Aircraft travel data card acquisition method based on 5G enhanced positioning

Abstract

The invention discloses an aircraft travel data card acquisition method based on 5G enhanced positioning. The method comprises the following steps: pre-constructing a plurality of enhanced positioning platforms based on 5G signal communication; the enhanced positioning platform realizes positioning of the aircraft through one or more LPP sessions and performs data communication with the aircraft; the enhanced positioning platform shares the request load of the aircraft to the platform through a load balancing technology, and supports the processing capability of a thermal expansion platform so as to adapt to various different operator networks; real-time travel data and historical travel data of an aircraft are obtained through an enhanced positioning platform, and the obtained real-time travel data and historical travel data of a network access target aircraft are sorted to obtain travel card data of the aircraft. The monitoring and communication capabilities of the low-altitude general aircraft can be improved, track recording and position monitoring of the aircraft and the unmanned aerial vehicle target are realized, and the problems that low, small and slow network access unmanned aerial vehicles are difficult to monitor, and an efficient and convenient monitoring management platform is lacked are solved.

Description

Technical Field

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

[0002] General aviation aircraft are gradually being used in civil applications, with typical application scenarios including urban logistics, feeder logistics, emergency rescue, etc. In these scenarios, general aviation aircraft have the characteristics of low flight altitude, a wide variety of aircraft, and complex and changeable flight areas. The existing ground-based civil air traffic control surveillance radars and communication equipment are difficult to effectively guarantee the surveillance and communication capabilities during low-altitude flights, and cannot meet the requirements of continuous, reliable, and cost-effective surveillance and communication in all time domains, airspaces, and 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 monitoring 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 an aircraft travel data card based on 5G enhanced positioning. The present invention can improve the monitoring and communication capabilities of low-altitude general aviation 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-access 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 itinerary 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 shares the aircraft's request load to the platform through load balancing technology, and supports hot expansion of platform processing capabilities to adapt to a variety of different operator networks;

[0010] The real-time and historical itinerary data of aircraft can be acquired through the enhanced positioning platform, and the real-time and historical itinerary data of the target aircraft in the network can be sorted out to obtain the aircraft itinerary card data.

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

[0012] Positioning capability transmission is one-way transmission, which 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 shared by different positioning methods.

[0013] Auxiliary data transmission is one-way transmission, which only supports the transmission of auxiliary data from the server to the target. The auxiliary data is transmitted through a unicast channel. In addition to sending 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 the accuracy of positioning data.

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

[0015] Error handling means that one party notifies the other party that the LPP message it received is wrong 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 two-way 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, calculating the average longitude and latitude 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 to the average position exceeds a preset distance threshold, the position with the largest distance to the average position is eliminated, and then steps S1 to S2 are repeated until the distance between all positioning points and 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, and 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] Preferably, the enhanced positioning platform chooses the message queue Kafka system for construction, and maximizes the system throughput by optimizing the parameter configuration of the Kafka system. 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] The orthogonal experimental design method 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 optimal performance configuration of given resources.

[0026] Preferably, the Kafka system performs feature configuration by using key-value;

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

[0028] The features that affect the performance of the Kafka system 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 to compress, batch, the number of failed retries, the message timeout, and the serialization method;

[0031] The Kafka system presents the travel data in the form of a table, which includes the name, description, type, default value, valid value and importance of the feature. By analyzing the table, the effective information of the feature is obtained, so as to perform feature screening.

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

[0033] Predetermine the communication distance of each of the multiple enhanced positioning platforms and define the coverage 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, determine its location in real time based on its corresponding itinerary data;

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

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

[0038] After receiving the reply, 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 when it lost contact.

