An airport geographic information data management method, system, device and medium

Abstract

An airport geographic information data management method, system, device and medium, which relates to the field of data management. In this method, a three-dimensional geographic base is constructed based on multi-source heterogeneous geographic data; sensor data is aligned and fused with the three-dimensional geographic base in terms of time and space to obtain a digital twin model; an interaction relationship between passenger movement and aircraft taxiing paths is established through a spatio-temporal association map; the passenger flow prediction results, aircraft taxiing paths, interaction relationship and digital twin model are combined to deduce the airport operation situation, and the deduced data is encrypted and stored through a private blockchain; in response to receiving a user's interaction instruction, the user's permission is identified, and target data matching the interaction instruction is selected from the deduced data based on the permission and displayed to the user. Implementing the technical solution provided by this application achieves the effects of improving airport operation efficiency, optimizing resource scheduling and enhancing safety.

Description

Technical Field

[0001] This application relates to the technical field of data management, and particularly relates to an airport geographic information data management method, system, device, and medium. Background Art

[0002] In recent years, the technology of airport geographic information data management has developed rapidly, and it has played an important role in improving airport operation efficiency and ensuring flight safety. This technology not only improves the utilization rate of airport resources, but also provides a more convenient service experience for passengers. At the same time, in the context of the rapid development of the global aviation industry, it promotes the overall process of smart airport construction.

[0003] Currently, for the problems of airport geographic information data management, the commonly used methods in the industry include: one is to integrate geographic information from different sources onto a two-dimensional map through a simple data overlay method for basic navigation and display; the other is to combine sensor data with traditional GIS (Geographic Information System) to achieve dynamic monitoring of local areas. Although these methods solve some problems to a certain extent, they have obvious deficiencies in dealing with real-time data fusion, establishing dynamic interaction relationships, and ensuring data security, resulting in limited airport operation efficiency and unable to meet the growing intelligent needs. Summary of the Invention

[0004] This application provides an airport geographic information data management method, system, device, and medium, which improves airport operation efficiency, optimizes resource scheduling, and enhances data security.

[0005] In the first aspect of this application, an airport geographic information data management method is provided, which is applied to an airport geographic information data management platform. The method includes:

[0006] Obtain multi-source heterogeneous geographic data, and construct a three-dimensional geographic base according to the multi-source heterogeneous geographic data. The multi-source heterogeneous geographic data includes airport drawing data, sensor coordinate data, point cloud data, and satellite images;

[0007] Obtain sensor data through edge computing nodes within the preset range of sensors, and align and fuse the sensor data with the three-dimensional geographic base in terms of time and space to obtain a digital twin model. The digital twin model includes real-time runway status, meteorological impact area, and equipment location heat map;

[0008] Obtain a passenger flow prediction result according to flight information and historical passenger flow data, predict the aircraft taxiing path according to the parking position allocation, the position of ground support vehicles, and the runway occupancy status, and establish an interaction relationship between passenger movement and the aircraft taxiing path through a spatio-temporal association graph;

[0009] Combine the predicted passenger flow results, the aircraft taxiing path, the interaction relationship with the digital twin model to deduce the airport operation situation, and encrypt and store the deduced data through a private blockchain;

[0010] In response to receiving an interaction instruction from a user, identify the user's permission, select target data matching the interaction instruction from the deduced data based on the permission, and display it to the user.

[0011] Optionally, the process of aligning and fusing the sensor data with the three-dimensional geographical base to obtain a digital twin model includes:

[0012] Use spatial interpolation and time synchronization algorithms to align the timestamps of the sensor data with the time reference of the three-dimensional geographical base, and use a spatial matching algorithm to match the spatial coordinates of the sensor data with the geographical entities in the three-dimensional geographical base to obtain the aligned sensor data;

[0013] Fuse the aligned sensor data with the three-dimensional geographical base, and add dynamic information to the three-dimensional geographical base to generate a digital twin model, where the dynamic information includes real-time runway status, meteorological impact areas, and equipment location heat maps.

[0014] Optionally, the process of predicting the aircraft taxiing path based on parking position allocation, ground support vehicle positions, and runway occupancy status includes:

[0015] Analyze the parking position allocation information to determine the starting parking position and target runway position of each flight;

[0016] Based on the starting parking position and the target runway position, combined with the ground support vehicle position information, determine a candidate set of aircraft taxiing paths, where the ground support vehicle position information includes vehicle type, real-time coordinates, and driving direction;

[0017] According to the preset taxiing time and runway occupancy status information, screen out candidate paths from the candidate set of aircraft taxiing paths, where the runway occupancy status information includes whether the runway is occupied during the preset taxiing time period, and the estimated takeoff or landing time of the occupied flight;

[0018] Based on the taxiway layout information of the airport, obstacle information, the taxiing speed and acceleration of the aircraft, calculate the target taxiing path from the starting parking position to the target runway position, where the taxiway layout information includes the length, width, slope, and turning radius of the taxiway.

[0019] Optionally, the establishment of the interaction relationship between passenger movement and aircraft taxiing path through the spatio-temporal association map includes:

[0020] Create a graph data structure, map the physical space and time dimension of the airport into the graph data structure to obtain a spatio-temporal association map, where the nodes in the spatio-temporal association map represent different areas of the airport and different positions of the aircraft, and the edges represent the connection relationships between areas and the movement trajectories of the aircraft;

[0021] Map the passenger flow prediction result and the aircraft taxiing path into the spatio-temporal association map to obtain a spatio-temporal risk map with dynamic weights;

[0022] Calculate the influence coefficient between passenger movement and aircraft taxiing path in the spatio-temporal risk map.

[0023] Optionally, the combination of the passenger flow prediction result, the aircraft taxiing path, the interaction relationship and the digital twin model to deduce the airport operation situation includes:

[0024] Establish an interaction relationship between passenger movement and aircraft taxiing path in the digital twin model, start the deduction function of the digital twin model, and simulate the airport operation situation according to the set time step;

[0025] During the deduction process, update the positions and states of passengers and aircraft in real time according to the passenger flow prediction result, the aircraft taxiing path and the interaction relationship;

[0026] Monitor the deduction process in real time, collect key index data, and judge whether the key index data is abnormal. The key index data includes passenger waiting time, aircraft taxiing time, and regional congestion degree;

[0027] When the key index data is abnormal, generate a suggestion message, and send a reminder message and the suggestion message to the target object.

[0028] Optionally, the identification of the user's permission and the selection of target data matching the interaction instruction from the deduction data based on the permission includes:

[0029] Analyze the interaction instruction to determine the intent label, spatio-temporal range and operation type, and verify the user's role through a token;

[0030] Input the user role and the intent label into the airport ontology knowledge graph to obtain an authorized data field white list;

[0031] Determine a set of encrypted data block hashes that meet the permissions from the deduction data according to the authorized data field white list;

[0032] Decrypt the encrypted data block hash set to obtain the target data.

[0033] Optionally, the inputting the user role and the intent label into the airport ontology knowledge graph to obtain the authorized data field whitelist includes:

[0034] Construct an airport ontology knowledge graph according to various entities in the airport and the relationships between various entities. The entities include runways, aircraft, and personnel roles, and the relationships include permission associations and data access rules;

[0035] Construct a query statement according to the user role and the intent label, and screen out a set of data fields associated with the user role and the intent label from the airport ontology knowledge graph according to predefined rules and relationships;

[0036] Check whether the data fields in the data field set are within the target time and space range corresponding to the permissions;

[0037] Organize the data fields within the target time and space range into an authorized data field whitelist.

