CN116775940A - Digital twinning-based freight airport data processing method and related device - Google Patents
Digital twinning-based freight airport data processing method and related device Download PDFInfo
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Abstract
The application provides a freight airport data processing method based on digital twinning and a related device, wherein the method comprises the following steps: acquiring transportation relation map information of flights of a freight airport within a preset time range, wherein the transportation relation map information comprises association relations among a plurality of transportation entities of different levels, and the transportation entities of different levels at least comprise freight aircrafts, freight vehicles and containers; acquiring a planned departure time parameter and an actual departure time parameter of each flight in flights within a preset time range; determining a transit time score parameter corresponding to each flight based on the transportation relation map information, the planned departure time parameter and the actual departure time parameter; and determining a target flight with abnormal transit time score from flights in a preset time range based on the transit time score parameter corresponding to each flight. The embodiment of the application is convenient for airport operation control personnel to optimize the abnormal condition of the freight airport, and improves the logistics transportation efficiency of the freight airport.
Description
Technical Field
The application relates to the technical field of logistics transportation, in particular to a freight airport data processing method based on digital twinning and a related device.
Background
The rapid development of the air freight industry becomes an important foundation for supporting the development of economic trade in China. Currently, the economic development of China steps into the development period of speed-increasing gear shifting, power conversion and structural optimization. The general laws of economic development and industry upgrades overlap with the advent of emerging industries and new business models, resulting in significant changes in the air freight demand side architecture, pushing air freight supply side architecture reform. The air freight is changed into specialized and logistics transformation and is upgraded to be a necessary trend, and the trend is required for the construction and development of freight airports in China.
At a cargo airport, there is typically a substantial amount of logistics transportation management, such as cargo aircraft, cargo vehicles, etc., as well as emergency management, etc. These can all affect logistics transportation efficiency.
Therefore, how to improve the logistics transportation efficiency of the freight airport is a technical problem to be solved in the technical field of current logistics transportation.
Disclosure of Invention
The application provides a digital twinning-based freight airport data processing method and a related device, and aims to solve the technical problem of how to improve the logistics transportation efficiency of a freight airport.
In one aspect, the present application provides a digital twinning-based freight airport data processing method, the method comprising:
Acquiring transportation relation map information of flights of a freight airport within a preset time range, wherein the transportation relation map information comprises association relations among a plurality of transportation entities of different levels, and the transportation entities of different levels at least comprise freight aircrafts, freight vehicles and containers;
acquiring a planned departure time parameter and an actual departure time parameter of each flight in the flights within the preset time range;
determining a transit time score parameter corresponding to each flight based on the transportation relation map information, the planned departure time parameter and the actual departure time parameter;
and determining the target flight with abnormal transit time score from flights in the preset time range based on the transit time score parameter corresponding to each flight.
In one possible implementation manner of the present application, the determining 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 includes:
determining a transit time score ratio parameter of each flight based on the planned departure time parameter and the actual departure time parameter;
Determining total number parameters of tickets corresponding to the freight aircrafts in each flight based on the transportation relation map information;
and determining the transit time score parameter corresponding to each flight based on the transit time score proportion parameter of each flight and the total number parameter of the tickets corresponding to the freight aircrafts in each flight.
In one possible implementation manner of the present application, the determining the transit time ageing score proportion parameter of each flight based on the planned departure time parameter and the actual departure time parameter includes:
calculating the difference value between the actual take-off time parameter and the planned take-off time parameter;
and determining the transit time effect score proportion parameter of each flight based on the difference and the corresponding relation between the preset transit time effect score proportion parameter and the difference.
In one possible implementation manner of the present application, the acquiring the transportation relationship map information of flights in the preset time range of the freight airport includes:
acquiring motion trail data and business operation data of each entity in each flight in a preset time range from a preset freight airport digital twin platform;
Determining the association relation among the entities in each flight in the preset time range based on the motion trail data and the business operation data of the entities in each flight in the preset time range;
and constructing transportation relation map information of flights of the freight airport in the preset time range based on the association relation among entities in each flight in the preset time range.
In one possible implementation manner of the present application, the determining, based on the transit time score parameter corresponding to each flight, a target flight with abnormal transit time score from flights within the preset time range includes:
sequencing the transit time effect score parameters corresponding to each flight;
and ranking the last preset flights of the transit time score parameter, and determining the last preset flights as target flights with abnormal transit time scores.
In one possible implementation manner of the present application, after determining a target flight with abnormal transit time score from flights within the preset time range based on the transit time score parameter, the method further includes:
and determining a target transportation child entity with abnormal transit time score from the target flights based on the target flights and the transportation relation map information, wherein the transportation child entity is a transportation entity with a transportation level lower than that of a parent entity.
In one possible implementation manner of the present application, the determining, based on the target flight and the transportation relationship map information, a target transportation sub-entity with abnormal transit time score from the target flight includes:
based on the transport relationship map information, determining association relationships among a plurality of transport entities corresponding to the freight aircraft in the target flight;
determining a duty ratio parameter of the transit time efficiency score of each transport sub-entity in the transport sub-entities to the transit time efficiency score of a father entity based on the association relation among the transport entities corresponding to the freight aircraft in the target flight;
and determining a target transportation child entity with abnormal transit time score from the target flights based on the duty ratio parameter of the transit time score of each transportation child entity in the plurality of transportation child entities to the transit time score of the parent entity.
