Method and device for optimizing high-precision map data in flying area and electronic equipment

Abstract

The invention provides a flight area high-precision map data optimization method and device and electronic equipment, and relates to the field of data processing. The method comprises the following steps: acquiring aircraft taxiing trajectory data, ground service vehicle path data and navigation light network data for an airport flight area; analyzing the aircraft taxiing trajectory data, the ground service vehicle path data and the navigation light network data, and determining to-be-optimized features of the airport flight area; performing inversion on the to-be-optimized features to generate an optimization strategy; and performing data optimization on the high-precision map corresponding to the airport flight area through an optimization strategy. By implementing the technical scheme provided by the invention, the data optimization accuracy of the high-precision map can be conveniently improved.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and specifically to a method, device and electronic equipment for optimizing high-precision map data of an airfield. Background Art

[0002] High-precision maps are an important infrastructure to ensure the safety and efficiency of airport flight zone operations, and are widely used in scenarios such as aircraft taxiing, ground vehicle scheduling, and navigation lighting system optimization.

[0003] At present, the traditional high-precision map update optimization method mainly relies on a single perception method, such as manual survey. However, this method is limited by labor costs and efficiency, has a long update cycle, and is difficult to respond to the dynamic needs of dynamic behaviors in airport operations in a timely manner, resulting in low accuracy of data optimization for high-precision maps.

[0004] Therefore, there is an urgent need for a method, device and electronic equipment for optimizing high-precision map data of an airfield. Summary of the invention

[0005] The present application provides a method, device and electronic equipment for optimizing high-precision map data of an airfield, which is convenient for improving the accuracy of data optimization of high-precision maps.

[0006] In a first aspect of the present application, a method for optimizing high-precision map data of an airfield is provided, the method comprising: obtaining aircraft taxi trajectory data, ground vehicle path data, and navigation light network data for an airport airfield; analyzing the aircraft taxi trajectory data, the ground vehicle path data, and the navigation light network data to determine features to be optimized for the airport airfield; inverting the features to be optimized to generate an optimization strategy; and optimizing data of a high-precision map corresponding to the airport airfield through the optimization strategy.

[0007] By adopting the above technical solutions, the lack of comprehensive information of a single data source is made up by comprehensively utilizing aircraft taxi trajectories, ground vehicle paths and navigation light network data. Taxi trajectory data reflects the actual path of the aircraft on different runways and taxiways, revealing potential trajectory deviations or inaccurate taxiway markings. Vehicle path data captures abnormal behaviors in the operation of ground vehicles and helps locate obstacles or path design problems. Navigation light data reflects the coverage and status of lights, and finds navigation blind spots or unreasonable distribution of light signals. The dynamic operation behavior of the airport is analyzed from multiple dimensions, covering static infrastructure and dynamic operation environment, making map optimization more comprehensive and accurate. By analyzing the collected data in real time, problems such as taxiway deviation, obstacle distribution changes or navigation light anomalies can be quickly identified. The optimization strategy is generated through data-driven, without long-term manual intervention, and can significantly shorten the map update cycle. The airport operation environment is complex and changeable, and the dynamic perception and optimization method ensures that the map reflects the latest operation status in a timely manner. Based on intelligent analysis of multi-source data, the features to be optimized are automatically inverted, avoiding the inefficiency of relying on manual experience in traditional optimization. By generating a specific optimization plan through the algorithm, human errors are reduced and the reliability of the optimization results is improved. Therefore, it is easy to improve the accuracy of data optimization for high-precision maps.

[0008] Optionally, the acquiring of aircraft taxi trajectory data, ground vehicle path data and navigation light network data for an airport flight zone specifically includes: receiving original aircraft taxi trajectory data sent by an ADS-B system; receiving original ground vehicle path data sent by a UWB base station; receiving original navigation light network data sent by a lighting control system; performing data processing on the original aircraft taxi trajectory data, the original ground vehicle path data and the original navigation light network data to obtain the aircraft taxi trajectory data, the ground vehicle path data and the navigation light network data, wherein the data processing includes denoising, filtering and normalization processing.

[0009] By adopting the above technical solutions, ADS-B data provides high-precision position information during the taxiing process of the aircraft, monitors the taxiing path of the aircraft in real time, and accurately reflects the use of the taxiway and potential problems. The high positioning accuracy of UWB technology is suitable for capturing the running trajectory of ground handling vehicles, helping to find obstacles, detours and unreasonable traffic planning problems. The lighting control system collects the status and distribution information of navigation lights in real time, and identifies lighting blind spots, insufficient lighting coverage or signal abnormalities. The three types of data complement each other, covering dynamic behavior and static feature information such as aircraft, vehicles and navigation facilities, forming a full range of operating environment perception. Denoising can remove noise signals from the original data to ensure the authenticity and accuracy of the data. Through filtering technology, data fluctuations are smoothed, the real trajectory and path characteristics are extracted, and transient anomalies caused by environmental changes or equipment errors are eliminated. Normalization processing standardizes data from different sources by unifying data formats and dimensions, facilitating subsequent fusion analysis and improving data compatibility and applicability.

[0010] Optionally, the analyzing the aircraft taxi trajectory data, the ground vehicle path data and the navigation light network data to determine the features to be optimized of the airport flight area specifically includes: clustering the aircraft taxi trajectory data using a density clustering algorithm to obtain a high-frequency trajectory; determining a target taxi runway based on the high-frequency trajectory; obtaining a runway map of the target taxi runway from the high-precision map, and determining a center line marked in the runway map; calculating a deviation between the high-frequency trajectory and the center line marked in the runway map; if it is determined that the deviation exceeds a preset error range, determining a trajectory abnormal point corresponding to the deviation, and determining the trajectory abnormal point as the feature to be optimized.

[0011] By adopting the above technical solution, the taxiing trajectory data is automatically analyzed by the density clustering algorithm, which can effectively identify high-frequency taxiing trajectories, replace the traditional method of relying on manual detection, and improve the analysis efficiency. The deviation between the trajectory and the runway centerline is calculated, and the abnormal points where the deviation exceeds the error range are automatically identified, making feature recognition more efficient and accurate. This method is based on algorithms and data-driven, avoiding subjective bias and missed judgment problems that may be caused by manual analysis. The high-frequency trajectory in the taxiing data is extracted by the clustering algorithm to reflect the actual taxiing behavior and operation rules of the aircraft, covering the main dynamic characteristics of airport operation. Combined with the taxiing trajectory data and the runway map data, the airport operation status is comprehensively analyzed from both dynamic and static aspects to ensure the comprehensiveness of feature recognition. By accurately calculating the deviation between the high-frequency trajectory and the runway centerline, the deviation degree of the taxiing trajectory is accurately quantified, providing data support for subsequent optimization. By analyzing the taxiing trajectory data, the deviation problem in the taxiway centerline design or marking can be dynamically discovered, and the operation requirements can be responded to in a timely manner. By analyzing the high-frequency trajectory deviation, the centerline marked in the runway map can be corrected, so that the high-precision map is more in line with the actual operation requirements. If the deviation problem involves runway design defects, the taxiway design can be improved through annotation adjustment and operation optimization. Deviation detection and outlier identification can provide a reliable basis for dynamic map updates, ensuring that the map is always consistent with the actual operation status of the airport.

