Aircraft taxiing conflict prediction method, device and storage medium

Abstract

The present invention discloses a method, device and storage medium for predicting aircraft taxiing conflicts, which belongs to the technical field of civil aviation airport surface traffic control. The method comprises: constructing an aircraft taxiing workflow network based on pre-acquired airport and aircraft information; setting an aircraft taxiing activity time interval based on the aircraft taxiing workflow network; fuzzy processing the aircraft taxiing activity time interval using a trapezoidal fuzzy number, and converting the fuzzy processed aircraft taxiing activity time interval into a trapezoidal time function to obtain an aircraft taxiing fuzzy workflow network; and performing time dynamic verification on the aircraft taxiing fuzzy workflow network to predict aircraft taxiing conflicts. The method can predict the probability and time of aircraft taxiing conflicts, and provide a reference method for airport taxiing conflict management.

Description

Technical Field

[0001] The invention relates to an aircraft taxiing conflict prediction method, device and storage medium, belonging to the technical field of civil aviation airport surface traffic control. Background Art

[0002] With the rapid development of the global economy and the continuous advancement of science and technology, the civil aviation transportation industry has also ushered in rapid development. At the same time, the operational efficiency and safety assurance capabilities of the air traffic system have also become prominent. Airport taxiways are important nodes in the entire air traffic network. Their operational efficiency and safety level directly affect the operating status of the air traffic transportation system, and thus have an impact on the aviation economy.

[0003] With the rapid development of my country's aviation industry, the traffic volume at airports has continued to increase, leading to increasingly serious ground traffic congestion at airports. Airport taxiing conflicts not only cause flight delays, affect the operating efficiency of airports, and bring inconvenience and economic losses to passengers and airlines, but in serious cases may also lead to accidents and casualties, posing a threat to aviation safety. In order to further improve the development level of the civil aviation transportation industry, enhance the safety and operating efficiency of airports, and promote the sustainable development of the aviation industry, a scientific, accurate and reasonable aircraft taxiing conflict prediction method is particularly important.

[0004] Most of the existing studies on taxi conflict prediction aim to predict and control potential conflicts in taxi schedules, but they ignore the interference that occurs during the actual taxiing process, which is the main cause of airport ground delays. In addition, when formulating methods to resolve conflicts, only detours or queueing solutions are simply proposed. These studies are more theoretical modeling, prediction and control, and cannot predict and resolve actual taxi conflicts in a timely and accurate manner. Summary of the invention

[0005] The purpose of the present invention is to provide a method, device and storage medium for predicting aircraft taxiing conflicts, which can predict the probability and time of aircraft taxiing conflicts and provide a reference method for airport taxiing conflict management.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides an aircraft taxiing conflict prediction method, comprising:

[0008] Based on the pre-acquired airport and aircraft information, an aircraft taxiing workflow network is constructed;

[0009] Based on the aircraft taxiing workflow network, setting an aircraft taxiing activity time interval;

[0010] Using trapezoidal fuzzy numbers to perform fuzzy processing on the aircraft taxiing activity time interval, and converting the fuzzy processed aircraft taxiing activity time interval into a trapezoidal time function to obtain an aircraft taxiing fuzzy workflow network;

[0011] The aircraft taxiing fuzzy workflow network is verified dynamically over time to predict aircraft taxiing conflicts.

[0012] In combination with the first aspect, further, based on the pre-acquired airport and aircraft information, constructing an aircraft taxiing workflow network includes:

[0013] Based on the pre-acquired airport and aircraft information, obtain the aircraft taxiing workflow and aircraft taxiing activities;

[0014] Using the pre-acquired Petri net to describe the connection relationship between the aircraft taxiing activities, and using the aircraft taxiing workflow to connect the aircraft taxiing activities to obtain the aircraft taxiing workflow net;

[0015] The aircraft taxiing workflow includes: the aircraft enters the taxiway, taxis on the straight road, enters the intersection, turns or waits at the intersection, leaves the intersection, and leaves the taxiway, and: the aircraft enters the taxiway, taxis on the straight road, passes the intersection, and leaves the taxiway;

[0016] The aircraft taxiing activity is composed of a place and two transitions. The place includes: an aircraft straight-ahead taxiing state and an aircraft intersection parking state. The transitions include: an aircraft entering an intersection and an aircraft leaving an intersection.

