Aircraft taxiing trajectory management system and method, medium and device

Abstract

The embodiment of the invention provides an aircraft taxiing trajectory management method, comprising: acquiring path management data, the path management data comprising equipment data and airport environment data corresponding to an aircraft, the equipment data comprising current setting data, historical trajectory data and / or current position data of the aircraft; predicting future trajectory data of the aircraft at a future time point based on the path management data; acquiring real-time trajectory data of the aircraft at the future time point; judging whether the real-time trajectory data and the future trajectory data meet a preset early warning condition or not; and giving an early warning in response to the satisfaction. According to the method, the safety and efficiency of airport scene operation can be remarkably improved, accidents and conflicts are reduced, and normal operation of flights is ensured.

Description

Technical Field

[0001] The present specification relates to the technical field of aircraft taxiing trajectory management, and in particular to an aircraft taxiing trajectory management system and method, medium, and device. Background Art

[0002] During busy hours, it is crucial to plan the taxiing paths of aircraft (such as airplanes) in a reasonable manner to improve the efficiency of airport operations. If an aircraft deviates from the scheduled taxiing plan, it may cause chaos in the field operations. Therefore, it is necessary to propose an aircraft taxiing trajectory management method, system, device and storage medium, which can significantly improve the safety and efficiency of airport field operations, reduce the occurrence of accidents and conflicts, and ensure the normal operation of flights. Summary of the invention

[0003] One aspect of an embodiment of the present specification provides a method for managing an aircraft taxiing trajectory, including: acquiring path management data, the path management data including equipment data and airport environment data corresponding to the aircraft, the equipment data including current setting data, historical trajectory data and / or current position data of the aircraft; predicting future trajectory data of the aircraft at a future point in time based on the path management data; acquiring real-time trajectory data of the aircraft at the future point in time; determining whether the real-time trajectory data and the future trajectory data satisfy preset warning conditions; and issuing a warning in response to satisfaction.

[0004] In some embodiments, the acquiring of the path management data includes: acquiring basic path management data; and preprocessing the basic path management data based on a data preprocessing algorithm to obtain the path management data.

[0005] In some embodiments, the data preprocessing algorithm includes: obtaining basic information of the path management basic data, the basic information including at least one of a column name, a data type, and missing values; based on the basic information, determining a data cleaning method, the data cleaning method including at least one of filling missing values, removing duplicate data, and correcting erroneous data; based on the data cleaning method, cleaning the path management basic data to obtain cleaned data; converting the cleaned data to a preset data type to obtain converted data; exporting the converted data based on a preset export format to obtain the path management data.

[0006] In some embodiments, converting the cleaned data into a preset data type to obtain converted data also includes: determining whether the preset data in the converted data is in a preset format and whether the distribution characteristics of the preset data meet preset integration conditions; in response to the preset data not being in the preset format, converting the data format of the preset data into the preset format based on a preset service; in response to the distribution characteristics of the preset data meeting the preset integration conditions, integrating the preset data; and creating a new data column to record the preset data.

[0007] In some embodiments, the airport environment data includes location data of airport buildings and / or location data of airport equipment; the acquisition of path management data includes: acquiring a first reference coordinate system of the aircraft and a second reference coordinate system of an airport management system; in response to the first reference coordinate system and the second reference coordinate system not being the same reference coordinate system, converting the first reference coordinate system and the second reference coordinate system into the same target coordinate system; acquiring historical trajectory data and / or current location data of the aircraft in the target coordinate system, and acquiring the location data of the airport buildings and / or location data of the airport equipment in the target coordinate system.

[0008] In some embodiments, predicting the future trajectory data of the aircraft at a future point in time based on the path management data includes: determining the airport space and the flight space corresponding to the aircraft based on the path management data; determining the spatial characteristics and spatial relationships of the airport space and the flight space; the spatial characteristics include data distribution and data patterns, and the spatial relationships include overlapping areas and adjacent areas; determining the spatial data summary, spatial object relationship, and spatial connection relationship of the airport space and the flight space based on the spatial characteristics and spatial relationship of the airport space and the flight space; predicting the future trajectory data of the aircraft at a future point in time based on the spatial data summary, spatial object relationship, and spatial connection relationship of the airport space and the flight space.

[0009] In some embodiments, based on the spatial data summaries, spatial object relationships, and spatial connection relationships of the airport space and the flight space, predicting the future trajectory data of the aircraft at a future point in time includes: based on the spatial data summaries, spatial object relationships, and spatial connection relationships of the airport space and the flight space, as well as the equipment data and airport environment data corresponding to the aircraft, through a prediction model, predicting the future trajectory data of the aircraft at a future point in time, wherein the prediction model is a machine learning model.

[0010] In some embodiments, predicting the future trajectory data of the aircraft at a future point in time based on the spatial data summaries, spatial object relationships, and spatial connection relationships of the airport space and the flight space includes: determining spatial relationship features based on the spatial data summaries, spatial object relationships, and spatial connection relationships of the airport space and the flight space; querying spatial relationship features using spatial indexes based on the spatial relationship features; the queries on the spatial relationship features include intersection queries, inclusion relationship queries, overlapping queries, adjacent queries, covering queries, covered queries, cross queries, distance queries, and buffer queries; and predicting the future trajectory data of the aircraft at a future point in time based on the query results.