[0038] In a second aspect of the present application, an airport geographic information data management system is provided, including a collection module, a fusion module, a prediction module, a deduction module, and an interaction module, where:

[0039] The collection module is configured to obtain multi-source heterogeneous geographic data and construct a three-dimensional geographic base according to the multi-source heterogeneous geographic data. The multi-source heterogeneous geographic data includes airport drawing data, sensor coordinate data, point cloud data, and satellite images;

[0040] The fusion module is configured to obtain sensor data through edge computing nodes within a preset range of sensors, and perform time and space alignment and fusion of the sensor data with the three-dimensional geographic base to obtain a digital twin model. The digital twin model includes real-time runway status, meteorological impact areas, and equipment location heat maps;

[0041] The prediction module is configured to obtain a passenger flow prediction result according to flight information and historical passenger flow data, predict the aircraft taxiing path according to the parking position allocation, the position of ground support vehicles, and the runway occupancy status, and establish an interaction relationship between passenger movement and the aircraft taxiing path through a spatio-temporal association map;

[0042] The deduction module is configured to combine the passenger flow prediction result, the aircraft taxiing path, and the interaction relationship with the digital twin model to deduce the airport operation situation, and encrypt and store the deduction data through a private blockchain;

[0043] An interaction module is configured to, in response to receiving an interaction instruction from a user, identify the user's permissions, select target data that matches the interaction instruction from the deduction data based on the permissions, and display the target data to the user.

[0044] In a third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, and both the user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory, so that the electronic device executes the method described in any one of the above.

[0045] In a fourth aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores instructions, and when the instructions are executed, the method described in any one of the above is executed.

[0046] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0047] 1. By acquiring multi-source heterogeneous geographic data such as airport drawing data, sensor coordinate data, point cloud data, and satellite images, different types and sources of geographic information can be integrated to construct a three-dimensional geographic base covering rich details. This provides a comprehensive and accurate basic framework for the management and analysis of airport geographic information, making subsequent operations and analyses based on geographic information more reliable. The sensor data is acquired by edge computing nodes within the preset range of the sensors and aligned and fused with the three-dimensional geographic base in terms of time and space to form a digital twin model. This model contains information such as real-time runway status, meteorological impact areas, and equipment location heat maps, and can reflect the actual operating conditions of the airport in real time, providing an intuitive and dynamic visualization model for the monitoring and management of the airport's operating situation, and helping to detect potential problems and risks in a timely manner;

[0048] 2. Obtaining the passenger flow prediction results based on flight information and historical passenger flow data helps the airport plan resources in advance, such as security check channels, waiting areas, catering services, etc., to cope with the passenger flow peaks at different time periods, improve the airport's operation efficiency and service quality, and avoid safety hazards and service quality degradation caused by passenger flow congestion; Predicting the aircraft taxiing path through information such as parking bay allocation, position of ground support vehicles, and runway occupancy status can optimize the ground operation process of the aircraft, reduce taxiing time and conflicts, improve the flight takeoff and landing efficiency of the airport, and reduce the safety risks of the aircraft during ground operation; Establishing the interaction relationship between passenger movement and aircraft taxiing path through the spatio-temporal correlation graph helps to deeply understand the dynamic correlation between passengers and aircraft in the airport, providing a more comprehensive decision-making basis for resource allocation, service optimization, and emergency response of the airport;

[0049] 3. Combine the passenger flow prediction results, aircraft taxiing paths, interaction relationships with the digital twin model to deduce the airport operation situation. This deduction can simulate the airport operation status under different conditions, help airport management personnel discover potential problems in advance, formulate coping strategies, optimize the airport operation plan and resource allocation, and improve the overall operation efficiency and safety of the airport; Encrypt and store the deduction data through a private blockchain, and utilize the distributed ledger, immutability and encryption characteristics of the blockchain to ensure the security and integrity of the deduction data. This helps prevent data from being maliciously tampered with or leaked, and provides reliable data support for the long-term operation analysis and decision-making of the airport;

[0050] 4. In response to receiving the user's interaction instruction, identify the user's permission, and based on the permission, select the target data that matches the interaction instruction from the deduction data for display. This personalized data display method can meet the needs of different users (such as airport management personnel, air traffic control personnel, security personnel, etc.), provide key information related to their responsibilities, improve the efficiency and accuracy of users' access to information, and help users better perform their duties. Brief Description of the Drawings

[0051] Figure 1 is a schematic flowchart of a method for managing airport geographic information data disclosed in an embodiment of the present application;

[0052] Figure 2 is a schematic block diagram of a system for managing airport geographic information data disclosed in an embodiment of the present application;

[0053] Figure 3 is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application.

[0054] Description of the Reference Numerals: 201, acquisition module; 202, fusion module; 203, prediction module; 204, deduction module; 205, interaction module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Embodiments

[0055] In order to enable those skilled in the art to better understand the technical solutions in this specification, the following will clearly and completely describe the technical solutions in the embodiments of this specification in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments.

[0056] In the description of the embodiments of the present application, words such as "for example" or "for instance" are used to give examples, provide illustrations, or make explanations. Any embodiment or design solution described as "for example" or "for instance" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "for example" or "for instance" is intended to present relevant concepts in a specific manner.

[0057] In the description of the embodiments of the present application, the term "a plurality of" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "comprise", "include", "have" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0058] This embodiment discloses an airport geographic information data management method, which is applied to an airport geographic information data management platform. Figure 1 It is a schematic flowchart of an airport geographic information data management method disclosed in the embodiments of the present application, as Figure 1 shown, the method includes the following steps:

[0059] S101. Obtain multi-source heterogeneous geographic data, and construct a three-dimensional geographic base according to the multi-source heterogeneous geographic data, where the multi-source heterogeneous geographic data includes airport drawing data, sensor coordinate data, point cloud data, and satellite images;

[0060] S102. Obtain sensor data through edge computing nodes within the preset range of the sensors, and perform time and space alignment and fusion of the sensor data with the three-dimensional geographic base to obtain a digital twin model, where the digital twin model includes real-time runway status, meteorological impact areas, and equipment location heat maps;

[0061] S103. Obtain a passenger flow prediction result according to flight information and historical passenger flow data, predict the aircraft taxiing path according to parking position allocation, the position of ground support vehicles, and runway occupancy status, and establish an interaction relationship between passenger movement and the aircraft taxiing path through a spatio-temporal association map;

[0062] S104. Combine the passenger flow prediction result, the aircraft taxiing path, the interaction relationship with the digital twin model to deduce the airport operation situation, and encrypt and store the deduced data through a private blockchain;

[0063] S105. In response to receiving an interaction instruction from a user, identify the user's permissions, select target data that matches the interaction instruction from the deduction data based on the permissions, and display it to the user.

[0064] Obtain multi-source heterogeneous geographical data. The multi-source heterogeneous geographical data has a wide range of sources, including airport drawing data (such as design drawing information of airport layout, runways, taxiways, etc.), sensor coordinate data (location information of various sensors within the airport), point cloud data (a set of three-dimensional space points obtained through laser scanning, etc., which can accurately reflect the surface characteristics of objects), and satellite images (image information of the airport and its surrounding areas obtained from the air). Use the above multi-source heterogeneous geographical data to construct a three-dimensional geographical base, which is the basis for subsequent data fusion and analysis, and provides an intuitive and three-dimensional display framework for the geographical information of the airport. Obtain sensor data through edge computing nodes within the preset range of the sensors. Edge computing nodes can process data near the data source, reduce data transmission latency, and improve the real-time performance of data processing. Sensor data may include real-time data collected by runway status monitoring sensors, meteorological sensors, equipment positioning sensors, etc. Align and fuse the obtained sensor data with the three-dimensional geographical base in terms of time and space. Time alignment ensures that the data from different data sources is consistent in the time dimension, and space alignment ensures the accurate correspondence of the data in the geographical space. Through fusion, a digital twin model is obtained. The digital twin model includes information such as real-time runway status (such as whether the runway is slippery, whether there are obstacles, etc.), meteorological impact areas (the impact range of different meteorological conditions on each area of the airport), and equipment location heat maps (reflecting the distribution and usage frequency of equipment within the airport), and can reflect the operating status of the airport in real time and accurately. According to flight information and historical passenger flow data, use data analysis and prediction algorithms to obtain passenger flow prediction results. Flight information provides key information such as the arrival and departure times and numbers of passengers, and historical passenger flow data helps analyze the changing patterns of passenger flow, so as to more accurately predict future passenger flow conditions. Predict the taxiing path of aircraft based on information such as parking position allocation, ground support vehicle positions, and runway occupancy status. Parking position allocation determines the starting position of the aircraft, the positions of ground support vehicles may affect the taxiing route of the aircraft, and the runway occupancy status restricts the runways available for the aircraft. Considering these factors comprehensively can more reasonably predict the taxiing path of the aircraft. Establish an interaction relationship between passenger movement and aircraft taxiing path through a spatio-temporal correlation map. The spatio-temporal correlation map can visually display the correlation between passengers and aircraft in terms of time and space. For example, how the movement of passengers in the terminal building affects the taxiing, docking, etc. operations of aircraft, which helps to better understand the overall situation of airport operations. Combine the passenger flow prediction results, aircraft taxiing path, interaction relationship with the digital twin model to deduce the airport operation situation. By comprehensively considering various factors, simulate the operating status of the airport under different conditions, discover potential problems and risks in advance, and provide a basis for airport management decisions. Encrypt and store the deduced data through a private blockchain.Private blockchains have characteristics such as data immutability and traceability, which can ensure the security and integrity of the deduced data and facilitate subsequent data querying and analysis. In response to receiving a user's interaction instruction, the user's permissions are identified. Different users may have different access permissions. For example, airport management personnel may have higher permissions and can view more detailed data, while ordinary staff may only be able to view the part of the data related to their work. Based on the user's permissions, target data that matches the interaction instruction is selected from the deduced data and presented to the user. This can ensure that users can only obtain the data they have permission to access, meet the users' query needs, and improve the efficiency and security of data use.

