017] The technical effects and advantages of the multi-entity collaborative intelligent airport surface dispatching system and method of this invention are as follows:

[0018] 1. This invention reduces waiting time and unnecessary idle operations through intelligent collaborative scheduling, ensuring efficient resource utilization. Through real-time data and intelligent algorithm analysis, the system can automatically calculate the optimal resource allocation scheme, avoiding delays or errors in manual decision-making and improving the efficiency of scheduling decisions. Based on multi-dimensional data such as flights, equipment, personnel, and weather, the system adjusts and optimizes airport resource allocation in real time, enabling flexible resource allocation among different entities and avoiding bottlenecks and resource waste. According to real-time changes in the airport situation, such as flight delays, equipment failures, and weather changes, the system can dynamically adjust scheduling schemes and respond promptly to emergencies. Intelligent algorithms automatically complete scheduling decisions, task allocation, and instruction issuance, reducing manual intervention and mitigating efficiency losses and safety risks caused by human error. The system can monitor the status of all entities in real time, automatically issuing warnings and taking corresponding measures when potential problems are detected, avoiding scheduling conflicts or safety accidents caused by human judgment errors.

[0019] 2. This invention, through real-time data sharing and intelligent decision-making, ensures safe scheduling distances and times between different entities, preventing collisions or conflicts between aircraft, vehicles, and personnel. If the system detects anomalies (such as equipment malfunctions, flight delays, weather changes, etc.), it can promptly issue warnings and take corresponding emergency measures to ensure the safe operation of the airport. Through intelligent scheduling and precise resource allocation, the system can avoid congestion at the airport, improve the smoothness of boarding, transfers, baggage claim, and other processes, and enhance the overall passenger experience. Through real-time analysis of passenger data and flight information, the system can provide personalized services to passengers, such as timely flight change notifications and gate guidance, optimizing ground equipment, flight scheduling, and vehicle operations. By utilizing resources such as air traffic control, the system reduces wasted time and resources, thereby lowering overall operating costs. Through intelligent scheduling, precise operational plans, and optimized resource allocation, it can effectively reduce flight delays and associated costs. The system can collect and analyze a large amount of airport operational data, including flight information, ground equipment status, and personnel arrangements. Based on this data, it can make predictions, optimize operations, and provide decision support. Through historical data analysis and machine learning, the system can continuously optimize scheduling strategies, improving the efficiency and safety of future airport operations. The airport environment is dynamic, complex, and ever-changing. The intelligent scheduling system can flexibly adapt to complex changes in the environment, including high flight density, weather changes, and emergencies, ensuring the efficient operation of the airport. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the multi-entity collaborative intelligent airport surface dispatching system of the present invention.

[0021] Figure 2 This is a schematic diagram of the structure of the multi-entity collaborative intelligent airport surface scheduling method of the present invention. Detailed Implementation

[0022] 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. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0023] Example 1, Figure 1 The present invention provides a multi-entity collaborative intelligent airport surface dispatching system.

[0024] S10: Obtain the aircraft's arrival and departure times, flight distance, and fuel consumption during flight. Compare and analyze the arrival and departure times with the scheduled times to calculate the flight delay time, and calculate the additional fuel consumption based on the flight delay time and fuel consumption.

[0025] Obtaining aircraft arrival and departure times, fuel consumption during flight, calculating flight delays, and analyzing additional fuel consumption are crucial parts of aviation operations, especially in flight scheduling, flight delay management, and fuel cost control.

[0026] Obtain the aircraft's actual departure time and scheduled departure time, and subtract the scheduled departure time from the actual departure time to obtain the takeoff delay time;

[0027] Obtain the actual arrival time and scheduled arrival time of the aircraft, and subtract the scheduled arrival time from the actual arrival time to obtain the arrival delay time;

[0028] The flight delay time is obtained by adding the departure delay time and the arrival delay time together. The specific calculation formula is as follows: t y =(t s1 -t d1 )+(t s2 -t d2 In the formula, t y It refers to the flight delay time, t s1 This is the actual departure time, t d1 This is the scheduled departure time, t s2 It is the actual arrival time, t d2 This is the scheduled arrival time;

[0029] Flight time is obtained by subtracting the actual arrival time from the actual departure time.

