Cargo airport data processing method based on digital twinning and related device

Abstract

The application provides a cargo airport data processing method based on digital twinning and related devices, which comprises the following steps: obtaining transport relationship graph information of flights of a cargo airport within a preset time range, the transport relationship graph information comprising the correlation between a plurality of different levels of transport entities, the plurality of different levels of transport entities at least comprising cargo aircrafts, cargo vehicles and containers; obtaining a planned takeoff time parameter and an actual takeoff time parameter of each flight within the preset time range; determining a transfer timeliness score parameter corresponding to each flight based on the transport relationship graph information, the planned takeoff time parameter and the actual takeoff time parameter; and determining a target flight with an abnormal transfer timeliness score from the flights within the preset time range based on the transfer timeliness score parameter corresponding to each flight. The embodiment of the application facilitates the optimization of abnormal conditions of the cargo airport by the airport operation control personnel, and improves the logistics transportation efficiency of the cargo airport.

Description

Technical Field

[0001] This application relates to the field of logistics and transportation technology, specifically to a method and related apparatus for processing cargo airport data based on digital twins. Background Technology

[0002] The rapid development of the air cargo industry has become a crucial foundation for supporting my country's economic and trade development. Currently, my country's economic development is entering a period of slower growth, a shift in growth drivers, and structural optimization. The general laws of economic development and industrial upgrading, combined with the emergence of new industries and new business models, have led to significant structural changes on the demand side of air cargo, driving structural reforms on the supply side. The transformation and upgrading of air cargo towards specialization and logistics has become an inevitable trend, placing demands on the construction and development of my country's cargo airports.

[0003] At cargo airports, there is typically a large volume of logistics and transportation management involved, such as cargo aircraft, cargo vehicles, and emergency management. All of these can affect logistics and transportation efficiency.

[0004] Therefore, how to improve the logistics and transportation efficiency of cargo airports is a technical problem that urgently needs to be solved in the field of logistics and transportation technology. Summary of the Invention

[0005] This application provides a data processing method and related apparatus for cargo airports based on digital twins, aiming to solve the technical problem of how to improve the logistics and transportation efficiency of cargo airports.

[0006] On the one hand, this application provides a method for processing cargo airport data based on digital twins, the method comprising:

[0007] Obtain the transport relationship map information of flights at the cargo airport within a preset time range. The transport relationship map information includes the association between multiple transport entities at different levels. The multiple transport entities at different levels include at least cargo aircraft, cargo vehicles, and containers.

[0008] Obtain the planned departure time parameters and actual departure time parameters for each flight within the preset time range;

[0009] Based on the transportation relationship map information, the planned departure time parameter, and the actual departure time parameter, the transit time score parameter corresponding to each flight is determined;

[0010] Based on the transit timeliness score parameter corresponding to each flight, target flights with abnormal transit timeliness scores are identified from the flights within the preset time range.

[0011] In one possible implementation of this application, determining the transit time score parameter for each flight based on the transport relationship map information, the planned departure time parameter, and the actual departure time parameter includes:

[0012] Based on the planned departure time parameter and the actual departure time parameter, the transfer time efficiency score ratio parameter for each flight is determined;

[0013] Based on the transportation relationship map information, the total number of tickets corresponding to each cargo aircraft in each flight is determined.

[0014] Based on the transit time efficiency score ratio parameter for each flight and the total number of tickets corresponding to cargo aircraft in each flight, the transit time efficiency score parameter for each flight is determined.

[0015] In one possible implementation of this application, determining the transfer time efficiency score ratio parameter for each flight based on the planned departure time parameter and the actual departure time parameter includes:

[0016] Calculate the difference between the actual takeoff time parameter and the planned takeoff time parameter;

[0017] Based on the difference and the correspondence between the preset transfer time efficiency score ratio parameter and the difference, the transfer time efficiency score ratio parameter for each flight is determined.

[0018] In one possible implementation of this application, obtaining the transport relationship map information of flights at a cargo airport within a preset time range includes:

[0019] From the pre-set digital twin platform of the cargo airport, obtain the motion trajectory data and business operation data of each entity in each flight within a pre-set time range;

[0020] Based on the motion trajectory data and business operation data of each entity in each flight within the preset time range, the correlation between each entity in each flight within the preset time range is determined;

[0021] Based on the relationships between entities in each flight within the preset time range, a transportation relationship map of the cargo airport within the preset time range is constructed.

[0022] In one possible implementation of this application, determining the target flight with an abnormal transfer timeliness score from the flights within the preset time range based on the transfer timeliness score parameter corresponding to each flight includes:

[0023] Sort the transit time score parameters corresponding to each flight;

[0024] Flights ranked last in transit timeliness score are identified as target flights with abnormal transit timeliness scores.

[0025] In one possible implementation of this application, after determining the target flight with an abnormal transfer timeliness score from the flights within the preset time range based on the transfer timeliness score parameter, the method further includes:

[0026] Based on the target flight and the transportation relationship graph information, target transportation sub-entities with abnormal transit time scores are identified from the target flights, wherein the transportation sub-entities are transportation entities with a transportation level lower than their parent entities.

[0027] In one possible implementation of this application, determining the target transportation sub-entity with an abnormal transit time score from the target flight based on the target flight and the transportation relationship graph information includes:

[0028] Based on the transportation relationship map information, the association relationships between multiple transportation entities corresponding to the cargo aircraft in the target flight are determined;

[0029] Based on the relationship between multiple transport entities corresponding to the cargo aircraft in the target flight, determine the percentage of the transit time score of each transport sub-entity to the transit time score of the parent entity.