[0011] In some embodiments, based on the spatial relationship feature, querying the spatial relationship feature using a spatial index includes: determining a target spatial search range; constructing an R-tree index based on the path management data; and querying the spatial relationship feature based on the R-tree index.

[0012] In some embodiments, predicting the future trajectory data of the aircraft at a future point in time based on the query results includes: creating a static map and a dynamic map based on the query results; predicting the future trajectory data of the aircraft at a future point in time based on the static map and the dynamic map; wherein creating a static map includes: creating a chart and an axis; determining the center distance of spatial objects within the airport based on the query results; transferring the center distance and spatial objects to the axis to determine the airport spatial data; obtaining basic map data, the basic map data including coastline, boundaries, grid lines, and longitude and latitude label data; generating a static map based on the basic map data and airport spatial data; creating a dynamic map includes: determining single-frame data information based on the query results; wherein the time interval between two adjacent frames is determined based on a first preset parameter, and the sequence of each frame is determined based on a second preset parameter; removing the current axis content in the single-frame static map, and adding the information of the dynamic element in the current frame to obtain the current frame; and obtaining the dynamic map based on the current frames corresponding to several moments.

[0013] In some embodiments, predicting the future trajectory data of the aircraft at a future point in time based on the path management data includes: predicting the future trajectory data of the aircraft at a future point in time based on the path management data using a time series analysis algorithm or a regression algorithm.

[0014] In some embodiments, determining whether the real-time trajectory data and the future trajectory data satisfy a preset warning condition comprises: determining an abnormal trajectory in the real-time trajectory data based on an anomaly detection algorithm and the future trajectory data; and determining that the preset warning condition is satisfied in response to the presence of the abnormal trajectory.

[0015] In some embodiments, the method further includes: constructing a trajectory network map based on the historical trajectory data; determining the path analysis data of the aircraft based on the trajectory network map using a preset analysis algorithm; and performing an interactive visualization of the path analysis data; wherein the preset analysis algorithm includes: determining the data to be eliminated based on the trajectory network map and eliminating it to obtain clean data; mapping the spatial nodes in the clean data to a geographic grid based on the clean data and a preset matching algorithm to obtain raster data; performing trajectory processing based on the raster data to generate path analysis data, wherein the trajectory processing includes trajectory line type generation, trajectory analysis, trajectory point densification or sparseness, motion feature calculation, and trajectory processing; wherein the trajectory in the trajectory line type generation is created based on multiple data sources, and the trajectory analysis includes detecting stop points, segmenting trajectories, and simplifying and aggregating trajectories; the motion feature calculation includes calculating trajectory speed, trajectory direction, and acceleration; and the trajectory processing includes cleaning, simplification, generalization, and aggregation of trajectories.

[0016] Another aspect of an embodiment of the present specification provides an aircraft taxiing trajectory management system, including: a first acquisition module, configured to acquire path management data, the path management data including equipment data and airport environment data corresponding to the aircraft, the equipment data including current setting data, historical trajectory data and / or current position data of the aircraft; a prediction module, configured to predict future trajectory data of the aircraft at a future point in time based on the path management data; a second acquisition module, configured to acquire real-time trajectory data of the aircraft at the future point in time; a judgment module, configured to judge whether the real-time trajectory data and the future trajectory data meet a preset warning condition; and a warning module, configured to issue a warning when the real-time trajectory data and the future trajectory data meet the preset warning condition.

[0017] Another aspect of an embodiment of the present specification provides an aircraft taxi trajectory management device, the device comprising at least one processor and at least one memory; the at least one memory is used to store computer instructions; the at least one processor is used to execute at least part of the computer instructions to implement an aircraft taxi trajectory management method.

[0018] Another aspect of the embodiments of the present specification provides a computer-readable storage medium, wherein the storage medium stores computer instructions, and when the computer instructions are executed by a computer, a method for managing an aircraft taxiing trajectory is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0020] Figure 1 is a system module diagram of an aircraft taxiing trajectory management system according to some embodiments of this specification;

[0021] Figure 2 is an exemplary flow chart of an aircraft taxiing trajectory management method according to some embodiments of this specification;

[0022] Figure 3 is an exemplary flow chart of a data preprocessing algorithm according to some embodiments of this specification;

[0023] Figure 4 It is an exemplary schematic diagram of the data display process shown in some embodiments of this specification. DETAILED DESCRIPTION

[0024] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0025] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0026] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0027] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0028] As the number of flights increases, the efficiency of the airport ground taxi network decreases, and there is a potential risk of conflict. For example, conflicts and runway blockages may occur during the taxiing process of aircraft. And when the aircraft deviates from the track, emergency situations such as chaos may occur. Therefore, it is necessary to provide an aircraft taxiing trajectory management system that can detect and warn of possible conflicts that may occur during the taxiing process of aircraft. At the same time, in order to ensure the smooth taxiing route of the aircraft, the aircraft taxiing trajectory management system needs to detect and prompt possible road blockages or other factors that affect passability and issue corresponding warnings, so as to reasonably plan the taxiing path of the aircraft during busy periods to improve the efficiency of airport operations. At the same time, it can also help airport managers and pilots make correct decisions.