[0012] Optionally, the analyzing the aircraft taxi trajectory data, the ground vehicle path data and the navigation light network data to determine the features to be optimized of the airport flight area specifically includes: determining the ground vehicle trajectory based on the ground vehicle path data; comparing the ground vehicle trajectory with the high-precision map to mark the paths outside the map coverage area; obtaining the operating mode of the ground vehicle, the operating mode including sharp turns and detours; determining path abnormality factors based on the operating mode of the ground vehicle; if it is determined that the number of occurrences of the path abnormality factors is greater than a preset number, determining the sharp turn points and repeated detour points in the ground vehicle trajectory as the features to be optimized.

[0013] By adopting the above technical solutions, by comparing the ground handling vehicle trajectory with the high-precision map, the paths outside the map coverage area can be accurately identified, thereby discovering the map blind spots or insufficiently marked areas. By marking the paths outside the coverage area, it helps to improve the high-precision map data so that it can more comprehensively and accurately reflect the actual operating status of the airport flight area. By analyzing the operating mode of the ground handling vehicle, the hidden problems in the vehicle operation are deeply explored, providing comprehensive information support for subsequent optimization. The statistical analysis of path abnormality factors can dynamically capture abnormal conditions in operation, which is convenient for rapid response and resolution. Through the classification analysis of the operating mode, the sharp turning points and repeated detour points are accurately located, which significantly improves the efficiency and accuracy of abnormality identification. By counting the number of occurrences of abnormal factors, high-frequency problem areas are automatically screened, and the optimization priority is clarified to avoid blind optimization. The accurate identification of sharp turning points and repeated detour points helps to locate the areas with design or marking problems in the ground handling vehicle path, so as to concentrate resources for improvement. According to the data generated by the operating mode and abnormal analysis results, scientific path optimization decisions are supported to reduce resource waste. Sharp turning points and detour points are identified and corrected, the operating path of ground handling vehicles is simplified, unnecessary detours and complex operations are reduced, and operational efficiency is improved. Analyzing ground vehicle path anomalies helps optimize vehicle scheduling and avoid multiple round trips or repeated detours.

[0014] Optionally, the analyzing the aircraft taxi trajectory data, the ground vehicle path data and the navigation light network data to determine the features to be optimized of the airport flight area specifically includes: determining the navigation light signal according to the navigation light network data; associating the time series of the on state and the off state of the navigation light signal with the aircraft taxi trajectory data and the ground vehicle path data to mark the signal loss area; calculating the effective coverage range of the navigation light through the distribution of signal strength and trajectory points; screening and obtaining the signal coverage blind area distribution according to the signal loss area and the effective coverage range; determining potential hardware problems and / or coverage areas to be expanded in the navigation light network according to the signal coverage blind area distribution; determining the potential hardware problems and / or coverage areas to be expanded in the navigation light network as the features to be optimized.

[0015] By adopting the above technical solution, by associating the on and off time series of the navigation light signal with the aircraft taxiing trajectory and ground vehicle path data, the signal loss area can be accurately identified. In this way, the airport can quickly identify the signal interruption area and repair it in time to ensure the stability of the navigation light network. By calculating the signal strength and trajectory point distribution, the effective coverage of the navigation light can be determined more accurately, avoiding the errors and inefficiencies in traditional methods and improving the accuracy of light signal coverage. The screening of signal loss areas and effective coverage helps to find potential signal coverage blind spots. By identifying these blind spots, the airport can expand the coverage of the lighting facilities in a targeted manner and reduce the blind spots and potential risks of flight operations in the area. According to the distribution of coverage blind spots, data support can be provided for expanding the coverage area of ​​the navigation lights, accurately planning the location and number of new facilities, and avoiding ineffective investment. Signal loss areas and blind spots may cause aircraft and ground vehicles to lose navigation guidance during taxiing, increasing the risk of accidents. By identifying and repairing these problems in advance, the accident rate of the airport can be significantly reduced. After the navigation lighting network is optimized, the movement of aircraft and ground handling vehicles within the airport is more accurate and safer, especially when visibility is low or at night, the reliability of navigation lighting is critical to safety. According to the signal loss area and coverage, the airport dispatch system can adjust the operation routes of flight and ground handling vehicles in real time to avoid delays or detours caused by navigation lighting problems.

[0016] Optionally, the inversion of the features to be optimized to generate an optimization strategy specifically includes: according to the features to be optimized, fitting the taxiway centerline using the density center of the aircraft taxi trajectory data to obtain the position to be optimized; according to the features to be optimized, extracting the trajectory sharp turning points of the detour area, and calculating the obstacle boundary using a convex hull algorithm; according to the features to be optimized, extracting unmarked areas and / or areas with insufficient navigation light coverage; generating the optimization strategy based on the position to be optimized and the obstacle boundary, as well as the unmarked areas and / or areas with insufficient navigation light coverage.

[0017] By adopting the above technical solution, the position of the taxiway centerline can be accurately calculated by fitting the density center of the aircraft taxi trajectory data, ensuring that the taxiway marking in the map and navigation system is more accurate. Based on the fitting of the taxiway centerline, the offset and path inconsistency of the taxiway can be identified, thereby correcting the errors on the map and improving the accuracy of the taxiway marking. By analyzing the sharp turning points of the trajectory of the detour area, the area that needs to be detoured during the taxiing of the vehicle or aircraft can be identified. Identifying these points helps to optimize the flight route and the path planning of ground handling vehicles. The convex hull algorithm can accurately calculate the obstacle boundary of the detour area, thereby helping to identify and eliminate obstacles that may affect operational safety. This algorithm can ensure that the calculation of the obstacle boundary is more accurate and reduce omissions. By extracting unlabeled areas and areas with insufficient coverage of navigation lights, they can be supplemented and corrected in high-precision maps and light networks to ensure that the map data is more complete and avoid blind spots. Optimizing unlabeled and insufficiently covered areas can improve the accuracy and reliability of the navigation lighting system, especially in complex airport environments, and enhance the comprehensiveness of the lighting system. By integrating multiple features such as the location to be optimized, obstacle boundaries, and unmarked areas, a more comprehensive optimization strategy can be formulated to ensure that the airport flight area map, navigation lighting system, and path planning can be effectively improved.

