An aircraft flight data card acquisition method based on 5g enhanced positioning

Abstract

The application discloses an aircraft flight data card acquisition method based on 5G enhanced positioning, comprising the following steps: a plurality of enhanced positioning platforms based on 5G signal communication are constructed in advance; the enhanced positioning platform realizes the positioning of the aircraft through one or more LPP sessions, and communicates data with the aircraft; the enhanced positioning platform shares the request load of the aircraft to the platform through load balancing technology, and supports the heat expansion platform processing capacity to adapt to various different operator networks; the real-time flight data and the historical flight data of the aircraft are acquired through the enhanced positioning platform, and the obtained real-time flight data and the historical flight data of the network entry target aircraft are sorted to obtain the aircraft flight card data. The application can improve the low-altitude general aircraft monitoring and communication capability, realize the track recording and position monitoring of the aircraft and unmanned aerial vehicle target, and solve the problems of low, small and slow network entry unmanned aerial vehicle monitoring difficulty and lack of efficient and convenient monitoring management platform.

Description

Technical Field

[0001] The present invention belongs to the field of avionics communication technology, and in particular relates to a method for obtaining an aircraft trip data card based on 5G enhanced positioning. Background Art

[0002] General aviation aircraft are gradually gaining popularity in civilian applications, with typical scenarios including urban logistics, feeder logistics, and emergency rescue. In these scenarios, general aviation aircraft operate at low altitudes, with a wide variety of aircraft types and in complex and changing flight areas. Existing ground-based civil air traffic control surveillance radars and communications equipment are unable to effectively guarantee surveillance and communication capabilities during low-altitude flights, and cannot meet the requirements for continuous, reliable, and cost-effective surveillance and communication across all time, air, and geographical regions.

[0003] Due to the lack of a reliable general aviation aircraft monitoring platform, general aviation aircraft may pose serious safety hazards to people, property and infrastructure. Therefore, it is necessary to study a method that can improve the surveillance and communication capabilities of low-altitude general aviation aircraft and realize the trajectory recording and position monitoring of aircraft and UAV targets. Summary of the Invention

[0004] In view of this, the present invention proposes a method for obtaining aircraft travel data cards based on 5G enhanced positioning. The present invention can improve the monitoring and communication capabilities of low-altitude general-purpose aircraft, realize the trajectory recording and position monitoring of aircraft and UAV targets, and solve the problems of difficulty in monitoring low, small, and slow-accessing UAVs and the lack of an efficient and convenient monitoring and management platform.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] The present invention provides a method for obtaining an aircraft trip data card based on 5G enhanced positioning, comprising:

[0007] Pre-build multiple enhanced positioning platforms based on 5G signal communications;

[0008] The enhanced positioning platform locates the aircraft through one or more LPP sessions and communicates data with the aircraft.

[0009] The enhanced positioning platform uses load balancing technology to share the load of aircraft requests to the platform, and supports hot expansion of platform processing capabilities to adapt to a variety of different operator networks;

[0010] The real-time and historical travel data of aircraft are acquired through the enhanced positioning platform, and the real-time and historical travel data of the target aircraft in the network are collated to obtain the aircraft travel card data.

[0011] Preferably, multiple LPP sessions are supported concurrently, including positioning capability transmission, assistance data transmission, location information transmission, error handling, and session termination, wherein:

[0012] Positioning capability transmission is one-way and only supports the transmission of the target's positioning capability to the server. Positioning capability refers to the ability to support different positioning methods, the ability to support different aspects of a specific positioning method, and the ability to be shared by different positioning methods.

[0013] Assisted data transmission is one-way and only supports the transmission of the server's auxiliary data to the target. The auxiliary data is transmitted through a unicast channel. In addition to sending the auxiliary data that matches the target request, the server also supports sending one or more additional LPP messages to transmit other auxiliary data. The 5G-based enhanced positioning platform uses the BNC method to obtain auxiliary positioning data, which is then aggregated on the auxiliary positioning server and sent to other enhanced positioning platforms to optimize positioning data accuracy.

[0014] Position information transmission is one-way and only supports sending the target's position information to the server. The position information includes the position estimate and the parameters used for position calculation. In addition to sending the position information that matches the server request, the target also supports sending one or more additional LPP messages to convey other position information.

[0015] Error handling refers to one party notifying the other party that the LPP message it received is erroneous or does not match the request it sent. Error handling supports bidirectional operation, that is, both the target and the server can send error handling to each other;

[0016] Session termination means that one party notifies the other party that it will terminate the ongoing LPP session. Session termination supports bidirectional occurrence, that is, both the target and the server can terminate the ongoing LPP session.