[0029] Figure 1 It is a system module diagram of an aircraft taxi trajectory management system according to some embodiments of the present specification.

[0030] In some embodiments, the aircraft taxiing trajectory management system 100 may include a first acquisition module 110, a prediction module 120, a second acquisition module 130, a determination module 140, and an early warning module 150. In some embodiments, some or all modules of the aircraft taxiing trajectory management system 100 may be integrated into a processor.

[0031] The first acquisition module is configured to acquire path management data, wherein the path management data includes equipment data corresponding to the aircraft and airport environment data, and the equipment data includes current setting data, historical trajectory data and / or current location data of the aircraft.

[0032] In some embodiments, the first acquisition module is further configured to acquire path management basic data; and preprocess the path management basic data based on a data preprocessing algorithm to obtain the path management data.

[0033] In some embodiments, the first acquisition module is further configured to acquire basic information of the path management basic data, the basic information including at least one of a column name, a data type, and missing values; based on the basic information, determine a data cleaning method, the data cleaning method including at least one of filling missing values, removing duplicate data, and correcting erroneous data; clean the path management basic data based on the data cleaning method to obtain cleaned data; convert the cleaned data into a preset data type to obtain converted data; export the converted data based on a preset export format to obtain the path management data.

[0034] In some embodiments, the first acquisition module is also configured to determine whether the preset data in the converted data is in a preset format and whether the distribution characteristics of the preset data meet the preset integration conditions; in response to the preset data not being in the preset format, converting the data format of the preset data into the preset format based on a preset service; in response to the distribution characteristics of the preset data meeting the preset integration conditions, integrating the preset data; and creating a new data column to record the preset data.

[0035] In some embodiments, the first acquisition module is further configured to acquire a first reference coordinate system of the aircraft and a second reference coordinate system of an airport management system; in response to the first reference coordinate system and the second reference coordinate system not being the same reference coordinate system, convert the first reference coordinate system and the second reference coordinate system into the same target coordinate system; acquire historical trajectory data and / or current position data of the aircraft in the target coordinate system, and acquire position data of the airport buildings and / or position data of airport equipment in the target coordinate system.

[0036] The prediction module is configured to predict future trajectory data of the aircraft at a future time point based on the path management data.

[0037] In some embodiments, the prediction module is also configured to determine the airport space and the flight space corresponding to the aircraft based on the path management data; determine the spatial characteristics and spatial relationships of the airport space and the flight space; the spatial characteristics include data distribution and data patterns, and the spatial relationships include overlapping areas and adjacent areas; based on the spatial characteristics and spatial relationships of the airport space and the flight space, determine the spatial data summary, spatial object relationship, and spatial connection relationship of the airport space and the flight space; based on the spatial data summary, spatial object relationship, and spatial connection relationship of the airport space and the flight space, predict the future trajectory data of the aircraft at a future point in time.

[0038] In some embodiments, the prediction module is also configured to predict the future trajectory data of the aircraft at a future point in time through a prediction model based on the spatial data summary of the airport space and the flight space, the spatial object relationship, the spatial connection relationship, and the equipment data and airport environment data corresponding to the aircraft, and the prediction model is a machine learning model.

[0039] In some embodiments, the prediction module is further configured to determine spatial relationship characteristics based on the spatial data summary, spatial object relationship, and spatial connection relationship of the airport space and the flight space; based on the spatial relationship characteristics, use the spatial index to query the spatial relationship characteristics; the query of the spatial relationship characteristics includes intersection query, inclusion relationship query, overlapping query, adjacent query, covering query, covered query, cross query, distance query, buffer zone query; based on the query results, predict the future trajectory data of the aircraft at a future point in time.

[0040] In some embodiments, the prediction module is further configured to determine a target space search range; construct an R-tree index based on the path management data; and query spatial relationship features based on the R-tree index.

[0041] In some embodiments, the prediction module is also configured to create a static map and a dynamic map based on the query results; predict the future trajectory data of the aircraft at a future point in time based on the static map and the dynamic map; wherein creating a static map includes: creating a chart and an axis; determining the center distance of spatial objects within the airport based on the query results; transferring the center distance and spatial objects to the axis to determine the airport spatial data; obtaining basic map data, the basic map data including coastline, boundaries, grid lines, and longitude and latitude label data; generating a static map based on the basic map data and airport spatial data; creating a dynamic map includes: determining single-frame data information based on the query results; wherein the time interval between two adjacent frames is determined based on a first preset parameter, and the sequence of each frame is determined based on a second preset parameter; removing the current axis content in the single-frame static map, and adding the information of the dynamic element in the current frame to obtain the current frame; obtaining the dynamic map based on the current frames corresponding to several moments.

[0042] In some embodiments, the prediction module is further configured to predict the future trajectory data of the aircraft at a future time point based on the path management data using a time series analysis algorithm or a regression algorithm.

[0043] The second acquisition module is configured to acquire the real-time trajectory data of the aircraft at the future time point.