In another aspect, the present application provides a digital twinning-based freight airport data processing apparatus, the apparatus comprising:
the first acquisition unit is used for acquiring transportation relation map information of flights of the freight airport within a preset time range, wherein the transportation relation map information comprises association relations among a plurality of different levels of transportation entities, and the plurality of different levels of transportation entities at least comprise freight airplanes, freight vehicles, containers and tickets loaded by the containers;
The second acquisition unit is used for acquiring the planned departure time parameter and the actual departure time parameter of each flight in the flights within the preset time range;
the first determining unit is used for determining a transit time score parameter corresponding to each flight based on the transportation relation map information, the planned departure time parameter and the actual departure time parameter;
and the second determining unit is used for determining a target flight with abnormal transit time score from flights within the preset time range based on the transit time score parameters corresponding to each flight.
In one possible implementation manner of the present application, the first determining unit specifically includes:
a third determining unit, configured to determine a transit time score proportion parameter of each flight based on the planned departure time parameter and the actual departure time parameter;
a fourth determining unit, configured to determine, based on the transportation relationship map information, a total number parameter of tickets corresponding to the cargo aircraft in each flight;
and a fifth determining unit, configured to determine a transit time score parameter corresponding to each flight based on the transit time score proportion parameter of each flight and the total number parameter of tickets corresponding to the freight aircraft in each flight.
In a possible implementation manner of the present application, the third determining unit is specifically configured to:
calculating the difference value between the actual take-off time parameter and the planned take-off time parameter;
and determining the transit time effect score proportion parameter of each flight based on the difference and the corresponding relation between the preset transit time effect score proportion parameter and the difference.
In one possible implementation manner of the present application, the first obtaining unit is specifically configured to:
acquiring motion trail data and business operation data of each entity in each flight in a preset time range from a preset freight airport digital twin platform;
determining the association relation among the entities in each flight in the preset time range based on the motion trail data and the business operation data of the entities in each flight in the preset time range;
and constructing transportation relation map information of flights of the freight airport in the preset time range based on the association relation among entities in each flight in the preset time range.
In a possible implementation manner of the present application, the second determining unit is specifically configured to:
Sequencing the transit time effect score parameters corresponding to each flight;
and ranking the last preset flights of the transit time score parameter, and determining the last preset flights as target flights with abnormal transit time scores.
In one possible implementation manner of the present application, after determining a target flight with abnormal transit time score from flights within the preset time range based on the transit time score parameter, the apparatus further includes:
and a sixth determining unit, configured to determine a target transportation child entity with abnormal transit time score from the target flights based on the target flights and the transportation relationship map information, where the transportation child entity is a transportation entity with a transportation level lower than that of a parent entity.
In a possible implementation manner of the present application, the sixth determining unit is specifically configured to:
based on the transport relationship map information, determining association relationships among a plurality of transport entities corresponding to the freight aircraft in the target flight;
determining a duty ratio parameter of the transit time efficiency score of each transport sub-entity in the transport sub-entities to the transit time efficiency score of a father entity based on the association relation among the transport entities corresponding to the freight aircraft in the target flight;
And determining a target transportation child entity with abnormal transit time score from the target flights based on the duty ratio parameter of the transit time score of each transportation child entity in the plurality of transportation child entities to the transit time score of the parent entity.
In another aspect, the present application also provides a server, including:
one or more processors;
a 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 twinning-based airport data processing method.
In another aspect, the present application also provides a computer readable storage medium having stored thereon a computer program to be loaded by a processor for performing the steps of the digital twinning based airport data processing method.
The application provides a digital twin-based freight airport data processing method, which comprises the steps of obtaining transportation relation map information of flights of a freight airport within a preset time range, wherein the transportation relation map information comprises association relations among a plurality of transportation entities of different levels, and the transportation entities of different levels at least comprise a freight plane, a freight vehicle and a container; acquiring a planned departure time parameter and an actual departure time parameter of each flight in the flights within the preset time range; determining a transit time score parameter corresponding to each flight based on the transportation relation map information, the planned departure time parameter and the actual departure time parameter; and determining the target flight with abnormal transit time score from flights in the preset time range based on the transit time score parameter corresponding to each flight. Compared with the traditional digital twin-based freight airport data processing method, under the background that the freight airport data cannot be comprehensively analyzed and the abnormality problem cannot be rapidly located, the method creatively locates the target flights with abnormal transit aging scores by comprehensively analyzing the transport relationship maps of flights within the preset time and the take-off time parameters of each flight, effectively shortens the locating time and reduces the analysis cost, thereby facilitating airport transportation control personnel to optimize the abnormality condition of the freight airport and further improving the logistics transportation efficiency of the freight airport.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic illustration of a scenario of a digital twinning-based airport data processing system provided by an embodiment of the present application;
FIG. 2 is a flow chart of one embodiment of a digital twinning-based airport data processing method provided in an embodiment of the application;
FIG. 3 is a flow chart of one embodiment of step 203 provided in an embodiment of the present application;
FIG. 4 is a schematic diagram of one embodiment of a digital twinning-based airport data processing device provided in an embodiment of the application;
FIG. 5 is a schematic diagram of an embodiment of a server provided in an embodiment of the present application;
fig. 6 is a schematic diagram of transportation relationship map information of flights provided in an embodiment of the application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to fall within the scope of the application.
In the description of the present 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", "outer", etc. indicate orientations or positional relationships based on the drawings are merely for convenience in describing the present application and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present application. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the present application, the term "exemplary" is used to mean "serving as an example, instance, or illustration. Any embodiment described as "exemplary" in this disclosure is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is presented to enable any person skilled in the art to make and use the application. In the following description, details are set forth for purposes of explanation. It will be apparent to one of ordinary skill in the art that the present application may be practiced without these specific details. In other instances, well-known structures and processes have not been described in detail so as not to obscure the description of the application with unnecessary detail. Thus, the present application is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.