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

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

[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 is greater than a preset density threshold, a miniaturized enhanced positioning platform is deployed to the aircraft;

[0045] After deploying the miniaturized enhanced positioning platform, the aircraft actively initiates positioning communication with the aircraft that lost contact outside the first range and obtains one-time trip data and returns it to the enhanced positioning platform;

[0046] After the lost aircraft has acquired 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-access 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 may be taught from the practice of the present invention. The objectives and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to make the purpose, technical solution and beneficial effects of the present invention clearer, 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 in an embodiment of the present invention;

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

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

[0057] Figure 5 It is a process diagram of the 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 in conjunction with 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 an 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 shares the aircraft's request load to the platform through load balancing technology, and supports hot expansion of platform processing capabilities to adapt to a variety of different operator networks;

[0064] The real-time and historical itinerary data of aircraft can be acquired through the enhanced positioning platform, and the real-time and historical itinerary data of the target aircraft in the network can be sorted out to obtain the aircraft itinerary 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 itinerary information of aircraft, the aircraft itinerary information acquisition technology is studied, and the enhanced positioning based on 5G network access targets is studied. The real-time itinerary data and historical itinerary data of the network access target aircraft are sorted out, and the aircraft / drone itinerary card data (marking a type of target) is given, and the data interface with the drone is added, and the drone itinerary data, positioning data and other data of the access platform are provided to realize the trajectory recording and location monitoring of aircraft and drone targets. And it has: 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) standardization organization. The platform uses load balancing to share the request load of drones 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 shown in the figure below. Figure 2 As shown:

[0066] 1) The platform uses load balancing to share the request load of drones to the platform, and supports hot expansion of platform processing capabilities;

[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 are modular in structure;

[0072] Supported protocols and algorithms:

[0073] 1) Satellite systems supported by the platform: support GPS satellites; support BeiDou (BD) satellites; support 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), the mobile terminal performs positioning and navigation measurements and is responsible for positioning calculations, while orbital auxiliary information comes from the network. It is suitable for continuous positioning mode, but has high requirements on the computing resources of the terminal system.

[0076] 4) Bearer protocols 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, auxiliary data transmission, location information transmission, error handling and session termination, wherein:

[0079] Positioning capability transmission is one-way transmission, which 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 shared by different positioning methods.

[0080] Auxiliary data transmission is one-way transmission, which only supports the transmission of auxiliary data from the server to the target. The auxiliary data is transmitted through a unicast channel. In addition to sending 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 the accuracy of positioning data.

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

[0082] Error handling means that one party notifies the other party that the LPP message it received is wrong 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 two-way 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 embodied 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. Multiple LPP sessions can be concurrent, that is, before one LPP session ends, another LPP session can be initiated. The two parties of the LPP session are referred to as "target" and "server", and they correspond to SET and SLP respectively in the user plane.

[0085] 1) Positioning capability transmission: One-way transmission, only supports the transmission of the target's positioning capability to the server. Positioning capability refers 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: One-way transmission, only supports the transmission of the server's auxiliary data to the target. Auxiliary data can only be transmitted through unicast channels. In addition to sending auxiliary data that matches the target 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 the accuracy of the positioning data.

[0087] 3) Location information transmission: One-way transmission, only supports the transmission of the target's location information to the server. The location information contains the location estimate and the parameters used for location calculation (such as wireless measurement and location measurement). In addition to sending the location information that matches the server request, the target may also send one or more additional LPP messages to transmit other location information.

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

[0089] 5) Session termination: Session termination means one party notifies the other party that it will terminate the ongoing LPP session. Session termination can occur in both directions, that is, both the target and the server can terminate the 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, calculating the average longitude and latitude 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 to the average position exceeds a preset distance threshold, the position with the largest distance to the average position is eliminated, and then steps S1 to S2 are repeated until the distance between all positioning points and 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, and 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 cell data self-learning function uses the numerous locations where actual business occurs to infer the real location of the cell. Regularly using recent business data to infer the cell location, the calculated cell location is more accurate, and the location information of the newly added cells can be quickly obtained, and the data of the demolished cells can also be gradually filtered out. In the calculation of the cell location, when in actual business, 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 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 the positions involved in the calculation in step 1 and the average position one by one. If the position with the largest distance from the average position exceeds 10 kilometers, eliminate the position with the largest distance from the average position, and then 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 community is small and the probability of the calculated position being biased is high, so the calculation of the community position is abandoned.