[0017] In combination with the first aspect, further, the airport and aircraft information includes: airport taxiway location, airport taxiway structure, airport apron location, airport runway nodes, airport taxiway nodes, aircraft performance and aircraft planned taxi route.

[0018] In combination with the first aspect, further, the aircraft taxiing activity time interval includes a straight taxiing activity time interval and a cross taxiing activity time interval. The straight taxiing activity time interval is set to be greater than the shortest straight taxiing time under an ideal environment and less than the maximum straight taxiing time under a disturbance environment, and the cross taxiing activity time interval is set to be greater than the shortest cross taxiing time under an ideal environment and less than the maximum cross taxiing time under a disturbance environment.

[0019] In combination with the first aspect, further, using a trapezoidal fuzzy number to perform fuzzy processing on the taxiing activity time interval of the aircraft includes: assigning a trapezoidal fuzzy number to each taxiing activity time interval of the aircraft;

[0020] The aircraft taxiing activity time interval after fuzzy processing is converted into a trapezoidal time function, and the aircraft taxiing fuzzy workflow network is obtained, including:

[0021] Calculate the statistical mean and standard deviation of the time interval of aircraft taxiing activities after fuzzy processing;

[0022] Based on the mean value and the standard deviation, a confidence interval of the aircraft taxiing activity time interval after fuzzy processing is obtained;

[0023] Based on the confidence interval, the aircraft taxiing activity time interval after fuzzy processing is converted into a trapezoidal time function to obtain the aircraft taxiing fuzzy workflow network;

[0024] Among them, the trapezoidal fuzzy number of the aircraft taxiing activity time interval is:

[0025] ;

[0026] The confidence interval is:

[0027] ;

[0028] in, is the statistical average of the time interval of aircraft taxiing activities, is the standard deviation of the time interval of the taxiing activity, is the number of times the aircraft taxiing activity time interval is recorded, is the quantile, is the confidence level, is the time interval of aircraft taxiing activities, The preset buffer time.

[0029] In combination with the first aspect, further, the aircraft taxiing fuzzy workflow network is subjected to time dynamic verification, and the aircraft taxiing conflict prediction includes:

[0030] Performing sequence mode time verification on the aircraft taxiing fuzzy workflow network to predict the conflict between the end time and the deadline of the aircraft's own taxiing activity;

[0031] The aircraft taxiing fuzzy workflow network is subjected to selection mode time verification to predict conflicts among different aircraft competing for the same taxiway resources.

[0032] In combination with the first aspect, further, the sequence mode time verification includes:

[0033] The duration from the current taxiing activity to the next taxiing activity of the aircraft is represented by trapezoidal fuzzy numbers.

[0034] Based on the duration from the current taxiing activity to the next taxiing activity of the aircraft, the trapezoidal fuzzy number is used to predict the end time of the next taxiing activity of the aircraft;

[0035] Compare the end time of the next taxiing activity of the aircraft with the cut-off time. If the end time of the next taxiing activity of the aircraft is less than or equal to the cut-off time, the end time of the taxiing activity of the aircraft and the cut-off time do not conflict. Otherwise, the end time of the taxiing activity of the aircraft and the cut-off time conflict.

[0036] The probability of conflict between the end time and the deadline of the aircraft taxiing activity is:

[0037] ;

[0038] in, is the probability of conflict between the end time and the deadline of the aircraft taxiing activity, Trapezoid The area of Trapezoid The area of Trapezoid With trapezoid The area of ​​the overlap, , , , is the trapezoidal fuzzy number of the cut-off time of aircraft taxiing activities, , , , is the trapezoidal fuzzy number of the end time of the aircraft taxiing activity;

[0039] The time when the end time of aircraft taxiing activities conflicts with the deadline is:

[0040] ;

[0041] in, For aircraft No. The end time of the sliding activity conflicts with the deadline.

[0042] In combination with the first aspect, further, the selection mode time verification includes:

[0043] The trapezoidal fuzzy number is used to calculate the taxiing start time of two aircraft competing for the same taxiway resources.