[0017] Preferably, the enhanced positioning platform has a cell data self-learning function, and the learning steps include:

[0018] Step S1: Calculate the average latitude and longitude of all recent positioning results under the current enhanced positioning platform;

[0019] Step S2: traverse each positioning point and determine the distance between the position calculated in step S1 and the average position one by one. If the position with the largest distance from the average position exceeds a preset distance threshold, the position with the largest distance from the average position is eliminated, and then steps S1 to S2 are repeated until the distance from all positioning points to the average position is less than the distance threshold.

[0020] Step S3: Determine the number of remaining positioning points. If there is only one, it is considered that the sample size of the cell is small, and the probability of the calculated position being biased is high, so the calculation of the cell position is abandoned.

[0021] Step S4: If the number is greater than 1, the positioning results of the remaining positioning points are used to calculate the average value of the longitude and latitude as the location of the cell.

[0022] Optimally, the enhanced positioning platform chooses the message queue Kafka system for construction, and optimizes the parameter configuration of the Kafka system to maximize the system throughput. During the optimization process:

[0023] First, through two feature screenings, we obtained the feature set that has the greatest impact on performance in the trip data;

[0024] Orthogonal experimental design was used to select representative samples, and the LASSO algorithm of machine learning was used to screen the samples to obtain a prediction model between performance and characteristics.

[0025] Finally, based on the feature correlation and feature boundary problems, the final performance prediction model is obtained, and the optimization algorithm of the genetic algorithm is designed and implemented to obtain the performance-optimal configuration of the given resources.

[0026] Preferably, the Kafka system uses key-value pairs for feature configuration.

[0027] Supports modification of features in travel data through configuration files or programs;

[0028] The features that affect Kafka system performance are divided into two modules: Broker module and Produce module.

[0029] The Broke module mainly affects the message writing disk cycle, message clearing threshold, log control, cluster control, and Zookeeper control of the server cluster;

[0030] The Producer module mainly affects the way the Producer sends messages to the cluster, whether it is compressed, whether it is batched, the number of failed retries, the message timeout, and the serialization method;

[0031] The Kafka system presents travel data in a table format, which includes the feature name, description, type, default value, valid value, and importance. By analyzing the table, effective information about the feature is obtained, and feature screening is performed.

[0032] Preferably, during the process of obtaining the aircraft itinerary data card, the following operations are further performed:

[0033] Predetermine the communication distances of multiple enhanced positioning platforms and define the coverage areas of the signals of each enhanced positioning platform;

[0034] Summarize and count the coverage ranges of multiple enhanced positioning platform signals to obtain a first range in the world coordinate system;

[0035] For any aircraft, its location can be determined in real time based on its corresponding travel data;

[0036] When the aircraft reaches the boundary of the first range, a boundary crossing reminder message is sent to the aircraft via the enhanced positioning platform;

[0037] After receiving the over-boundary warning message, the aircraft turns on the over-boundary positioning mode and sends a final reply to the enhanced positioning platform, while returning the current coordinate position information;

[0038] After receiving the response, the enhanced positioning platform records the aircraft number and its coordinate position information;

[0039] After the aircraft turns on the cross-border positioning mode, it will actively record its own travel trajectory and upload the travel trajectory synchronously after it accesses the enhanced positioning platform next time;

[0040] After the aircraft is connected again, the enhanced positioning platform searches for the coordinate location information returned by the aircraft in its last reply, and uses the aircraft's travel trajectory when it was not connected to reproduce the travel data of the aircraft during the period of loss of contact.

[0041] Preferably, when the aircraft reaches the boundary of the first range, the enhanced positioning platform further performs the following operations:

[0042] Collecting statistics on the data of the aircraft that lost contact outside the first range based on the coordinate position information and time of the last return of the multiple aircraft determined and recorded by the multiple enhanced positioning platforms;

[0043] Determine the distribution density of missing aircraft in the current time period and the area where the current aircraft is located based on the statistical results;

[0044] When the distribution density of the missing aircraft in the location area exceeds a preset density threshold, a miniaturized enhanced positioning platform is deployed toward the aircraft;

[0045] After deploying a miniaturized enhanced positioning platform, the aircraft proactively initiates positioning communication with the aircraft that has lost contact outside the first range and obtains one-time travel data, which is then returned to the enhanced positioning platform;

[0046] After the lost aircraft obtains its trip data through the miniaturized enhanced positioning platform, it releases the trip data record and starts recording new trip data;

[0047] The enhanced positioning platform updates the travel data of the lost aircraft based on the one-time travel data.

[0048] The present invention has achieved at least the following beneficial effects:

[0049] 1. The present invention can improve the monitoring and communication capabilities of low-altitude general-purpose aircraft, realize the trajectory recording and position monitoring of aircraft and UAV targets, and solve the problems of difficulty in monitoring low-altitude, small, and slow-accessing UAVs and the lack of an efficient and convenient monitoring and management platform.

[0050] 2. Use load balancing to share the request load of drones to the platform, and support hot expansion of platform processing capabilities, which can improve the platform's ability to receive and process massive aircraft location data.