The embodiment of the application provides a freight airport data processing method based on digital twinning and a related device, and the method and the related device are respectively described in detail below.
As shown in fig. 1, fig. 1 is a schematic view of a digital twin-based airport data processing system according to an embodiment of the present application, where the digital twin-based airport data processing system may include a plurality of terminals 100 and a server 200, where the terminals 100 are connected to the server 200 through a network, and where a digital twin-based airport data processing device, such as the server in fig. 1, is integrated in the server 200, and the terminals 100 may access the server 200.
The server 200 in the embodiment of the present application is mainly used for obtaining the transportation relationship map information of flights in the preset time range of the freight airport, wherein the transportation relationship map information comprises the association relationship among a plurality of different levels of transportation entities, and the plurality of different levels of transportation entities at least comprise freight aircrafts, freight vehicles and containers; acquiring a planned departure time parameter and an actual departure time parameter of each flight in flights within a preset time range; determining a transit time score parameter corresponding to each flight based on the transportation relation map information, the planned departure time parameter and the actual departure time parameter; and determining a target flight with abnormal transit time score from flights in a preset time range based on the transit time score parameter corresponding to each flight.
In the embodiment of the present application, the server 200 may be an independent server, or may be a server network or a server cluster formed by servers, for example, the server 200 described in the embodiment of the present application includes, but is not limited to, a computer, a network terminal, a single network server, a plurality of network server sets, or a cloud server formed by a plurality of servers. Wherein the Cloud server is composed of a large number of computers or web servers based on Cloud Computing (Cloud Computing). In embodiments of the present application, communication between the server and the terminal may be achieved by any communication means, including, but not limited to, mobile communication based on the third generation partnership project (3rd Generation Partnership Project,3GPP), long term evolution (Long Term Evolution, LTE), worldwide interoperability for microwave access (Worldwide Interoperability for Microwave Access, wiMAX), or computer network communication based on the TCP/IP protocol family (TCP/IP Protocol Suite, TCP/IP), user datagram protocol (User Datagram Protocol, UDP), etc.
It will be appreciated that the terminal 100 used in embodiments of the present application may be a device that includes both receive and transmit hardware, i.e., a device that has both receive and transmit hardware capable of performing bi-directional communications over a bi-directional communication link. Such a terminal may include: a cellular or other communication device having a single-line display or a multi-line display or a cellular or other communication device without a multi-line display. The terminal 100 may be a desktop terminal or a mobile terminal, and the terminal 100 may be one of a mobile phone, a tablet computer, a notebook computer, and the like.
It will be appreciated by those skilled in the art that the application environment shown in fig. 1 is merely an application scenario of the present application, and is not limited to the application scenario of the present application, and other application environments may include more or fewer terminals than those shown in fig. 1, or a server network connection relationship, for example, only 1 server and 2 terminals are shown in fig. 1. It will be appreciated that the digital twinning-based freight airport data processing system may also include one or more other servers, or/and one or more terminals connected to a server network, as is not limited in this regard.
In addition, as shown in FIG. 1, the digital twinning-based airport data processing system may also include a memory 300 for storing data, such as flight data for a stored airport and digital twinning-based airport data processing data, such as digital twinning-based airport data processing data when the digital twinning-based airport data processing system is in operation.
It should be noted that, the schematic view of the scenario of the digital twin-based airport data processing system shown in fig. 1 is only an example, and the digital twin-based airport data processing system and scenario described in the embodiments of the present application are for more clearly describing the technical solution of the embodiments of the present application, and do not constitute a limitation of the technical solution provided by the embodiments of the present application, and those skilled in the art can know that, with the evolution of the digital twin-based airport data processing system and the appearance of new service scenarios, the technical solution provided by the embodiments of the present application is equally applicable to similar technical problems.
Next, a method for processing data of a freight airport based on digital twin provided by the embodiment of the application is described.
In the embodiment of the digital twin-based freight airport data processing method of the present application, a digital twin-based freight airport data processing device is used as an execution body, and in order to simplify and facilitate the description, the execution body will be omitted in the subsequent method embodiments, and the digital twin-based freight airport data processing device is applied to a server, and the method includes: acquiring transportation relation map information of flights of a freight airport within a preset time range, wherein the transportation relation map information comprises association relations among a plurality of transportation entities of different levels, and the transportation entities of different levels at least comprise freight aircrafts, freight vehicles and containers; acquiring a planned departure time parameter and an actual departure time parameter of each flight in flights within a preset time range; determining a transit time score parameter corresponding to each flight based on the transportation relation map information, the planned departure time parameter and the actual departure time parameter; and determining a target flight with abnormal transit time score from flights in a preset time range based on the transit time score parameter corresponding to each flight.
Referring to fig. 2 to 6, fig. 2 is a flowchart illustrating an embodiment of a digital twin-based airport data processing method according to an embodiment of the present application, where the digital twin-based airport data processing method includes:
201. and acquiring transportation relation map information of flights in a preset time range of the freight airport.
The transportation relationship graph information includes associations between a plurality of different levels of transportation entities including at least cargo aircraft, cargo vehicles, and containers.