[0044] Compare the taxiing activity start times of two aircraft competing for the same taxiway resources. If the taxiing activity start times of the two aircraft competing for the same taxiway resources overlap, the two aircraft conflict when competing for the same taxiway resources. Otherwise, the two aircraft do not conflict when competing for the same taxiway resources.

[0045] The probability of conflict between two aircraft competing for the same taxiway resource is:

[0046] ;

[0047] in, is the probability of conflict between two aircraft when competing for the same taxiway resource, Trapezoid The area of Trapezoid The area of Trapezoid With trapezoid The area of ​​the overlap, , , , is the trapezoidal fuzzy number of the taxiing start time of an aircraft, , , , is the trapezoidal fuzzy number of the taxiing activity start time of another aircraft;

[0048] The time when two aircraft conflict when competing for the same taxiway resource is:

[0049] ;

[0050] in, The time when two aircraft conflict when competing for the same taxiway resources. The safety interval of the aircraft.

[0051] In a second aspect, the present invention provides an aircraft taxiing conflict prediction device, comprising:

[0052] Aircraft taxiing workflow network construction module: used to build an aircraft taxiing workflow network based on pre-acquired airport and aircraft information;

[0053] An aircraft taxiing activity time interval setting module: used to set an aircraft taxiing activity time interval based on the aircraft taxiing workflow network;

[0054] An aircraft taxiing fuzzy workflow network construction module is used to perform fuzzy processing on the aircraft taxiing activity time interval using a trapezoidal fuzzy number, and convert the fuzzy processed aircraft taxiing activity time interval into a trapezoidal time function to obtain an aircraft taxiing fuzzy workflow network;

[0055] Prediction module: used for performing time dynamic verification on the aircraft taxiing fuzzy workflow network and predicting aircraft taxiing conflicts.

[0056] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] The aircraft taxiing conflict prediction method provided by the present invention constructs an aircraft taxiing workflow network based on pre-acquired airport and aircraft information, sets an aircraft taxiing activity time interval, considers the time uncertainty caused by interference factors during the actual taxiing process of the aircraft, uses trapezoidal fuzzy numbers to perform fuzzy processing on the aircraft taxiing activity time interval, and converts the fuzzy-processed aircraft taxiing activity time interval into a trapezoidal time function, obtains the aircraft taxiing fuzzy workflow network, performs time dynamic verification on the aircraft taxiing fuzzy workflow network, and uses time consistency to predict the probability and time of aircraft taxiing conflicts, thereby providing a reference method for airport taxiing conflict management. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a flow chart of an aircraft taxiing conflict prediction method provided by an embodiment of the present invention;

[0060] Figure 2 is a schematic diagram of an aircraft taxiing workflow provided by an embodiment of the present invention;

[0061] Figure 3 1. It is a schematic diagram of the conflict between the end time and the deadline of the taxiing activity of the aircraft itself provided by an embodiment of the present invention;

[0062] Figure 4 It is a schematic diagram of conflicts among different aircraft competing for the same taxiway resources provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The technical solution of the present application is further described in detail below in conjunction with specific implementation methods.

[0064] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and should not be construed as limitations on the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments may be combined with each other.

[0065] Embodiment 1:

[0066] Figure 1 is a flow chart of a method for predicting aircraft taxiing conflicts provided by this embodiment. This flow chart only shows the logical sequence of the method of this embodiment. Different methods can be used without conflict. Figure 1 The steps shown or described are accomplished in the order shown.

[0067] The aircraft taxiing conflict prediction method provided in this embodiment can be applied to a terminal and can be executed by an aircraft taxiing conflict prediction device, which can be implemented by software and / or hardware, and can be integrated in a terminal, for example: any tablet computer or computer device with communication function.

[0068] See also Figure 1 The method of this embodiment specifically includes the following steps:

[0069] Step 1: Construct an aircraft taxiing workflow network based on the pre-acquired airport and aircraft information;

[0070] In this embodiment, the airport and aircraft information includes: airport taxiway location, airport taxiway structure, airport apron location, airport runway node, airport taxiway node, aircraft performance and aircraft planned taxi route.