[0065] Optionally, the aligning and fusing the sensor data with the three-dimensional geographic base in terms of time and space to obtain a digital twin model includes:

[0066] Using a spatial interpolation and time synchronization algorithm to align the timestamps of the sensor data with the time reference of the three-dimensional geographic base, and using a spatial matching algorithm to match the spatial coordinates of the sensor data with the geographical entities in the three-dimensional geographic base to obtain the aligned sensor data;

[0067] Fusing the aligned sensor data with the three-dimensional geographic base and adding dynamic information to the three-dimensional geographic base to generate a digital twin model, where the dynamic information includes real-time runway status, meteorological impact areas, and equipment location heat maps.

[0068] Spatial interpolation algorithms are usually used to estimate the values of unknown points between known data points and may assist in handling the distribution of data in the time dimension during time alignment; time synchronization algorithms are to ensure that the timestamps of different data sources can be unified to the same time reference. Align the timestamps of sensor data with the time reference of the three-dimensional geographic base. Since sensor data is collected in real time, while the three-dimensional geographic base may have a preset time reference system, adjust the time of sensor data through the time synchronization algorithm to make it consistent with the time reference of the three-dimensional geographic base, ensuring the accuracy of data in the time dimension during subsequent fusion. The spatial matching algorithm can match according to the spatial coordinates of sensor data (such as longitude, latitude, altitude, etc.) and the spatial features of geographical entities in the three-dimensional geographic base (such as the boundaries of buildings, the contours of runways, etc.). Match the spatial coordinates of sensor data with the geographical entities in the three-dimensional geographic base to determine the accurate position of sensor data in the three-dimensional geographic base. For example, a sensor installed beside a runway can accurately determine its corresponding runway position in the three-dimensional geographic base through the spatial matching algorithm, thus obtaining the aligned sensor data. Integrate the sensor data that has been aligned in time and space with the three-dimensional geographic base. The integration process is to integrate the real-time information contained in the sensor data into the three-dimensional geographic base, enabling the three-dimensional geographic base to not only have static geographical structure information but also reflect real-time dynamic changes. Real-time runway status: Sensors can monitor the surface conditions of the runway in real time, such as whether it is slippery, whether there is snow accumulation, whether there are obstacles, etc. Add this real-time status information to the three-dimensional geographic base, and managers can intuitively see the real-time situation of the runway in the digital twin model to make timely decisions, such as whether to close the runway for maintenance. Meteorological impact area: Meteorological sensors can collect meteorological data around the airport, such as wind speed, wind direction, rainfall, etc. By analyzing and processing these data, the impact ranges of different meteorological conditions on various areas of the airport can be determined and this information can be incorporated into the three-dimensional geographic base. For example, in strong wind weather, the areas affected by relatively strong winds can be clearly seen, so as to reasonably arrange the parking and takeoff / landing of aircraft. Equipment location heat map: By arranging positioning sensors within the airport, the location information of equipment (such as ground support vehicles, baggage handling equipment, etc.) can be obtained in real time. Statistically analyze this location information to generate an equipment location heat map, showing the distribution and usage frequency of equipment within the airport.

[0069] The time stamps of sensor data are aligned with the time reference of the 3D geographical base by using spatial interpolation and time synchronization algorithms, which can eliminate the time differences between different data sources. Accurate time alignment is the basis for data fusion. Only when the sensor data and the 3D geographical base are aligned in time can the data at different times be correctly associated and integrated. The spatial coordinates of sensor data are matched with geographical entities in the 3D geographical base by using a spatial matching algorithm, which can accurately locate the sensor data to the corresponding positions in the 3D geographical space. The spatially aligned data can be accurately displayed on the 3D geographical base, enabling users to more intuitively understand the operating conditions of the airport. The aligned sensor data is fused with the 3D geographical base, and dynamic information (such as real-time runway status, meteorological impact areas, and equipment location heat maps) is added to the 3D geographical base, which can generate a digital twin model with real-time dynamic characteristics. This model can reflect the operating conditions of the airport in real time, providing the latest information for airport management personnel. For example, when there is water accumulation on the runway, the real-time runway status information will be updated to the digital twin model in a timely manner, and the management personnel can immediately take measures, such as notifying the aircraft to decelerate or adjusting the taxiing route. The digital twin model integrates multi-source heterogeneous data, can provide more comprehensive and accurate information, and helps airport management personnel make more scientific decisions. The digital twin model can provide strong support for the planning and design of the airport. By analyzing historical data and real-time data, designers can understand the operating bottlenecks and development trends of the airport, so as to optimize the layout and facility configuration of the airport. For example, according to the equipment location heat map, the installation location and maintenance area of the equipment can be reasonably planned to improve the use efficiency and maintenance convenience of the equipment.

[0070] Optionally, the predicting the aircraft taxiing path according to the parking bay allocation, the ground support vehicle position, and the runway occupancy status includes:

[0071] Analyze the parking bay allocation information to determine the starting parking bay and the target runway position of each flight;

[0072] According to the starting parking bay and the target runway position, combined with the ground support vehicle position information, determine a candidate set of aircraft taxiing paths, where the ground support vehicle position information includes vehicle type, real-time coordinates, and driving direction;

[0073] According to the preset taxiing time and the runway occupancy status information, screen out candidate paths from the candidate set of aircraft taxiing paths, where the runway occupancy status information includes whether the runway is occupied during the preset taxiing time period and the estimated takeoff or landing time of the occupied flight;

[0074] Based on the taxiway layout information, obstacle information, taxiing speed and acceleration of the aircraft at the airport, calculate the target taxiing path from the starting parking position to the target runway position. The taxiway layout information includes the length, width, slope and turning radius of the taxiway.