It should be noted that, the transportation relationship map information may also include ticket information, that is, information about the number of tickets specifically carried by each container. The association relationship among the transport entities of the different levels comprises a hierarchical relationship and a binding relationship among the transport entities of the different levels. As shown in fig. 6, for example, in a flight a (corresponding to the freight aircraft), there are three freight vehicles (corresponding to the freight vehicles) a, b, and c, and there are a plurality of containers such as ULD1, ULD2, ULD3, ULD 4..uldn, and a plurality of tickets (corresponding to the tickets), wherein all the tickets on the flight a are respectively packaged by the plurality of containers, and then the plurality of containers are respectively packaged by the three vehicles, and the container with the ticket is transported to the freight aircraft corresponding to the flight a by the three vehicles, and as can be understood from fig. 6, the freight aircraft corresponding to the flight a is a first-stage transport entity, the freight vehicle corresponding to the vehicle a, and the like is a second-stage transport entity, and the container is a third-stage entity, wherein the first-stage transport entity is set as a parent entity of the second-stage transport entity, the second-stage transport entity is set as a child entity of the third-stage transport entity, and the third-stage transport entity is set as a child entity of the third-stage transport entity.
In this embodiment, the preset time range may be a plurality of months, a month, a week, a few days, a day, or a few hours in a day, specifically, may be set according to actual requirements, for example, when the situation of a flight of each day needs to be analyzed, the preset time range may be set to be one day, and similarly, when the situation of a flight in a week needs to be analyzed, the preset time range may be set to be one week.
In some embodiments of the present application, obtaining transportation relationship profile information for flights of a freight airport within a predetermined time frame includes: acquiring motion trail data and business operation data of each entity in each flight in a preset time range from a preset freight airport digital twin platform; determining the association relation between the entities in each flight in the preset time range based on the motion trail data and the business operation data of the entities in each flight in the preset time range; and constructing transportation relation map information of flights of the freight airport in the preset time range based on the association relation among entities in each flight in the preset time range.
Specifically, before using the preset digital twin platform of the freight airport, the digital twin platform of the freight airport needs to be built, and the specific process is as follows: data are collected, a three-dimensional model and event animation are established, and the method comprises the following steps: constructing three-dimensional visual basic simulation scenes such as an apron, a road base map and the like according to airport geographic information and building model data; combining a roadside courtyard model and a sorting center model to construct an airport building and greening scene; establishing a three-dimensional model and event animation according to basic data of the acquired aircraft and related flights, including appearance patterns, machine types, cargo capacity, loading rate, landing sliding speed, fuselage length, landing places, the number of ULD (ULD) aviation boxes storable in each cabin and the like; according to basic trailer data including appearance, boxing number, length of a vehicle body and running speed, a three-dimensional model and event animation are built; according to the appearance of other service vehicles (conveyor belt vehicles, lifting platform vehicles, fuelling vehicles, ferry vehicles, dirt cleaning vehicles, tractors and boarding vehicles) and the length, width and running speed data of the vehicle body, a three-dimensional model and event animation are established; according to the appearance, the size, the type and other data of various ULD aviation boxes, a three-dimensional model and an event animation are established; according to the basic data of the ticket and the related logistics data, a three-dimensional model and an event animation are established; and establishing a three-dimensional model and event animation according to sorting equipment data, such as equipment parameters, paths, configuration and the like, and business rule data, such as traffic operation, path planning, flight guarantee and the like.
Specifically, the data is processed and synchronized, and the data and the three-dimensional animation are synchronized through digital mapping, and the method comprises the following steps: data warehouse centralized processing: inputting the collected data of each entity of the airport into a digital twin platform, and storing the data in a specific set to an original data warehouse through machine learning; the method comprises the steps of intensively processing original data of each entity of an airport stored to a digital twin platform original data warehouse, removing error, invalid and unnecessary noise data, and storing the rest data; dividing the reserved data into dynamic data and static data, wherein the dynamic data are motion trail data, business event data, dynamic statistical data and the like, and the static data are geographic information data, road point location data, aircraft vehicle attribute data and the like; the data protocol module reduces the dimension of dynamic and static data based on the preserved dynamic and static data by PCA technology, and comprises the following specific steps: feature centering, subtracting the mean of dynamic and static data for each dimension, i.e., converting the mean of one flight, vehicle, ULD feature (or flight, vehicle, ULD attribute) to 0. Covariance matrices of the feature-centered static data and dynamic data matrices are calculated. Eigenvalues and eigenvectors of the covariance matrix are calculated. And selecting the feature vector corresponding to the large feature value to obtain a data set of new static data and dynamic data. The method comprises the steps of performing data transformation operation on each dimension in a data set of the static and real-time dynamic data through a dispersion normalization (X1= (X-min)/(max-min)) and the like, effectively increasing the usability and robustness of digital mapping data, normalizing the data generated by digital twin, solving the problems of data distribution, stable sequence problems, data explosion and the like, and obtaining downstream data to be synchronized. Synchronizing the processed downstream data: if the data are real-time dynamic data, such as motion trail data, business event data, dynamic statistics data and the like, synchronizing the data from a data warehouse to a digital twin platform background database in real time for calling at any time; if the data is static type data, such as geographic information data, road point location data, aircraft vehicle attribute data and the like, the data is synchronized to a digital twin platform background database from a data warehouse at fixed time for calling at any time. Three-dimensional animation is synchronized with data: motion trail animation synchronization: the data twin platform invokes the stored three-dimensional model of the moving entity according to the object data provided by the current moving track data; and then matching the position, the moment, the gesture and the speed according to the coordinate data, the moment data, the direction data and the speed data of the motion trail, and reproducing the motion condition of the entity on the field. Business operation animation synchronization: the data twin platform invokes the stored three-dimensional business animation according to the data such as the object, the event type, the coordinates and the like provided by the current business event data; and matching the animation effect with the duration according to the record of the operation starting time point and the operation ending time point of the business event, and reproducing the business operation condition of the physical site, thereby correspondingly generating business operation data.
