Airport support vehicle conflict diagnosis method based on digital twin drive

Abstract

The invention relates to the technical field of conflict detection, in particular to an airport support vehicle conflict diagnosis method based on digital twin driving. Comprising the following steps: acquiring attribute information of an airport object, and generating a corresponding digital twin monomer in a twin space; associating the digital twin monomers according to the service logic to form a flight ground support twin service; identifying guarantee conflicts, acquiring conflict occurrence time, places and reasons, and performing conflict analysis; adjusting the guarantee plan, and performing simulation deduction; through the implementation of the mode, a guarantee service process with real-time data interaction is provided, the number of guarantee service conflicts is reduced, and the dynamic adjustment capability of a scheduling plan is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of conflict detection, and particularly to a method for diagnosing conflicts of airport support vehicles driven by digital twin. Background Art

[0002] With the rapid development of the civil aviation industry, the number of inbound and outbound flights per unit time has increased sharply. In order to complete the ground support services for flights within the specified time, airport support vehicles and personnel often need to operate overload. And how to effectively identify service conflict problems at each node of the support process has become increasingly prominent. In the prior art, it is impossible to effectively monitor, diagnose and adjust service conflicts at support nodes, resulting in various equipment and personnel unable to work efficiently in coordination.

[0003] With the development of edge computing, cloud computing and digital twin technologies, it provides technical support for accurately obtaining real-time information of airport equipment and personnel. Digital twin technology can model the three-dimensional model, business data and business logic of the physical space to form a virtual space that accurately maps to the physical space, and the virtual space is synchronously updated according to the physical space.

[0004] In summary, it is very necessary to propose a method for diagnosing conflicts of airport support vehicles driven by digital twin with a support service process of real-time data interaction, reducing the number of support service conflicts and improving the dynamic adjustment ability of the scheduling plan. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for diagnosing conflicts of airport support vehicles driven by digital twin, with a support service process of real-time data interaction, aiming to reduce the number of support service conflicts and improve the dynamic adjustment ability of the scheduling plan.

[0006] To achieve the above purpose, a method for diagnosing conflicts of airport support vehicles driven by digital twin adopted by the present invention includes the following steps: Obtain the attribute information of airport objects and generate corresponding digital twin monomers in the twin space; Associate the digital twin monomers according to the business logic to form a twin service for flight ground support; Identify support conflicts, obtain the time, location and cause of the conflict occurrence, and conduct conflict analysis; Adjust the support plan and conduct simulation deduction.

[0007] Among them, in the step of obtaining the attribute information of airport objects: Obtain the real-time information of support nodes of all flights and extract the real-time data through the twin model.

[0008] Among them, in the step of obtaining the real-time information of support nodes of all flights: The real-time information includes the refined 3D model, position, speed, direction of the guarantee vehicle, and the real-time data of the task nodes.

[0009] Among them, in the step of associating digital twin monomers according to business logic to form the twin business of flight ground guarantee: Associate business objects, business scenarios, and business logic in the digital twin space with twin monomers according to various modeling methods.

[0010] Among them, after the step of associating business objects, business scenarios, and business logic in the digital twin space with twin monomers according to various modeling methods: Conduct deviation analysis based on the real-time guarantee status and guarantee plan of the digital twin monomers, conduct future deduction for a specific time period according to the guarantee plan, and save the deduction information.

[0011] Among them, in the step of identifying guarantee conflicts, obtaining the time, location, and cause of the conflict occurrence, and conducting conflict analysis: Conduct conflict diagnosis according to the conflict model based on the real-time monitoring data and deduction data.

[0012] Among them, in the step of adjusting the guarantee plan and conducting simulation deduction: Obtain the conflicts that occur in real-time and deduction, adjust the guarantee plan, and synchronously upload the updated information to the digital twin space. The computing engine in the digital twin space conducts simulation verification and updates the tasks to the guarantee equipment.

[0013] A method for diagnosing conflicts of airport guarantee vehicles driven by digital twins according to the present invention obtains the attribute information of airport objects, generates corresponding digital twin monomers in the twin space; associates the digital twin monomers according to business logic to form the twin business of flight ground guarantee; identifies guarantee conflicts, obtains the time, location, and cause of the conflict occurrence, and conducts conflict analysis; adjusts the guarantee plan and conducts simulation deduction; through the implementation of the above methods, a guarantee service process with real-time data interaction is realized, reducing the number of guarantee service conflicts and improving the dynamic adjustment ability of the scheduling plan. Description of the Drawings

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0015] Figure 1 It is the flowchart of the steps of the method for diagnosing conflicts of airport guarantee vehicles driven by digital twins according to the present invention. Specific Embodiments

[0016] Here, exemplary embodiments will be described in detail, and examples thereof are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application.