[0075] Perform a detailed analysis of the parking position allocation information, and extract the starting parking position and the target runway position corresponding to each flight. The starting parking position is the position where the flight parks at the airport waiting for takeoff or has just landed, and the target runway position is the runway used by the flight during takeoff or landing. For example, if a flight is assigned to parking position 3 and plans to take off from runway 2, it can be determined through analysis that the starting parking position of this flight is 3, and the target runway position is runway 2. Combine the starting parking position, the target runway position and the ground support vehicle position information to initially screen out a set of possible taxiing paths. Based on the starting parking position and the target runway position, consider the position information of the ground support vehicles. The ground support vehicle position information includes vehicle types (such as tractors, refueling vehicles, etc.), real-time coordinates and driving directions. Different vehicle types and driving states will affect the taxiing of the aircraft. For example, if a tractor is operating on a certain taxiway, the aircraft may need to avoid that taxiway. Based on these factors, determine the candidate set of aircraft taxiing paths. Each path in the candidate set is a possible route from the starting parking position to the target runway position. According to the preset taxiing time and runway occupancy status information, further screen out more suitable paths from the candidate set. The preset taxiing time is the time required for the aircraft to taxi from the starting parking position to the target runway position preset according to factors such as the operating experience of the airport and the performance of the aircraft. The runway occupancy status information includes whether the runway is occupied during the preset taxiing time period and the estimated takeoff or landing time of the occupied flight. If a runway passed by a certain candidate path is occupied by other flights during the preset taxiing time period of the aircraft, then this path does not meet the requirements. By comparing the preset taxiing time and the runway occupancy status information, screen out the candidate paths that meet the conditions from the candidate set of aircraft taxiing paths. Considering multiple factors comprehensively, calculate the optimal target taxiing path from the screened candidate paths. Consider the taxiway layout information of the airport, including the length, width, slope and turning radius of the taxiway, etc. These factors will affect the taxiing performance and safety of the aircraft. For example, a narrower taxiway may limit the taxiing speed of the aircraft, and a larger turning radius may require the aircraft to adjust its taxiing attitude. Combine the obstacle information to ensure that there are no obstacles on the taxiing path that affect the safe taxiing of the aircraft. Consider the taxiing speed and acceleration of the aircraft. Different aircraft have different taxiing performances, and the taxiing speed and acceleration will affect the taxiing time and path selection. Based on the above factors, calculate the target taxiing path from the starting parking position to the target runway position through professional algorithms and models, and this path should meet the requirements of safety and efficiency.

[0076] The starting and target positions are determined, and personalized taxiing path planning schemes can be formulated according to the specific conditions of different flights. The starting parking positions and target runways of different flights may vary. By accurately analyzing the parking position allocation information, the most suitable taxiing path can be customized for each flight, improving the operation efficiency of the airport. Generating multiple candidate sets of taxiing paths provides more options for subsequent path screening. In actual airport operations, various emergencies may occur, such as the temporary closure of taxiways and vehicle failures. With multiple candidate paths, quick adjustments can be made according to the actual situation, enhancing the airport's ability to handle emergencies. According to the preset taxiing time and runway occupancy status information, candidate paths are screened from the candidate sets of aircraft taxiing paths. The preset taxiing time helps evaluate the efficiency of different paths and select paths that can complete taxiing within a reasonable time. Considering the runway occupancy status information can effectively avoid conflicts between aircraft during taxiing and other flights on the runway. By screening paths that can complete taxiing, takeoff, and landing within the available runway time period, the safety and orderliness of airport operations are ensured. According to the taxiway layout information of the airport (including the length, width, slope, and turning radius of the taxiways), obstacle information, the taxiing speed and acceleration of the aircraft, the target taxiing path from the starting parking position to the target runway position is calculated. The taxiway layout and obstacle distribution of the airport directly affect the taxiing of the aircraft. Considering these factors can ensure that the calculated path is feasible and safe. For example, the slope and turning radius of the taxiway limit the taxiing speed and turning ability of the aircraft, and these factors need to be fully considered when calculating the path to avoid danger during the aircraft's taxiing process.

[0077] Optionally, the establishment of the interaction relationship between passenger movement and aircraft taxiing path through the spatio-temporal association map includes:

[0078] Create a graph data structure, map the physical space and time dimension of the airport into the graph data structure to obtain a spatio-temporal association map, where the nodes in the spatio-temporal association map represent different areas of the airport and different positions of the aircraft, and the edges represent the connection relationships between areas and the movement trajectories of the aircraft;

[0079] Map the passenger flow prediction result and the aircraft taxiing path into the spatio-temporal association map to obtain a spatio-temporal risk map with dynamic weights;

[0080] Calculate the influence coefficient between passenger movement and aircraft taxiing path in the spatio-temporal risk map.

[0081] Create a graph data structure and map the physical space and time dimension of the airport into this graph data structure, thus forming a spatio-temporal correlation graph. The graph data structure is a data representation composed of nodes and edges. The nodes in the spatio-temporal correlation graph represent different areas of the airport and different positions of the aircraft. Different areas of the airport can include various boarding gates, security check channels, baggage claim areas, etc. inside the terminal building, as well as outdoor areas such as airport runways, taxiways, and apron areas. Different positions of the aircraft represent various key nodes during the operation of the aircraft at the airport, such as take-off points, landing points, specific positions during taxiing, etc. The edges represent the connection relationships between areas and the movement trajectories of the aircraft. The connection relationships between areas reflect the flow paths of passengers between different areas in the airport. For example, the path from a certain boarding gate in the terminal building to the corresponding security check channel. The movement trajectories of the aircraft reflect the movement processes of the aircraft such as taxiing, taking off, and landing at the airport, such as the trajectory from the apron to the runway. Mapping the physical space and time dimension into the graph data structure enables the spatio-temporal correlation graph to simultaneously reflect the relationships between different areas and aircraft positions in the airport in terms of time and space. This mapping helps to more comprehensively analyze the operation of the airport, taking into account the positions and movements of passengers and aircraft at different time points. Map the passenger flow prediction results and the aircraft taxiing paths into the spatio-temporal correlation graph. The passenger flow prediction results contain the passenger flow distribution in different areas of the airport during different time periods, and the aircraft taxiing paths clarify the movement routes of the aircraft in the airport. Mapping the passenger flow prediction results into the graph can visually show the passenger flow density in different areas at different times. For example, during the peak periods of flight take-off and landing, there may be passenger flow peaks at the boarding gates and baggage claim areas in the terminal building. Mapping the aircraft taxiing paths into the graph can clearly display the movement trajectories and the areas passed by the aircraft in the airport. This helps to analyze the interaction between the aircraft and passengers in space. Obtain a spatio-temporal risk graph with dynamic weights. The dynamic weights are assigned according to the real-time situations of the passenger flow and the aircraft taxiing paths, reflecting the risk levels of different areas and paths at different times. The magnitude of the weights may be affected by various factors, such as the size of the passenger flow, the speed of the aircraft, the functions of the areas, etc. For example, when the passenger flow in a certain area is very large and there is an aircraft taxiing near this area at the same time, the weight of this area may be relatively high, indicating a higher risk. The weights are dynamic and will change with the changes in the passenger flow and the aircraft taxiing paths. This enables the spatio-temporal risk graph to reflect the risk status of the airport operation in real time, providing timely and effective decision-making basis for airport management personnel. Calculate the influence coefficient between the passenger movement and the aircraft taxiing paths in the spatio-temporal risk graph. The influence coefficient is used to quantify the mutual influence degree between the passenger movement and the aircraft taxiing paths. By calculating the influence coefficient, the interaction relationship between passengers and aircraft in the airport can be deeply understood.For example, if the influence coefficient of a certain area is high, it indicates that there is a significant mutual influence between passenger movement and aircraft taxiing paths in that area. Appropriate measures may need to be taken to reduce risks, such as adjusting flight schedules and optimizing passenger flow lines. The calculation results of the influence coefficient provide important decision-making support for the management and operation of the airport. Airport managers can arrange resources reasonably according to the magnitude of the influence coefficient, formulate more scientific and reasonable operation plans, and improve the operation efficiency and safety of the airport.