[0071] Based on the pre-acquired airport and aircraft information, building an aircraft taxiing workflow network specifically includes the following steps:

[0072] Step ①: Based on the pre-acquired airport and aircraft information, obtain the aircraft taxiing workflow and aircraft taxiing activities;

[0073] In this embodiment, Figure 2 As shown, the aircraft taxiing workflow includes: the aircraft enters the taxiway, taxis on the straight road, enters the intersection, turns or waits at the intersection, leaves the intersection, and leaves the taxiway, and: the aircraft enters the taxiway, taxis on the straight road, passes the intersection, and leaves the taxiway.

[0074] The aircraft taxiing activity consists of one place and two transitions. The place includes: the aircraft straight-ahead taxiing state and the aircraft intersection parking state. The transitions include: the aircraft entering the intersection and the aircraft leaving the intersection.

[0075] Step ②: Use the pre-acquired Petri net to describe the connection relationship between each aircraft taxiing activity, and use the aircraft taxiing workflow to connect each aircraft taxiing activity to obtain the aircraft taxiing workflow net.

[0076] Petri net is a mathematical model used to model, describe and analyze system behavior, which consists of places, transitions, directed arcs and tokens.

[0077] This embodiment uses Petri nets to describe the state change process of aircraft straight-ahead operation and intersection operation. According to the model characteristics that the workflow can reflect a series of interconnected business activities or tasks, each aircraft taxiing process is connected according to the connection relationship between the aircraft taxiing activities by combining Petri nets, and the aircraft taxiing workflow is used to connect them. All aircraft taxiing workflows on the taxiway are combined together to construct an aircraft taxiing workflow network.

[0078] Step 2: Based on the aircraft taxiing workflow network, set the aircraft taxiing activity time interval;

[0079] Time is a key factor in aircraft taxiing and is also a major component of the aircraft taxiing workflow network. Adding the time concept of aircraft straight-ahead operation and intersection parking can improve the aircraft taxiing workflow network. When taxiing in the taxiing workflow network, the aircraft is susceptible to various uncertain factors, causing the actual taxiing time of the aircraft to deviate from the planned taxiing time.

[0080] In this embodiment, a time interval is specified for the taxiing activity time of the aircraft, the uncertainty of the taxiing activity time of the aircraft is analyzed, and the taxiing activity time interval recorded during the taxiing process of the aircraft is fuzzy processed using trapezoidal fuzzy numbers, so that the taxiing time of the aircraft is distributed with a certain probability within a time period.

[0081] The aircraft taxiing activity time interval includes the straight taxiing activity time interval and the cross taxiing activity time interval. The straight taxiing activity time interval is set to be greater than the shortest straight taxiing time under an ideal environment and less than the maximum straight taxiing time under a disturbed environment. The cross taxiing activity time interval is set to be greater than the shortest cross taxiing time under an ideal environment and less than the maximum cross taxiing time under a disturbed environment.

[0082] Step 3: Use trapezoidal fuzzy numbers to perform fuzzy processing on the time interval of aircraft taxiing activities, and convert the fuzzy processed time interval of aircraft taxiing activities into a trapezoidal time function to obtain the aircraft taxiing fuzzy workflow network;

[0083] The fuzzy processing of the time interval of aircraft taxiing activities is performed using trapezoidal fuzzy numbers, that is, a trapezoidal fuzzy number is assigned to the taxiing activity time interval of each aircraft.

[0084] In this embodiment, the trapezoidal fuzzy number of the aircraft taxiing activity time interval is:

[0085] ;

[0086] in, is the statistical average of the time interval of aircraft taxiing activities, is the standard deviation of the time interval of the taxiing activity, is the number of times the aircraft taxiing activity time interval is recorded, is the quantile, is the confidence level, is the time interval of aircraft taxiing activities, The preset buffer time.

[0087] The fuzzy processed aircraft taxiing activity time interval is converted into a trapezoidal time function, and the aircraft taxiing fuzzy workflow network is obtained, which specifically includes the following steps:

[0088] Step 1: Calculate the statistical mean and standard deviation of the time interval of aircraft taxiing activities after fuzzy processing;

[0089] Step 2: Based on the mean value and standard deviation, obtain the confidence interval of the aircraft taxiing activity time interval after fuzzy processing;

[0090] In this embodiment, the confidence interval is:

[0091] ;

[0092] in, is the statistical average of the time interval of aircraft taxiing activities, is the standard deviation of the time interval of the taxiing activity, is the number of times the aircraft taxiing activity time interval is recorded, is the quantile, is the confidence level.