[0030] The aircraft's detour distance is obtained based on the flight distance and the predetermined flight distance, and the flight speed is calculated based on the flight distance and flight time.

[0031] The fuel consumption rate per unit distance is calculated based on fuel consumption and flight distance during the flight.

[0032] Additional fuel consumption is calculated based on flight delay time, flight time, and fuel consumption rate per unit distance, using the following formula: In the formula, W is the additional fuel consumption, Q is the fuel consumption during flight, d is the flight distance, Δd is the circumnavigation distance, and t is the distance traveled. y It refers to the flight delay time, t s2 It is the actual arrival time, t s1 This is the actual departure time. It is the fuel consumption rate per unit distance. It refers to flight speed.

[0033] S20: Obtain the total taxiing distance and taxiing time of the aircraft during arrival and departure, calculate the taxiing speed, construct a taxiing consumption model based on the taxiing speed and taxiing time, and obtain taxiing consumption data;

[0034] Obtain the aircraft's approach and departure taxi distances and taxi times. Add the approach and departure taxi distances and taxi times to obtain the total taxi distance and taxi time. Divide the total taxi distance by the taxi time to calculate the aircraft's taxi speed. Construct a taxiing consumption model based on the taxiing speed and taxiing time to obtain taxiing consumption data. The specific formula is as follows: F = a * t h +b*V h 2 In the formula, F represents the gliding consumption data, and t represents the gliding consumption data. h It is the taxiing time, 'a' is the taxiing time weighting factor, and 'V' is the taxiing time weighting factor. h 'b' is the gliding speed, and 'b' is the gliding speed weighting factor.

[0035] S30 uses fuzzy inference to determine the aircraft's fuel consumption based on additional fuel consumption and taxiing consumption data, and reduces the aircraft's fuel consumption by shortening taxiing distance and reducing delay time.

[0036] The specific steps for determining aircraft fuel consumption based on fuzzy inference using additional fuel consumption and taxiing consumption data are as follows:

[0037] Step C1: Define the additional fuel consumption and coasting consumption data as input variables, and divide them into different fuzzy sets.

[0038] For example, "Low", "Medium", "High" refers to additional fuel consumption, while "Low", "Medium", "High" refers to coasting consumption data.

[0039] Step C2: Define aircraft fuel consumption as an output variable and divide it into fuzzy sets, such as "Low" and "High", for aircraft fuel consumption.

[0040] Step C3 involves developing a set of fuzzy rules to describe the impact of different input variables on the output variable. The rules can be defined based on professional knowledge or obtained through data analysis and experimentation. For example:

[0041] By labeling additional fuel consumption as L, taxiing consumption data as [missing information], and aircraft fuel consumption status as P, we can define...

[0042] Rule 1: If (W is High) and (F is High), then (P is High).

[0043] Rule 2: If (W is Low) and (F is Low), then (P is Low). ...

[0045] Step C4: Perform fuzzy inference based on fuzzy rules to determine the aircraft's fuel consumption.

[0046] It should be noted that the division of fuzzy sets can be adjusted according to the actual situation. For example, although this embodiment uses three fuzzy sets as an example, in reality, the additional fuel consumption, taxiing consumption data, and aircraft fuel consumption can be divided into more than three sets to facilitate more accurate identification.

[0047] Furthermore, regarding the determination of whether the additional fuel consumption or coasting consumption data is high or low, thresholds can be set according to the actual situation; additional fuel consumption above 100L / km can be labeled as "High", coasting consumption data above 100L / km can be labeled as "High", etc., which will not be elaborated here.

[0048] If P is High, the aircraft consumes more fuel. In this case, the fuel consumption can be reduced by shortening the taxiing distance and reducing the delay time.