[0018] Optionally, the method also includes: generating an optimized high-precision map; importing the optimized high-precision map into a simulation tool to generate data to be verified; determining a personalized optimization strategy based on the data to be verified; and performing data optimization on the optimized high-precision map according to the personalized optimization strategy.

[0019] By adopting the above technical solutions, by generating optimized high-precision maps and importing them into simulation tools for verification, it is ensured that the optimization results of each stage are verified by actual scenarios. This multi-stage optimization process can ensure the accuracy and applicability of the final map and reduce the gap between theory and practical application. According to the data to be verified, a personalized optimization strategy can be formulated, which can be optimized for specific scenarios and needs, so that the map can adapt to different airport environments and operating conditions, and improve the flexibility and breadth of application of the map. After importing the optimized high-precision map into the simulation tool, various complex situations can be simulated in a virtual environment, and potential problems or imperfections of the map can be identified in advance. Simulation verification helps test the adaptability of the map in different operating scenarios to ensure that it can provide accurate navigation and path planning in actual use. Through the simulation tool, the data to be verified can be visualized, the effect of map optimization can be intuitively understood, and the simulation results can be used to verify whether the map meets the actual operation requirements to ensure accuracy and practicality. After generating the data to be verified, the personalized optimization strategy is determined according to specific needs. The personalized characteristics of this strategy can ensure that different airports or different flight areas can get tailored optimization solutions under specific operating requirements, thereby improving the adaptability and flexibility of the system.

[0020] In a second aspect of the present application, a device for optimizing high-precision map data of an airfield is provided, the device comprising an acquisition module and a processing module, wherein the acquisition module is used to acquire aircraft taxi trajectory data, ground vehicle path data and navigation light network data for an airport airfield; the processing module is used to analyze the aircraft taxi trajectory data, the ground vehicle path data and the navigation light network data to determine the features to be optimized of the airport airfield; the processing module is also used to invert the features to be optimized and generate an optimization strategy; the processing module is also used to optimize the data of the high-precision map corresponding to the airport airfield through the optimization strategy.

[0021] In the third aspect of the present application, an electronic device is provided, which includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs the method described above.

[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, the method described above is executed.

[0023] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages: By comprehensively utilizing aircraft taxi trajectories, ground vehicle paths, and navigation light network data, the lack of comprehensive information from a single data source is made up. Taxi trajectory data reflects the actual path of aircraft on different runways and taxiways, revealing potential trajectory deviations or inaccurate taxiway markings. Vehicle path data captures abnormal behavior in ground vehicle operations and helps locate obstacles or path design problems. Navigation light data reflects the coverage and status of lights, and finds navigation blind spots or unreasonable distribution of light signals. The dynamic operation behavior of airports is analyzed from multiple dimensions, covering static infrastructure and dynamic operation environments, making map optimization more comprehensive and accurate. By analyzing the collected data in real time, problems such as taxiway deviations, changes in obstacle distribution, or abnormal navigation lights can be quickly identified. The optimization strategy is generated through data-driven, without the need for long-term manual intervention, and can significantly shorten the map update cycle. The airport operation environment is complex and changeable, and dynamic perception and optimization methods ensure that the map reflects the latest operation status in a timely manner. Based on intelligent analysis of multi-source data, the features to be optimized are automatically inverted, avoiding the inefficiency of relying on manual experience in traditional optimization. The specific optimization plan is generated through algorithms, reducing human errors and improving the reliability of optimization results. Therefore, it is convenient to improve the accuracy of data optimization for high-precision maps. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of a flow chart of a method for optimizing high-precision map data of a flight zone provided in an embodiment of the present application; Figure 2 Another schematic diagram of a flow chart of a method for optimizing high-precision map data of a flight zone provided in an embodiment of the present application; Figure 3 A schematic diagram of a module of a flight zone high-precision map data optimization device provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0025] Explanation of the reference numerals: 31, acquisition module; 32, processing module; 41, processor; 42, communication bus; 43, user interface; 44, network interface; 45, memory. DETAILED DESCRIPTION

[0026] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0027] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0028] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0029] High-precision maps are key infrastructure to ensure the safety and efficiency of airport airfield operations, and are widely used in areas such as aircraft taxiing, ground vehicle scheduling, and navigation lighting system optimization.

[0030] At present, traditional high-precision map updates and optimizations mainly rely on a single perception method, such as manual surveys. However, this method is limited by labor costs and efficiency, has a long update cycle, and is difficult to quickly respond to the dynamic changes in airport operations, which in turn affects the data optimization accuracy of high-precision maps.

[0031] In order to solve the above technical problems, this application provides a method for optimizing high-precision map data of a flight area. Figure 1 , Figure 1 A flowchart of a method for optimizing high-precision map data of a flight zone provided in an embodiment of the present application. The method is applied to a server and includes steps S110 to S140, which are as follows: S110, obtaining aircraft taxiing trajectory data, ground handling vehicle path data, and navigation lighting network data for the airport flight area.

[0032] Specifically, a server is a computer hardware or software system specially designed to provide services and resources to other computers or devices. In the embodiment of the present application, the main function of the server is to manage, store and process the above data, and provide these data or services to the client device. The aircraft taxiing trajectory data is obtained through the track monitoring equipment on the aircraft, such as ADS-B, which records the specific path of the aircraft taxiing on the airport ground. Through this data, the taxiing route, speed, position and time of the aircraft can be known. Assuming that an aircraft is taxiing from the boarding gate to the runway, the ADS-B system will record each position point of the aircraft and generate a trajectory. Through the analysis of these trajectory data, the specific path of the aircraft taxiing can be accurately understood. Ground handling vehicle path data involves how ground handling vehicles in the airport, such as push trucks, garbage trucks, fuel trucks, etc., operate in the airport. Through sensors, such as UWB base stations or GPS positioning, the location and path of these vehicles are tracked to obtain their real-time operation information. In the airport, ground handling vehicles may need to drive from the cargo area to the aircraft, or perform maintenance in the runway area. Through UWB base stations or GPS technology, the server can obtain the path data of these vehicles in real time and ensure that their travel does not conflict with the aircraft's taxi route. The airport's navigation lighting system is used to guide aircraft to drive safely on runways, taxiways and aprons. Through sensors and control systems, the status of navigation lights, such as on or off, and the coverage of lights are monitored. Navigation lights may indicate which areas the aircraft is taxiing in, or guide pilots to find the correct taxi route. By monitoring the on and off status of these lights, the server can obtain real-time data on which areas have insufficient lighting or faults, helping to make timely maintenance or adjustments.