[0093] Step 4: Based on the confidence interval, obtain the trapezoidal time function of the aircraft taxiing activity time interval after fuzzy processing, and obtain the aircraft taxiing fuzzy workflow network.

[0094] Step 4: Perform temporal dynamic verification on the aircraft taxiing fuzzy workflow network to predict aircraft taxiing conflicts.

[0095] The time dynamic verification of the aircraft taxiing fuzzy workflow network and the prediction of aircraft taxiing conflicts specifically include the following steps:

[0096] Step (1): Perform sequence pattern time verification on the aircraft taxiing fuzzy workflow network to predict the conflict between the end time and deadline of the aircraft's own taxiing activities;

[0097] In this embodiment, the sequence mode time verification specifically includes the following steps:

[0098] Step i: Use trapezoidal fuzzy numbers to represent the duration from the current taxiing activity to the next taxiing activity of the aircraft;

[0099] Step II: Based on the duration from the current taxiing activity to the next taxiing activity of the aircraft, the end time of the next taxiing activity of the aircraft is predicted using trapezoidal fuzzy numbers;

[0100] Step iii: Compare the end time of the next taxiing activity of the aircraft with the cut-off time. If the end time of the next taxiing activity of the aircraft is less than or equal to the cut-off time, the end time of the taxiing activity of the aircraft does not conflict with the cut-off time. Otherwise, the end time of the taxiing activity of the aircraft conflicts with the cut-off time.

[0101] like Figure 3 As shown in the figure, the probability of conflict between the end time and the deadline of the aircraft taxiing activity is:

[0102] ;

[0103] in, is the probability of conflict between the end time and the deadline of the aircraft taxiing activity, Trapezoid The area of Trapezoid The area of Trapezoid With trapezoid The area of ​​the overlap, , , , is the trapezoidal fuzzy number of the cut-off time of aircraft taxiing activities, , , , It is the trapezoidal fuzzy number of the end time of the aircraft taxiing activity.

[0104] The time when the end time of aircraft taxiing activities conflicts with the deadline is:

[0105] ;

[0106] in, For aircraft No. The end time of the sliding activity conflicts with the deadline.

[0107] Step (2): Perform time verification on the aircraft taxiing fuzzy workflow network to predict conflicts among different aircraft competing for the same taxiway resources.

[0108] In this embodiment, the selection mode time verification specifically includes the following steps:

[0109] Step I: Use trapezoidal fuzzy numbers to calculate the taxiing start time of two aircraft competing for the same taxiway resources;

[0110] Step II: Compare the taxiing activity start times of the two aircraft competing for the same taxiway resources. If the taxiing activity start times of the two aircraft competing for the same taxiway resources overlap, the two aircraft conflict when competing for the same taxiway resources. Otherwise, the two aircraft do not conflict when competing for the same taxiway resources.

[0111] like Figure 4 As shown in Figure 2, the probability of conflict between two aircraft when competing for the same taxiway resource is:

[0112] ;

[0113] in, is the probability of conflict between two aircraft when competing for the same taxiway resource, Trapezoid The area of Trapezoid The area of Trapezoid With trapezoid The area of ​​the overlap, , , , is the trapezoidal fuzzy number of the taxiing start time of an aircraft, , , , is the trapezoidal fuzzy number of the taxiing activity start time of another aircraft.

[0114] The time when two aircraft conflict when competing for the same taxiway resource is:

[0115] ;

[0116] in, The time when two aircraft conflict when competing for the same taxiway resources. The safety interval of the aircraft.

[0117] Figure 4 middle, is a possible value of the conflict time, that is, a value within the time when the conflict occurs, and its membership function obtains the maximum value.