[0017] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit the present application. The singular forms "a", "the", and "said" used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0018] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining".

[0019] Please refer to Figure 1 , the present invention provides a method for diagnosing conflicts of airport support vehicles driven by digital twins, including the following steps: S100: Obtain the attribute information of airport objects and generate corresponding digital twin monomers in the twin space.

[0020] Further, in the step of obtaining the attribute information of airport objects: Obtain the real-time information of the guarantee nodes of all flights and extract the real-time data through the twin model.

[0021] Further, in the step of obtaining the real-time information of the guarantee nodes of all flights: The real-time information includes the refined three-dimensional model, position, speed, direction, and real-time data of the task nodes of the support vehicles.

[0022] In this embodiment, a business twin model for flight ground support is constructed in a virtual space, including a visual three-dimensional model, mechanism, state, and behavior models. To construct an accurate twin model, it is first necessary to obtain real-time data such as equipment, production, etc. with the help of devices such as monitors, sensors, and wireless networks. Clean and extract these real-time data to initialize the business twin model, and use digital twin technology to capture subsequent data updates and synchronously update the business twin model. The business twin model needs to obtain and model the following data. For flight objects, it includes route information, flight information, taxiing data, etc. For aircraft stands, it is necessary to accurately segment the time of business processes, including wheel chock removal, jet bridge docking, and passenger stairs vehicle docking, etc. For support vehicles, it includes vehicle basic information and vehicle operation information. Since a business twin model for flight ground support is constructed, passenger information can be integrated with flight information because there is a unique correspondence between passenger information and flight information. Except for support vehicle data, other data can update the business twin model before the start of the support task. The data of support vehicles requires even more precision and needs to cooperate with various real-time monitoring devices to obtain information. The basic information of the vehicle can be obtained from the dispatching center, and this data is relatively fixed and rarely updated, such as vehicle number, vehicle type, and affiliated department, etc. The operation information of the vehicle needs to be obtained by more edge devices, including vehicle location, operation trajectory, work tasks, and work status, etc. The above information are all attribute information of support vehicles. To construct a single model of support vehicles, a precise visual three-dimensional model is also required. A one-to-one three-dimensional model is constructed in the business twin model according to the shape, size, and physical properties of the support vehicle. Integrate the basic information and operation information into the three-dimensional model to form an independent twin single entity in the business twin model. Similarly, other devices also construct twin single entities according to a similar method.

[0023] S200: Associate digital twin single entities according to business logic to form a flight ground support twin service.

[0024] Further, in the step of associating digital twin single entities according to business logic to form a flight ground support twin service: Associate twin single entities in the digital twin space for business objects, business scenarios, and business logic according to various modeling methods.

[0025] Further, after the step of associating twin single entities in the digital twin space for business objects, business scenarios, and business logic according to various modeling methods: Conduct deviation analysis based on the real-time support status and support plan of digital twin single entities, conduct future deduction for a specific time period according to the support plan, and save the deduction information.

[0026] In this embodiment, a twin monomer is constructed based on the real-time information of the monomer model. Since the business twin model of flight ground support is a complex system, it is necessary to associate various twin monomers in the business twin model with business objects, business scenarios, and business logic according to multiple modeling methods. Based on the analysis of twin body characteristics, clarify the constituent elements of the twin monomer and determine the regular relationship of the twin object. Clarify the key characteristics of business logic events, realize the standardized description of event rules, and achieve the high-order functional application of the twin body and the transformation of unordered data into business network data. Based on the analysis of airport business association processes, realize the expression of twin body hierarchical association rules. The associated information includes operation status association and business logic association, etc. For the business twin model of flight ground support, it is mainly to associate the aircraft twin monomer and the support vehicle twin monomer, and the support personnel and the support vehicle are bound, which can be regarded as a combined twin monomer. The business association between the aircraft and the support vehicle is achieved by establishing a flight ground support scheduling model. These models mainly constrain the services of the support vehicles, including the start / finish time of wheel chocks on / off, the start / finish time of jet bridge docking / evacuation, the time of passenger boarding bridge docking / evacuation, the time of cabin door opening / closing, the time of cargo door opening / closing, etc. These specific support node times are the calculation results of the solution algorithm on the scheduling model, and these solution methods are selected according to the calculation overhead, real-time requirements, and robustness requirements. Considering that the digital twin model has high real-time requirements, artificial intelligence algorithms, including machine learning and reinforcement learning, can be considered first, and manual layout and some classic heuristic algorithms should be avoided as much as possible to further improve the real-time performance of the entire system and ensure that each twin monomer is updated with equal timestamps and frame rates. Other types of twin monomers are also associated according to the above methods.