[0033] In a possible implementation, obtaining aircraft taxi trajectory data, ground vehicle path data and navigation light network data for an airport flight zone specifically includes: receiving original aircraft taxi trajectory data sent by an ADS-B system; receiving original ground vehicle path data sent by a UWB base station; receiving original navigation light network data sent by a lighting control system; processing the original aircraft taxi trajectory data, the original ground vehicle path data and the original navigation light network data to obtain the aircraft taxi trajectory data, the ground vehicle path data and the navigation light network data, wherein the data processing includes denoising, filtering and normalization processing.

[0034] Specifically, the ADS-B system is an automatic dependent surveillance broadcast system, which is a system for aircraft monitoring that can obtain the aircraft's location information, speed and flight trajectory in real time. The ADS-B system sends the aircraft's current location, speed, heading and other information through broadcast signals on the aircraft. Assuming that an aircraft is taxiing from the boarding gate to the runway, the ADS-B system will broadcast its location information regularly. After receiving these broadcast signals, the server can obtain the aircraft's real-time taxi trajectory data. The UWB base station is a high-precision positioning system used in complex environments such as airports to accurately track the path of ground handling vehicles. The UWB base station uses radio waves to locate ground handling vehicles with an accuracy of several centimeters to several decimeters. For example, ground handling vehicles may be located in real time through the UWB base station, and the system will record the movement path of each vehicle inside the airport, including the trajectory of the vehicle from the cargo area to the boarding gate. The navigation lighting system is used to provide visual guidance for aircraft guidance paths in the airport flight area, especially at night or when the visibility is low. The lighting control system records the on and off status of the lights and their positions. Suppose the taxiway lighting system of an airport is turned on on a certain section of the runway, the control system will record the status of the lights and provide this information to the server. The server can use this data to understand which areas of navigation lights are turned on or off.

[0035] After obtaining the raw data, the data needs to be processed to ensure the accuracy and consistency of the data for subsequent analysis and optimization. The purpose of denoising is to remove noise or unnecessary interference signals from sensor data. For example, aircraft taxi trajectory data may produce erroneous position information due to equipment errors or environmental interference. Denoising can delete these inaccurate data points. Filtering can help smooth data, eliminate high-frequency noise, and improve the accuracy of trajectory data. For example, in ground vehicle path data, filtering can remove instantaneous errors caused by low positioning accuracy. Normalization is to convert data from different sources into the same scale or format to facilitate unified analysis of data. For example, the data generated by the ADS-B system, UWB system, and lighting control system may have different units or coordinate systems. Normalization can ensure that these data can be compared and integrated under a unified standard.

[0036] S120, analyzing the aircraft taxiing trajectory data, ground handling vehicle path data, and navigation lighting network data to determine the features of the airport flight area to be optimized.

[0037] Specifically, features to be optimized refer to those issues discovered through data analysis that may affect the safety, efficiency or performance of airport operations. Through comprehensive analysis of aircraft taxi trajectories, ground vehicle paths and navigation lighting data, the server is able to identify potential areas for improvement. Suppose the server analyzes the data and finds that aircraft often deviate from the center line on a certain taxiway, and ground vehicles in the area also frequently detour. Coupled with insufficient coverage of navigation light signals in the area, this may increase the complexity of aircraft taxiing, especially in low visibility conditions. The server identifies these areas as features to be optimized and proposes optimization suggestions such as strengthening path marking, adjusting ground vehicle traffic paths, and improving navigation lighting facilities.

[0038] In a possible implementation, the aircraft taxi trajectory data, the ground vehicle path data, and the navigation light network data are analyzed to determine the features to be optimized in the airport flight area, specifically including: clustering the aircraft taxi trajectory data using a density clustering algorithm to obtain a high-frequency trajectory; determining a target taxi runway based on the high-frequency trajectory; obtaining a runway map of the target taxi runway from a high-precision map, and determining the center line marked in the runway map; calculating the deviation between the high-frequency trajectory and the center line marked in the runway map; if it is determined that the deviation exceeds a preset error range, determining a trajectory abnormal point corresponding to the deviation, and determining the trajectory abnormal point as a feature to be optimized.

[0039] Specifically, the density clustering algorithm is a method for identifying clusters based on the density relationship between data points. Here, it is used to analyze the aircraft taxi trajectory data to find which trajectory points are dense and repeated. High-frequency trajectories refer to the trajectory paths that appear repeatedly during the taxiing process of the aircraft, which may be the common routes for the aircraft to taxi. Assuming that multiple aircraft taxi from the same boarding gate to the same runway, the density clustering algorithm, such as the DBSCAN algorithm, will identify that these taxi paths are repeated, forming a dense cluster of trajectories, which are high-frequency trajectories. High-frequency trajectories represent the taxi paths commonly used by aircraft, so these trajectories can be used to determine the most commonly used taxi runways for aircraft. The target taxi runway refers to the runway or taxiway that is most commonly used by aircraft when taxiing. After analyzing the taxi trajectories of all aircraft, it may be found that the path to taxi to runway A is the most common, which can be determined as the target taxi runway.

[0040] Among them, the high-precision map contains detailed geographical information of the airport, including information such as taxiways, runways, and facility locations. After obtaining the runway map of the target taxiway, the centerline of the runway will be marked on the map as a reference line for the aircraft to taxi. The centerline of the target taxiway A is clearly marked in the high-precision map, and this centerline represents the ideal taxiing trajectory of the aircraft. The deviation between the high-frequency trajectory and the centerline in the runway map is calculated to see whether the aircraft taxis along the ideal centerline. If the aircraft's taxiing trajectory deviates from the centerline, it may indicate a problem, such as an unreasonable taxiway design or environmental factors during aircraft taxiing. Assuming that there is a deviation between the centerline of the target taxiway A and the aircraft's high-frequency trajectory, the calculated deviation is 5 meters, indicating that the aircraft failed to taxi accurately along the centerline during taxiing. If the calculated deviation exceeds a preset error range, for example, the deviation is greater than 5 meters, it is considered to be a trajectory anomaly, indicating that the aircraft taxiing has deviated from the normal path. These deviated trajectory points are marked as features to be optimized. Assume that the set error range is 5 meters, and it is found that the taxiing trajectories of some aircraft deviate from the center line by more than 10 meters. These deviated trajectory points will be identified as abnormal points and used as features to be optimized. The airport runway needs to be optimized, which may be improvements to the markings, taxiway width, guide lights, etc.