[0118] The aircraft taxiing conflict prediction method provided in this embodiment constructs an aircraft taxiing workflow network based on pre-acquired airport and aircraft information, sets an aircraft taxiing activity time interval, considers the time uncertainty caused by interference factors during the actual taxiing process of the aircraft, uses trapezoidal fuzzy numbers to perform fuzzy processing on the aircraft taxiing activity time interval, and converts the fuzzy-processed aircraft taxiing activity time interval into a trapezoidal time function to obtain the aircraft taxiing fuzzy workflow network, perform time dynamic verification on the aircraft taxiing fuzzy workflow network, and use time consistency to predict the probability and time of aircraft taxiing conflicts, thereby providing a reference method for airport taxiing conflict management.

[0119] Embodiment 2:

[0120] This embodiment provides an aircraft taxiing conflict prediction device, comprising:

[0121] Aircraft taxiing workflow network construction module: used to build an aircraft taxiing workflow network based on pre-acquired airport and aircraft information;

[0122] Aircraft taxiing activity time interval setting module: used to set the aircraft taxiing activity time interval based on the aircraft taxiing workflow network;

[0123] Aircraft taxiing fuzzy workflow network construction module: used to perform fuzzy processing on the aircraft taxiing activity time interval using trapezoidal fuzzy numbers, and convert the fuzzy processed aircraft taxiing activity time interval into a trapezoidal time function to obtain the aircraft taxiing fuzzy workflow network;

[0124] Prediction module: used to perform time-dynamic verification of the aircraft taxiing fuzzy workflow network and predict aircraft taxiing conflicts.

[0125] The aircraft taxiing conflict prediction device provided in the embodiments of the present application can execute the aircraft taxiing conflict prediction method provided in any embodiment of the present application, and has the corresponding functional modules and beneficial effects of the execution method.

[0126] Embodiment 3:

[0127] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method in Embodiment 1 are implemented.

[0128] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0129] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0130] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0131] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0132] The above are only preferred implementations of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for predicting aircraft taxiing conflicts, characterized in that: include: Based on the pre-acquired airport and aircraft information, an aircraft taxiing workflow network is constructed; Based on the aircraft taxiing workflow network, setting an aircraft taxiing activity time interval; Using trapezoidal fuzzy numbers to perform fuzzy processing on the aircraft taxiing activity time interval, and converting the fuzzy processed aircraft taxiing activity time interval into a trapezoidal time function to obtain an aircraft taxiing fuzzy workflow network; Performing time-dynamic verification on the aircraft taxiing fuzzy workflow network to predict aircraft taxiing conflicts; The fuzzy processing of the time interval of the taxiing activity of the aircraft using the trapezoidal fuzzy number includes: Assigning a trapezoidal fuzzy number to each of the taxiing activity time intervals of the aircraft; The aircraft taxiing activity time interval after fuzzy processing is converted into a trapezoidal time function, and the aircraft taxiing fuzzy workflow network is obtained, including: Calculate the statistical mean and standard deviation of the time interval of aircraft taxiing activities after fuzzy processing; Based on the mean value and the standard deviation, a confidence interval of the aircraft taxiing activity time interval after fuzzy processing is obtained; Based on the confidence interval, the aircraft taxiing activity time interval after fuzzy processing is converted into a trapezoidal time function to obtain the aircraft taxiing fuzzy workflow network; Among them, the trapezoidal fuzzy number of the aircraft taxiing activity time interval is: ； The confidence interval is: ； in, is the statistical average of the time interval of aircraft taxiing activities, is the standard deviation of the time interval of the taxiing activity, is the number of times the aircraft taxiing activity time interval is recorded, is the quantile, is the confidence level, is the time interval of aircraft taxiing activities, To preset the buffer time; The aircraft taxiing fuzzy workflow network is verified dynamically over time to predict aircraft taxiing conflicts including: Performing sequence mode time verification on the aircraft taxiing fuzzy workflow network to predict the conflict between the end time and the deadline of the aircraft's own taxiing activity; Performing time verification on the aircraft taxiing fuzzy workflow network to select a mode and predict conflicts between different aircraft competing for the same taxiway resources; The sequential mode time verification includes: The duration from the current taxiing activity to the next taxiing activity of the aircraft is represented by trapezoidal fuzzy numbers. Based on the duration from the current taxiing activity to the next taxiing activity of the aircraft, the trapezoidal fuzzy number is used to predict the end time of the next taxiing activity of the aircraft; Compare the end time of the next taxiing activity of the aircraft with the cut-off time. If the end time of the next taxiing activity of the aircraft is less than or equal to the cut-off time, the end time of the taxiing activity of the aircraft and the cut-off time do not conflict. Otherwise, the end time of the taxiing activity of the aircraft and the cut-off time conflict. The probability of conflict between the end time and the deadline of the aircraft taxiing activity is: ； in, is the probability of conflict between the end time and the deadline of the aircraft taxiing activity, Trapezoid The area of Trapezoid The area of Trapezoid With trapezoid The area of ​​the overlap, , , , is the trapezoidal fuzzy number of the cut-off time of aircraft taxiing activities, , , , is the trapezoidal fuzzy number of the end time of the aircraft taxiing activity; The time when the end time of aircraft taxiing activities conflicts with the deadline is: ； in, For aircraft No. The end time of the gliding activity conflicts with the deadline; The selection mode time verification includes: The trapezoidal fuzzy number is used to calculate the taxiing start time of two aircraft competing for the same taxiway resources. Compare the taxiing activity start times of two aircraft competing for the same taxiway resources. If the taxiing activity start times of the two aircraft competing for the same taxiway resources overlap, the two aircraft conflict when competing for the same taxiway resources. Otherwise, the two aircraft do not conflict when competing for the same taxiway resources. The probability of conflict between two aircraft competing for the same taxiway resource is: ； in, is the probability of conflict between two aircraft when competing for the same taxiway resource, Trapezoid The area of Trapezoid The area of Trapezoid With trapezoid The area of ​​the overlap, , , , is the trapezoidal fuzzy number of the taxiing start time of an aircraft, , , , is the trapezoidal fuzzy number of the taxiing activity start time of another aircraft; The time when two aircraft conflict when competing for the same taxiway resource is: ； in, The time when two aircraft conflict when competing for the same taxiway resources. The safety interval of the aircraft.