[0030] Based on the ratio of the transit time score of each of the multiple transit sub-entities to the transit time score of the parent entity, the target transit sub-entities with abnormal transit time scores are identified from the target flights.

[0031] On the other hand, this application provides a cargo airport data processing device based on digital twins, the device comprising:

[0032] The first acquisition unit is used to acquire the transport relationship map information of flights at the cargo airport within a preset time range. The transport relationship map information includes the association between multiple transport entities at different levels. The multiple transport entities at different levels include at least cargo aircraft, cargo vehicles, containers, and the tickets loaded in the containers.

[0033] The second acquisition unit is used to acquire the planned departure time parameters and actual departure time parameters of each flight in the preset time range.

[0034] The first determining unit is used to determine the transit time score parameter corresponding to each flight based on the transportation relationship map information, the planned departure time parameter, and the actual departure time parameter;

[0035] The second determining unit is used to determine the target flight with abnormal transfer timeliness score from the flights within the preset time range based on the transfer timeliness score parameter corresponding to each flight.

[0036] In one possible implementation of this application, the first determining unit specifically includes:

[0037] The third determining unit is used to determine the transfer time efficiency score ratio parameter for each flight based on the planned departure time parameter and the actual departure time parameter.

[0038] The fourth determining unit is used to determine the total number of tickets corresponding to each cargo aircraft in each flight based on the transportation relationship map information.

[0039] The fifth determining unit is used to determine the transit time score parameter for each flight based on the transit time score ratio parameter for each flight and the total number of tickets corresponding to cargo aircraft in each flight.

[0040] In one possible implementation of this application, the third determining unit is specifically used for:

[0041] Calculate the difference between the actual takeoff time parameter and the planned takeoff time parameter;

[0042] Based on the difference and the correspondence between the preset transfer time efficiency score ratio parameter and the difference, the transfer time efficiency score ratio parameter for each flight is determined.

[0043] In one possible implementation of this application, the first acquisition unit is specifically used for:

[0044] From the pre-set digital twin platform of the cargo airport, obtain the motion trajectory data and business operation data of each entity in each flight within a pre-set time range;

[0045] Based on the motion trajectory data and business operation data of each entity in each flight within the preset time range, the correlation between each entity in each flight within the preset time range is determined;

[0046] Based on the relationships between entities in each flight within the preset time range, a transportation relationship map of the cargo airport within the preset time range is constructed.

[0047] In one possible implementation of this application, the second determining unit is specifically used for:

[0048] Sort the transit time score parameters corresponding to each flight;

[0049] Flights ranked last in transit timeliness score are identified as target flights with abnormal transit timeliness scores.

[0050] In one possible implementation of this application, after determining the target flight with an abnormal transfer timeliness score from the flights within the preset time range based on the transfer timeliness score parameter, the device further includes:

[0051] The sixth determining unit is used to determine, based on the target flight and the transportation relationship graph information, a target transportation sub-entity with an abnormal transit time score from the target flight, wherein the transportation sub-entity is a transportation entity with a transportation level lower than its parent entity.

[0052] In one possible implementation of this application, the sixth determining unit is specifically used for:

[0053] Based on the transportation relationship map information, the association relationships between multiple transportation entities corresponding to the cargo aircraft in the target flight are determined;

[0054] Based on the relationship between multiple transport entities corresponding to the cargo aircraft in the target flight, determine the percentage of the transit time score of each transport sub-entity to the transit time score of the parent entity.

[0055] Based on the ratio of the transit time score of each of the multiple transit sub-entities to the transit time score of the parent entity, the target transit sub-entities with abnormal transit time scores are identified from the target flights.

[0056] On the other hand, this application also provides a server, the server comprising:

[0057] One or more processors;

[0058] Memory; and

[0059] One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the digital twin-based cargo airport data processing method.

[0060] On the other hand, this application also provides a computer-readable storage medium having a computer program stored thereon, the computer program being loaded by a processor to perform the steps in the digital twin-based cargo airport data processing method.

[0061] This application provides a digital twin-based cargo airport data processing method, which includes acquiring transport relationship graph information of flights at a cargo airport within a preset time range. The transport relationship graph information includes the associations between multiple transport entities at different levels, including at least cargo aircraft, cargo vehicles, and containers. The method also involves acquiring the planned departure time parameters and actual departure time parameters for each flight within the preset time range; determining the transit time efficiency score parameter for each flight based on the transport relationship graph information, the planned departure time parameters, and the actual departure time parameters; and identifying target flights with abnormal transit time efficiency scores from the flights within the preset time range based on the transit time efficiency score parameters for each flight. Compared to traditional digital twin-based cargo airport data processing methods, which cannot comprehensively analyze cargo airport data and quickly locate anomalies, this application creatively uses a comprehensive analysis of the transport relationship graph of flights within a preset time range and the departure time parameters of each flight to quickly locate target flights with abnormal transit time efficiency scores. This effectively shortens the location time, reduces analysis costs, and facilitates airport operations control personnel in optimizing abnormal situations at cargo airports, thereby improving the logistics efficiency of cargo airports. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 This is a schematic diagram of a scenario for a cargo airport data processing system based on digital twins, as provided in an embodiment of this application.

[0064] Figure 2 This is a schematic flowchart of an embodiment of the cargo airport data processing method based on digital twin provided in this application.