[0041] For example, suppose that at an airport, there are multiple track points along the taxiing path from the boarding gate to runway A. Through the density clustering algorithm, the server identifies this series of tracks as high-frequency tracks because multiple aircraft will taxi along this path. Then, the server extracts the runway map of runway A from the high-precision map and determines the marked centerline. Next, the server compares the high-frequency tracks with the centerline and finds that some aircraft's taxiing paths deviate from the centerline, for example, some deviations reach 6 meters, while the preset error range is 5 meters. Therefore, these track points where the taxiing tracks deviate from the centerline by more than 5 meters will be marked as "track anomalies" and used as features to be optimized. These anomalies may indicate problems in runway design, guidance signs, taxiway width, etc., which need to be optimized and improved. Ultimately, the airport can take measures based on these features to be optimized, such as adding or adjusting taxiway signs, improving flight path planning, and strengthening taxiway guide lights, thereby improving the safety and efficiency of airport operations.

[0042] In one possible implementation, the aircraft taxi trajectory data, ground vehicle path data, and navigation light network data are analyzed to determine the features to be optimized in the airport flight area, which specifically includes: determining the ground vehicle trajectory based on the ground vehicle path data; comparing the ground vehicle trajectory with the high-precision map to mark the path outside the map coverage area; obtaining the operating mode of the ground vehicle, the operating mode includes sharp turns and detours; determining path abnormality factors based on the operating mode of the ground vehicle; if it is determined that the number of occurrences of the path abnormality factors is greater than a preset number, then determining the sharp turn points and repeated detour points in the ground vehicle trajectory as features to be optimized.

[0043] Specifically, the high-precision map includes the detailed locations and layouts of roads, taxiways, runways, etc. within the airport. The actual driving trajectory of the ground handling vehicle is compared with these map data to identify which trajectories exceed the area marked on the map and mark them as paths outside the map coverage area. If a ground handling vehicle deviates from the planned route while driving along taxiway A and enters an unmarked area, this can be marked as a path outside the map coverage area, indicating that there may be a problem with the marking of the area, or the ground handling vehicle needs to adjust its driving path. The operation mode of the ground handling vehicle refers to the typical driving behavior of the vehicle within the airport, such as sharp turns and detours. These operation modes may affect the driving path of the ground handling vehicle due to road design, obstacles, environmental conditions, etc. If the ground handling vehicle frequently makes sharp turns on a certain section of road or bypasses certain obstacles, these behaviors can be identified as sharp turns and detours. By analyzing the operation mode of the ground handling vehicle, it can be determined whether there are abnormal factors that affect the normal driving of the vehicle. For example, frequent sharp turns may be due to unreasonable road design, unclear signs, or obstacles; while repeated detours may be due to certain areas that cannot be directly reached or obstacles. Suppose a ground handling vehicle frequently makes sharp turns on a certain taxiway at an airport, and detours are also common. The system will analyze these behaviors and determine their causes, such as possible problems with the taxiway design that prevent the vehicle from passing smoothly.

[0044] For example, if path anomalies occur frequently over a period of time and exceed the preset number threshold, these points are considered to be features to be optimized. These features indicate that certain areas or designs of the airport may have problems and need to be optimized. Suppose that over a period of time, there are 20 sharp turns in the same area by ground handling vehicles and 15 detours. These behaviors exceed the preset threshold, so the sharp turns and detours in the area will be marked as features to be optimized. It may be necessary to consider replanning this section of the road, adjusting the design of the taxiway, or adding guidance signs.

[0045] In a possible implementation, the aircraft taxi trajectory data, the ground vehicle path data, and the navigation light network data are analyzed to determine the features to be optimized in the airport flight area, which specifically includes: determining the navigation light signal according to the navigation light network data; associating the time series of the on and off states of the navigation light signal with the aircraft taxi trajectory data and the ground vehicle path data to mark the signal loss area; calculating the effective coverage range of the navigation light through the signal strength and the distribution of trajectory points; screening the signal coverage blind area distribution according to the signal loss area and the effective coverage range; determining the potential hardware problems and / or the coverage area to be expanded in the navigation light network according to the signal coverage blind area distribution; determining the potential hardware problems and / or the coverage area to be expanded in the navigation light network as the features to be optimized.

[0046] Specifically, signal loss areas refer to those areas where the paths of aircraft or ground handling vehicles pass when the navigation light signal is turned off. By comparing the on and off status of the lights with the aircraft taxi track and ground handling vehicle path data, it is possible to find out which areas vehicles or aircraft still need to pass through when the lights are turned off, thereby determining that there are problems with signal coverage in these areas. Suppose that during a certain period of time, the navigation lights of a taxiway in the airport are turned off, while aircraft or ground handling vehicles are driving in this area. Through analysis, the system can identify that the areas where these tracks are located are signal loss areas. Signal strength refers to the strength of the signal of the navigation light system in a specific area. The stronger the signal, the wider the coverage, and vice versa. Combined with the distribution of aircraft taxi tracks and ground handling vehicle path points, the effective coverage of the lights can be calculated through an algorithm, that is, which areas can be effectively covered by the light signal. If the light signal strength in a certain area is low and there are fewer track points distributed in this area, this means that the navigation light coverage in this area is insufficient. Signal coverage blind areas refer to those areas that are both in the signal loss area and not in the effective coverage of the navigation light. These areas are blind spots that the lighting system cannot cover, which may affect the normal operation of aircraft or ground handling vehicles and increase safety risks. Assuming that in a certain area, the intensity of the navigation light signal is weak and aircraft and ground handling vehicles frequently pass through this area, this area will be identified as a signal coverage blind area. If the signal coverage blind areas are distributed in a specific area, it may mean that there is a problem with the navigation light hardware in this area, or the lighting system needs to be expanded to cover these blind areas. Therefore, the system will analyze potential hardware failures or areas that need to be expanded based on the location and distribution of the blind areas. Assuming there is a blind area near the start of the runway, it may be because the lighting equipment in this area is faulty, or the lighting equipment is not sufficient to cover the area. The system can mark this area as a potential hardware problem area, or as an area where light coverage needs to be expanded.

[0047] Through the above analysis, the server determines which areas have coverage issues for navigation lights. These issues can be classified as features to be optimized, including potential hardware failures and expanded coverage areas. If the lighting equipment in a certain area is aging or malfunctioning, resulting in insufficient signal coverage, this area will be marked as a feature to be optimized. Similarly, if an area has insufficient lighting coverage and is frequently passed by aircraft taxiing or ground handling vehicle paths, this area will also be expanded as an area to be optimized.

[0048] S130, invert the features to be optimized and generate an optimization strategy.