[0051] Other advantages, objectives and features of the present invention will be described in the following description and will be apparent to those skilled in the art to some extent, or those skilled in the art can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to make the purpose, technical solutions and beneficial effects of the present invention more clear, the present invention provides the following drawings for illustration:

[0053] Figure 1 This is a flowchart of a method for obtaining an aircraft itinerary data card based on 5G enhanced positioning in an embodiment of the present invention;

[0054] Figure 2 This is a system architecture diagram of a mobile communication positioning enhancement platform according to an embodiment of the present invention;

[0055] Figure 3 Schematic diagram of enhanced bit platform base station data self-learning in an embodiment of the present invention;

[0056] Figure 4 A diagram showing the working principle of the optimization configuration step in an embodiment of the present invention;

[0057] Figure 5 A diagram showing the processing process of a performance prediction model based on LASSO in an embodiment of the present invention;

[0058] Figure 6 Schematic diagram of the LPP protocol in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0060] The present invention provides a method for obtaining aircraft itinerary data card based on 5G enhanced positioning, referring to Figure 1 ,include:

[0061] Pre-build multiple enhanced positioning platforms based on 5G signal communications;

[0062] The enhanced positioning platform locates the aircraft through one or more LPP sessions and communicates data with the aircraft.

[0063] The enhanced positioning platform uses load balancing technology to share the load of aircraft requests to the platform, and supports hot expansion of platform processing capabilities to adapt to a variety of different operator networks;

[0064] The real-time and historical travel data of aircraft are acquired through the enhanced positioning platform, and the real-time and historical travel data of the target aircraft in the network are collated to obtain the aircraft travel card data.

[0065] The working principle and beneficial effects of the above technical solution are as follows: To achieve the acquisition of real-time and historical aircraft itinerary information, aircraft itinerary information acquisition technology is studied, and enhanced positioning based on 5G network targets is studied. The real-time and historical itinerary data of the target aircraft connected to the network is organized and provided as aircraft / drone itinerary card data (with a Class I target labeled). A data interface with drones is added, and drone itinerary data, positioning data, and other data are provided to the access platform, enabling trajectory recording and location monitoring of aircraft and drone targets. It also has the following features: 1. Positioning capability transmission capability; 2. Auxiliary data transmission capability; 3. Location information transmission capability; 4. Error handling capability; 5. Session termination capability. The enhanced positioning platform is based on the Secure User Plane Location Service (SUPL) protocol defined by the Open Mobile Alliance (OMA) standards organization. The platform uses load balancing to share the load of drone requests to the platform, and supports hot expansion of platform processing capabilities, and can adapt to the networks of China Mobile, China Telecom, and China Unicom. The drone positioning process of the enhanced positioning platform is reflected by one or more LPP (LTE Positioning Protocol) sessions. LPP sessions include the following types: positioning capability transmission, auxiliary data transmission, location information transmission, error handling, and session termination. The network structure diagram is as follows: Figure 2 As shown:

[0066] 1) The platform uses load balancing to share the load of drone requests to the platform and supports hot expansion of platform processing capacity;

[0067] 2) The reference network directly adopts the RINEX data format, which enhances the versatility and compatibility of the reference network;

[0068] 3) Support multi-mode positioning;

[0069] 4) Support sending multi-mode auxiliary enhanced data to the terminal;

[0070] 5) Adapt to the networks of China Mobile, China Telecom and China Unicom;

[0071] The platform supports distributed deployment; the platform functions in a modular structure;

[0072] Supported protocols and algorithms:

[0073] 1) Satellite systems supported by the platform: GPS satellites; BeiDou (BD) satellites; GLONASS satellites

[0074] 2) Auxiliary data format supported by the platform: RINEX format

[0075] 3) Position calculation method supported by the platform: MS-Based (MSB), in which the mobile terminal performs positioning and navigation measurements and is responsible for its own positioning calculations, while orbital auxiliary information comes from the network. This method is suitable for continuous positioning mode, but it places high demands on the computing resources of the terminal system.

[0076] 4) Bearer protocol supported by the platform: LPP

[0077] 5) Proxy module supported by the platform: Proxy mode.

[0078] In a preferred embodiment, multiple LPP sessions are supported concurrently, including positioning capability transmission, assistance data transmission, location information transmission, error handling, and session termination, wherein:

[0079] Positioning capability transmission is one-way and only supports the transmission of the target's positioning capability to the server. Positioning capability refers to the ability to support different positioning methods, the ability to support different aspects of a specific positioning method, and the ability to be shared by different positioning methods.

[0080] Assisted data transmission is one-way and only supports the transmission of the server's auxiliary data to the target. The auxiliary data is transmitted through a unicast channel. In addition to sending the auxiliary data that matches the target request, the server also supports sending one or more additional LPP messages to transmit other auxiliary data. The 5G-based enhanced positioning platform uses the BNC method to obtain auxiliary positioning data, which is then aggregated on the auxiliary positioning server and sent to other enhanced positioning platforms to optimize positioning data accuracy.