[0065] Figure 3 This is a flowchart illustrating a specific embodiment of step 203 provided in this application.

[0066] Figure 4 This is a schematic diagram of an embodiment of the cargo airport data processing device based on digital twin provided in this application.

[0067] Figure 5 This is a schematic diagram of the structure of one embodiment of the server provided in this application;

[0068] Figure 6This is a schematic diagram of the transportation relationship map information of flights provided in the embodiments of this application. Detailed Implementation

[0069] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0070] In the description of this application, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

[0071] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0072] This application provides a method and related apparatus for processing cargo airport data based on digital twins, which will be described in detail below.

[0073] like Figure 1 As shown, Figure 1This is a schematic diagram of a scenario for a digital twin-based cargo airport data processing system provided in an embodiment of this application. The system may include multiple terminals 100 and a server 200, which are network-connected. The server 200 integrates a digital twin-based cargo airport data processing device, such as... Figure 1 In the server, terminal 100 can access server 200.

[0074] In this embodiment, server 200 is mainly used to obtain the transport relationship map information of flights at cargo airports within a preset time range. The transport relationship map information includes the association between multiple transport entities at different levels, and the multiple transport entities at different levels include at least cargo aircraft, cargo vehicles, and containers. It obtains the planned departure time parameters and actual departure time parameters of each flight within the preset time range. Based on the transport relationship map information, planned departure time parameters, and actual departure time parameters, it determines the transfer timeliness score parameter corresponding to each flight. Based on the transfer timeliness score parameter corresponding to each flight, it identifies target flights with abnormal transfer timeliness scores from the flights within the preset time range.

[0075] In this embodiment, the server 200 can be a standalone server, a server network, or a server cluster. For example, the server 200 described in this embodiment includes, but is not limited to, a computer, a network terminal, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. The cloud server is composed of a large number of computers or network servers based on cloud computing. In this embodiment, communication between the server and the terminal can be achieved through any communication method, including but not limited to, mobile communication based on the 3rd Generation Partnership Project (3GPP), Long Term Evolution (LTE), and Worldwide Interoperability for Microwave Access (WiMAX), or computer network communication based on the TCP / IP Protocol Suite (TCP / IP) and User Datagram Protocol (UDP).

[0076] It is understood that the terminal 100 used in this application embodiment can be a device that includes both receiving and transmitting hardware, i.e., a device with receiving and transmitting hardware capable of performing bidirectional communication on a bidirectional communication link. Such a terminal may include: cellular or other communication devices having a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. Specifically, the terminal 100 may be a desktop terminal or a mobile terminal, and the terminal 100 may also be one of a mobile phone, tablet computer, laptop computer, etc.

[0077] Those skilled in the art will understand that Figure 1 The application environment shown is merely one application scenario of the solution in this application and does not constitute a limitation on the application scenario of the solution in this application. Other application environments may include more than one application scenario. Figure 1 The number of more or fewer terminals, or server network connections shown, for example Figure 1 Only one server and two terminals are shown in the diagram. It is understood that this digital twin-based cargo airport data processing system may also include one or more other servers, and / or one or more terminals connected to the server network, which is not limited here.

[0078] In addition, such as Figure 1 As shown, the digital twin-based cargo airport data processing system may also include a memory 300 for storing data, such as cargo airport flight data and digital twin-based cargo airport data processing data, for example, digital twin-based cargo airport data processing data during the operation of the digital twin-based cargo airport data processing system.

[0079] It should be noted that, Figure 1 The schematic diagram of the digital twin-based cargo airport data processing system shown is merely an example. The digital twin-based cargo airport data processing system and scenario described in this application are intended to more clearly illustrate the technical solutions of this application and do not constitute a limitation on the technical solutions provided in this application. As those skilled in the art will know, with the evolution of digital twin-based cargo airport data processing systems and the emergence of new business scenarios, the technical solutions provided in this application are also applicable to similar technical problems.

[0080] Next, we will introduce the cargo airport data processing method based on digital twin provided in the embodiments of this application.

[0081] In this embodiment of the digital twin-based cargo airport data processing method, a digital twin-based cargo airport data processing device is used as the execution subject. For simplicity and ease of description, this execution subject will be omitted in subsequent method embodiments. The digital twin-based cargo airport data processing device is applied to a server. The method includes: acquiring the transport relationship map information of flights at a cargo airport within a preset time range, the transport relationship map information including the association between multiple transport entities at different levels, and the multiple transport entities at different levels including at least cargo aircraft, cargo vehicles, and containers; acquiring the planned departure time parameters and actual departure time parameters of each flight within the preset time range; determining the transfer time efficiency score parameter corresponding to each flight based on the transport relationship map information, the planned departure time parameters, and the actual departure time parameters; and determining the target flights with abnormal transfer time efficiency scores from the flights within the preset time range based on the transfer time efficiency score parameters corresponding to each flight.

[0082] Please see Figures 2 to 6 , Figure 2 This is a schematic flowchart of an embodiment of the digital twin-based cargo airport data processing method provided in this application. The digital twin-based cargo airport data processing method includes:

[0083] 201. Obtain the transportation relationship map information of flights at cargo airports within a preset time range.

[0084] The transportation relationship map information includes the relationships between multiple transportation entities at different levels, which at least include cargo aircraft, cargo vehicles, and containers.