[0044] The judgment module is configured to judge whether the real-time trajectory data and the future trajectory data meet a preset warning condition.

[0045] In some embodiments, the judgment module is further configured to determine an abnormal trajectory in the real-time trajectory data based on an abnormality detection algorithm and the future trajectory data; and in response to the existence of the abnormal trajectory, determine that a preset warning condition is met.

[0046] The early warning module is configured to issue an early warning when the real-time trajectory data and the future trajectory data meet a preset early warning condition.

[0047] In some embodiments, the aircraft taxiing trajectory management system further includes a construction module, a determination module, and a display module. The construction module is configured to construct a trajectory network map based on the historical trajectory data; the determination module is configured to determine the path analysis data of the aircraft based on the trajectory network map using a preset analysis algorithm; and the display module is configured to perform an interactive visual display of the path analysis data.

[0048] Through the above modules and their functions, the aircraft taxiing trajectory management system can significantly improve the safety and efficiency of airport surface operations, reduce accidents and conflicts, and ensure the normal operation of flights. For a detailed description of the aircraft taxiing trajectory management system and the functions of its various modules, please refer to this manual. Figure 2-Figure 4 Related content.

[0049] It should be understood that Figure 1 The system and its modules shown can be implemented in various ways. For example, by hardware, software, or a combination of software and hardware. The system and its modules of this specification can be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, but also by software implemented by, for example, various types of processors, and can also be implemented by a combination of the above hardware circuits and software (e.g., firmware).

[0050] It should be noted that the above description of the aircraft taxiing trajectory management system and its modules is only for the convenience of description and does not limit the present specification to the scope of the embodiments. It is understandable that for those skilled in the art, after understanding the principle of the system, it is possible to arbitrarily combine the modules or form a subsystem to connect with other modules without deviating from the principle. In some embodiments, Figure 1 The first acquisition module 110, prediction module 120, second acquisition module 130, judgment module 140, and early warning module 150 disclosed in the specification can be different modules in a system, or one module can realize the functions of two or more modules. For example, each module can share a storage module, or each module can have its own storage module. Such variations are all within the protection scope of this specification.

[0051] Figure 2 is an exemplary flow chart of an aircraft taxiing trajectory management method according to some embodiments of this specification. Figure 2 As shown, the process 200 includes the following steps. In some embodiments, the process 200 may be executed based on an aircraft taxi trajectory management system or a processor.

[0052] Step 210, obtaining path management data.

[0053] In some embodiments, the path management data includes equipment data corresponding to the aircraft and airport environment data, wherein the equipment data includes current setting data, historical trajectory data and / or current location data of the aircraft. The airport environment data may include surrounding environment, road conditions, obstacle detection conditions, weather information, etc.

[0054] The processor can obtain the equipment data based on reading the log data of the aircraft control system. The processor can also obtain the airport environment data based on the airport monitoring data, airport database, weather forecast software, etc.

[0055] In some embodiments, the processor may obtain basic path management data, and preprocess the basic path management data based on a data preprocessing algorithm to obtain the path management data. Figure 3 Related content.

[0056] In some embodiments, the airport environment data includes the location data of airport buildings and / or the location data of airport equipment. The processor may also obtain the first reference coordinate system of the aircraft and the second reference coordinate system of the airport management system, and in response to the first reference coordinate system and the second reference coordinate system not being the same reference coordinate system, convert the first reference coordinate system and the second reference coordinate system into the same target coordinate system, obtain the historical trajectory data and / or current location data of the aircraft in the target coordinate system, and obtain the location data of the airport buildings and / or the location data of the airport equipment in the target coordinate system.

[0057] Just as an example, when the coordinates of the aircraft and the airport are in different coordinate reference systems, the processor can perform coordinate system conversion based on tools such as Pyproj so that the data are in the same reference system.

[0058] Step 220 , predicting future trajectory data of the aircraft at a future time point based on the path management data.

[0059] In some embodiments, the processor can determine the airport space and the flight space corresponding to the aircraft based on the path management data; determine the spatial characteristics and spatial relationships of the airport space and the flight space; the spatial characteristics include data distribution and data patterns, and the spatial relationships include overlapping areas and adjacent areas; based on the spatial characteristics and spatial relationships of the airport space and the flight space, determine the spatial data summary, spatial object relationship, and spatial connection relationship of the airport space and the flight space; based on the spatial data summary, spatial object relationship, and spatial connection relationship of the airport space and the flight space, predict the future trajectory data of the aircraft at a future point in time.

[0060] For example, the processor can use Geopandas to read and write the previously processed path management data and perform geospatial-specific analysis on the data, such as spatial connections, overlay analysis, etc., to better understand the distribution, characteristics, and patterns of spatial data. By way of example only, the processor can identify and quantify the relationships between spatial objects, such as which areas overlap or are adjacent to each other, to determine spatial relationships. The processor can also discover patterns in the data, such as hot spots, clustering phenomena, or trends in spatial distribution, to determine spatial features.

[0061] In some embodiments, based on the above analysis, a specific spatial data summary and the relationship between various spatial objects can be obtained; at the same time, the geographic spatial data can be merged with other data sets and connected based on spatial relationships.