[0049] Specifically, inversion in the embodiment of the present application refers to the process of analyzing the determined features to be optimized, such as track deviation, signal loss area, path anomaly, etc., to deduce the cause or optimization measures. Through the inversion process, the direction and strategy of optimization are identified. Assuming that it has been found through analysis that there are track deviations on certain taxiways, the process of inversion is to find out the cause of this deviation, which may be due to the calibration error of the runway or the layout problem of the navigation lights. Through inversion, specific areas that need improvement can be identified. Generating an optimization strategy refers to formulating a specific optimization plan or adjustment measures based on the results of the inversion analysis. The purpose of the optimization strategy is to improve the operational safety and efficiency of the airport flight area and ensure that it can respond to various changes or emergencies in real time. If the inversion analysis finds that there are frequent track deviations on a taxiway, the optimization strategy may be to adjust the centerline of the runway or enhance the light signal coverage in the area.

[0050] In a possible implementation, the features to be optimized are inverted to generate an optimization strategy, specifically including: according to the features to be optimized, fitting the taxiway centerline using the density center of the aircraft taxi trajectory data to obtain the position to be optimized; according to the features to be optimized, extracting the sharp turning points of the trajectory in the detour area, and calculating the obstacle boundary using the convex hull algorithm; according to the features to be optimized, extracting the unlabeled areas and / or the areas with insufficient coverage of navigation lights; and generating an optimization strategy based on the position to be optimized and the obstacle boundary, as well as the unlabeled areas and / or the areas with insufficient coverage of navigation lights.

[0051] Specifically, the density center refers to clustering the aircraft taxi trajectory data to find the areas with the densest trajectories, which represent the paths where aircraft often taxi. Through density center fitting, the center line of the taxiway can be determined. If this center line deviates from the existing annotated center line in the map, then these areas are considered to be locations to be optimized. Assume that some areas of a taxiway at an airport (such as taxiway A) frequently experience trajectory deviations, reflecting that the aircraft's taxi path is inconsistent with the existing taxiway center line. Through density center fitting, the correct center line of taxiway A can be determined more accurately, marking the areas that need to be optimized. By analyzing the path data of ground handling vehicles or the taxi trajectory of the aircraft, the sharp turning points that occur during the taxiing process can be identified. These sharp turning points may occur due to obstacles in the path, incorrect guidance, or insufficient navigation lights. The convex hull refers to the minimum closed boundary formed by a set of points, which is used to represent the outer boundary of an area. Here, the convex hull algorithm is used to calculate the obstacle boundary around the sharp turning point of the trajectory and identify the location of the obstacle. In the path data of a taxiway, ground vehicles or aircraft make a sharp turn at a certain point, indicating that there may be obstacles (such as equipment, buildings, or other obstructions) in this part of the area. By extracting these sharp turn points and applying the convex hull algorithm, you can determine the boundaries of these obstacles and mark the areas that need to be optimized. Unlabeled areas are areas that have not been marked or are not clearly marked in the existing high-precision map. These areas may be new facilities, remodeled parts, or places where changes have occurred in operation. Inadequate navigation light coverage areas refer to areas that are not covered by navigation light signals. These areas may have navigation blind spots, resulting in aircraft or ground vehicles being unable to accurately navigate in these areas. By analyzing the navigation light network data, it may be found that some areas of the airport (such as a taxiway or parking area) have insufficient light signal coverage, which may cause safety hazards. On the other hand, some areas are not fully labeled, perhaps because these places were missed when the map was updated, or there was new construction recently and the map has not been updated.

[0052] Finally, by combining the previous analysis results, a specific optimization plan is formulated. The optimization strategy includes adjusting the calibration of the taxiway (according to the taxiway centerline fitted by the density center), clearing or bypassing obstacles (by adjusting the path or removing obstacles), updating the map to cover unmarked areas, and expanding or improving the coverage of navigation lights to ensure that all key areas are fully covered. Suppose through analysis, it is found that the centerline of taxiway A is deviated, and the navigation light coverage in a certain area is insufficient, and some areas are not marked. The optimization strategy may include: recalibrating the centerline of taxiway A, adding lighting facilities in this area, updating the map to include unmarked areas, and ensuring that the light coverage is more comprehensive to solve the blind spot problem.

[0053] For example, suppose an airport has the following problems: the aircraft taxiing trajectory on taxiway A deviates, and the centerline deviates from the actual taxiing route. There is a sharp turn on taxiway B, and it is suspected that there are obstacles (such as equipment, buildings, etc.) in this area. Some areas of taxiway C have insufficient navigation lighting coverage, which makes it difficult for ground handling vehicles to navigate accurately at night. There is a new area on taxiway D, but it has not been marked in the existing map. First, analyze taxiway A through aircraft taxiing trajectory data, determine the center path that aircraft most often pass through, refit the centerline of taxiway A, and mark the areas that need to be optimized. Through the ground handling vehicle path data, find the sharp turn points of taxiway B, apply the convex hull algorithm to determine the obstacle boundaries of these sharp turn points, and mark the parts that need to be optimized. Through the data of the lighting control system, find the blind area of ​​lighting coverage of taxiway C, and plan to add lighting facilities to improve the navigation coverage of this area. Through the high-precision map data, identify the new area of ​​taxiway D and update the map to ensure that the area is included in the map. Finally, based on these analysis results, the generated optimization strategies include: recalibrating the centerline of taxiway A, adding obstacle handling to taxiway B, improving the navigation lighting coverage of taxiway C, and updating the map annotation of taxiway D. By implementing these strategies, the operational efficiency and safety of the airport flight area can be improved.

[0054] S140. Optimize data of the high-precision map corresponding to the airport flight area through an optimization strategy.

[0055] Specifically, data optimization refers to the correction and adjustment of existing map data by applying optimization strategies, so that the high-precision map is more in line with the actual operational needs of the airport. The optimized map not only improves accuracy, but also provides more accurate positioning, path planning and navigation services to ensure the safety and efficiency of the airport flight area. Suppose that in the high-precision map of a taxiway in the airport flight area, there is a deviation in the calibration of the centerline of the taxiway, and there are blind spots in the coverage of navigation lights, and facilities in some areas have not yet been marked on the map. Through the previous analysis, an optimization strategy was generated, including refitting the centerline of the taxiway, expanding the coverage of navigation lights, and updating unmarked areas in the map. After receiving these strategies, the server will modify the taxiway centerline, light coverage area, and new facilities in the high-precision map to optimize the map data.

[0056] In one possible implementation, refer to Figure 2 , Figure 2Another flow chart of a method for optimizing high-precision map data of a flight zone provided in an embodiment of the present application. The method includes steps S210 to S240, which are as follows: S210, generating an optimized high-precision map; S220, importing the optimized high-precision map into a simulation tool to generate data to be verified; S230, determining a personalized optimization strategy based on the data to be verified; S240, optimizing the data of the optimized high-precision map according to the personalized optimization strategy.