[0081] Position information transmission is one-way and only supports sending the target's position information to the server. The position information includes the position estimate and the parameters used for position calculation. In addition to sending the position information that matches the server request, the target also supports sending one or more additional LPP messages to convey other position information.

[0082] Error handling refers to one party notifying the other party that the LPP message it received is erroneous or does not match the request it sent. Error handling supports bidirectional operation, that is, both the target and the server can send error handling to each other;

[0083] Session termination means that one party notifies the other party that it will terminate the ongoing LPP session. Session termination supports bidirectional occurrence, that is, both the target and the server can terminate the ongoing LPP session.

[0084] The working principle and beneficial effects of the above technical solution are as follows: The drone positioning process of the 5G-based drone enhanced positioning platform is implemented by one or more LPP (LTE Positioning Protocol) sessions. LPP sessions include the following types: positioning capability transmission, assistance data transmission, location information transmission, error handling, and session termination. Multiple LPP sessions can be concurrent; that is, before one LPP session ends, another can be initiated. The two parties in an LPP session are referred to as the "target" and "server," corresponding to the SET and SLP, respectively, in the user plane.

[0085] 1) Positioning Capability Transfer: This is a one-way transfer that only supports the transmission of the target's positioning capabilities to the server. Positioning capabilities refer to the ability to support different positioning methods (such as A-GNSS, OTDOA, and E-CID), the ability to support different aspects of a specific positioning method (such as different types of assistance data for A-GNSS), and certain capabilities shared by different positioning methods (such as the ability to manage concurrent LPP sessions).

[0086] 2) Auxiliary Data Transmission: This is a one-way transmission method, supporting only the transmission of auxiliary data from the server to the target. Auxiliary data can only be transmitted via unicast channels. In addition to sending auxiliary data matching the target's request, the server may also send one or more LPP messages to transmit other auxiliary data. The 5G-based drone enhanced positioning platform uses the BNC method to obtain auxiliary positioning data, which is then aggregated on the auxiliary positioning server and sent to each enhanced positioning platform to optimize positioning data accuracy.

[0087] 3) Location Information Transmission: This is a one-way transmission, supporting only the transmission of the target's location information to the server. The location information includes the estimated location and parameters used for the location calculation (such as radio and position measurements). In addition to sending the location information that matches the server request, the target may also send one or more additional LPP messages to convey additional location information.

[0088] 4) Error Handling: Error handling is when one party notifies the other party that a received LPP message is erroneous or does not match the request it sent. Error handling can occur in both directions, meaning both the target and the server can send error handling to each other.

[0089] 5) Session Termination: Session termination occurs when one party notifies the other party that it is terminating an ongoing LPP session. Session termination can occur in both directions, meaning both the target and the server can terminate an ongoing LPP session.

[0090] In a preferred embodiment, the enhanced positioning platform has a cell data self-learning function, and the learning steps include:

[0091] Step S1: Calculate the average latitude and longitude of all recent positioning results under the current enhanced positioning platform;

[0092] Step S2: traverse each positioning point and determine the distance between the position calculated in step S1 and the average position one by one. If the position with the largest distance from the average position exceeds a preset distance threshold, the position with the largest distance from the average position is eliminated, and then steps S1 to S2 are repeated until the distance from all positioning points to the average position is less than the distance threshold.

[0093] Step S3: Determine the number of remaining positioning points. If there is only one, it is considered that the sample size of the cell is small, and the probability of the calculated position being biased is high, so the calculation of the cell position is abandoned.

[0094] Step S4: If the number is greater than 1, the positioning results of the remaining positioning points are used to calculate the average value of the longitude and latitude as the location of the cell.

[0095] The working principle and beneficial effects of the above technical solution are as follows: The self-learning function of cell data uses the numerous locations where actual business operations occur to infer the true location of the cell. Regularly using recent business data to infer the cell location makes the calculated cell location more accurate, and the location information of newly added cells can be quickly obtained, while the data of dismantled cells can also be gradually filtered out. In the calculation of cell location, when in actual business operations, it often happens that the collected terminal location deviates from the actual location by more than 10 kilometers. The probability of such deviation location data occurring is small, but if there is a location deviation, it will also have a huge impact on the cell location calculation. Therefore, the following method of discovering data that deviates from the actual location is used to eliminate the deviated location.

[0096] The method for finding deviation position data is as follows, refer to Figure 3 :

[0097] 1) Calculate the average value of all recent longitude and latitude in the cell;

[0098] 2) Determine the distance between each of the locations involved in the calculation in step 1 and the average location. If the location with the largest distance from the average location exceeds 10 kilometers, eliminate the location with the largest distance from the average location and repeat step 1.