[0101] In a preferred embodiment, the enhanced positioning platform selects 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] The orthogonal experimental design method 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 optimal performance configuration of given resources.

[0105] The working principle and beneficial effects of the above technical solution are as follows: With the surge in massive real-time travel data, the drone enhanced positioning platform generally adopts the strategy of increasing the number of Kafka server clusters to process massive travel data, thereby improving the throughput of the entire system. How to improve the throughput of the system through reasonable configuration of parameters based on existing resources has become a current research hotspot. Kafka server clusters will have a default configuration during use, but for different platforms, the scale and resources of server clusters are different, and the default configuration is not suitable for all platforms. For different platforms, the throughput required to be achieved is different, and how to customize parameter configuration has also become a current problem to be solved. The real-time data access platform chooses the current mainstream message queue Kafka system to build. Kafka's throughput is the most concerned performance in the platform. Guided by actual project requirements, this paper mainly studies how to optimize the parameter configuration of the Kafka system to maximize the system throughput under the premise of given computing resources. First, through two feature screenings, we obtained the feature set that has 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 the 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 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 machine learning methods to obtain prediction models and uses optimized genetic algorithms 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 a key-value format;

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

[0108] The features that affect the performance of the Kafka system 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 to compress, batch, the number of failed retries, the message timeout, and the serialization method;

[0111] The Kafka system presents the travel data in the form of a table, which includes the name, description, type, default value, valid value and importance of the feature. By analyzing the table, the effective information of the feature is obtained, so as to perform feature screening.

[0112] The working principle and beneficial effects of the above technical solution are as follows: the travel data system uses the form of key-value to configure features, and the modification of features can be done through configuration files or programs. The features that affect the performance of the travel data system are mainly divided into two modules, the Broker module and the Produce module. The Broke module mainly affects the message writing disk cycle, message clearing threshold, log control, cluster control, zookeeper control, etc. of the server cluster, and the Producer module mainly affects the way the Producer sends messages to the cluster and whether it is compressed, whether it is batched, the number of failed retries, the message timeout, the serialization method, etc. The features of the travel data system are given in the form of a table, including the name, description, type, default value, valid value and importance of the feature. By analyzing the table, the effective information of the feature is obtained, so as to select the feature. Based on the official definition, the features have been screened twice, but the impact of the screened features on the performance and the model between them need to be predicted. The LASSO algorithm can reduce the feature set and give a prediction model, which is a compression estimation method. The LASSO algorithm can obtain a prediction model by constructing a penalty function. 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 of the 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 all related, only some of them are related, and the correlation is transitive between features. Based on the official definition analysis and expert advice, a set of analysis results of related features are given below. The feature batch.size is correlated with the feature buffer.memory, the feature timeout.ms, and the feature compression.type. According to the official definition, we can find out that the relationship between the related features batch.size is inversely proportional to the square of timeout.ms, while the comprehensive relationship between buffer.memory and batch.size and compression.type is a logarithmic function relationship. Through the analysis of the four features and related experiments, the values ​​of other features are fixed, and the values ​​of these four variables are changed to obtain 64 sets of training samples, which are re-substituted into the LASSO in Section 1.4.2 for learning, so as to obtain 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 distance of each of the multiple enhanced positioning platforms and define the coverage 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, determine its location in real time based on its corresponding itinerary data;

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

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

[0119] After receiving the reply, 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 when it lost contact.