2. The aircraft taxiing conflict prediction method according to claim 1, characterized in that: Based on the pre-acquired airport and aircraft information, the aircraft taxiing workflow network is constructed including: Based on the pre-acquired airport and aircraft information, obtain the aircraft taxiing workflow and aircraft taxiing activities; Using the pre-acquired Petri net to describe the connection relationship between the aircraft taxiing activities, and using the aircraft taxiing workflow to connect the aircraft taxiing activities to obtain the aircraft taxiing workflow net; The aircraft taxiing workflow includes: the aircraft enters the taxiway, taxis on the straight road, enters the intersection, turns or waits at the intersection, leaves the intersection, and leaves the taxiway, and: the aircraft enters the taxiway, taxis on the straight road, passes the intersection, and leaves the taxiway; The aircraft taxiing activity is composed of a place and two transitions. The place includes: an aircraft straight-ahead taxiing state and an aircraft intersection parking state. The transitions include: an aircraft entering an intersection and an aircraft leaving an intersection.

3. The aircraft taxiing conflict prediction method according to claim 1, characterized in that: The airport and aircraft information includes: airport taxiway location, airport taxiway structure, airport apron location, airport runway nodes, airport taxiway nodes, aircraft performance and aircraft planned taxi route.

4. The aircraft taxiing conflict prediction method according to claim 1, characterized in that: The aircraft taxiing activity time interval includes a straight taxiing activity time interval and a cross taxiing activity time interval. The straight taxiing activity time interval is set to be greater than the shortest straight taxiing time under an ideal environment and less than the maximum straight taxiing time under a disturbed environment. The cross taxiing activity time interval is set to be greater than the shortest cross taxiing time under an ideal environment and less than the maximum cross taxiing time under a disturbed environment.