[0049] S40 uses Dijkstra's algorithm to obtain passenger walking distance, and uses the NSGA-II algorithm to optimize passenger walking distance and aircraft fuel consumption, and outputs Pareto front solution set, and selects optimization scheme according to actual needs.

[0050] The passenger walking network within an airport can be represented by a directed graph. Each node in the graph represents an intersection, each edge represents a pedestrian path connecting two nodes, and the weight of the edge represents the distance a passenger needs to walk on that path.

[0051] Let G = (V, E) be the pedestrian network graph of the airport, where V is the set of nodes representing intersections, E is the set of edges representing pedestrian path connections, and d(u, v) is the distance from node u to node v. The goal of path planning is to find the shortest path from the starting node s to the target node t, minimizing the walking distance.

[0052] First, the population is initialized. Then, a fast non-dominated sort is performed on the individuals in the population. Individuals in the first layer are completely independent and not dominated by any other individual in the population. Individuals in the second layer are dominated only by individuals in the first layer, and so on. Each layer is a completely non-dominated solution starting from that layer. After the non-dominated sort is completed, the next step is to sort the individuals in each layer according to their crowding, which describes the distance between an individual and its neighbors. Next, selection operations are performed based on the results of the previous non-dominated and crowding sorts. At the same time, crossover and mutation operations are performed on the individuals in the population to generate a new population. This completes one iteration. The iteration process is then repeated until the convergence condition is met.

[0053] It is important to note that the solution set generated by the NSGA-II algorithm in each iteration is only a non-dominated solution set, and not an optimal solution.

[0054] The steps for optimizing passenger walking distance and aircraft fuel consumption based on the NSGA-II algorithm are as follows:

[0055] □ Initialize the population: Randomly generate a certain number of initial solutions, each solution representing a scheduling scheme. Each solution includes information such as passenger walking paths and aircraft taxiing paths.

[0056] □ Target Assessment: For each individual (solution), calculate the corresponding passenger walking distance and aircraft fuel consumption.

[0057] □ Non-dominated sorting: The solution set is non-dominated and sorted according to the objective value, dividing the solution set into different levels. For the solutions in each level, the crowding degree is calculated to ensure the diversity of the solution set.

[0058] □ Genetic operations: Selection, crossover, and mutation operations are used to generate new populations, and the optimal solution is selected to continue evolving into the next generation. This process continuously optimizes the solution set, tending towards the Pareto front.

[0059] □ Termination condition: The algorithm terminates and outputs the Pareto front solution set after the maximum number of iterations is reached.

[0060] After several generations of evolution, the NSGA-II algorithm outputs a set of Pareto optimal solutions. These solutions represent the optimal trade-off between passenger walking distance and aircraft fuel consumption under given objective constraints. Each solution has different passenger walking distances and fuel consumption, allowing decision-makers to choose the most suitable option based on actual needs.

[0061] The optimized scheduling system can reduce passengers' walking distance, improve their travel comfort, and reduce passenger dissatisfaction and delays caused by excessive walking distances.

[0062] □ Reduced fuel consumption: Fuel consumption is reduced by optimizing aircraft taxiing paths and standby strategies. Especially during peak hours, reasonable taxiing paths and optimized scheduling strategies can significantly reduce airline operating costs.

[0063] □ System flexibility and adaptability: The NSGA-II algorithm can adjust the scheduling scheme according to different airport layouts, flight schedules and other dynamic factors, so it has strong flexibility and adaptability and can cope with complex scheduling scenarios.

[0064] □ Multi-objective balancing: Because the NSGA-II algorithm can optimize multiple objectives simultaneously, airport managers can choose the optimal solution to balance passenger experience and fuel consumption based on specific needs. For example, during peak hours, more emphasis may be placed on passenger walking distance; while during off-peak hours, more attention may be paid to reducing fuel consumption. Compared with traditional scheduling methods, the NSGA-II multi-objective optimization-based scheduling system can achieve more efficient airport surface scheduling while ensuring safety, especially in large airports and complex flight networks, significantly improving airport operational efficiency.