[0085] It should be noted that the transportation relationship graph information can also include ticket information, that is, the specific number of tickets carried by each container. The relationships between multiple transportation entities at different levels include hierarchical relationships and binding relationships between them. For example... Figure 6 As shown, for example, on a certain flight A (corresponding to the aforementioned cargo aircraft), there are three cargo vehicles (corresponding to the aforementioned cargo vehicles) a, b, and c, multiple containers (ULD1, ULD2, ULD3, ULD4...ULDn), and multiple tickets (corresponding to the aforementioned tickets). All tickets on flight A are repackaged from these containers, and then these containers are further repackaged from the three vehicles. The containers containing the tickets are then transported by the three vehicles to the cargo aircraft corresponding to flight A. Figure 6It can be seen that the cargo plane corresponding to flight A is the first-level transport entity, the cargo vehicle corresponding to vehicle a, etc., is the second-level transport entity, and the container is the third-level entity. The first-level transport entity can be set as the parent entity of the second-level transport entity, the second-level transport entity can be set as the child entity of the first-level transport entity, and the second-level transport entity can be set as the parent entity of the third-level transport entity, and the third-level transport entity can be set as the child entity of the second-level transport entity.

[0086] In this embodiment, the preset time range can be multiple months, one month, one week, several days, one day, or several hours within one day. Specifically, it can be set according to actual needs. For example, when it is necessary to analyze the flight situation of each day, the preset time range can be set to one day. Similarly, when it is necessary to analyze the flight situation within a week, the preset time range can be set to one week.

[0087] In some embodiments of this application, obtaining the transport relationship map information of flights at a cargo airport within a preset time range includes: obtaining the motion trajectory data and business operation data of each entity in each flight within the preset time range from a preset cargo airport digital twin platform; determining the association relationship between each entity in each flight within the preset time range based on the motion trajectory data and business operation data of each entity in each flight within the preset time range; and constructing the transport relationship map information of flights at a cargo airport within the preset time range based on the association relationship between each entity in each flight within the preset time range.

[0088] Specifically, before using the pre-set digital twin platform for cargo airports, it is necessary to establish a digital twin platform for cargo airports. The specific process is as follows: data collection and the creation of 3D models and event animations, including the following steps: based on airport geographic information and building model data, construct a 3D visualization basic simulation scene such as apron and road base maps; combine roadside yard models and sorting center models to construct airport building and greening scenes; based on the collected basic aircraft data and related flights, including appearance, aircraft type, cargo capacity, load factor, landing taxiing speed, fuselage length, landing point, number of ULD flight boxes that can be stored in each compartment, etc., to create 3D models and event animations; based on trailer basic data, package The system includes: creating 3D models and event animations based on appearance, number of boxes, vehicle length, and travel speed; creating 3D models and event animations based on the appearance, length, width, and travel speed data of other service vehicles (conveyor trucks, lifting platform trucks, refueling trucks, shuttle trucks, sewage cleaning trucks, tractor trucks, and boarding ladder trucks); creating 3D models and event animations based on the appearance, size, and type data of various ULD flight cases; creating 3D models and event animations based on the basic data of tickets and related logistics data; and creating 3D models and event animations based on sorting equipment data, such as equipment parameters, paths, and configurations, as well as business rule data, such as traffic operation, path planning, and flight support.

[0089] Specifically, data processing and synchronization, achieved through digital mapping, involves the following steps to synchronize data with 3D animation: Centralized data warehouse processing: Data from various airport entities is input into the digital twin platform and centrally stored in the original data warehouse using machine learning; Centralized processing of the original data from various airport entities stored in the digital twin platform's original data warehouse removes erroneous, invalid, and unnecessary noise data, retaining the remaining data; The retained data is divided into dynamic and static data, where dynamic data includes motion trajectory data, business event data, dynamic statistical data, etc., and static data includes geographic information data, road location data, aircraft and vehicle attribute data, etc.; The data reduction module, based on the retained dynamic and static data, reduces the dimensions of dynamic and static data using PCA technology. Specific steps include: Feature centering: Subtracting the mean of each dimension of dynamic and static data, transforming the mean of a flight, vehicle, or ULD feature (or flight, vehicle, or ULD attribute) to 0. Calculating the covariance matrix of the feature-centered static and dynamic data matrices. Calculating the eigenvalues ​​and eigenvectors of the covariance matrix. The eigenvectors corresponding to the largest eigenvalues ​​are selected to obtain new static and dynamic data datasets. Deviation standardization (X1 = (X-min) / (max-min)) and other methods are used to transform each dimension of the static and real-time dynamic data datasets, effectively increasing the usability and robustness of the digitized mapping data. This standardizes the data generated by the digital twin, addressing issues such as data distribution, stationary sequences, and data explosion, and yielding downstream data to be synchronized. The processed downstream data is synchronized: If it is real-time dynamic data, such as motion trajectory data, business event data, or dynamic statistical data, it is synchronized from the data warehouse to the digital twin platform's backend database for immediate access; if it is static data, such as geographic information data, road location data, or aircraft and vehicle attribute data, it is synchronized from the data warehouse to the digital twin platform's backend database periodically for immediate access. 3D Animation and Data Synchronization: Motion Trajectory Animation Synchronization: The data twin platform retrieves the saved 3D model of the moving entity based on the object data provided by the current motion trajectory data. Then, it matches the position, time, attitude, and velocity based on the coordinate, time, direction, and velocity data of the motion trajectory to reproduce the entity's motion on the site. Business Operation Animation Synchronization: The data twin platform retrieves the saved 3D business animation based on the object, event type, and coordinate data provided by the current business event data. Then, it matches the animation effects and duration based on the start and end times of the business event to reproduce the entity's business operations on the site, thereby generating corresponding business operation data.