202. Acquiring a planned departure time parameter and an actual departure time parameter of each flight in flights within a preset time range;
specifically, the planned departure time parameter and the actual departure time parameter of each flight in the flights in the preset time range can be obtained from the server.
203. Determining a transit time score parameter corresponding to each flight based on the transportation relation map information, the planned departure time parameter and the actual departure time parameter;
the transit time aging score parameter is a parameter for evaluating the transit time aging speed of a flight, specifically, when the transit time aging score parameter of a certain flight is higher, the transit time aging of the flight is about to be quick, otherwise, when the transit time aging score parameter of the certain flight is lower, the transit time aging of the flight is about to be slow.
How 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 is described in the following examples, which are not repeated herein.
204. And determining a target flight with abnormal transit time score from flights in a preset time range based on the transit time score parameter corresponding to each flight.
In some embodiments of the present application, determining a target flight with abnormal transit time score from flights within a preset time range based on a transit time score parameter corresponding to each flight includes: sequencing the transit time effect score parameters corresponding to each flight; and ranking the last preset flights of the transit time score parameter, and determining the last preset flights as target flights with abnormal transit time scores. For example, as shown in step 303, the parameters of the transit time score corresponding to the flight a, the flight B and the flight C are 960, 960 and 800, respectively, wherein the transit time score of the flight C is the lowest, so that the flight C is determined to be the target flight with abnormal transit time score.
The application provides a digital twin-based freight airport data processing method, which comprises the steps of obtaining transportation relation map information of flights of a freight airport within a preset time range, wherein the transportation relation map information comprises association relations among a plurality of transportation entities of different levels, and the transportation entities of different levels at least comprise a freight aircraft, a freight vehicle and a container; acquiring a planned departure time parameter and an actual departure time parameter of each flight in flights within a preset time range; determining a transit time score parameter corresponding to each flight based on the transportation relation map information, the planned departure time parameter and the actual departure time parameter; and determining a target flight with abnormal transit time score from flights in a preset time range based on the transit time score parameter corresponding to each flight. Compared with the traditional digital twin-based freight airport data processing method, under the background that the freight airport data cannot be comprehensively analyzed and the abnormality problem cannot be rapidly located, the method creatively locates the target flights with abnormal transit aging scores by comprehensively analyzing the transport relationship maps of flights within the preset time and the take-off time parameters of each flight, effectively shortens the locating time and reduces the analysis cost, thereby facilitating airport transportation control personnel to optimize the abnormality condition of the freight airport and further improving the logistics transportation efficiency of the freight airport.
In some embodiments of the present application, as shown in fig. 3, step 203, determining a 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 includes:
301. determining a transit time efficiency score proportion parameter of each flight based on the planned departure time parameter and the actual departure time parameter;
the transit time aging score proportion parameter is a proportion parameter for evaluating the transit time aging speed of flights.
In some embodiments of the application, determining the transit time score ratio parameter for each flight based on the planned departure time parameter and the actual departure time parameter includes: calculating the difference value between the actual take-off time parameter and the planned take-off time parameter; and determining the transit time effect score proportion parameter of each flight based on the difference value and the corresponding relation between the preset transit time effect score proportion parameter and the difference value. For example, when the difference between the actual take-off time parameter and the planned take-off time parameter is within [15, +infinite ], the transit time score ratio parameter is 1.2, if the difference is 1 between [0,15], the transit time score ratio parameter is 0.8 between [15, 0], and if the difference is between [ 30, -15], the score is 0.2.
302. Determining total number parameters of tickets corresponding to the freight aircraft in each flight based on the transport relationship map information;
as known from step 201, the transport relationship profile information may also include ticket information, i.e., number of tickets each container specifically carries. Therefore, the total number parameter of the tickets corresponding to the freight aircraft in each flight can be calculated according to the relation among a plurality of entities in each flight and the number information of the tickets specifically borne by each container. For example, flight a corresponds to two freight vehicles, each carrying 2 and 3 containers, and each container carries 100 couriers, and then the total number parameter y= (2+3) ×100=500 for the tickets corresponding to the freight aircraft in flight a.
303. And determining the transit time score parameter corresponding to each flight based on the transit time score proportion parameter of each flight and the total number parameter of the tickets corresponding to the freight aircrafts in each flight.
In one embodiment, the transit time score ratio parameter for each flight may be multiplied by the total number of tickets corresponding to the cargo aircraft in each flight to obtain the transit time score parameter for each flight. For example, the actual departure times of the flights a, B and C are 20 minutes earlier, 12 minutes earlier and 10 minutes later than the planned departure time, respectively, and then the corresponding transit ageing score ratio parameters are 1.2, 1 and 0.8, respectively; and the total number of the corresponding tickets of the flight A, the flight B and the flight C is 800, 960 and 1000 respectively, the transit time score parameters of the flight A, the flight B and the flight C are 960, 960 and 800 respectively.
In another embodiment of the present application, after determining a 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: and determining a target transportation child entity with abnormal transit time score from the target flights based on the target flights and the transportation relation map information, wherein the transportation child entity is a transportation entity with a lower transportation level than the parent entity.