[0099] 3) When the distance between all points and the average position is less than 10 kilometers, the remaining valid positions are used to estimate the location of the cell;

[0100] 4) If there is only one remaining position after step 2, it is considered that the sample size of the cell is small and the probability of the calculated position being biased is high, so the calculation of the cell position is abandoned.

[0101] In a preferred embodiment, the enhanced positioning platform uses the message queue Kafka system for construction and optimizes the parameter configuration of the Kafka system to maximize the system throughput. During the optimization process:

[0102] First, through two feature screenings, we obtained the feature set that has the greatest impact on performance in the trip data;

[0103] Orthogonal experimental design was used to select representative samples, and the LASSO algorithm of machine learning was used to screen the samples to obtain a prediction model between performance and characteristics.

[0104] Finally, based on the feature correlation and feature boundary problems, the final performance prediction model is obtained, and the optimization algorithm of the genetic algorithm is designed and implemented to obtain the performance-optimal configuration of the given resources.

[0105] The working principle and beneficial effects of the above technical solution are as follows: With the surge in massive amounts of real-time travel data, drone enhanced positioning platforms generally adopt a strategy of increasing the number of Kafka server clusters to process this massive amount of travel data, thereby improving the overall system throughput. Improving system throughput through appropriate parameter configuration, based on available resources, has become a current research hotspot. Kafka server clusters have a default configuration during use, but the server cluster size and resources vary across platforms, and the default configuration is not suitable for all platforms. Furthermore, the required throughput varies across platforms, making customized parameter configuration a pressing issue. The real-time data access platform utilizes the popular message queue Kafka system. Kafka throughput is a key performance concern within the platform. Guided by actual project requirements, this study focuses on optimizing Kafka system parameter configuration to maximize system throughput within a given set of computing resources. First, through two feature screenings, we obtained a set of features that have a greater impact on performance. Then, we used the orthogonal experimental design method to select some representative samples for testing. Next, we used the LASSO algorithm of machine learning to screen the samples and preliminarily obtained a prediction model between performance and features. Then, we considered the feature correlation and feature boundary issues to obtain the final performance prediction model. We designed and implemented the optimization algorithm of the genetic algorithm to obtain the optimal performance configuration of the given resources. Finally, through relevant experiments, we compared and analyzed the research results. The data platform mainly studies how to improve the performance of the system. It mainly uses the machine learning method to obtain the prediction model and the optimized genetic algorithm to obtain the optimal configuration. The specific working principle diagram is as follows Figure 4 shown.

[0106] In a preferred embodiment, the Kafka system performs feature configuration by using key-value pairs.

[0107] Supports modification of features in travel data through configuration files or programs;

[0108] The features that affect Kafka system performance are divided into two modules: Broker module and Produce module.

[0109] The Broke module mainly affects the message writing disk cycle, message clearing threshold, log control, cluster control, and Zookeeper control of the server cluster;

[0110] The Producer module mainly affects the way the Producer sends messages to the cluster, whether it is compressed, whether it is batched, the number of failed retries, the message timeout, and the serialization method;

[0111] The Kafka system presents travel data in a table format, which includes the feature name, description, type, default value, valid value, and importance. By analyzing the table, effective information about the feature is obtained, and feature screening is performed.

[0112] The working principle and beneficial effects of the above technical solution are as follows: The travel data system uses a key-value format to configure features. Features can be modified either through configuration files or programs. Features that affect the performance of the travel data system are primarily divided into two modules: the Broker module and the Producer module. The Broker module primarily affects the server cluster's message write cycle, message purge threshold, log control, cluster control, and Zookeeper control. The Producer module primarily affects how the Producer sends messages to the cluster, including compression, batching, number of failed retries, message timeout, and serialization methods. The features of the travel data system are presented in a table, including the feature name, description, type, default value, valid values, and importance. By analyzing the table, valid feature information is obtained, allowing for feature selection. Based on the official definition, features have been screened twice, but the performance impact of the selected features and the relationship between them and performance require prediction. The LASSO algorithm, a compression estimation method, can narrow the feature set and generate a prediction model. The LASSO algorithm can construct a penalty function to obtain a prediction model. By minimizing the residual sum of squares, the coefficients of the features can be compressed and the regression coefficients of some features can be made zero, thereby achieving the purpose of feature selection and effectively explaining the prediction model. The LASSO algorithm is better than the ordinary least squares estimation (OLS). There are two problems with the least squares estimation when establishing a prediction model. The first problem is the prediction accuracy. If there is a very obvious linear relationship between the predicted feature and the corresponding feature, the least squares estimation will be biased. The second problem is the model's explanatory power. If there are multiple features in the multivariate linear regression model that are not related to the predicted feature, the least squares estimation itself cannot perform feature selection, which weakens the model's explanatory power. The LASSO algorithm adds L1 as a penalty constraint in the calculation process of RSS minimization, which can shrink some coefficients to be estimated to zero. Using the LASSO algorithm as the core of this machine learning, through continuous learning, feature selection and performance model prediction are performed. The entire processing process is as follows: Figure 5As shown. The correlation between the 12 features after LASSO screening is not present in all features, only in some, and the correlation is transitive between features. Based on official definition analysis and expert advice, the following is an analysis of a set of correlated features. The feature batch.size is correlated with the features buffer.memory, timeout.ms, and compression.type. According to the official definition, we can find that the relationship between the related features is inversely proportional to the square of timeout.ms, while the relationship between buffer.memory and the combined relationship of batch.size and compression.type is a logarithmic function. Through analysis of the four features and related experiments, the values ​​of the other features are fixed. By changing the values ​​of these four variables, 64 sets of training samples are obtained and re-substituted into the LASSO in Section 1.4.2 for learning, thereby obtaining the correlation between the four features.