[0027] The flight ground support business twin model forms a model with business perception capabilities through the association of various twins and real-time information. That is, it can realize the current flight support operation situation perception. For example, according to the real-time information of the three-dimensional model and attributes of the digital twin monomer, combined with the flight support plan and business logic, the deviation analysis is performed based on the real-time support status of the digital twin monomer and the support plan to obtain whether all support vehicles are currently serving in advance, serving as planned, or serving late. It can also realize the flight support operation situation perception at future moments. According to the flight support plan and the current digital twin real-time data, the flight ground support simulation is performed for a specific time period in the future, and the key node support information is saved. The key node support information is used for analysis in the diagnosis stage, and the support status of the support vehicle at a specific time in the future can be obtained. Business perception requires two aspects of deviation analysis. The first is the deviation between the actual operation situation of the airport and the operation situation in the digital twin space. Due to the inconsistency between the physical entity and the virtual entity caused by the untimely update of data or other reasons, the data of the deviation time node needs to be saved for subsequent conflict diagnosis and tracing. The second is the deviation between the actual operation status and the established guarantee plan. The cause may be unexpected events, such as abnormal conditions and equipment failures, or the guarantee plan may not be robust enough to match the actual operation status. Business perception can provide deviation information for subsequent conflict diagnosis, and further provide reliable data support for subsequent conflict handling, to find the cause of the conflict and the time node corresponding to the conflict.

[0028] S300: Identify security conflicts, obtain the time, location and cause of the conflict, and conduct conflict analysis.

[0029] Further, in the steps of identifying guarantee conflicts, obtaining the time, location and cause of conflicts, and conducting conflict analysis: According to the real-time monitoring data and deduction data, conflict diagnosis is carried out according to the conflict model.

[0030] In this embodiment, a support conflict is defined as: the support vehicle provides service to the aircraft too early or too late, directly or indirectly causing the aircraft to be unable to perform the next flight mission as planned. There are three specific conflict types. First, the vehicle arrives at the stand in advance before the allowed time window to wait, and the preceding support node of the corresponding support node has not yet been completed. Second, the vehicle arrives at the stand late after the allowed time window to provide support for the support node, and the following support node cannot provide service. Third, the abnormal service completion time of the current support vehicle in this aircraft service will affect the normal service completion time of the next aircraft.

[0031] Based on the real-time perception of the flight guarantee operation situation at the current moment, the application algorithms in the twin database are called to detect all guarantee conflicts, and then boundary settings are made according to specific requirements to accurately diagnose the conflicts. Finally, detailed conflict information and distribution are obtained. Next, a sliding window with a soft time window is used to conduct a deduction and review based on the twin data to find out which type of conflict causes the current guarantee task not to be completed within the specified time window, and to determine whether the delay can be offset by adjusting other guarantee tasks. The occurrence nodes, causes, and relevant data of this delay are uploaded to the twin data center for later analysis and review. Secondly, according to the existing information and future uncertain events, the guarantee process for a period of time in the future can be simulated and deduced to find out the guarantee breakpoints that may be brought about by the uncertain events. Specifically, first, data is input into the simulation engine, and the uncertain events are described using the parameter area. The uncertain events can be parameterized according to the Gaussian distribution and empirical data. The simulation engine can start the future deduction in an uncertain environment based on basic information such as flight schedules and guarantee vehicle fleets, and upload the potential guarantee conflicts in the specified future time period to the dispatching center in the form of a simulation report. The dispatching center then uses the sliding window mechanism of the soft time window to eliminate the acceptable conflicts and find out the guarantee conflicts that cause serious delays. All conflict data is uploaded to the twin space for reference and analysis by other related services to ensure the consistency of the entire airport system. In addition to guarantee conflict diagnosis, it is also necessary to diagnose the real-time trajectory of guarantee vehicles to achieve real-time diagnosis of the driving trajectories of guarantee vehicles in the airfield area. Through the vehicle positioning data of the video service platform and the ASMGCS system, combined with the driving rules of vehicles in the airfield area, the driving trajectories of the vehicles are monitored and diagnosed in real time, and warning is given to the guarantee vehicles with abnormal trajectories or deviated planned paths. For the diagnosis of the shortage of guarantee vehicle resources, the remaining resources of the flight guarantee vehicles are diagnosed, and real-time warning is given when the resources are in short supply. By counting the usage of vehicle resources in the airport airfield area, combined with the remaining guarantee vehicle resources and the flight guarantee vehicle scheduling plan, a real-time early warning diagnosis is carried out on whether the flight guarantee vehicles can complete the overall guarantee task of the current airport, so as to realize the early scheduling of vehicle resources and reduce the delay risk.