[0065] Example 2, Figure 2 This is a schematic diagram of the structure of the multi-entity collaborative intelligent airport surface scheduling method of the present invention.

[0066] A multi-agent collaborative intelligent airport surface scheduling method includes the following steps: acquiring aircraft arrival and departure times, flight distances, and fuel consumption during flight; comparing and analyzing arrival and departure times with scheduled times to calculate flight delays; and calculating additional fuel consumption based on flight delays and fuel consumption. The method also includes acquiring total taxiing distances and taxiing times for aircraft arrival and departure, calculating taxiing speeds, constructing taxiing consumption models based on taxiing speeds and times, and obtaining taxiing consumption data. Furthermore, it involves using fuzzy inference to determine aircraft fuel consumption based on additional fuel consumption and taxiing consumption data, and reducing aircraft fuel consumption by shortening taxiing distances and reducing delay times. Finally, it uses the Dijkstra algorithm to acquire passenger walking distances, and the NSGA-II algorithm to optimize passenger walking distances and aircraft fuel consumption, outputting a Pareto solution set, and selecting an optimization scheme based on actual needs.

[0067] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0068] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0069] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0070] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0071] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0072] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-entity collaborative intelligent airport surface dispatching system, characterized in that: It includes an acquisition module, a construction module, an inference module, and an optimization module, with connections between the modules: The acquisition module is used to acquire the aircraft's arrival and departure times, flight distance, and fuel consumption during flight. It compares and analyzes the arrival and departure times with the scheduled times to calculate the flight delay time and calculates the additional fuel consumption based on the flight delay time and fuel consumption. A construction module is used to obtain the total taxiing distance and taxiing time of the aircraft during arrival and departure, calculate the taxiing speed, and construct a taxiing consumption model based on the taxiing speed and taxiing time to obtain taxiing consumption data; wherein, the specific calculation formula for the taxiing consumption data is as follows: In the formula, This is gliding consumption data. This represents the taxiing time, and 'a' is the taxiing time weighting factor. It's the gliding speed. It is the gliding speed weighting factor; The inference module is used to determine the aircraft's fuel consumption based on fuzzy inference using data on additional fuel consumption and taxiing consumption, and to reduce the aircraft's fuel consumption by shortening taxiing distance and reducing delay time. The optimization module is used to obtain passenger walking distance based on Dijkstra's algorithm, optimize passenger walking distance and aircraft fuel consumption based on NSGA-II algorithm, and output Pareto solution set, and select optimization scheme according to actual needs.

2. The multi-entity collaborative intelligent airport surface dispatching system according to claim 1, characterized in that, The specific calculation process for the flight delay time is as follows: Obtain the aircraft's actual departure time and scheduled departure time, and subtract the scheduled departure time from the actual departure time to obtain the takeoff delay time; Obtain the actual arrival time and scheduled arrival time of the aircraft, and subtract the scheduled arrival time from the actual arrival time to obtain the arrival delay time; The flight delay time is obtained by adding the departure delay time and the arrival delay time together. The specific calculation formula is as follows: In the formula, It refers to the flight delay time. This is the actual departure time. This is the scheduled departure time. This is the actual arrival time. This is the scheduled arrival time.

3. The multi-entity collaborative intelligent airport surface dispatching system according to claim 2, characterized in that, The specific process for obtaining the additional fuel consumption of the flight is as follows: Flight time is obtained by subtracting the actual arrival time from the actual departure time. The aircraft's detour distance is obtained based on the flight distance and the predetermined flight distance, and the flight speed is calculated based on the flight distance and flight time. The fuel consumption rate per unit distance is calculated based on fuel consumption and flight distance during the flight. Additional fuel consumption is calculated based on flight delay time, flight speed, and fuel consumption rate per unit distance.

4. The multi-entity collaborative intelligent airport surface dispatching system according to claim 3, characterized in that, The specific process for obtaining the gliding consumption data is as follows: Obtain the aircraft's approach and departure taxi distances and taxi times, and add the approach and departure taxi distances and taxi times together to obtain the total taxi distance and taxi time; The aircraft's taxiing speed is calculated by dividing the total taxiing distance by the taxiing time. A gliding consumption model is constructed based on gliding speed and gliding time to obtain gliding consumption data.