[0113] In a preferred embodiment, during the process of acquiring the aircraft itinerary data card, the following operations are further performed:

[0114] Predetermine the communication distances of multiple enhanced positioning platforms and define the coverage areas of the signals of each enhanced positioning platform;

[0115] Summarize and count the coverage ranges of multiple enhanced positioning platform signals to obtain a first range in the world coordinate system;

[0116] For any aircraft, its location can be determined in real time based on its corresponding travel data;

[0117] When the aircraft reaches the boundary of the first range, a boundary crossing reminder message is sent to the aircraft via the enhanced positioning platform;

[0118] After receiving the over-boundary warning message, the aircraft turns on the over-boundary positioning mode and sends a final reply to the enhanced positioning platform, while returning the current coordinate position information;

[0119] After receiving the response, the enhanced positioning platform records the aircraft number and its coordinate position information;

[0120] After the aircraft turns on the cross-border positioning mode, it will actively record its own travel trajectory and upload the travel trajectory synchronously after it accesses the enhanced positioning platform next time;

[0121] After the aircraft is connected again, the enhanced positioning platform searches for the coordinate location information returned by the aircraft in its last reply, and uses the aircraft's travel trajectory when it was not connected to reproduce the travel data of the aircraft during the period of loss of contact.

[0122] The operating principle and beneficial effects of the above technical solution are as follows: First, the communication range of each enhanced positioning platform is determined, which may be based on the platform's transmit power, antenna characteristics, and environmental factors. Then, based on these communication ranges, the signal coverage range of each platform is delineated, forming multiple local coverage areas. The local coverage areas of all enhanced positioning platforms are aggregated to form a larger coverage area, defined in the world coordinate system and referred to as the first range. For each aircraft, the system determines its position in real time based on its travel data, ensuring the precise location of each aircraft. When an aircraft approaches the boundary of the first range, the system sends a boundary crossing alert to the aircraft through the enhanced positioning platform, alerting the pilot or automatic control system to the boundary. Upon receiving the boundary crossing alert, the aircraft enters boundary crossing positioning mode and sends a final reply to the enhanced positioning platform, including the aircraft number and current coordinates. The enhanced positioning platform records the aircraft's reply. While in boundary crossing positioning mode, the aircraft records its travel trajectory for simultaneous upload upon its next connection to the enhanced positioning platform. By monitoring the aircraft's position in real time and issuing boundary crossing alerts, the occurrence of boundary crossings and other unsafe incidents can be reduced. The enhanced positioning platform's coverage aggregation provides a wider monitoring area, enabling continuous positioning services for aircraft as they transition between different areas. Even if an aircraft loses contact, its journey data can be reproduced by recording and synchronizing its trajectory, ensuring data integrity and continuity. The cross-border positioning mode and last reply mechanism ensure a swift response when an aircraft approaches a boundary, providing timely handling for potential emergencies. Accurate journey data recording and analysis enable more effective management and allocation of air traffic resources, improving aviation efficiency.

[0123] In a preferred embodiment, when the aircraft reaches the boundary of the first range, the enhanced positioning platform further performs the following operations:

[0124] Collecting statistics on the data of the aircraft that lost contact outside the first range based on the coordinate position information and time of the last return of the multiple aircraft determined and recorded by the multiple enhanced positioning platforms;

[0125] Determine the distribution density of missing aircraft in the current time period and the area where the current aircraft is located based on the statistical results;

[0126] When the distribution density of the missing aircraft in the location area exceeds a preset density threshold, a miniaturized enhanced positioning platform is deployed toward the aircraft;

[0127] After deploying a miniaturized enhanced positioning platform, the aircraft proactively initiates positioning communication with the aircraft that has lost contact outside the first range and obtains one-time travel data, which is then returned to the enhanced positioning platform;

[0128] After the lost aircraft obtains its trip data through the miniaturized enhanced positioning platform, it releases the trip data record and starts recording new trip data;

[0129] The enhanced positioning platform updates the travel data of the lost aircraft based on the one-time travel data.