[0090] 202. Obtain the planned departure time parameters and actual departure time parameters for each flight within the preset time range;

[0091] Specifically, the planned departure time parameters and actual departure time parameters for each flight within a preset time range can be retrieved from the server.

[0092] 203. Based on the transportation relationship map information, planned departure time parameters, and actual departure time parameters, determine the transfer timeliness score parameters for each flight;

[0093] The transit time score is a parameter used to evaluate the speed of flight transit. Specifically, the higher the transit time score of a flight, the faster the transit time. Conversely, the lower the transit time score of a flight, the slower the transit time.

[0094] For details on how to determine the transit time score parameters for each flight based on transportation relationship map information, planned departure time parameters, and actual departure time parameters, please refer to the following example, which will not be elaborated here.

[0095] 204. Based on the transit timeliness score parameter corresponding to each flight, identify the target flights with abnormal transit timeliness scores from the flights within the preset time range.

[0096] In some embodiments of this application, based on the transit timeliness score parameter corresponding to each flight, the target flight with abnormal transit timeliness score is determined from flights within a preset time range. This includes: sorting the transit timeliness score parameters corresponding to each flight; and determining the flight with the lowest preset ranking in the transit timeliness score parameter as the target flight with abnormal transit timeliness score. For example, as illustrated in step 303, the transit timeliness score parameters corresponding to flights A, B, and C are 960, 960, and 800, respectively. Flight C has the lowest transit timeliness score, thus determining flight C as the target flight with abnormal transit timeliness score.

[0097] This application provides a digital twin-based cargo airport data processing method, which includes acquiring transport relationship graph information of flights at a cargo airport within a preset time range. The transport relationship graph information includes the associations between multiple transport entities at different levels, including at least cargo aircraft, cargo vehicles, and containers. The method also involves acquiring the planned departure time parameters and actual departure time parameters for each flight within the preset time range; determining the transit time efficiency score parameters for each flight based on the transport relationship graph information, planned departure time parameters, and actual departure time parameters; and identifying target flights with abnormal transit time efficiency scores from the flights within the preset time range based on the transit time efficiency score parameters for each flight. Compared to traditional digital twin-based cargo airport data processing methods, which cannot comprehensively analyze cargo airport data and quickly locate anomalies, this application creatively uses a comprehensive analysis of the transport relationship graph of flights within a preset time range and the departure time parameters of each flight to quickly locate target flights with abnormal transit time efficiency scores. This effectively shortens the location time, reduces analysis costs, and facilitates airport operations control personnel in optimizing anomalies at cargo airports, thereby improving the logistics efficiency of cargo airports.

[0098] In some embodiments of this application, such as Figure 3 As shown, step 203, based on the transport relationship map information, planned departure time parameters, and actual departure time parameters, determines the transit time efficiency score parameters for each flight, including:

[0099] 301. Based on the planned departure time parameters and the actual departure time parameters, determine the transfer time efficiency score ratio parameters for each flight;

[0100] Among them, the transfer timeliness score ratio parameter is a ratio parameter used to evaluate the speed of flight transfers.

[0101] In some embodiments of this application, the transfer time efficiency score ratio parameter for each flight is determined based on the planned departure time parameter and the actual departure time parameter. This includes: calculating the difference between the actual departure time parameter and the planned departure time parameter; and determining the transfer time efficiency score ratio parameter for each flight based on the difference and the pre-defined correspondence between the difference and the transfer time efficiency score ratio parameter. For example, if the difference between the actual departure time parameter and the planned departure time parameter is in the range of [15, +infinity], the transfer time efficiency score ratio parameter is 1.2; if the difference is between [0, 15], the transfer time efficiency score ratio parameter is 1; if the difference is between [-15, 0], the transfer time efficiency score ratio parameter is 0.8; and if the difference is between [-30, -15], the score is 0.2.

[0102] 302. Based on the transportation relationship map information, determine the total number of tickets corresponding to each cargo aircraft in each flight;

[0103] As shown in step 201, the transportation relationship graph information can also include ticket information, that is, the specific number of tickets carried by each container. Therefore, based on the relationships between multiple entities in each flight and the specific number of tickets carried by each container, the total number of tickets corresponding to each cargo plane in each flight can be calculated. For example, if flight A corresponds to two cargo vehicles, each carrying 2 and 3 containers respectively, and each container contains 100 express parcels, then the total number of tickets corresponding to the cargo plane in flight A is Y = (2 + 3) * 100 = 500.

[0104] 303. Based on the transit time efficiency score ratio parameter for each flight and the total number of tickets corresponding to cargo aircraft in each flight, determine the transit time efficiency score parameter for each flight.

[0105] In one specific embodiment, the transit time efficiency score ratio parameter for each flight can be multiplied by the total number of tickets corresponding to the cargo aircraft in each flight to obtain the transit time efficiency score parameter for each flight. For example, if the actual departure times of flights A, B, and C are 20 minutes earlier than the scheduled departure time, 12 minutes earlier than the scheduled departure time, and 10 minutes later than the scheduled departure time, respectively, then their corresponding transit time efficiency score ratio parameters are 1.2, 1, and 0.8; and if the total number of tickets corresponding to flights A, B, and C are 800, 960, and 1000, respectively, then their corresponding transit time efficiency score parameters are 960, 960, and 800.