[0062] After the above processing, the processor can predict the future trajectory data of the aircraft at a future point in time based on the spatial data summary, spatial object relationship, and spatial connection relationship of the airport space and the flight space.

[0063] For example, the processor may predict the future trajectory data of the aircraft at a future time point through a prediction model based on the spatial data summary of the airport space and the flight space, the spatial object relationship, the spatial connection relationship, the equipment data corresponding to the aircraft, and the airport environment data, wherein the prediction model is a machine learning model. For example, the prediction model is a convolutional neural network model, etc.

[0064] The input of the prediction model includes the spatial data summary of the airport space and the flight space, the spatial object relationship, the spatial connection relationship, and the equipment data and airport environment data corresponding to the aircraft. The output of the prediction model includes the future trajectory data of the aircraft at a future point in time.

[0065] In some embodiments, the prediction model can be trained based on a training data set, and the training data set can include multiple training samples and labels corresponding to the training samples. Specifically, the training samples with labels are input into the prediction model, and the parameters of the prediction model are updated through training.

[0066] The training sample may include the sample space data summary, sample space object relationship, sample space connection relationship, and sample equipment data and sample airport environment data corresponding to the sample aircraft at the first historical time point of the historical airport space and the historical flight space. The label corresponding to the training sample may be the actual trajectory data of the sample aircraft at the second historical time point. The first historical time point is before the second historical time point.

[0067] In some embodiments, the processor may train the prediction model through multiple rounds of iterations: in each round of iterations, the processor selects training samples from the training data set, inputs the prediction model and obtains the prediction output, calculates the loss function value based on the prediction output and the loss function, and uses the back propagation algorithm to update the model parameters to minimize the loss. The iteration is performed until the end condition is met (such as reaching a specified number of iterations or the loss function converges), and the model training ends.

[0068] In some embodiments, the processor may also determine spatial relationship characteristics based on the spatial data summary, spatial object relationship, and spatial connection relationship of the airport space and the flight space; based on the spatial relationship characteristics, use the spatial index to query the spatial relationship characteristics; the query of the spatial relationship characteristics includes intersection query, inclusion relationship query, overlapping query, adjacent query, covering query, covered query, cross query, distance query, and buffer zone query; based on the query results, predict the future trajectory data of the aircraft at a future point in time.

[0069] As an example only, the processor may determine the spatial relationship feature based on a variety of methods. For example, using a spatial selection method, etc. In some embodiments, when querying the spatial relationship feature, the processor may determine the target space search range, build an R-tree index based on the path management data, and query the spatial relationship feature based on the R-tree index.

[0070] For example, the processor can accelerate queries based on spatial relationships based on the creation of spatial indexes. In some embodiments, the processor can optimize large-scale spatial searches of airports using R-trees. R-trees are tree-like data structures used for spatial data indexing, which can effectively handle query problems in multidimensional spaces, especially in large-scale spatial searches.

[0071] As an example, the processor can use spatial data to build an R-tree index. This step is key to optimizing the search process because the quality of index construction directly affects search efficiency. It includes determining the spatial range you want to search, and then using the R-tree index to perform spatial queries. Among them, spatial queries can be based on Geopandas and other methods.

[0072] In some embodiments of this specification, the performance of spatial relationship query may be affected by the size and complexity of the data set, so when processing large data sets, using spatial indexes can improve query efficiency. Querying spatial relationship features through R-tree indexes can significantly reduce the search space and quickly exclude irrelevant areas through the tree structure, thereby speeding up the search.

[0073] In some embodiments, an intersection query refers to checking whether a geometric object intersects with another geometric object; a containment relationship query refers to checking whether a geometric object is completely contained in another geometric object or checking whether a geometric object completely contains another geometric object; an overlap query refers to checking whether two geometric objects overlap in space; an adjacent query refers to checking whether two geometric objects are adjacent; a covering query refers to checking whether a geometric object covers another geometric object; a covered query refers to checking whether a geometric object is covered by another geometric object; a cross query refers to checking whether a geometric object intersects with another geometric object; a distance query refers to calculating the distance between two geometric objects; a buffer query refers to creating a buffer for a geometric object, which can be used to check whether other objects are within the buffer.

[0074] The above query methods can be applied directly to the geometry column of a GeoDataFrame to perform spatial relationship queries.

[0075] In some embodiments, the processor may also predict the future trajectory data of the aircraft at a future time point based on other methods. For example, the processor may create a static map and a dynamic map based on the query results, and predict the future trajectory data of the aircraft at a future time point based on the static map and the dynamic map.

[0076] Among them, creating a static map includes: creating a chart and an axis; determining the center distance of the spatial objects in the airport based on the query results; transferring the center distance and the spatial objects to the axis to determine the airport spatial data; obtaining basic map data, the basic map data including coastline, boundaries, grid lines, longitude and latitude label data; generating a static map based on the basic map data and airport spatial data; creating a dynamic map includes: determining single-frame data information based on the query results; wherein the time interval between two adjacent frames is determined based on a first preset parameter, and the sequence of each frame is determined based on a second preset parameter; removing the current axis content in the single-frame static map, and adding the information of the dynamic element in the current frame to obtain the current frame; obtaining the dynamic map based on the current frames corresponding to several moments.