[0057] Specifically, in the previous steps, optimization strategies were generated based on various data sources and data optimization was performed on the original map. The optimized high-precision map is corrected, updated and improved data that can better reflect the actual situation of the airport flight area. Assuming that the original map has problems such as deviation of the taxiway centerline and blind spots in light coverage, after optimization, the centerline position is corrected in the map and the light facility coverage area is added. The simulation tool is used to simulate and test the effect of actual operations on the optimized map. After importing the optimized map into the simulation tool, "data to be verified" can be generated. These data are relevant information obtained by simulating the operation process of aircraft, ground handling vehicles, etc. on the optimized map. Import the optimized taxiway centerline, lighting facilities, etc. into the simulation tool, simulate the actual taxiing path of aircraft and ground handling vehicles on this map, and generate actual operation data, such as taxiing trajectory, movement path of ground handling vehicles, etc. The generation of data to be verified will expose some new problems or optimization space. For example, the taxiing trajectory in some areas may not meet expectations, or there are still blind spots in light coverage. Based on these data to be verified, the system can analyze and propose personalized optimization strategies to solve specific problems. Suppose that through the simulation tool test, it is found that although the deviation of the optimized taxiway A has been reduced, there are still problems in certain specific weather conditions, making it difficult for the aircraft to taxi accurately. In this case, the simulation results will indicate these problems and propose personalized optimization strategies, such as increasing the density of navigation lights or re-adjusting the calibration of the taxiway. According to the problems verified in the simulation tool, the map data is further adjusted and the personalized optimization strategy is applied. This means that the high-precision map is revised again to address the specific problems exposed in the simulation and ensure that the map is more accurate and efficient under different operating conditions. If the simulation shows that taxiway A has problems in haze weather, the personalized optimization strategy may be to strengthen the lighting markings in the area or adjust the design of the taxiway, such as increasing the brightness of the markings or modifying them locally. The optimized map will include these changes, making the map more adaptable to various complex operating environments in actual use.

[0058] This application also provides a high-precision map data optimization device for the flight area, referring to Figure 3 , Figure 3A schematic diagram of a module of a high-precision map data optimization device for an airfield provided in an embodiment of the present application. The device is a server, and the server includes an acquisition module 31 and a processing module 32, wherein the acquisition module 31 acquires aircraft taxiing trajectory data, ground vehicle path data, and navigation light network data for an airport airfield; the processing module 32 analyzes the aircraft taxiing trajectory data, ground vehicle path data, and navigation light network data to determine the features to be optimized for the airport airfield; the processing module 32 inverts the features to be optimized and generates an optimization strategy; the processing module 32 optimizes the data of the high-precision map corresponding to the airport airfield through the optimization strategy.

[0059] In a possible implementation, the acquisition module 31 acquires the aircraft taxi trajectory data, ground vehicle path data and navigation light network data for the airport flight zone, specifically including: the acquisition module 31 receives the original aircraft taxi trajectory data sent by the ADS-B system; the acquisition module 31 receives the original ground vehicle path data sent by the UWB base station; the acquisition module 31 receives the original navigation light network data sent by the lighting control system; the processing module 32 processes the original aircraft taxi trajectory data, the original ground vehicle path data and the original navigation light network data to obtain the aircraft taxi trajectory data, the ground vehicle path data and the navigation light network data, and the data processing includes denoising, filtering and normalization processing.

[0060] In one possible implementation, the processing module 32 analyzes the aircraft taxi trajectory data, the ground vehicle path data, and the navigation light network data to determine the features to be optimized in the airport flight area, specifically including: the processing module 32 uses a density clustering algorithm to cluster the aircraft taxi trajectory data to obtain a high-frequency trajectory; the processing module 32 determines the target taxi runway based on the high-frequency trajectory; the processing module 32 obtains the runway map of the target taxi runway from the high-precision map, and determines the center line marked in the runway map; the processing module 32 calculates the deviation between the high-frequency trajectory and the center line marked in the runway map; if the processing module 32 determines that the deviation exceeds a preset error range, it determines the trajectory anomaly point corresponding to the deviation, and determines the trajectory anomaly point as the feature to be optimized.

[0061] In one possible implementation, the processing module 32 analyzes the aircraft taxi trajectory data, the ground vehicle path data, and the navigation light network data to determine the features to be optimized in the airport flight area, and specifically includes: the processing module 32 determines the ground vehicle trajectory based on the ground vehicle path data; the processing module 32 compares the ground vehicle trajectory with the high-precision map, and marks the path outside the map coverage area; the acquisition module 31 acquires the operating mode of the ground vehicle, and the operating mode includes sharp turns and detours; the processing module 32 determines the path abnormality factors based on the operating mode of the ground vehicle; if the processing module 32 determines that the path abnormality factors occur more than the preset number of times, then the sharp turn points and repeated detour points in the ground vehicle trajectory are determined as features to be optimized.

[0062] In a possible implementation, the processing module 32 analyzes the aircraft taxi trajectory data, the ground vehicle path data and the navigation light network data to determine the features to be optimized in the airport flight zone, which specifically includes: the processing module 32 determines the navigation light signal according to the navigation light network data; the processing module 32 associates the time series of the on state and the off state of the navigation light signal with the aircraft taxi trajectory data and the ground vehicle path data to mark the signal loss area; the processing module 32 calculates the effective coverage range of the navigation light through the signal strength and the distribution of trajectory points; the processing module 32 screens the signal coverage blind area distribution according to the signal loss area and the effective coverage range; the processing module 32 determines the potential hardware problems and / or the coverage area to be expanded in the navigation light network according to the signal coverage blind area distribution; the processing module 32 determines the potential hardware problems and / or the coverage area to be expanded in the navigation light network as the features to be optimized.

[0063] In a possible implementation, the processing module 32 inverts the features to be optimized and generates an optimization strategy, which specifically includes: the processing module 32 uses the density center of the aircraft taxi trajectory data to fit the taxiway centerline according to the features to be optimized to obtain the position to be optimized; the processing module 32 extracts the sharp turning points of the detour area according to the features to be optimized, and uses the convex hull algorithm to calculate the obstacle boundary; the processing module 32 extracts the unmarked area and / or the area with insufficient navigation light coverage according to the features to be optimized; the processing module 32 generates an optimization strategy based on the position to be optimized and the obstacle boundary, as well as the unmarked area and / or the area with insufficient navigation light coverage.

[0064] In a possible implementation, the processing module 32 generates an optimized high-precision map; the processing module 32 imports the optimized high-precision map into a simulation tool to generate data to be verified; the processing module 32 determines a personalized optimization strategy based on the data to be verified; the processing module 32 optimizes the data of the optimized high-precision map according to the personalized optimization strategy.

[0065] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, 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.

[0066] The present application also provides an electronic device, referring to Figure 4 , Figure 4 The electronic device may include: at least one processor 41 , at least one network interface 44 , a user interface 43 , a memory 45 , and at least one communication bus 42 .