[0032] To achieve future guarantee conflict diagnosis and adjustment, the following deductions need to be implemented in the simulation engine: aircraft taxiing arrival time simulation, which conducts a twin deduction of the taxiing arrival time for the scenario where the aircraft docks at a specified parking position. By collecting the historical data of aircraft taxiing, an aircraft taxiing simulation model is established to deduce indicators such as the aircraft arrival time and taxiing takeoff time. Guarantee vehicle arrival time simulation, which conducts a twin deduction of the arrival time of future guarantee tasks for guarantee vehicles according to their task execution situations. By collecting the historical data of vehicle guarantee tasks, a vehicle flight guarantee simulation model is established to deduce indicators such as the arrival time of guarantee vehicles and the expected arrival time of the next task. Auxiliary verification for flight delay adjustment, according to the recovery and adjustment plan for flight delays, using the simulation model to conduct a twin deduction of the adjustment plan. By statistically analyzing the indicators of relevant links in the adjustment process, the evaluation results of different plans are obtained, thereby verifying the feasibility of the adjustment plan and selecting the optimal plan.

[0033] S400: Adjust the guarantee plan and conduct simulation deductions.

[0034] Further, in the step of adjusting the guarantee plan and conducting simulation deductions: Obtain the conflicts that occur in real time and during deductions, adjust the guarantee plan, and simultaneously upload the updated information to the digital twin space. The computing engine in the digital twin space conducts simulation verification and updates the tasks to the guarantee equipment.

[0035] In this embodiment, the decision-making personnel adjust the existing guarantee plan and the future guarantee plan according to the conflict diagnosis report. For the conflicts caused by abnormal weather and resource shortages, the vehicle is compactly adjusted according to the actual situation. For the conflicts caused by unreasonable scheduling plans themselves, more advanced algorithms are used to re-solve them, making full use of the advantages of mathematical methods and deep reinforcement learning algorithms, and using professional knowledge and experience to design the algorithms by experts to meet the airport scheduling requirements. The adjusted plan is synchronously uploaded to the twin space for twin operation deduction and prediction. Twin operation deduction and prediction, as the core functional module of the digital twin platform, is positioned to predict and pre-judge the future long-term and short-term operation trends of the airport. By analyzing the real-time data of each business platform of the airport and using the digital twin CVM computing engine to conduct simulation deduction calculations on the airport operation status, the output simulation data can be presented in three-dimensional visualization through the twin model, realizing the verification of the rationality of the future long-term airport resource allocation and scheduling plan, predicting and evaluating the bottlenecks of future short-term airport operation resources, achieving a more accurate grasp of the future operation trends, and global interactive control.

[0036] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the content disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include common general knowledge or conventional technical means in the technical field not disclosed in the present application.

[0037] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A method for diagnosing conflicts of airport support vehicles driven by digital twins, characterized in that, It includes the following steps: Obtain the attribute information of airport objects and generate corresponding digital twin monomers in the twin space; Associate the digital twin monomers according to the business logic to form the twin business of flight ground support; Identify support conflicts, obtain the time, location and cause of the conflicts, and conduct conflict analysis; Adjust the support plan and conduct simulation deduction.

2. The method for diagnosing conflicts of airport support vehicles driven by digital twins as described in claim 1, wherein, In the step of obtaining the attribute information of airport objects: Obtain the real-time information of the support nodes of all flights and extract the real-time data through the twin model.

3. The method for diagnosing conflicts of airport support vehicles driven by digital twin as claimed in claim 2, wherein In the step of obtaining the real-time information of the support nodes of all flights: The real-time information includes the refined three-dimensional model, position, speed, direction of the support vehicle and the real-time data of the task nodes.

4. The conflict diagnosis method for airport support vehicles driven by digital twin as claimed in claim 1, wherein In the step of associating the digital twin monomers according to the business logic to form the twin business of flight ground support: Associate the business objects, business scenarios and business logic in the digital twin space with the twin monomers according to various modeling methods.

5. The method for diagnosing conflicts of airport support vehicles driven by digital twin as claimed in claim 4, wherein After the step of associating the business objects, business scenarios and business logic in the digital twin space with the twin monomers according to various modeling methods: Conduct deviation analysis based on the real-time support status and support plan of the digital twin monomers, conduct future deduction for a specific time period according to the support plan, and save the deduction information.

6. The method for diagnosing conflicts of airport support vehicles driven by digital twin as claimed in claim 1, wherein, In the step of identifying support conflicts, obtaining the time, location and cause of the conflicts, and conducting conflict analysis: Conduct conflict diagnosis according to the conflict model based on the real-time monitoring data and deduction data.

7. The method for diagnosing conflicts of airport support vehicles driven by digital twin as claimed in claim 6, wherein, In the step of adjusting the support plan and conducting simulation deduction: Obtain the conflicts that occur in real-time and during deduction, adjust the support plan, and simultaneously upload the updated information to the digital twin space. The computing engine in the digital twin space conducts simulation verification and updates the tasks to the support equipment.

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