[0077] In some embodiments, the processor can predict the future trajectory data of the aircraft at a future point in time based on the static map and the dynamic map; wherein, creating a static map includes: creating a chart and an axis; determining the center distance of spatial objects within the airport based on the query results; transferring the center distance and spatial objects to the axis to determine the airport spatial data; obtaining basic map data, the basic map data including coastline, boundaries, grid lines, longitude and latitude label data; generating a static map based on the basic map data and airport spatial data; creating a dynamic map includes: determining single-frame data information based on the query results; wherein, the time interval between two adjacent frames is determined based on a first preset parameter, and the sequence of each frame is determined based on a second preset parameter; removing the current axis content in the single-frame static map, and adding the information of the dynamic element in the current frame to obtain the current frame; obtaining the dynamic map based on the current frames corresponding to several moments.

[0078] In some embodiments, the static map is a map of fixed buildings and equipment at the airport, and the dynamic map is a map based on real-time updated data of the aircraft. The processor can construct the above map in a variety of ways. For example, the processor can use Geopy to convert the position of the aircraft into longitude and latitude coordinates and calculate the relative distance, and finally use Matplotlib and Cartopy to draw the analysis results to create static and dynamic maps.

[0079] In some embodiments, the processor can import Cartopy and Matplotlib, use Matplotlib to create charts and axes, and set up the map by passing the corresponding CRS to the axes. Use the methods provided by Cartopy to add coastlines, boundaries, grid lines, longitude and latitude labels, etc., draw the geospatial data on the map, and then obtain a static map. Among them, the processor can also add custom elements such as titles, legends, colors, etc. as needed, and use Matplotlib's `savefig` method to save the map as an image file, or use the `show` method to display the map.

[0080] In some embodiments, the processor can combine Cartopy with the animation function of Matplotlib to create a dynamic map. For example, the processor can generate animation based on the `animation` module provided by Matplotlib, and define an update function `update` to clear all the contents of the current axis in each frame and redraw some elements that change over time. Among them, `FuncAnimation` is used to generate animation, the `frames` parameter specifies the sequence of animation frames, and the `interval` parameter defines the time interval between frames.

[0081] In some embodiments, according to actual needs, the processor can use Folium to generate an interactive web map for display on a web page. Among them, Folium can generate interactive maps, and users can pan, zoom, and interact with elements such as markers and pop-up windows on the map, allowing users to customize the style of the map, including color, size, icons, etc. After saving the map as an HTML file, the map can be viewed in any browser that supports HTML, allowing the map to be displayed to third parties.

[0082] In the above map generation method, the processor can perform complex scientific calculations in the analysis based on Scipy, and use Numpy as the numerical calculation foundation of the entire workflow, providing support for Pandas and Scipy. SciPy and Numpy, as basic scientific computing libraries, provide underlying support for the entire system.

[0083] In some embodiments, the processor may predict future trajectory data of the aircraft at a future time point based on the path management data using a time series analysis algorithm or a regression algorithm.

[0084] For example, the processor can use Geopy to convert the position of the aircraft into longitude and latitude coordinates and calculate the relative distance, and use Scikit-learn to perform pattern recognition and deep learning analysis of the trajectory data based on the trajectory data processed by Geopandas. As an example only, the processor can use an anomaly detection algorithm to identify abnormal aircraft trajectories, and can use time series analysis or regression algorithms to predict the future position and trajectory of the aircraft.

[0085] Step 230, obtaining the real-time trajectory data of the aircraft at the future time point.

[0086] In some embodiments, when the time reaches a future time point, the processor may obtain the real-time trajectory data of the aircraft at the future time point based on the control system or positioning software of the aircraft.

[0087] Step 240: determine whether the real-time trajectory data and the future trajectory data meet a preset warning condition.

[0088] For example, the processor may determine the positioning deviation between the real-time trajectory data and the future trajectory data. If the deviation is greater than a deviation threshold, it is considered that a preset warning condition is met.

[0089] In some embodiments, the processor may further determine an abnormal trajectory in the real-time trajectory data based on an anomaly detection algorithm and the future trajectory data, and determine that a preset warning condition is satisfied in response to the presence of the abnormal trajectory. For example, the processor may use a combination of NetworkX and Scikit-learn to analyze and display aircraft trajectory networks and path analysis. For more information, see Figure 4 corresponding instructions.

[0090] Step 250, in response to satisfaction, an early warning is issued.

[0091] In some embodiments, the processor may issue an early warning when the real-time trajectory data and the future trajectory data meet preset early warning conditions, alerting the abnormality and helping airport managers and pilots make correct decisions.

[0092] Figure 3 is an exemplary flow chart of a data preprocessing algorithm according to some embodiments of this specification. Figure 3 As shown, the process 300 includes the following steps. In some embodiments, the process 300 may be executed based on an aircraft taxi trajectory management system or a processor.

[0093] Step 310: Acquire basic information of the path management basic data.