[0106] In another embodiment of this application, after determining the target flight with abnormal transit time score from flights within a preset time range based on the transit time score parameter, the method further includes: determining the target transportation sub-entity with abnormal transit time score from the target flight based on the target flight and transportation relationship graph information, wherein the transportation sub-entity is a transportation entity with a transportation level lower than the parent entity.

[0107] In some embodiments of this application, determining the target transportation sub-entities with abnormal transit time scores from the target flight based on target flight and transportation relationship graph information includes: determining the association relationships between multiple transportation entities corresponding to cargo aircraft in the target flight based on transportation relationship graph information; determining the proportion parameter of the transit time score of each transportation sub-entity to the transit time score of its parent entity based on the association relationships between multiple transportation entities corresponding to cargo aircraft in the target flight; and determining the target transportation sub-entities with abnormal transit time scores from the target flight based on the proportion parameter of the transit time score of each transportation sub-entity to the transit time score of its parent entity.

[0108] In another embodiment of this application, based on the three-dimensional spatial situational data with a time dimension obtained in step 201 above, a time window is defined as every 10 minutes starting from the first recording, and a time-sensitive tag is recorded for each time window. The average OTT score of all entities within a 100-square-meter area centered on the obtained vehicle entity and ULD entity information is calculated.

[0109] In another embodiment of this application, based on the several time-sensitive tags obtained in the above embodiments, the time-sensitive tags are sorted according to their size to obtain the time window with the smallest time-sensitive tag. The entity event records and statistical data records of the entire time window are traced back. Based on the records, the model and animation are automatically matched, and the operation of the entity within the time window is automatically displayed in the digital twin 3D environment.

[0110] To better implement the digital twin-based cargo airport data processing method in the embodiments of this application, this application also provides a digital twin-based cargo airport data processing apparatus, such as... Figure 4 As shown, the digital twin-based cargo airport data processing device 400 includes:

[0111] The first acquisition unit 401 is used to acquire the transport relationship map information of flights at the cargo airport within a preset time range. The transport relationship map information includes the association between multiple transport entities at different levels. The multiple transport entities at different levels include at least cargo aircraft, cargo vehicles, containers, and the tickets loaded in the containers.

[0112] The second acquisition unit 402 is used to acquire the planned departure time parameters and actual departure time parameters of each flight in the preset time range.

[0113] The first determining unit 403 is used to determine the transit time score parameter for each flight based on the transportation relationship map information, the planned departure time parameter, and the actual departure time parameter.

[0114] The second determining unit 404 is used to determine the target flight with abnormal transfer timeliness score from the flights within a preset time range based on the transfer timeliness score parameter corresponding to each flight.

[0115] In some embodiments of this application, the first determining unit 403 specifically includes:

[0116] The third determining unit is used to determine the transfer time efficiency score ratio parameter for each flight based on the planned departure time parameter and the actual departure time parameter;

[0117] The fourth determining unit is used to determine the total number of tickets corresponding to each cargo aircraft in each flight based on the transportation relationship map information;

[0118] The fifth determining unit is used to determine the transit time score parameter for each flight based on the transit time score ratio parameter for each flight and the total number of tickets corresponding to cargo aircraft in each flight.

[0119] In some embodiments of this application, the third determining unit is specifically used for:

[0120] Calculate the difference between the actual takeoff time parameter and the planned takeoff time parameter;

[0121] Based on the difference and the correspondence between the preset transfer time efficiency score ratio parameter and the difference, the transfer time efficiency score ratio parameter for each flight is determined.

[0122] In some embodiments of this application, the first acquisition unit 401 is specifically used for:

[0123] From the pre-set digital twin platform of the cargo airport, obtain the motion trajectory data and business operation data of each entity in each flight within a pre-set time range;

[0124] Based on the motion trajectory data and business operation data of each entity in each flight within a preset time range, determine the relationship between each entity in each flight within the preset time range;

[0125] Based on the relationships between entities in each flight within a preset time range, a transportation relationship graph of cargo airport flights within the preset time range is constructed.

[0126] In some embodiments of this application, the second determining unit 404 is specifically used for:

[0127] Sort the transit time score parameters corresponding to each flight;

[0128] Flights ranked last in transit timeliness score are identified as target flights with abnormal transit timeliness scores.

[0129] In some embodiments of this application, after determining the target flight with an abnormal transit time score from flights within a preset time range based on the transit time score parameter, the device further includes:

[0130] The sixth determining unit is used to identify target transportation sub-entities with abnormal transit time scores from target flights based on target flight and transportation relationship graph information. The transportation sub-entities are transportation entities with a transportation level lower than their parent entities.

[0131] In some embodiments of this application, the sixth determining unit is specifically used for:

[0132] Based on transportation relationship graph information, the relationships between multiple transportation entities corresponding to cargo aircraft in the target flight are determined.

[0133] Based on the relationship between multiple transport entities corresponding to cargo aircraft in the target flight, determine the percentage of the transit time score of each transport sub-entity to the transit time score of the parent entity.

[0134] Based on the ratio of the transit time score of each transit sub-entity to the transit time score of the parent entity, target transit sub-entities with abnormal transit time scores are identified from the target flights.