[0130] The working principle and beneficial effects of the above technical solution are as follows: The enhanced positioning platform uses the last recorded coordinate position information and time data of multiple aircraft to collect data statistics for aircraft that have lost contact outside the first range. Based on the statistical results, the platform calculates the distribution density of lost aircraft within a specific area during the current time period. If the distribution density of lost aircraft exceeds a preset threshold, the platform automatically deploys a miniaturized enhanced positioning platform toward the aircraft flying toward the boundary. The deployed miniaturized platform proactively establishes positioning communication with the lost aircraft outside the first range, obtains its one-time trip data, and returns it to the main enhanced positioning platform. After the miniaturized platform obtains the trip data, the lost aircraft releases the old trip data record and starts a new one. The main enhanced positioning platform uses this one-time trip data to update the lost aircraft's trip information. The working principle of the technical solution is based on real-time data analysis and an automated response mechanism to ensure continuous monitoring of aircraft, even if they are at the edge of the monitoring range or have lost contact. By deploying miniaturized enhanced positioning platforms, this solution can expand the monitoring range, improve the positioning accuracy of lost aircraft, and ensure the continuity of trip data. It can also effectively release the travel data storage of the missing aircraft and extend the length of time the data is stored during the loss of contact.

[0131] The technical solutions of the present invention include:

[0132] Load balancing technology:

[0133] Elastic Load Balancing (ELB) automatically distributes access traffic to multiple elastic cloud hosts. Tenants should first create an ELB through the portal or API, and then create one or more ELB listeners, such as HTTP 80. Then, tenants can add members to the Listener. The ELB service system will automatically map tenant operations to the ELB service system and distribute workloads to ELB members. It can expand the external service capabilities of the application system, achieve a higher level of application fault tolerance, and provide the load balancing capabilities required to distribute traffic. Users can create ELB, configure the ports to be listened to for the service, and configure cloud hosts through a browser-based, unified view cloud computing management graphical interface. Eliminate single points of failure and improve the availability of the entire system.

[0134] LPP positioning technology:

[0135] In the process of 5G-based UAV enhanced positioning, refer to Figure 6 , LPP sessions are used between the location server and the target drone to obtain location-related measurements or position estimates, or to transmit assistance data. A single LPP session is used to support a single location request (for example, a single MT-LR, MO-LR, or NI-LR). Multiple LPP sessions can be used between the same endpoints to support multiple different location requests. Each LPP session consists of one or more LPP transactions, and each LPP transaction performs a single operation (capability exchange, assistance data transmission, or location information transmission). In NG-RAN, LPP transactions are implemented as LPP procedures. The initiator of an LPP session always starts the first LPP transaction, but subsequent transactions may be initiated by either end. LPP transactions in a session can occur serially or in parallel. LPP transactions are represented at the LPP protocol level using transaction IDs to associate messages with each other.

[0136] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A method for obtaining aircraft trip data cards based on 5G enhanced positioning, characterized in that: include: Pre-build multiple enhanced positioning platforms based on 5G signal communications; The enhanced positioning platform locates the aircraft through one or more LPP sessions and communicates data with the aircraft. The enhanced positioning platform uses load balancing technology to share the load of aircraft requests to the platform, and supports hot expansion of platform processing capabilities to adapt to a variety of different operator networks; The real-time and historical itinerary data of aircraft are acquired through the enhanced positioning platform, and the acquired real-time and historical itinerary data of the target aircraft in the network are collated to obtain aircraft itinerary card data. The enhanced positioning platform uses the message queue Kafka system for construction and optimizes the parameter configuration of the Kafka system to maximize the system throughput. During the optimization process: First, through two feature screenings, we obtained the feature set that has the greatest impact on performance in the trip data; Orthogonal experimental design was used to select representative samples, and the LASSO algorithm of machine learning was used to screen the samples to obtain a prediction model between performance and characteristics. Finally, based on the feature correlation and feature boundary problems, the final performance prediction model is obtained, and the optimization algorithm of the genetic algorithm is designed and implemented to obtain the performance-optimal configuration of the given resources.