5. An aircraft taxiing conflict prediction device, characterized in that: include: Aircraft taxiing workflow network construction module: used to build an aircraft taxiing workflow network based on pre-acquired airport and aircraft information; An aircraft taxiing activity time interval setting module: used to set an aircraft taxiing activity time interval based on the aircraft taxiing workflow network; An aircraft taxiing fuzzy workflow network construction module is used to perform fuzzy processing on the aircraft taxiing activity time interval using a trapezoidal fuzzy number, and convert the fuzzy processed aircraft taxiing activity time interval into a trapezoidal time function to obtain an aircraft taxiing fuzzy workflow network; Prediction module: used for performing time dynamic verification on the aircraft taxiing fuzzy workflow network and predicting aircraft taxiing conflicts; The fuzzy processing of the time interval of the taxiing activity of the aircraft using the trapezoidal fuzzy number includes: Assigning a trapezoidal fuzzy number to each of the taxiing activity time intervals of the aircraft; The aircraft taxiing activity time interval after fuzzy processing is converted into a trapezoidal time function, and the aircraft taxiing fuzzy workflow network is obtained, including: Calculate the statistical mean and standard deviation of the time interval of aircraft taxiing activities after fuzzy processing; Based on the mean value and the standard deviation, a confidence interval of the aircraft taxiing activity time interval after fuzzy processing is obtained; Based on the confidence interval, the aircraft taxiing activity time interval after fuzzy processing is converted into a trapezoidal time function to obtain the aircraft taxiing fuzzy workflow network; Among them, the trapezoidal fuzzy number of the aircraft taxiing activity time interval is: ； The confidence interval is: ； in, is the statistical average of the time interval of aircraft taxiing activities, is the standard deviation of the time interval of the taxiing activity, is the number of times the aircraft taxiing activity time interval is recorded, is the quantile, is the confidence level, is the time interval of aircraft taxiing activities, To preset the buffer time; The aircraft taxiing fuzzy workflow network is verified dynamically over time to predict aircraft taxiing conflicts including: Performing sequence mode time verification on the aircraft taxiing fuzzy workflow network to predict the conflict between the end time and the deadline of the aircraft's own taxiing activity; Performing time verification on the aircraft taxiing fuzzy workflow network to select a mode and predict conflicts between different aircraft competing for the same taxiway resources; The sequential mode time verification includes: The duration from the current taxiing activity to the next taxiing activity of the aircraft is represented by trapezoidal fuzzy numbers. Based on the duration from the current taxiing activity to the next taxiing activity of the aircraft, the trapezoidal fuzzy number is used to predict the end time of the next taxiing activity of the aircraft; Compare the end time of the next taxiing activity of the aircraft with the cut-off time. If the end time of the next taxiing activity of the aircraft is less than or equal to the cut-off time, the end time of the taxiing activity of the aircraft and the cut-off time do not conflict. Otherwise, the end time of the taxiing activity of the aircraft and the cut-off time conflict. The probability of conflict between the end time and the deadline of the aircraft taxiing activity is: ； in, is the probability of conflict between the end time and the deadline of the aircraft taxiing activity, Trapezoid The area of Trapezoid The area of Trapezoid With trapezoid The area of ​​the overlap, , , , is the trapezoidal fuzzy number of the cut-off time of aircraft taxiing activities, , , , is the trapezoidal fuzzy number of the end time of the aircraft taxiing activity; The time when the end time of aircraft taxiing activities conflicts with the deadline is: ； in, For aircraft No. The end time of the gliding activity conflicts with the deadline; The selection mode time verification includes: The trapezoidal fuzzy number is used to calculate the taxiing start time of two aircraft competing for the same taxiway resources. Compare the taxiing activity start times of two aircraft competing for the same taxiway resources. If the taxiing activity start times of the two aircraft competing for the same taxiway resources overlap, the two aircraft conflict when competing for the same taxiway resources. Otherwise, the two aircraft do not conflict when competing for the same taxiway resources. The probability of conflict between two aircraft competing for the same taxiway resource is: ； in, is the probability of conflict between two aircraft when competing for the same taxiway resource, Trapezoid The area of Trapezoid The area of Trapezoid With trapezoid The area of ​​the overlap, , , , is the trapezoidal fuzzy number of the taxiing start time of an aircraft, , , , is the trapezoidal fuzzy number of the taxiing activity start time of another aircraft; The time when two aircraft conflict when competing for the same taxiway resource is: ； in, The time when two aircraft conflict when competing for the same taxiway resources. The safety interval of the aircraft.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in any one of claims 1 to 4 are implemented.

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