[0135] The digital twin-based cargo airport data processing device 400 provided in this application includes a first acquisition unit 401, used to acquire transport relationship map information of flights at a cargo airport within a preset time range. The transport relationship map information includes the association between multiple transport entities at different levels, and the multiple transport entities at different levels include at least cargo aircraft, cargo vehicles, containers, and tickets loaded in the containers; a second acquisition unit 402, used to acquire the planned departure time parameters and actual departure time parameters of each flight within the preset time range; a first determination unit 403, used to determine the transfer time efficiency score parameter corresponding to each flight based on the transport relationship map information, the planned departure time parameters, and the actual departure time parameters; and a second determination unit 404, used to determine the target flight with abnormal transfer time efficiency score from the flights within the preset time range based on the transfer time efficiency score parameter corresponding to each flight. Compared to traditional digital twin-based cargo airport data processing devices 400, which cannot comprehensively analyze cargo airport data or quickly locate anomalies, this application creatively analyzes the transportation relationship map of flights within a preset time period and the departure time parameters of each flight to quickly locate target flights with abnormal transit time scores. This effectively shortens the location time and reduces analysis costs, thereby facilitating airport operations control personnel to optimize abnormal situations at cargo airports and improve the logistics transportation efficiency of cargo airports.

[0136] In addition to the above-described methods and apparatus for processing cargo airport data based on digital twins, embodiments of this application also provide a server that integrates any of the cargo airport data processing apparatuses based on digital twins provided in the embodiments of this application. The server includes:

[0137] One or more processors;

[0138] Memory; and

[0139] One or more applications, wherein the applications are stored in memory and configured to be operated by a processor using any of the methods in any of the embodiments of the above-described digital twin-based cargo airport data processing method.

[0140] This application also provides a server that integrates any of the digital twin-based cargo airport data processing devices provided in this application. See also... Figure 5 , Figure 5 This is a schematic diagram of the structure of one embodiment of the server provided in this application.

[0141] like Figure 5 As shown, it illustrates the structural diagram of the cargo airport data processing device based on digital twin designed in the embodiments of this application. Specifically:

[0142] The digital twin-based cargo airport data processing device may include components such as a processor 501 with one or more processing cores, a storage unit 502 with one or more computer-readable storage media, a power supply 503, and an input unit 504. Those skilled in the art will understand that... Figure 5 The structure of the digital twin-based cargo airport data processing device shown does not constitute a limitation on the digital twin-based cargo airport data processing device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein:

[0143] The processor 501 is the control center of the digital twin-based cargo airport data processing device. It connects various parts of the device via various interfaces and lines, and performs various functions and processes data by running or executing software programs and / or modules stored in the storage unit 502, and by calling data stored in the storage unit 502, thereby providing overall monitoring of the digital twin-based cargo airport data processing device. Optionally, the processor 501 may include one or more processing cores; preferably, the processor 501 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 501.

[0144] Storage unit 502 can be used to store software programs and modules. Processor 501 executes various functional applications and data processing by running the software programs and modules stored in storage unit 502. Storage unit 502 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the digital twin-based cargo airport data processing device, etc. In addition, storage unit 502 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, storage unit 502 may also include a memory controller to provide processor 501 with access to storage unit 502.

[0145] The digital twin-based cargo airport data processing device also includes a power supply 503 that supplies power to the various components. Preferably, the power supply 503 can be logically connected to the processor 501 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 503 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0146] The digital twin-based cargo airport data processing device may also include an input unit 504, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0147] Although not shown, the digital twin-based cargo airport data processing device may also include a display unit, etc., which will not be described in detail here. Specifically, in the embodiments of this application, the processor 501 in the digital twin-based cargo airport data processing device loads the executable files corresponding to the processes of one or more application programs into the storage unit 502 according to the following instructions, and the processor 501 runs the application programs stored in the storage unit 502 to realize various functions, as follows:

[0148] Obtain the transport relationship map information of flights at the cargo airport within a preset time range. The transport relationship map information includes the association between multiple transport entities at different levels, which include at least cargo aircraft, cargo vehicles, and containers. Obtain the planned departure time parameters and actual departure time parameters for each flight within the preset time range. Based on the transport relationship map information, planned departure time parameters, and actual departure time parameters, determine the transfer timeliness score parameters corresponding to each flight. Based on the transfer timeliness score parameters corresponding to each flight, identify target flights with abnormal transfer timeliness scores from the flights within the preset time range.

[0149] This application provides a digital twin-based cargo airport data processing method, which includes acquiring transport relationship graph information of flights at a cargo airport within a preset time range. The transport relationship graph information includes the associations between multiple transport entities at different levels, including at least cargo aircraft, cargo vehicles, and containers. The method also involves acquiring the planned departure time parameters and actual departure time parameters for each flight within the preset time range; determining the transit time efficiency score parameters for each flight based on the transport relationship graph information, planned departure time parameters, and actual departure time parameters; and identifying target flights with abnormal transit time efficiency scores from the flights within the preset time range based on the transit time efficiency score parameters for each flight. Compared to traditional digital twin-based cargo airport data processing methods, which cannot comprehensively analyze cargo airport data and quickly locate anomalies, this application creatively uses a comprehensive analysis of the transport relationship graph of flights within a preset time range and the departure time parameters of each flight to quickly locate target flights with abnormal transit time efficiency scores. This effectively shortens the location time, reduces analysis costs, and facilitates airport operations control personnel in optimizing anomalies at cargo airports, thereby improving the logistics efficiency of cargo airports.