In some embodiments of the present application, determining a targeted transportation entity with an abnormal transit time score from a targeted flight based on targeted flight and transportation relationship profile information, comprises: based on the transport relationship map information, determining the association relationship among a plurality of transport entities corresponding to the freight aircraft in the target flight; determining a duty ratio parameter of the transit time efficiency score of each of the plurality of transportation sub-entities to the transit time efficiency score of the father entity based on the association relation among the plurality of transportation entities corresponding to the freight aircraft in the target flight; a target transportation child entity with an abnormal transit time score is determined from the target flights based on a ratio parameter of the transit time score of each of the plurality of transportation child entities to the transit time score of the parent entity.
In another embodiment of the present application, according to the three-dimensional space situation data with time dimension obtained in the above step 201, a time window is obtained every 10 minutes from the first recording, and an aging label is recorded for each time window. The resulting vehicle entity and ULD entity information is the average OTT score for all entities within 100 square meters of the center.
In another embodiment of the present application, based on the plurality of aging labels obtained in the above embodiment, sorting according to the sizes of the aging labels, obtaining a time window with the minimum aging label, and tracing the entity event record and the statistics data record of the whole time window; based on the recorded automatic matching model and animation, the running of the entity in the time window is automatically displayed in a digital twin three-dimensional environment.
In order to better implement the digital twin-based data processing method of the freight airport in the embodiment of the present application, on the basis of the digital twin-based data processing method of the freight airport, the embodiment of the present application further provides a digital twin-based data processing device of the freight airport, as shown in fig. 4, the digital twin-based data processing device 400 of the freight airport includes:
a first obtaining unit 401, configured to obtain transportation relationship map information of flights in a preset time range at a freight airport, where the transportation relationship map information includes association relationships between a plurality of transportation entities at different levels, and the plurality of transportation entities at different levels at least include a freight plane, a freight vehicle, a container, and a ticket loaded by the container;
A second obtaining unit 402, configured to obtain a planned departure time parameter and an actual departure time parameter of each of the flights within a preset time range;
a first determining unit 403, configured to determine a transit time score parameter corresponding to each flight based on the transportation relation map information, the planned departure time parameter, and the actual departure time parameter;
the second determining unit 404 is configured to determine, from flights within a preset time range, a target flight with an abnormal transit time score based on the transit time score parameter corresponding to each flight.
In some embodiments of the present application, the first determining unit 403 specifically includes:
the third determining unit is used for determining a transit time score proportion parameter of each flight based on the planned departure time parameter and the actual departure time parameter;
a fourth determining unit, configured to determine total number parameters of tickets corresponding to the cargo aircraft in each flight based on the transportation relationship map information;
and a fifth determining unit, configured to determine a transit time score parameter corresponding to each flight based on the transit time score proportion parameter of each flight and the total number parameter of tickets corresponding to the cargo aircraft in each flight.
In some embodiments of the present application, the third determining unit is specifically configured to:
Calculating the difference value between the actual take-off time parameter and the planned take-off time parameter;
and determining the transit time effect score proportion parameter of each flight based on the difference value and the corresponding relation between the preset transit time effect score proportion parameter and the difference value.
In some embodiments of the present application, the first obtaining unit 401 is specifically configured to:
acquiring motion trail data and business operation data of each entity in each flight in a preset time range from a preset freight airport digital twin platform;
determining the association relation between the entities in each flight in the preset time range based on the motion trail data and the business operation data of the entities in each flight in the preset time range;
and constructing transportation relation map information of flights of the freight airport in the preset time range based on the association relation among entities in each flight in the preset time range.
In some embodiments of the present application, the second determining unit 404 is specifically configured to:
sequencing the transit time effect score parameters corresponding to each flight;
and ranking the last preset flights of the transit time score parameter, and determining the last preset flights as target flights with abnormal transit time scores.
In some embodiments of the present application, after determining a target flight with abnormal transit time score from flights within a preset time range based on the transit time score parameter, the apparatus further includes:
and a sixth determining unit configured to determine a target transportation child entity with abnormal transit time score from the target flights based on the target flights and the transportation relationship map information, wherein the transportation child entity is a transportation entity with a transportation level lower than that of the parent entity.
In some embodiments of the present application, the sixth determining unit is specifically configured to:
based on the transport relationship map information, determining the association relationship among a plurality of transport entities corresponding to the freight aircraft in the target flight;
determining a duty ratio parameter of the transit time efficiency score of each of the plurality of transportation sub-entities to the transit time efficiency score of the father entity based on the association relation among the plurality of transportation entities corresponding to the freight aircraft in the target flight;
a target transportation child entity with an abnormal transit time score is determined from the target flights based on a ratio parameter of the transit time score of each of the plurality of transportation child entities to the transit time score of the parent entity.
The digital twin-based freight airport data processing device 400 provided by the application comprises a first acquisition unit 401, a second acquisition unit 401 and a third acquisition unit, wherein the first acquisition unit is used for acquiring transportation relation map information of flights of a freight airport within a preset time range, the transportation relation map information comprises association relations among a plurality of transportation entities with different levels, and the transportation entities with different levels at least comprise freight airplanes, freight vehicles, containers and tickets loaded by the containers; a second obtaining unit 402, configured to obtain a planned departure time parameter and an actual departure time parameter of each of the flights within a preset time range; a first determining unit 403, configured to determine a transit time score parameter corresponding to each flight based on the transportation relation map information, the planned departure time parameter, and the actual departure time parameter; the second determining unit 404 is configured to determine, from flights within a preset time range, a target flight with an abnormal transit time score based on the transit time score parameter corresponding to each flight. Compared with the traditional digital twin-based freight airport data processing device 400, the application creatively and rapidly positions the destination flights with abnormal transit time scores by comprehensively analyzing the transport relationship maps of flights within the preset time and comprehensively analyzing the take-off time parameters of each flight under the background that the freight airport data cannot be comprehensively analyzed and the abnormal problems cannot be rapidly positioned, effectively shortens the positioning time and reduces the analysis cost, thereby being convenient for airport operators to optimize the abnormal conditions of the freight airport and further improving the logistics transportation efficiency of the freight airport.