[0082] The spatio-temporal correlation graph represents different areas of the airport and different positions of aircraft with nodes, and represents the connection relationships between areas and the movement trajectories of aircraft with edges. It can visually display the complex relationships of the airport in time and space. For example, the spatial connections between different terminal buildings, runways, taxiways and other areas can be clearly seen, as well as the position changes and movement paths of aircraft at different time points. The spatio-temporal correlation graph provides a unified framework for airport operation analysis, enabling managers and researchers to more conveniently understand and analyze the operation status of the airport. By integrating various information into the graph, potential correlations between different areas and aircraft can be quickly discovered, providing support for subsequent analysis and decision-making. The results of passenger flow prediction reflect the degree of personnel density in different areas at different times, and the aircraft taxiing paths show the movement trajectories of aircraft. By combining this information and assigning different weights according to the actual situation, the risk status of different areas and time periods within the airport can be dynamically reflected. The spatio-temporal risk graph with dynamic weights can detect potential risk areas and time periods in advance. Through the analysis of the graph, areas where conflicts and congestion between personnel and aircraft may occur can be predicted, and timely measures can be taken for prevention and response. Calculating the influence coefficient between passenger movement and aircraft taxiing paths in the spatio-temporal risk graph can quantify the degree of mutual influence between passenger movement and aircraft taxiing. This coefficient can help managers more accurately understand the relationships between different factors, and thus formulate more reasonable management strategies. Through the analysis of the influence coefficient, the resource allocation of the airport can be optimized. For example, according to the magnitude of the influence coefficient, resources such as ground support personnel and guiding equipment can be reasonably arranged to ensure that there are sufficient resources to ensure the safety of passengers and aircraft in key areas and time periods. At the same time, the layout of airport facilities can also be optimized to reduce interference between passenger movement and aircraft taxiing and improve the operation efficiency of the airport.

[0083] Optionally, the combining the passenger flow prediction result, the aircraft taxiing path, the interaction relationship with the digital twin model to deduce the airport operation situation includes:

[0084] Establish an interaction relationship between passenger movement and aircraft taxiing paths in the digital twin model, activate the deduction function of the digital twin model, and simulate the operation situation of the airport according to the set time step.

[0085] During the deduction process, based on the passenger flow prediction results, the aircraft taxiing path, and the interaction relationship, the positions and states of passengers and aircraft are updated in real time;

[0086] Monitor the deduction process in real time, collect key indicator data, and determine whether there are any abnormalities in the key indicator data. The key indicator data includes passenger waiting time, aircraft taxiing time, and regional congestion level;

[0087] When there are abnormalities in the key indicator data, generate advice information and send reminder information and the advice information to the target object.

[0088] Establish the interactive relationship between passenger movement and aircraft taxi path in the digital twin model. The digital twin model is the basis for the airport operation situation simulation. It integrates multi-source information such as the airport's geographic information and real-time data. Incorporating the interactive relationship between passenger movement and aircraft taxi path enables the model to simulate the mutual influence between the two in actual operation. For example, the starting point of the aircraft taxi path is related to the path of passenger movement. Start the simulation function of the digital twin model and simulate the airport's operation situation according to the set time step. The time step setting can be adjusted according to actual needs, which determines the time accuracy of the simulation. For example, if the time step is set to 1 minute, the model will update the airport's operation status every 1 minute, simulating the changes in the location and behavior of passengers and aircraft at different time points, thereby gradually presenting the dynamic development process of the airport's operation situation. During the simulation process, the location and status of passengers and aircraft are updated in real time based on the passenger flow prediction results, aircraft taxi path and interactive relationship. The passenger flow prediction results provide the number and flow trends of passengers in different time periods and different areas. The aircraft taxiing path clarifies the movement trajectory of aircraft in the airport, and the interaction relationship determines the mutual influence between passengers and aircraft. For example, when it is predicted that a certain boarding gate will have a large passenger flow in a specific time period, the model will adjust the taxiing speed or route of nearby aircraft according to the interaction relationship, and update the movement status of passengers in the terminal building to ensure that the simulation results can reflect the actual situation. Through this real-time update mechanism, the digital twin model can dynamically simulate the operation process of the airport, so that managers can intuitively see the location and status changes of passengers and aircraft at different time points, understand the overall situation of airport operation, and provide a basis for subsequent decision-making. Monitor the simulation process in real time and collect key indicator data, such as passenger waiting time, aircraft taxiing time, and regional congestion. These key indicators can reflect the efficiency and quality of airport operations. Passenger waiting time may be too long, which may mean that the terminal layout is unreasonable or there are problems with flight scheduling; aircraft taxiing time may affect the punctuality of flights; and high regional congestion may cause safety hazards. Determine whether the key indicator data is abnormal. It can be judged by comparing with preset standard values or historical data. For example, if the waiting time of passengers exceeds the normal range, or the taxiing time of aircraft is significantly longer than the average level, it can be judged as an abnormality. Once an abnormality is detected, it means that there may be problems with the airport operation situation, and timely measures need to be taken to adjust it. When key indicator data is abnormal, suggestion information is generated. Suggestion information is formulated based on abnormal conditions and airport operation rules to solve the problems that arise. For example, if a boarding gate area is too crowded, the suggestion information may be to increase the number of guides, adjust the flight boarding time, or optimize the terminal layout. Send reminder information and suggestion information to the target object. The target object can be airport management personnel, ground support personnel, etc.By sending information in a timely manner, they can understand the abnormal situations of airport operations and take corresponding measures with reference to the recommended information, thus ensuring the safe and efficient operation of the airport.

[0089] By establishing the interactive relationship between passenger movement and aircraft taxiing path in the digital twin model and starting the simulation function, the airport's operation status can be simulated according to the set time step. This simulation takes into account the dynamic behavior of passengers and aircraft and their mutual influence, making the simulation results closer to the actual operation of the airport. For example, it can simulate the scenario of a large number of passengers gathering in the terminal during the peak flight period, and aircraft taxiing and taking off and landing frequently, helping managers to understand the possible congestion and conflict in advance. By adjusting the simulation parameters, such as different passenger flow prediction results, aircraft taxiing paths and interactive relationships, a variety of different airport operation scenarios can be simulated. This provides airport managers with a wealth of analysis cases, helping them evaluate the effects of different decision-making plans and formulate more scientific and reasonable operation strategies. During the simulation process, the location and status of passengers and aircraft are updated in real time based on the passenger flow prediction results, aircraft taxiing paths and interactive relationships. This enables the digital twin model to dynamically reflect the real-time distribution of passengers and aircraft in the airport, as well as their status changes. For example, you can see in real time the movement trajectory of passengers in the terminal, the queue situation at the boarding gate, and the taxiing speed and position of aircraft on the runway. The real-time updated information provides airport managers with the latest operation data, enabling them to make decisions in a timely manner. When it is found that the waiting time of passengers is too long or the taxiing of aircraft is abnormal, managers can immediately take measures, such as adding security inspection channels, adjusting the order of flight take-off and landing, etc., to improve the operation efficiency and service quality of the airport. The simulation process is monitored in real time, key indicator data such as passenger waiting time, aircraft taxiing time, regional congestion, etc. are collected, and it is determined whether these indicators are abnormal. In this way, risks and problems that may arise in the operation of the airport, such as passenger congestion and aircraft delays, can be discovered in advance. For example, when the waiting time of passengers exceeds the normal range, the system can issue an early warning in time to remind managers to take measures to alleviate congestion. Real-time monitoring of key indicator data helps to ensure the safety of airport operations. If the taxiing time of aircraft is abnormal or the regional congestion is too high, it may lead to safety accidents. By promptly discovering these abnormal situations, managers can take corresponding measures, such as adjusting aircraft taxi routes and increasing ground support personnel, to ensure the safety of airport operations. When key indicator data is abnormal, suggestion information is generated and reminder and suggestion information is sent to the target object. These suggestion information are derived from the results of the deduction and the analysis of abnormal situations, and are highly targeted. For example, if a passenger waits too long at a security checkpoint, the suggestion information may include specific measures such as increasing security personnel and optimizing the boarding process to help managers quickly solve the problem. Sending reminder and suggestion information to target objects, such as airport managers and ground support personnel, can improve the efficiency of collaboration between departments.Personnel from different departments can take corresponding actions in a timely manner based on the information received, jointly address the problems that occur during airport operations, and ensure the normal operation of the airport.

[0090] Optionally, the identifying the user's permissions and selecting target data that matches the interaction instruction from the deduced data includes:

[0091] Analyze the interaction instruction to determine the intent tag, spatio-temporal range, and operation type, and verify the user's role through a token.