[0150] Therefore, embodiments of this application provide a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc. The computer-readable storage medium stores multiple instructions that can be loaded by a processor to execute steps in any of the digital twin-based cargo airport data processing methods provided in embodiments of this application. For example, the instructions may execute the following steps:

[0151] Obtain the transport relationship map information of flights at the cargo airport within a preset time range. The transport relationship map information includes the association between multiple transport entities at different levels, which include at least cargo aircraft, cargo vehicles, and containers. Obtain the planned departure time parameters and actual departure time parameters for each flight within the preset time range. Based on the transport relationship map information, planned departure time parameters, and actual departure time parameters, determine the transfer timeliness score parameters corresponding to each flight. Based on the transfer timeliness score parameters corresponding to each flight, identify target flights with abnormal transfer timeliness scores from the flights within the preset time range.

[0152] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0153] The above provides a detailed description of a digital twin-based cargo airport data processing method and related apparatus provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for processing cargo airport data based on digital twins, characterized in that, The method includes: Obtain the transport relationship map information of flights at the cargo airport within a preset time range. The transport relationship map information includes the association relationship between multiple transport entities at different levels determined based on the motion trajectory data and business operation data of each entity in each flight. The multiple transport entities at different levels include at least cargo aircraft, cargo vehicles, containers, and ticket information loaded in the containers. The association relationship includes the hierarchical relationship and binding relationship between multiple transport entities at different levels. Obtain the planned departure time parameters and actual departure time parameters for each flight within the preset time range; Calculate the difference between the actual departure time parameter and the planned departure time parameter; based on the difference and the correspondence between the preset transfer time efficiency score ratio parameter and the difference, determine the transfer time efficiency score ratio parameter for each flight; Based on the transportation relationship map information, the total number of tickets corresponding to cargo aircraft in each flight is determined; based on the transit time efficiency score ratio parameter of each flight and the total number of tickets corresponding to cargo aircraft in each flight, the transit time efficiency score parameter corresponding to each flight is determined. Based on the transit timeliness score parameter corresponding to each flight, target flights with abnormal transit timeliness scores are identified from the flights within the preset time range.

2. The cargo airport data processing method based on digital twin according to claim 1, characterized in that, The acquisition of the transport relationship map information of flights at the cargo airport within a preset time range includes: From the pre-set digital twin platform of the cargo airport, obtain the motion trajectory data and business operation data of each entity in each flight within a pre-set time range; Based on the motion trajectory data and business operation data of each entity in each flight within the preset time range, the correlation between each entity in each flight within the preset time range is determined; Based on the relationships between entities in each flight within the preset time range, a transportation relationship map of the cargo airport within the preset time range is constructed.

3. The cargo airport data processing method based on digital twin according to claim 1, characterized in that, The step of determining target flights with abnormal transfer timeliness scores from flights within the preset time range based on the transfer timeliness score parameters corresponding to each flight includes: Sort the transit time score parameters corresponding to each flight; Flights ranked last in transit timeliness score are identified as target flights with abnormal transit timeliness scores.

4. The cargo airport data processing method based on digital twin according to claim 1, characterized in that, After identifying target flights with abnormal transfer timeliness scores from flights within the preset time range based on the transfer timeliness score parameter, the method further includes: Based on the target flight and the transportation relationship graph information, target transportation sub-entities with abnormal transit time scores are identified from the target flights, wherein the transportation sub-entities are transportation entities with a transportation level lower than their parent entities.

5. The cargo airport data processing method based on digital twin according to claim 4, characterized in that, The step of identifying target transportation sub-entities with abnormal transit time scores from the target flights based on the target flights and the transportation relationship graph information includes: Based on the transportation relationship map information, the association relationships between multiple transportation entities corresponding to the cargo aircraft in the target flight are determined; Based on the relationship between multiple transport entities corresponding to the cargo aircraft in the target flight, determine the percentage of the transit time score of each transport sub-entity to the transit time score of the parent entity. Based on the ratio of the transit time score of each of the multiple transit sub-entities to the transit time score of the parent entity, the target transit sub-entities with abnormal transit time scores are identified from the target flights.

6. A cargo airport data processing device based on digital twins, characterized in that, The device includes: The first acquisition unit is used to acquire the transportation relationship map information of flights at the cargo airport within a preset time range. The transportation relationship map information includes the association relationship between multiple different levels of transportation entities determined based on the motion trajectory data and business operation data of each entity in each flight. The multiple different levels of transportation entities include at least cargo aircraft, cargo vehicles, containers, and ticket information loaded in the containers. The association relationship includes the hierarchical relationship and binding relationship between multiple different levels of transportation entities. The second acquisition unit is used to acquire the planned departure time parameters and actual departure time parameters of each flight in the preset time range. The first determining unit is used to calculate the difference between the actual takeoff time parameter and the planned takeoff time parameter; and to determine the transfer time efficiency score ratio parameter for each flight based on the difference and the correspondence between the preset transfer time efficiency score ratio parameter and the difference. Based on the transportation relationship map information, the total number of tickets corresponding to cargo aircraft in each flight is determined; based on the transit time efficiency score ratio parameter of each flight and the total number of tickets corresponding to cargo aircraft in each flight, the transit time efficiency score parameter corresponding to each flight is determined. The second determining unit is used to determine the target flight with abnormal transfer timeliness score from the flights within the preset time range based on the transfer timeliness score parameter corresponding to each flight.

7. A server, characterized in that, The server includes: One or more processors; Memory; and One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the digital twin-based cargo airport data processing method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, It stores a computer program, which is loaded by a processor to perform the steps in the digital twin-based cargo airport data processing method according to any one of claims 1 to 5.

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