In addition to the above description of the method and apparatus for digital twin-based airport data processing, embodiments of the present application further provide a server, which integrates any of the digital twin-based airport data processing apparatuses provided in the embodiments of the present application, where the server includes:
one or more processors;
a memory; and
one or more applications, wherein the one or more applications are stored in memory and configured to perform the operations of any of the method embodiments of any of the digital twinning-based airport data processing method embodiments described above by a processor.
The embodiment of the application also provides a server which integrates any digital twin-based freight airport data processing device provided by the embodiment of the application. Referring to fig. 5, fig. 5 is a schematic structural diagram of an embodiment of a server according to an embodiment of the present application.
Referring now to FIG. 5, a schematic diagram of a digital twinning-based airport data processing device according to an embodiment of the present application is shown, in particular:
the digital twinning-based freight airport data processing apparatus may include one or more processors 501 of a processing core, one or more storage units 502 of a computer readable storage medium, a power supply 503, and an input unit 504, among other components. It will be appreciated by those skilled in the art that the digital twinning-based airport data processing apparatus structure shown in fig. 5 is not limiting and that digital twinning-based airport data processing apparatus may include more or fewer components than shown, or certain components may be combined, or a different arrangement of components. Wherein:
The processor 501 is a control center of the digital twin based airport data processing apparatus and connects various parts of the entire digital twin based airport data processing apparatus using various interfaces and lines to perform various functions and processing data of the digital twin based airport data processing apparatus by running or executing software programs and/or modules stored in the memory unit 502 and invoking data stored in the memory unit 502, thereby performing overall monitoring of the digital twin based airport data processing apparatus. Optionally, processor 501 may include one or more processing cores; preferably, the processor 501 may integrate an application processor that primarily handles operating systems, user interfaces, applications, etc., with a modem processor that primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 501.
The storage unit 502 may be used to store software programs and modules, and the processor 501 performs various functional applications and data processing by executing the software programs and modules stored in the storage unit 502. The storage unit 502 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data created from the use of digital twin based airport data processing devices, and the like. In addition, the storage unit 502 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory unit 502 may also include a memory controller to provide the processor 501 with access to the memory unit 502.
The digital twin based airport data processing device further comprises a power supply 503 for supplying power to the various components, preferably, the power supply 503 may be logically connected to the processor 501 through a power management system, so as to implement functions of managing charging, discharging, and power consumption management through the power management system. The power supply 503 may also include one or more of any of a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like.
The digital twinning based airport data processing apparatus may further comprise an input unit 504, which input unit 504 may be used to receive entered digital or character information and to generate keyboard, mouse, joystick, optical or trackball signal inputs in connection with user settings and function control.
Although not shown, the digital twinning-based freight airport data processing device may also include a display unit or the like, which is not described in detail herein. In particular, in the embodiment of the present application, the processor 501 in the digital twin-based airport data processing device loads 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, so as to implement various functions as follows:
Acquiring transportation relation map information of flights of a freight airport within a preset time range, wherein the transportation relation map information comprises association relations among a plurality of transportation entities of different levels, and the transportation entities of different levels at least comprise freight aircrafts, freight vehicles and containers; acquiring a planned departure time parameter and an actual departure time parameter of each flight in flights within a preset time range; determining a transit time score parameter corresponding to each flight based on the transportation relation map information, the planned departure time parameter and the actual departure time parameter; and determining a target flight with abnormal transit time score from flights in a preset time range based on the transit time score parameter corresponding to each flight.
The application provides a digital twin-based freight airport data processing method, which comprises the steps of obtaining transportation relation map information of flights of a freight airport within a preset time range, wherein the transportation relation map information comprises association relations among a plurality of transportation entities of different levels, and the transportation entities of different levels at least comprise a freight aircraft, a freight vehicle and a container; acquiring a planned departure time parameter and an actual departure time parameter of each flight in flights within a preset time range; determining a transit time score parameter corresponding to each flight based on the transportation relation map information, the planned departure time parameter and the actual departure time parameter; and determining a target flight with abnormal transit time score from flights in a preset time range based on the transit time score parameter corresponding to each flight. Compared with the traditional digital twin-based freight airport data processing method, under the background that the freight airport data cannot be comprehensively analyzed and the abnormality problem cannot be rapidly located, the method creatively locates the target flights with abnormal transit aging scores by comprehensively analyzing the transport relationship maps of flights within the preset time and the take-off time parameters of each flight, effectively shortens the locating time and reduces the analysis cost, thereby facilitating airport transportation control personnel to optimize the abnormality condition of the freight airport and further improving the logistics transportation efficiency of the freight airport.
To this end, embodiments of the present application provide a computer-readable storage medium, which may include: read Only Memory (ROM), random access Memory (RAM, random Access Memory), magnetic or optical disk, and the like. The computer readable storage medium has stored therein a plurality of instructions that can be loaded by a processor to perform the steps of any of the digital twinning-based airport data processing methods provided by embodiments of the present application. For example, the instructions may perform the steps of:
acquiring transportation relation map information of flights of a freight airport within a preset time range, wherein the transportation relation map information comprises association relations among a plurality of transportation entities of different levels, and the transportation entities of different levels at least comprise freight aircrafts, freight vehicles and containers; acquiring a planned departure time parameter and an actual departure time parameter of each flight in flights within a preset time range; determining a transit time score parameter corresponding to each flight based on the transportation relation map information, the planned departure time parameter and the actual departure time parameter; and determining a target flight with abnormal transit time score from flights in a preset time range based on the transit time score parameter corresponding to each flight.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
The foregoing has described in detail a digital twinning-based data processing method and related apparatus for a freight airport according to embodiments of the present application, and specific examples have been set forth herein to illustrate the principles and embodiments of the present application, the description of the foregoing examples being merely intended to assist in understanding the methods and core ideas of the present application; meanwhile, as those skilled in the art will vary in the specific embodiments and application scope according to the ideas of the present application, the present description should not be construed as limiting the present application in summary.