[0067] The communication bus 42 is used to realize the connection and communication between these components.

[0068] The user interface 43 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 43 may also include a standard wired interface and a wireless interface.

[0069] The network interface 44 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0070] Among them, the processor 41 may include one or more processing cores. The processor 41 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 45, and calling data stored in the memory 45. Optionally, the processor 41 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 41 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; 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 41, and it can be implemented separately through a chip.

[0071] Among them, the memory 45 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 45 includes a non-transitory computer-readable storage medium. The memory 45 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 45 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an 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 data involved in the above-mentioned method embodiments, etc. The memory 45 may also be optionally at least one storage device located away from the aforementioned processor 41. As Figure 4 As shown, the memory 45 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for a method for optimizing high-precision map data of a flight zone.

[0072] exist Figure 4 In the electronic device shown, the user interface 43 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 41 can be used to call an application program stored in the memory 45 for a method for optimizing high-precision map data of a flight zone. When executed by one or more processors, the electronic device executes one or more methods in the above-mentioned embodiments.

[0073] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0074] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors, the electronic device executes one or more of the methods described in the above embodiments.

[0075] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0076] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0077] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0078] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0079] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0080] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, it will be easy for those skilled in the art to think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for optimizing high-precision map data of an airfield, characterized in that: The method comprises: Obtain aircraft taxi trajectory data, ground handling vehicle path data, and navigation lighting network data for airport flight areas; Analyzing the aircraft taxi trajectory data, the ground handling vehicle path data, and the navigation light network data to determine the features of the airport flight area to be optimized; Inverting the features to be optimized to generate an optimization strategy; Through the optimization strategy, data optimization is performed on the high-precision map corresponding to the airport flight area.

2. The method for optimizing high-precision flight zone map data according to claim 1, characterized in that: The acquisition of aircraft taxiing trajectory data, ground handling vehicle path data, and navigation lighting network data for the airport flight area specifically includes: Receive the original aircraft taxi trajectory data sent by the ADS-B system; Receive the original ground vehicle path data sent by the UWB base station; Receive the original navigation lighting network data sent by the lighting control system; The original aircraft taxi trajectory data, the original ground vehicle path data and the original navigation light network data are processed to obtain the aircraft taxi trajectory data, the ground vehicle path data and the navigation light network data, wherein the data processing includes denoising, filtering and normalization processing.

3. The method for optimizing high-precision flight zone map data according to claim 1, characterized in that: The analyzing the aircraft taxiing trajectory data, the ground handling vehicle path data and the navigation light network data to determine the features to be optimized of the airport flight area specifically includes: Clustering the aircraft taxi trajectory data using a density clustering algorithm to obtain a high-frequency trajectory; determining a target taxiing runway according to the high-frequency trajectory; Acquire a runway map of the target taxiway from the high-precision map, and determine a center line marked in the runway map; calculating a deviation between the high frequency trajectory and a centerline marked in the runway map; If it is determined that the deviation exceeds the preset error range, a trajectory abnormal point corresponding to the deviation is determined, and the trajectory abnormal point is determined as the feature to be optimized.

4. The method for optimizing high-precision flight zone map data according to claim 3, characterized in that: The analyzing the aircraft taxiing trajectory data, the ground handling vehicle path data and the navigation light network data to determine the features to be optimized of the airport flight area specifically includes: Determining a ground handling vehicle trajectory according to the ground handling vehicle path data; Comparing the ground handling vehicle trajectory with the high-precision map, and marking the paths outside the map coverage area; acquiring an operation mode of a ground handling vehicle, wherein the operation mode includes sharp turns and detours; determining path abnormality factors according to the operation mode of the ground handling vehicle; If it is determined that the number of occurrences of the path abnormality factor is greater than a preset number, the sharp turning point and the repeated detour point in the ground handling vehicle trajectory are determined as the features to be optimized.

5. The method for optimizing high-precision flight zone map data according to claim 3, characterized in that: The analyzing the aircraft taxiing trajectory data, the ground handling vehicle path data and the navigation light network data to determine the features to be optimized of the airport flight area specifically includes: determining a navigation light signal according to the navigation light network data; Associating the time series of the on and off states of the navigation light signal with the aircraft taxiing trajectory data and the ground handling vehicle path data to mark the signal loss area; The effective coverage of the navigation light is calculated through the signal strength and the distribution of trajectory points; According to the signal loss area and the effective coverage range, the signal coverage blind area distribution is obtained by screening; Determine potential hardware problems and / or coverage areas to be expanded in the navigation light network according to the signal coverage blind area distribution; Potential hardware problems and / or coverage areas to be expanded in the navigation light network are determined as the features to be optimized.

6. The method for optimizing high-precision flight zone map data according to claim 1, characterized in that: The inversion of the feature to be optimized to generate an optimization strategy specifically includes: According to the features to be optimized, the center line of the taxiway is fitted using the density center of the aircraft taxi trajectory data to obtain a position to be optimized; According to the features to be optimized, the sharp turning points of the trajectory in the detour area are extracted, and the obstacle boundary is calculated by using the convex hull algorithm; Extracting unlabeled areas and / or areas with insufficient navigation light coverage according to the features to be optimized; The optimization strategy is generated based on the position to be optimized and the obstacle boundary, as well as the unlabeled area and / or the area with insufficient navigation light coverage.

7. The method for optimizing flight zone high-precision map data according to claim 1, characterized in that: The method further comprises: Generate optimized high-precision maps; Importing the optimized high-precision map into a simulation tool to generate data to be verified; Determine a personalized optimization strategy based on the data to be verified; According to the personalized optimization strategy, data optimization is performed on the optimized high-precision map.

8. A device for optimizing high-precision map data of an airfield, characterized in that: The device comprises an acquisition module (31) and a processing module (32), wherein: The acquisition module (31) is used to acquire aircraft taxiing trajectory data, ground handling vehicle path data and navigation lighting network data for the airport flight area; The processing module (32) is used to analyze the aircraft taxiing trajectory data, the ground handling vehicle path data and the navigation light network data to determine the features to be optimized of the airport flight area; The processing module (32) is further used to invert the feature to be optimized and generate an optimization strategy; The processing module (32) is also used to optimize the data of the high-precision map corresponding to the airport flight area through the optimization strategy.

9. An electronic device, characterized in that: The electronic device comprises a processor (41), a memory (45), a user interface (43) and a network interface (44), wherein the memory (45) is used to store instructions, the user interface (43) and the network interface (44) are both used to communicate with other devices, and the processor (41) is used to execute the instructions stored in the memory (45) so that the electronic device executes the method according to any one of claims 1 to 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 to 7 is performed.

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