2. The method for obtaining an aircraft itinerary data card based on 5G enhanced positioning according to claim 1, characterized in that: The multiple LPP sessions support concurrent execution, including positioning capability transmission, assistance data transmission, location information transmission, error handling, and session termination, wherein: Positioning capability transmission is one-way and only supports the transmission of the target's positioning capability to the server. Positioning capability refers to the ability to support different positioning methods, the ability to support different aspects of a specific positioning method, and the ability to be shared by different positioning methods. Assisted data transmission is one-way and only supports the transmission of the server's auxiliary data to the target. The auxiliary data is transmitted through a unicast channel. In addition to sending the auxiliary data that matches the target request, the server also supports sending one or more additional LPP messages to transmit other auxiliary data. The 5G-based enhanced positioning platform uses the BNC method to obtain auxiliary positioning data, which is then aggregated on the auxiliary positioning server and sent to other enhanced positioning platforms to optimize positioning data accuracy. Position information transmission is one-way and only supports sending the target's position information to the server. The position information includes the position estimate and the parameters used for position calculation. In addition to sending the position information that matches the server request, the target also supports sending one or more additional LPP messages to convey other position information. Error handling refers to one party notifying the other party that the LPP message it received is erroneous or does not match the request it sent. Error handling supports bidirectional operation, that is, both the target and the server can send error handling to each other; Session termination means that one party notifies the other party that it will terminate the ongoing LPP session. Session termination supports bidirectional occurrence, that is, both the target and the server can terminate the ongoing LPP session.

3. The method for obtaining an aircraft itinerary data card based on 5G enhanced positioning according to claim 1, characterized in that: The enhanced positioning platform has a cell data self-learning function, and the learning steps include: Step S1: Calculate the average latitude and longitude of all recent positioning results under the current enhanced positioning platform; Step S2: traverse each positioning point and determine the distance between the position calculated in step S1 and the average position one by one. If the position with the largest distance from the average position exceeds a preset distance threshold, the position with the largest distance from the average position is eliminated, and then steps S1 to S2 are repeated until the distance from all positioning points to the average position is less than the distance threshold. Step S3: Determine the number of remaining positioning points. If there is only one, it is considered that the sample size of the cell is small, and the probability of the calculated position being biased is high, so the calculation of the cell position is abandoned. Step S4: If the number is greater than 1, the positioning results of the remaining positioning points are used to calculate the average value of the longitude and latitude as the location of the cell.

4. The method for obtaining an aircraft itinerary data card based on 5G enhanced positioning according to claim 1, characterized in that: The Kafka system uses key-value to configure features; Supports modification of features in travel data through configuration files or programs; The features that affect Kafka system performance are divided into two modules: Broker module and Produce module. The Broke module mainly affects the message writing disk cycle, message clearing threshold, log control, cluster control, and Zookeeper control of the server cluster; The Producer module mainly affects the way the Producer sends messages to the cluster, whether it is compressed, whether it is batched, the number of failed retries, the message timeout, and the serialization method; The Kafka system presents travel data in a table format, which includes the feature name, description, type, default value, valid value, and importance. By analyzing the table, effective information about the feature is obtained, and feature screening is performed.

5. The method for obtaining an aircraft itinerary data card based on 5G enhanced positioning according to claim 1, characterized in that: During the process of obtaining the aircraft itinerary data card, the following operations are also performed: Predetermine the communication distances of multiple enhanced positioning platforms and define the coverage areas of the signals of each enhanced positioning platform; Summarize and count the coverage ranges of multiple enhanced positioning platform signals to obtain a first range in the world coordinate system; For any aircraft, its location can be determined in real time based on its corresponding travel data; When the aircraft reaches the boundary of the first range, a boundary crossing reminder message is sent to the aircraft via the enhanced positioning platform; After receiving the over-boundary warning message, the aircraft turns on the over-boundary positioning mode and sends a final reply to the enhanced positioning platform, while returning the current coordinate position information; After receiving the response, the enhanced positioning platform records the aircraft number and its coordinate position information; After the aircraft turns on the cross-border positioning mode, it will actively record its own travel trajectory and upload the travel trajectory synchronously after it accesses the enhanced positioning platform next time; After the aircraft is connected again, the enhanced positioning platform searches for the coordinate location information returned by the aircraft in its last reply, and uses the aircraft's travel trajectory when it was not connected to reproduce the travel data of the aircraft during the period of loss of contact.

6. The method for obtaining an aircraft itinerary data card based on 5G enhanced positioning according to claim 5, characterized in that: When the aircraft reaches the boundary of the first range, the enhanced positioning platform also performs the following operations: Collecting statistics on the data of the aircraft that lost contact outside the first range based on the coordinate position information and time of the last return of the multiple aircraft determined and recorded by the multiple enhanced positioning platforms; Determine the distribution density of missing aircraft in the current time period and the area where the current aircraft is located based on the statistical results; When the distribution density of the missing aircraft in the location area exceeds a preset density threshold, a miniaturized enhanced positioning platform is deployed toward the aircraft; After deploying a miniaturized enhanced positioning platform, the aircraft proactively initiates positioning communication with the aircraft that has lost contact outside the first range and obtains one-time travel data, which is then returned to the enhanced positioning platform; After the lost aircraft obtains its trip data through the miniaturized enhanced positioning platform, it releases the trip data record and starts recording new trip data; The enhanced positioning platform updates the travel data of the lost aircraft based on the one-time travel data.

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