[0094] For example, the processor may use GDAL to read the geospatial data of aircraft and airports, and then use Pandas to perform data preprocessing to obtain basic information of the path management basic data, wherein the basic information includes at least one of column names, data types, and missing values. For example, the basic information is obtained by checking the first few rows of the data.

[0095] Step 320: Determine a data cleaning method based on the basic information.

[0096] Among them, data cleaning methods can include processing missing values, removing duplicate data, correcting erroneous data, etc.

[0097] The processor can determine the required cleaning method based on the basic information. For example, when there are missing values ​​in the data, the missing values ​​need to be processed.

[0098] Handling missing values ​​can include dropping rows with missing values ​​using dropna() or filling missing values ​​with fillna(). Deduplication can include dropping duplicate rows using drop_duplicates(), and correcting erroneous data can include correcting some obviously erroneous data based on business logic.

[0099] Step 330: clean the path management basic data based on the data cleaning method to obtain cleaned data.

[0100] Step 340: convert the cleaned data into a preset data type to obtain converted data.

[0101] In some embodiments, converting the cleaned data into a preset data type to obtain converted data also includes: determining whether the preset data in the converted data is in a preset format and whether the distribution characteristics of the preset data meet preset integration conditions; in response to the preset data not being in the preset format, converting the data format of the preset data into the preset format based on a preset service; in response to the distribution characteristics of the preset data meeting the preset integration conditions, integrating the preset data; and creating a new data column to record the preset data.

[0102] For example, the processor can convert data types: ensure that the column of location data is the correct data type, for example, converting longitude and latitude to floating point numbers. For another example, the processor can create new columns as needed to record information such as longitude and latitude parsed from the address. For another example, if the data contains address information instead of longitude and latitude, the processor can use a geocoding service to convert the address to coordinates. For another example, if the location data is scattered in multiple tables or sources, the processor can use Pandas's `merge` or `join` method to integrate the data.

[0103] Step 350: export the converted data based on a preset export format to obtain the path management data.

[0104] After completing the above processing, the processor can export the data into different formats for further analysis or archiving.

[0105] It should be noted that the above description of the process 200 and the process 300 is only for example and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the process 200 and the process 300 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0106] like Figure 4 This is an exemplary schematic diagram of the data display process shown in some embodiments of this specification. Figure 4As shown, the processor can construct a trajectory network map 420 based on the historical trajectory data 410; determine the path analysis data 430 of the aircraft based on the trajectory network map using a preset analysis algorithm; and perform an interactive visualization 440 of the path analysis data.

[0107] Among them, the preset analysis algorithm includes: based on the trajectory network map, determining the data to be eliminated and eliminating it to obtain clean data; based on the clean data and the preset matching algorithm, mapping the spatial nodes in the clean data to the geographic grid to obtain raster data; performing trajectory processing based on the raster data to generate path analysis data, and the trajectory processing includes trajectory line type generation, trajectory analysis, trajectory point densification or sparseness, motion feature calculation, and trajectory processing; wherein the trajectory in the trajectory line type generation is created based on multiple data sources, and the trajectory analysis includes detecting stop points, segmenting trajectories, and simplifying and aggregating trajectories; the motion feature calculation includes calculating trajectory speed, trajectory direction, and acceleration; the trajectory processing includes cleaning, simplification, generalization, and aggregation of trajectories.

[0108] For example, the processor can combine Scikit-learn with NetworkX to regard the trajectory of the aircraft as a node 421 in the network, and construct the interaction or connection relationship between the trajectories as edges 422 to form a trajectory network map. The processor can also use the shortest path algorithm of NetworkX, such as Dijkstra or A* algorithm, to analyze the taxiing path of the aircraft.

[0109] In some embodiments, if the aircraft trajectory data is time-series, NetworkX can be used to analyze network structure and patterns over time.

[0110] By using NetworkX to fit graph theory models, such as small-world networks and scale-free networks, the network characteristics of aircraft trajectories can be reflected and assisted in understanding. Abnormal flight behaviors can be identified by analyzing abnormal connections or structural features in the network. Among them, NetworkX provides basic network visualization functions that can be used to display the flight trajectory and network structure of aircraft.

[0111] In some embodiments, the processor can use TransBigData and MovingPandas in combination to achieve more comprehensive data processing and analysis. For example, the processor can quickly calculate basic information such as data volume, time period, sampling interval, etc. based on TransBigData, and provide data cleaning methods, such as eliminating data outside the research scope, and provide methods for generating and matching various types of geographic rasters, mapping spatial point data to geographic rasters, and realizing interactive visualization of data based on keplergl.

[0112] The processor can create trajectories from a variety of data sources, such as CSV files, GIS file formats, DataFrames, etc.

[0113] Through the above processing, it is possible to more effectively process and analyze the trajectory data of the aircraft, thereby improving the accuracy and efficiency of trajectory prediction.

[0114] In some embodiments, the processor can use Matplotlib and Cartopy to plot the analysis results, create static maps of existing trajectory data and maps generated based on predicted trajectories, and if necessary, use Folium to generate an interactive aircraft trajectory map for display on a web page.

[0115] Scipy can be used to perform more complex scientific calculations in analysis. Numpy, as the numerical computing foundation of the entire workflow, provides support for Pandas and Scipy.