[0092] Input the user role and the intent tag into the airport ontology knowledge graph to obtain the authorized data field whitelist.

[0093] Determine the encrypted data block hash set that meets the permissions from the deduced data according to the authorized data field whitelist.

[0094] Decrypt the encrypted data block hash set to obtain the target data.

[0095] Analyze the interaction instruction to determine its intent label. The intent label represents the purpose for which the user initiates the interaction, such as querying the operating status of a specific flight, understanding the passenger flow in a certain area, etc. By analyzing the keywords, sentence structure, etc. in the interaction instruction, the user's intent can be accurately identified, providing a direction for subsequent data screening. Extract the spatio-temporal range information from the interaction instruction. The time range may include a specific time period, such as a certain hour of a certain day, or a relative time, such as "the last hour"; the spatial range may involve specific areas of the airport, such as a certain terminal building, runway, or taxiway, etc. Defining the spatio-temporal range helps narrow down the scope of data screening, improving the efficiency and accuracy of data acquisition. Determine the operation type corresponding to the interaction instruction, such as query, statistics, analysis, etc. Different operation types have different data processing methods and requirements. For example, a query operation may only need to obtain specific data records, while a statistics operation requires summarizing and analyzing the data. Defining the operation type can ensure that subsequent data processing meets the user's needs. Use a token to verify the user's role. A token is a security credential for authentication and authorization, usually containing the user's identity information, permission scope, etc. When the user initiates an interaction request, the system will require the user to provide the token and determine the user's role by verifying the legality and validity of the token. After token verification, the system can clarify the user's role, such as airport management personnel, ground support personnel, ordinary passengers, etc. Different user roles have different permissions. For example, airport management personnel may have higher data access permissions and can view more detailed and sensitive data, while ordinary passengers can only view the part of the information related to themselves. Input the user role and intent label into the airport ontology knowledge graph. The airport ontology knowledge graph is a method for modeling and representing airport domain knowledge. It contains various entities in the airport (such as flights, passengers, facilities, etc.) and the relationships between them, as well as the access permission rules for different user roles to various data fields. According to the rules and relationships in the airport ontology knowledge graph, combined with the user role and intent label, generate an authorized data field whitelist. The whitelist lists the data fields that the user has the right to access, such as flight number, departure time, arrival time, passenger name, seat number, etc. Through the whitelist mechanism, it can be ensured that the user can only access the data they are authorized to, protecting the security and privacy of the data. The deduced data is usually stored in the form of encrypted data blocks to improve data security. Each encrypted data block contains a part of the deduced data and has a unique hash value, which is used to identify and verify the integrity and uniqueness of the data block. According to the authorized data field whitelist, filter out the set of encrypted data block hashes that meet the permissions from the deduced data. The system will check the data fields contained in each encrypted data block to determine whether they are in the whitelist. Only the encrypted data blocks containing the authorized data fields will be selected to form a hash set. Decrypt the set of encrypted data block hashes that meet the permissions.The decryption process requires the use of the corresponding key and algorithm to restore the encrypted data block to the original target data. The decryption operation can only be performed when the user has the corresponding permissions to ensure the security and confidentiality of the data. After decryption, the target data required by the user is obtained. These data can be displayed, analyzed, or further processed according to the user's interaction instructions to meet the user's needs.

[0096] Analyzing the interaction instructions to determine the intent label, spatio-temporal range, and operation type can accurately understand the data content and operation purpose that the user wants to obtain. Verifying the user's role through a token can effectively confirm the user's identity and permission level. A token is a secure authentication mechanism, and only users with a legitimate token can access system resources. This can prevent unauthorized users from obtaining sensitive data and ensure the security of the system. Inputting the user role and intent label into the airport ontology knowledge graph to obtain the authorized data field whitelist can precisely define the data fields that the user can access according to the user's role and query intent. The airport ontology knowledge graph contains various concepts, relationships, and rules in the airport domain, and through it, fine-grained control of data access can be achieved. The authorized data field whitelist can be dynamically adjusted according to different user roles and query requirements, improving the flexibility of data access. When the user's role changes or the query requirement changes, the system can regenerate the whitelist to ensure that the user can obtain the latest data that conforms to their permissions. The deduced data is usually stored encrypted. By determining the set of encrypted data block hashes that conform to the permissions according to the authorized data field whitelist, data can be screened without decrypting the data. This method avoids leaking sensitive information during data transmission and processing, protecting the privacy and security of the data. Using the hash set can quickly locate the encrypted data blocks that conform to the permissions, improving the efficiency of data screening. The hash algorithm has the characteristics of fast calculation and uniqueness. By comparing the hash values, the system can quickly find the required data blocks, reducing unnecessary data processing and transmission.

[0097] Optionally, the inputting the user role and the intent label into the airport ontology knowledge graph to obtain the authorized data field whitelist includes:

[0098] Constructing an airport ontology knowledge graph based on various entities within the airport and the relationships between various entities. The entities include runways, aircraft, and personnel roles, and the relationships include permission associations and data access rules;

[0099] Constructing a query statement according to the user role and the intent label, and screening out the set of data fields associated with the user role and intent label from the airport ontology knowledge graph according to predefined rules and relationships;

[0100] Check whether the data fields in the set of data fields are within the target time and space range corresponding to the permission;

[0101] Collate the data fields within the target time and space range into a whitelist of authorized data fields.

[0102] Runway: The passageway at the airport for aircraft takeoff and landing, which is one of the important infrastructure facilities of the airport. In the knowledge graph, the runway, as an entity, has attributes such as length, width, runway number, surface material, etc. For example, a runway with the number 36L has a length of 3,800 meters, a width of 60 meters, and an asphalt surface. Aircraft: Include various flying tools such as airplanes and helicopters. The aircraft entity has attributes such as model, affiliated airline company, current status (such as in flight, on standby, under maintenance, etc.). For instance, a Boeing 747 aircraft, affiliated with [specific airline company name], is currently on standby. Personnel roles: Cover different personnel with various responsibilities within the airport, such as air traffic controllers, ground crew, security inspectors, airport management personnel, etc. Each role entity has corresponding permission levels, job responsibilities, etc. For example, air traffic controllers are responsible for directing the takeoff and landing of aircraft and have a relatively high permission level. Permission association: Describes the operation permissions of different personnel roles for various entities (such as runways, aircraft, etc.). For example, ground crew have the permission to perform maintenance and support work on aircraft, but may not have the permission to directly command aircraft takeoff and landing; air traffic controllers have the permission to direct the takeoff and landing operations of aircraft on the runway. Data access rules: Specify which entity-related data different personnel roles can access. For example, airport management personnel can access the overall operation data of the airport, including runway usage, aircraft traffic, personnel work arrangements, etc.; while ordinary ground crew may only access aircraft maintenance data directly related to their work. Query statement construction: Construct query statements based on the user role and intent tags. For example, if the user role is an airport operation analyst and the intent tag is to query the flight takeoff and landing situation of a specific runway during a certain period, then the query statement may be similar to "Query the data related to flight takeoff and landing of the runway with the number [specific number] during [specific period] for the user role of airport operation analyst". Data field screening: Screen out the set of data fields associated with the user role and intent tags from the airport ontology knowledge graph according to predefined rules and relationships. The predefined rules are formulated based on the business logic and security requirements of the airport. For example, it is stipulated that operation analysts can access data fields such as flight takeoff and landing times, flight numbers, runway usage, etc., but cannot access data fields related to the core confidential technical parameters of aircraft. By matching these rules and relationships, data fields that meet the conditions, such as flight takeoff and landing times, flight numbers, etc., are extracted from the knowledge graph. Spatiotemporal range check: Check whether the data fields in the set of screened data fields are within the target spatiotemporal range corresponding to the permissions. The target spatiotemporal range is determined according to the user role and intent tags. For example, the user may only want to query the data of a certain runway (spatial range) within a specific date (time range). If the entity event corresponding to the data field occurs outside this spatiotemporal range, it does not meet the requirements.For example, if the data queried is for Runway A from January 1st to January 7th, and a certain data field corresponds to the situation of Runway A on January 8th, then this data field is not within the target time - space range. Whitelist sorting: Sort the data fields within the target time - space range into a whitelist of authorized data fields. This whitelist specifies the specific data fields that users can access. Subsequently, the system will extract the corresponding target data from the deduced data according to this whitelist. For example, after screening and inspection, the final whitelist of authorized data fields may include data fields such as flight numbers, take - off and landing times, runway numbers, etc., and users can obtain the specific data corresponding to these fields.