Claims (10)
1. A digital twinning-based freight airport data processing method, the method comprising:
acquiring transportation relation map information of flights of a freight airport within a preset time range, wherein the transportation relation map information comprises association relations among a plurality of transportation entities of different levels, and the transportation entities of different levels at least comprise freight aircrafts, freight vehicles and containers;
Acquiring a planned departure time parameter and an actual departure time parameter of each flight in the flights within the preset time range;
determining a transit time score parameter corresponding to each flight based on the transportation relation map information, the planned departure time parameter and the actual departure time parameter;
and determining the target flight with abnormal transit time score from flights in the preset time range based on the transit time score parameter corresponding to each flight.
2. The digital twinning-based freight airport data processing method of claim 1, wherein said determining the transit time score parameter corresponding to each flight based on the transportation relationship map information, the planned departure time parameter, the actual departure time parameter comprises:
determining a transit time score ratio parameter of each flight based on the planned departure time parameter and the actual departure time parameter;
determining total number parameters of tickets corresponding to the freight aircrafts in each flight based on the transportation relation map information;
and determining the transit time score parameter corresponding to each flight based on the transit time score proportion parameter of each flight and the total number parameter of the tickets corresponding to the freight aircrafts in each flight.
3. The digital twinning-based freight airport data processing method of claim 2, wherein said determining said per flight transit time score scaling parameter based on said planned departure time parameter and said actual departure time parameter comprises:
calculating the difference value between the actual take-off time parameter and the planned take-off time parameter;
and determining the transit time effect score proportion parameter of each flight based on the difference and the corresponding relation between the preset transit time effect score proportion parameter and the difference.
4. The method for processing digital twin-based airport data according to claim 1, wherein the step of obtaining transport relationship map information of flights of the airport within a predetermined time range comprises:
acquiring motion trail data and business operation data of each entity in each flight in a preset time range from a preset freight airport digital twin platform;
determining the association relation among the entities in each flight in the preset time range based on the motion trail data and the business operation data of the entities in each flight in the preset time range;
And constructing transportation relation map information of flights of the freight airport in the preset time range based on the association relation among entities in each flight in the preset time range.
5. The digital twin based freight airport data processing method according to claim 1, wherein the determining, from the flights within the predetermined time range, the destination flights with abnormal transit time scores based on the transit time score parameters corresponding to each of the flights comprises:
sequencing the transit time effect score parameters corresponding to each flight;
and ranking the last preset flights of the transit time score parameter, and determining the last preset flights as target flights with abnormal transit time scores.
6. The digital twinning-based freight airport data processing method of claim 1, wherein after determining a destination flight with an abnormal transit time score from flights within the preset time range based on the transit time score parameter, the method further comprises:
and determining a target transportation child entity with abnormal transit time score from the target flights based on the target flights and the transportation relation map information, wherein the transportation child entity is a transportation entity with a transportation level lower than that of a parent entity.
7. The digital twinning-based freight airport data processing method of claim 6, wherein said determining a targeted transportation entity with an abnormal transit time score from said targeted flights based on said targeted flights and said transportation relationship profile information comprises:
based on the transport relationship map information, determining association relationships among a plurality of transport entities corresponding to the freight aircraft in the target flight;
determining a duty ratio parameter of the transit time efficiency score of each transport sub-entity in the transport sub-entities to the transit time efficiency score of a father entity based on the association relation among the transport entities corresponding to the freight aircraft in the target flight;
and determining a target transportation child entity with abnormal transit time score from the target flights based on the duty ratio parameter of the transit time score of each transportation child entity in the plurality of transportation child entities to the transit time score of the parent entity.
8. A digital twinning-based freight airport data processing apparatus, the apparatus comprising:
the first acquisition unit is used for acquiring transportation relation map information of flights of the freight airport within a preset time range, wherein the transportation relation map information comprises association relations among a plurality of different levels of transportation entities, and the plurality of different levels of transportation entities at least comprise freight airplanes, freight vehicles, containers and tickets loaded by the containers;
The second acquisition unit is used for acquiring the planned departure time parameter and the actual departure time parameter of each flight in the flights within the preset time range;
the first determining unit is used for determining a transit time score parameter corresponding to each flight based on the transportation relation map information, the planned departure time parameter and the actual departure time parameter;
and the second determining unit is used for determining a target flight with abnormal transit time score from flights within the preset time range based on the transit time score parameters corresponding to each flight.
9. A server, the server comprising:
one or more processors;
a 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 twinning-based airport data processing method of any one of claims 1 to 7.
10. A computer readable storage medium, having stored thereon a computer program, the computer program being loaded by a processor to perform the steps of the digital twinning based airport data processing method of any one of claims 1 to 7.
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| CN117933840B (en) * | 2024-03-21 | 2024-05-31 | 中国民用航空总局第二研究所 | Digital twin-driven flight ground guarantee delay diagnosis method, system and equipment |
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