[0116] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0117] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0118] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0119] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0120] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0121] For each patent, patent application, patent application disclosure and other materials cited in this specification, such as articles, books, specifications, publications, documents, etc., the entire contents are hereby incorporated into this specification as a reference. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the description, definition, and / or use of terms in the attached materials of this specification are inconsistent or conflicting with the contents described in this specification, the description, definition and / or use of terms in this specification shall prevail. Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, the alternative configurations of the embodiments of this specification may be considered to be consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the explicit introduction and description of this specification.

Claims

1. A method for managing an aircraft taxiing trajectory, characterized in that: include: Acquire path management data, wherein the path management data includes equipment data corresponding to the aircraft and airport environment data, and the equipment data includes current setting data, historical trajectory data and / or current position data of the aircraft; Predicting future trajectory data of the aircraft at a future time point based on the path management data; Acquire real-time trajectory data of the aircraft at the future time point; Determining whether the real-time trajectory data and the future trajectory data meet a preset warning condition; In response to satisfaction, an early warning is issued.

2. The method according to claim 1, characterized in that The acquiring of path management data comprises: Get basic data for route management; The path management basic data is preprocessed based on a data preprocessing algorithm to obtain the path management data.

3. The method according to claim 2, characterized in that The data preprocessing algorithm includes: Acquire basic information of the path management basic data, wherein the basic information includes at least one of a column name, a data type, and a missing value; Based on the basic information, determine a data cleaning method, wherein the data cleaning method includes at least one of filling missing values, removing duplicate data, and correcting erroneous data; Cleaning the path management basic data based on the data cleaning method to obtain cleaned data; Converting the cleaned data into a preset data type to obtain converted data; Based on a preset export format, the converted data is exported to obtain the path management data.

4. The method according to claim 3, characterized in that The step of converting the cleaned data into a preset data type to obtain converted data further comprises: Determining whether the preset data in the converted data is in a preset format and whether the distribution characteristics of the preset data meet a preset integration condition; In response to the preset data not being in the preset format, converting the data format of the preset data into the preset format based on a preset service; In response to the distribution characteristics of the preset data satisfying a preset integration condition, integrating the preset data; A new data column is created to record the preset data.

5. The method according to claim 4, characterized in that The airport environment data includes location data of airport buildings and / or location data of airport equipment; The acquiring of path management data comprises: Acquire a first reference coordinate system of the aircraft and a second reference coordinate system of an airport management system; In response to the first reference coordinate system and the second reference coordinate system not being the same reference coordinate system, converting the first reference coordinate system and the second reference coordinate system into the same target coordinate system; The historical trajectory data and / or the current position data of the aircraft in the target coordinate system are obtained, and the position data of the airport building and / or the position data of the airport equipment in the target coordinate system are obtained.

6. The method according to claim 5, characterized in that The predicting of future trajectory data of the aircraft at a future time point based on the path management data includes: Determining the airport space and the flight space corresponding to the aircraft based on the path management data; Determine the spatial characteristics and spatial relationship of the airport space and the flight space; the spatial characteristics include data distribution and data mode, and the spatial relationship includes overlapping areas and adjacent areas; Determine the spatial data summary, spatial object relationship, and spatial connection relationship of the airport space and the flight space based on the spatial characteristics and spatial relationship of the airport space and the flight space; Based on the spatial data summaries, spatial object relationships, and spatial connection relationships of the airport space and the flight space, future trajectory data of the aircraft at a future time point is predicted.

7. The method according to claim 6, characterized in that The predicting of the future trajectory data of the aircraft at a future time point based on the spatial data summary, spatial object relationship, and spatial connection relationship of the airport space and the flight space includes: Determine spatial relationship features based on spatial data summaries, spatial object relationships, and spatial connection relationships of the airport space and the flight space; Based on spatial relationship features, spatial index is used to query spatial relationship features; The query of the spatial relationship feature includes intersection query, inclusion relationship query, overlap query, adjacent query, covering query, covered query, intersection query, distance query, and buffer zone query; Based on the query result, future trajectory data of the aircraft at a future time point is predicted.

8. An aircraft taxiing trajectory management system, characterized in that: include: A first acquisition module is configured to acquire path management data, wherein the path management data includes equipment data corresponding to the aircraft and airport environment data, and the equipment data includes current setting data, historical trajectory data and / or current position data of the aircraft; A prediction module, configured to predict future trajectory data of the aircraft at a future time point based on the path management data; A second acquisition module is configured to acquire real-time trajectory data of the aircraft at the future time point; A judgment module is configured to judge whether the real-time trajectory data and the future trajectory data meet a preset warning condition; The early warning module is configured to issue an early warning when the real-time trajectory data and the future trajectory data meet a preset early warning condition.

9. An aircraft taxiing trajectory management device, characterized in that: The apparatus comprises at least one processor and at least one memory; The at least one memory is used to store computer instructions; The at least one processor is used to execute at least part of the computer instructions to implement the aircraft taxi trajectory management method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores computer instructions. When a computer reads the computer instructions, the computer executes the aircraft taxiing trajectory management method as described in any one of claims 1 to 7.

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