[0103] Constructing an airport ontology knowledge graph based on various entities within the airport (such as runways, aircraft, personnel roles) and the relationships between various entities (such as permission associations, data access rules) can comprehensively integrate the key information in airport operations. This enables various resources, roles, and rules of the airport to be presented in a structured manner, providing a solid foundation for subsequent data permission management and query. Constructing a query statement based on user roles and intent tags can accurately filter out the set of data fields associated with user roles and intent tags from the airport ontology knowledge graph. Different user roles have different responsibilities and requirements. In this way, it can be ensured that each user can only obtain the data related to their work, meeting personalized data access needs. Checking whether the data fields in the set of data fields are within the target time - space range corresponding to the permissions can ensure that the data obtained by users matches the actual airport operation scenario. The operation situation of the airport changes with time and space. Only by obtaining the data within the target time - space range can accurate and effective information be provided to users. Through the limitation of the time - space range, the security of the data is further enhanced. Even if a certain user has a certain role permission, they cannot access the relevant data outside the specific time - space range, preventing the abuse and leakage of data. The whitelist of authorized data fields facilitates the management and auditing of the system. System administrators can monitor and manage users' data access behaviors according to the whitelist to ensure the compliance of data access. At the same time, in case of data security issues, the problem can be quickly located through the whitelist.

[0104] This embodiment also discloses an airport geographic information data management system. Figure 2 It is a schematic diagram of the modules of an airport geographic information data management system disclosed in an embodiment of the present application. As Figure 2 shown, the system includes a collection module 201, a fusion module 202, a prediction module 203, a deduction module 204, and an interaction module 205, where:

[0105] The acquisition module 201 is configured to obtain multi-source heterogeneous geographic data and construct a three-dimensional geographic base according to the multi-source heterogeneous geographic data, where the multi-source heterogeneous geographic data includes airport drawing data, sensor coordinate data, point cloud data, and satellite images;

[0106] The fusion module 202 is configured to obtain sensor data through edge computing nodes within a preset range of sensors, align and fuse the sensor data with the three-dimensional geographic base in terms of time and space to obtain a digital twin model, where the digital twin model includes real-time runway status, meteorological impact areas, and equipment location heat maps;

[0107] The prediction module 203 is configured to obtain a passenger flow prediction result according to flight information and historical passenger flow data, predict the aircraft taxiing path based on parking position allocation, ground support vehicle positions, and runway occupancy status, and establish an interaction relationship between passenger movement and the aircraft taxiing path through a spatio-temporal association map;

[0108] The deduction module 204 is configured to combine the passenger flow prediction result, the aircraft taxiing path, the interaction relationship with the digital twin model to deduce the airport operation situation, and encrypt and store the deduction data through a private blockchain;

[0109] The interaction module 205 is configured to, in response to receiving a user's interaction instruction, identify the user's permission, select target data matching the interaction instruction from the deduction data based on the permission, and display it to the user.

[0110] Optionally, the fusion module 202 is configured to:

[0111] Adopt a spatial interpolation and time synchronization algorithm to align the time stamp of the sensor data with the time reference of the three-dimensional geographic base, and use a spatial matching algorithm to match the spatial coordinates of the sensor data with the geographic entities in the three-dimensional geographic base to obtain the aligned sensor data;

[0112] Fuse the aligned sensor data with the three-dimensional geographic base and add dynamic information to the three-dimensional geographic base to generate a digital twin model, where the dynamic information includes real-time runway status, meteorological impact areas, and equipment location heat maps.

[0113] Optionally, the prediction module 203 is configured to:

[0114] Analyze the parking position allocation information to determine the starting parking position and target runway position of each flight;

[0115] Determine a candidate set of aircraft taxi paths based on the starting parking position and the target runway position, in combination with the position information of ground support vehicles, where the position information of ground support vehicles includes vehicle type, real-time coordinates, and driving direction;

[0116] According to the preset taxi time and runway occupancy status information, screen out candidate paths from the candidate set of aircraft taxi paths, where the runway occupancy status information includes whether the runway is occupied during the preset taxi time period and the estimated takeoff or landing time of the occupied flight;

[0117] Calculate the target taxi path from the starting parking position to the target runway position based on the taxiway layout information of the airport, obstacle information, taxi speed, and acceleration of the aircraft, where the taxiway layout information includes the length, width, slope, and turning radius of the taxiway.

[0118] Optionally, the prediction module 203 is configured to:

[0119] Create a graph data structure, map the physical space and time dimension of the airport into the graph data structure to obtain a spatio-temporal correlation graph, where the nodes in the spatio-temporal correlation graph represent different areas of the airport and different positions of the aircraft, and the edges represent the connection relationships between areas and the movement trajectories of the aircraft;

[0120] Map the pedestrian flow prediction result and the aircraft taxi path into the spatio-temporal correlation graph to obtain a spatio-temporal risk graph with dynamic weights;

[0121] Calculate the influence coefficient between the movement of passengers and the aircraft taxi path in the spatio-temporal risk graph.

[0122] Optionally, the deduction module 204 is configured to:

[0123] Establish an interaction relationship between the movement of passengers and the aircraft taxi path in the digital twin model, start the deduction function of the digital twin model, and simulate the operation situation of the airport according to the set time step;

[0124] During the deduction process, update the positions and states of passengers and aircraft in real time according to the pedestrian flow prediction result, the aircraft taxi path, and the interaction relationship;

[0125] Monitor the deduction process in real time, collect key index data, and determine whether the key index data is abnormal. The key index data includes passenger waiting time, aircraft taxi time, and regional congestion level;

[0126] When the key index data is abnormal, generate advice information, and send a reminder message and the advice information to the target object.

[0127] Optionally, the interaction module 205 is configured to:

[0128] Analyze the interaction instruction to determine the intent label, spatio-temporal range, and operation type, and verify the user role of the user through a token;

[0129] Input the user role and the intent label into the airport ontology knowledge graph to obtain an authorized data field whitelist;

[0130] Determine a set of encrypted data block hashes that meet the permissions from the deduced data according to the authorized data field whitelist;

[0131] Decrypt the set of encrypted data block hashes to obtain the target data.

[0132] Optionally, the interaction module 205 is configured to:

[0133] Construct an airport ontology knowledge graph based on various entities in the airport and the relationships between various entities. The entities include runways, aircraft, and personnel roles, and the relationships include permission associations and data access rules;

[0134] Construct a query statement according to the user role and the intent label, and filter out a set of data fields associated with the user role and intent label from the airport ontology knowledge graph according to predefined rules and relationships;

[0135] Check whether the data fields in the set of data fields are within the target spatio-temporal range corresponding to the permissions;

[0136] Organize the data fields within the target spatio-temporal range into an authorized data field whitelist.

[0137] It should be noted that when the device provided in the above embodiment realizes its functions, only the above-mentioned division of each functional module is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0138] This embodiment also discloses an electronic device. Referring to Figure 3 , the electronic device may include: at least one processor 301, at least one communication bus 302, a user interface 303, a network interface 304, and at least one memory 305.

[0139] Among them, the communication bus 302 is used to realize the connection and communication between these components.

[0140] Among them, the user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may further include a standard wired interface and a wireless interface.

[0141] Among them, the network interface 304 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0142] Among them, the processor 301 may include one or more processing cores. The processor 301 connects various parts within the entire server through various interfaces and circuits. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling the data stored in the memory 305, the processor 301 performs various functions of the server and processes data. Optionally, the processor 301 may be implemented in at least one of the following hardware forms: digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor 301 may integrate one or a combination of several of the following: a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes the operating system, the user interface, and application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301 and may be implemented separately by a single chip.