5. The multi-entity collaborative intelligent airport surface dispatching system according to claim 4, characterized in that, The specific steps for determining aircraft fuel consumption based on fuzzy inference using additional fuel consumption and taxiing consumption data are as follows: The additional fuel consumption and coasting consumption data are defined as input variables, and they are divided into different fuzzy sets respectively; The aircraft fuel consumption is defined as an output variable and then divided into fuzzy sets. Develop fuzzy rules to describe the impact of additional fuel consumption and taxiing consumption data on aircraft fuel consumption; Fuzzy inference is performed based on fuzzy rules to determine the aircraft's fuel consumption.

6. The multi-entity collaborative intelligent airport surface dispatching system according to claim 5, characterized in that, The specific process for obtaining passenger walking distance based on Dijkstra's algorithm is as follows: The pedestrian network within the airport is represented by a directed graph G=(V,E). In the graph, V is the set of nodes, each node represents an intersection, and E is the set of edges, each edge represents a pedestrian path connection between two nodes, with the weight of the edge representing the walking distance of the pedestrian on that path. d(u,v) is the distance from node u to node v. The goal of path planning is to find the shortest path from the starting node s to the target node t, minimizing the sum of d(u,v).

7. The multi-entity collaborative intelligent airport surface dispatching system according to claim 6, characterized in that, The steps for optimizing passenger walking distance and aircraft fuel consumption based on the NSGA-II algorithm are as follows: An initial solution set is randomly generated, and each solution represents a scheduling scheme. This includes passenger walking routes, aircraft taxiing routes, and aircraft paths. For each solution, calculate the corresponding passenger walking distance and aircraft fuel consumption; The solution set is sorted non-dominated based on the target value, and the solution set is divided into different levels. For the solutions in each level, the crowding degree is calculated to ensure the diversity of the solution set. New populations are generated using selection, crossover, and mutation operations. The optimal solution is selected to continue evolving into the next generation. The solution set is continuously optimized and tends towards the Pareto front. Once the maximum number of iterations is reached, the algorithm terminates and outputs the Pareto front solution set. After several generations of evolution, the NSGA-II algorithm will output a set of Pareto optimal solutions, which represent the optimal trade-off between passenger walking distance and aircraft fuel consumption under given objective constraints. Each solution has different passenger walking distances and fuel consumption; choose the most suitable solution based on actual needs.

8. The multi-entity collaborative intelligent airport surface dispatching system according to claim 7, characterized in that, The specific formula for calculating the additional fuel consumption is as follows: In the formula, W is the additional fuel consumption, Q is the fuel consumption during flight, and d is the flight distance. It is the distance of the flight around the target. It refers to the flight delay time. This is the actual arrival time. This is the actual departure time. It is the fuel consumption rate per unit distance. It refers to flight speed.

9. A multi-agent collaborative intelligent airport surface dispatching method, based on the multi-agent collaborative intelligent airport surface dispatching system according to any one of claims 1-8, characterized in that, Includes the following steps: The system obtains the aircraft's arrival and departure times, flight distance, and fuel consumption during flight. It compares and analyzes the arrival and departure times with the scheduled times to calculate the flight delay time and calculate the additional fuel consumption based on the flight delay time and fuel consumption. Obtain the total taxiing distance and taxiing time of the aircraft during arrival and departure, calculate the taxiing speed, construct a taxiing consumption model based on the taxiing speed and taxiing time, and obtain taxiing consumption data; Based on fuzzy inference, the aircraft's fuel consumption is determined using additional fuel consumption and taxiing consumption data, and the aircraft's fuel consumption is reduced by shortening taxiing distance and reducing delay time. Passenger walking distance is obtained based on Dijkstra's algorithm, and passenger walking distance and aircraft fuel consumption are optimized based on NSGA-II algorithm, and Pareto solution set is output. The optimization scheme is selected according to actual needs.

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