[0143] Among them, the memory 305 may include random access memory (RAM) and may also include read-only memory. Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store the data involved in the above-mentioned method embodiments. Optionally, the memory 305 may further be at least one storage device located far from the aforementioned processor 301. Such as Figure 3As shown, in the memory 305 which is a computer storage medium, an operating system, a network communication module, a user interface module, and an application program of an airport geographic information data management method can be included.

[0144] In Figure 3 In the electronic device shown, the user interface 303 is mainly used to provide an interface for the user to obtain the data input by the user; and the processor 301 can be used to call the application program of an airport geographic information data management method stored in the memory 305. When executed by one or more processors 301, the electronic device executes the method of one or more of the above embodiments.

[0145] It should be noted that for the foregoing method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be in other sequences or performed simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0146] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0147] In the several embodiments provided by this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0148] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0149] In addition, in each embodiment of this application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0150] The foregoing are only exemplary embodiments of the present disclosure and should not be used to limit the scope of the present disclosure. That is, all equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. Those skilled in the art will readily conceive of other embodiments of the present disclosure after considering the disclosure of the specification. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include known common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for managing airport geographic information data, characterized in that Applied to the airport geographic information data management platform, the method includes: Obtain multi-source heterogeneous geographic data, and construct a three-dimensional geographic base according to the multi-source heterogeneous geographic data, where the multi-source heterogeneous geographic data includes airport drawing data, sensor coordinate data, point cloud data, and satellite images; Obtain sensor data through edge computing nodes within a preset range of the sensor, align and fuse the sensor data with the three-dimensional geographic base in terms of time and space to obtain a digital twin model, where the digital twin model includes real-time runway status, meteorological impact area, and equipment location heat map; Obtain a passenger flow prediction result based on flight information and historical passenger flow data, predict the aircraft taxiing path according to the parking position allocation, ground support vehicle position, and runway occupancy status, and establish an interaction relationship between passenger movement and the aircraft taxiing path through a spatio-temporal association map; Combine the passenger flow prediction result, the aircraft taxiing path, the interaction relationship with the digital twin model to deduce the airport operation situation, and encrypt and store the deduced data through a private blockchain; In response to receiving a user's interaction instruction, identify the user's permission, select target data matching the interaction instruction from the deduced data based on the permission, and display it to the user.

2. The airport geographic information data management method according to claim 1, wherein The aligning and fusing the sensor data with the three-dimensional geographic base in terms of time and space to obtain a digital twin model includes: Use spatial interpolation and time synchronization algorithms to align the time stamp of the sensor data with the time reference of the three-dimensional geographic base, and use a spatial matching algorithm to match the spatial coordinates of the sensor data with the geographic entities in the three-dimensional geographic base to obtain the aligned sensor data; Fuse the aligned sensor data with the three-dimensional geographic base, and add dynamic information to the three-dimensional geographic base to generate a digital twin model, where the dynamic information includes real-time runway status, meteorological impact area, and equipment location heat map.

3. The airport geographic information data management method according to claim 1, characterized in that The predicting the aircraft taxiing path according to the parking position allocation, ground support vehicle position, and runway occupancy status includes: Analyze the parking position allocation information to determine the starting parking position and the target runway position of each flight; According to the starting parking position and the target runway position, combined with the ground support vehicle position information, determine a candidate set of aircraft taxiing paths, where the ground support vehicle position information includes vehicle type, real-time coordinates, and driving direction; According to the preset taxiing time and runway occupancy status information, screen out candidate paths from the candidate set of aircraft taxiing paths, where the runway occupancy status information includes whether the runway is occupied during the preset taxiing time period, and the estimated takeoff or landing time of the occupied flight; According to the taxiway layout information, obstacle information, taxiing speed and acceleration of the aircraft in the airport, calculate the target taxiing path from the starting parking position to the target runway position, where the taxiway layout information includes the length, width, slope, and turning radius of the taxiway.

4. The airport geographic information data management method according to claim 3, wherein, The establishing an interaction relationship between passenger movement and the aircraft taxiing path through a spatio-temporal association map includes: Create a graph data structure, map the physical space and time dimension of the airport into the graph data structure to obtain a spatio-temporal correlation graph, where the nodes in the spatio-temporal correlation graph represent different areas of the airport and different positions of aircraft, and the edges represent the connection relationships between areas and the movement trajectories of aircraft; Map the passenger flow prediction result and the aircraft taxiing path into the spatio-temporal correlation graph to obtain a spatio-temporal risk graph with dynamic weights; Calculate the influence coefficient between passenger movement and aircraft taxiing path in the spatio-temporal risk graph.

5. The method for managing airport geographic information data according to claim 4, wherein The combination of the passenger flow prediction result, the aircraft taxiing path, the interaction relationship and the digital twin model to deduce the airport operation situation includes: Establish an interaction relationship between passenger movement and aircraft taxiing path in the digital twin model, start the deduction function of the digital twin model, and simulate the airport operation situation according to the set time step; During the deduction process, update the positions and states of passengers and aircraft in real time according to the passenger flow prediction result, the aircraft taxiing path and the interaction relationship; Monitor the deduction process in real time, collect key index data, and judge whether the key index data is abnormal. The key index data includes passenger waiting time, aircraft taxiing time, and regional congestion degree; When the key index data is abnormal, generate a suggestion message, and send a reminder message and the suggestion message to the target object.

6. The method for managing airport geographic information data according to claim 1, wherein The identification of the user's permission and the selection of target data matching the interaction instruction from the deduction data based on the permission includes: Analyze the interaction instruction to determine the intent label, spatio-temporal range and operation type, and verify the user's role through a token; Input the user role and the intent label into the airport ontology knowledge graph to obtain an authorized data field white list; Determine a set of encrypted data block hashes that meet the permission from the deduction data according to the authorized data field white list; Decrypt the set of encrypted data block hashes to obtain the target data.

7. The method for managing airport geographic information data according to claim 6, wherein The input of the user role and the intent label into the airport ontology knowledge graph to obtain an authorized data field white list includes: Construct an airport ontology knowledge graph according to various entities in the airport and the relationships between various entities. The entities include runways, aircraft, and personnel roles, and the relationships include permission associations and data access rules; Construct a query statement according to the user role and the intent label, and filter out a set of data fields associated with the user role and intent label from the airport ontology knowledge graph according to predefined rules and relationships; Check whether the data fields in the set of data fields are within the target spatio-temporal range corresponding to the permission; Organize the data fields within the target spatio-temporal range into an authorized data field white list.

8. An airport geographic information data management system, characterized in that, It includes a collection module, a fusion module, a prediction module, a deduction module and an interaction module, where: The collection module is configured to obtain multi-source heterogeneous geographic data and construct a three-dimensional geographic base according to the multi-source heterogeneous geographic data. The multi-source heterogeneous geographic data includes airport drawing data, sensor coordinate data, point cloud data and satellite images; A fusion module, configured to obtain sensor data through edge computing nodes within a preset range of sensors, align and fuse the sensor data with the three-dimensional geographical base in terms of time and space to obtain a digital twin model, where the digital twin model includes real-time runway status, meteorological impact areas, and equipment location heat maps; A prediction module, configured to obtain a passenger flow prediction result based on flight information and historical passenger flow data, predict the taxiing path of an aircraft according to the parking position allocation, the position of ground support vehicles, and the runway occupancy status, and establish an interaction relationship between passenger movement and the aircraft taxiing path through a spatio-temporal association graph; A deduction module, configured to combine the passenger flow prediction result, the aircraft taxiing path, the interaction relationship with the digital twin model to deduce the airport operation situation, and encrypt and store the deduction data through a private blockchain; An interaction module, configured to, in response to receiving an interaction instruction from a user, identify the user's permission, select target data matching the interaction instruction from the deduction data based on the permission, and display the target data to the user.

9. An electronic device, characterized in that, It includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions. Both the user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1-7 is executed.

Images