[0122] The working principle and beneficial effects of the above technical solution are as follows: First, determine the communication distance of each enhanced positioning platform, which may be based on the platform's transmission power, antenna characteristics and environmental factors. Then, based on these communication distances, delineate the signal coverage range of each platform to form multiple local coverage areas. Aggregate the local coverage areas of all enhanced positioning platforms to form a larger coverage range, which is defined in the world coordinate system and is called the first range. For each aircraft, the system determines its position in real time based on its travel data to ensure that the precise position of each aircraft is known. When the aircraft approaches the boundary of the first range, the system sends an over-boundary reminder message to the aircraft through the enhanced positioning platform to prompt the pilot or automatic control system to pay attention to the boundary. After receiving the over-boundary reminder, the aircraft will turn on the over-boundary positioning mode and send the last reply to the enhanced positioning platform, including the aircraft number and current coordinate position information. The enhanced positioning platform records the aircraft's reply information. In the over-boundary positioning mode, the aircraft will record its own travel trajectory so that it can be uploaded synchronously when it is connected to the enhanced positioning platform next time. By real-time monitoring of the aircraft's position and sending over-boundary reminders, the occurrence of crossing the boundary or other unsafe incidents can be reduced. The coverage aggregation of the enhanced positioning platform provides a wider monitoring area, allowing aircraft to maintain continuous positioning services when transitioning between different areas. Even if the aircraft loses contact, its travel data can be reproduced by recording and synchronizing the travel trajectory, ensuring the integrity and continuity of the data. The cross-border positioning mode and the last reply mechanism ensure a quick response when the aircraft approaches the border, providing timely handling for possible emergencies. Through accurate travel data recording and analysis, air traffic resources can be managed and allocated more effectively, improving aviation efficiency.

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

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

[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 is greater than a preset density threshold, a miniaturized enhanced positioning platform is deployed to the aircraft;

[0127] After deploying the miniaturized enhanced positioning platform, the aircraft actively initiates positioning communication with the aircraft that lost contact outside the first range and obtains one-time trip data and returns it to the enhanced positioning platform;

[0128] After the lost aircraft has acquired 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 coordinate position information and time data of multiple aircraft previously recorded to collect data statistics on aircraft that have lost contact outside the first range. Based on the statistical results, the platform calculates the distribution density of lost aircraft in a specific area within the current time period. If the distribution density of lost aircraft exceeds the preset threshold, the platform will automatically deploy a miniaturized enhanced positioning platform to the aircraft flying to the boundary. The deployed miniaturized platform actively establishes positioning communication with the aircraft that has lost contact 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 will release the old trip data record and start a new record. The main enhanced positioning platform uses these one-time trip data to update the trip information of the lost aircraft. The working principle of the technical solution is based on real-time data analysis and automated response mechanisms to ensure continuous monitoring of aircraft, even if they are at the edge of the monitoring range or have lost contact. Through the deployment of miniaturized enhanced positioning platforms, the 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 for storing data during the loss of contact.

[0131] The technical solution of the present invention includes:

[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 drone 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 (e.g., 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, auxiliary 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 rather than to limit it. 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 itinerary data card 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 shares the aircraft's request load to the platform through load balancing technology, 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 can be acquired through the enhanced positioning platform, and the real-time and historical itinerary data of the target aircraft in the network can be sorted out to obtain the aircraft itinerary card data.

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, auxiliary data transmission, location information transmission, error handling and session termination, wherein: Positioning capability transmission is one-way transmission, which 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 shared by different positioning methods. Auxiliary data transmission is one-way transmission, which only supports the transmission of auxiliary data from the server to the target. The auxiliary data is transmitted through a unicast channel. In addition to sending 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 the accuracy of positioning data. The location information transmission is one-way transmission, which only supports the transmission of the target's location information to the server. The location information includes the location estimate and the parameters used for location calculation. In addition to sending the location information that matches the server request, the target also supports sending one or more additional LPP messages to transmit other location information. Error handling means that one party notifies the other party that the LPP message it received is wrong 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 two-way 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, calculating the average longitude and latitude 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 to the average position exceeds a preset distance threshold, the position with the largest distance to the average position is eliminated, and then steps S1 to S2 are repeated until the distance between all positioning points and 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, and 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 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; The orthogonal experimental design method 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 optimal performance configuration of given resources.

5. The method for obtaining an aircraft itinerary data card based on 5G enhanced positioning according to claim 4, characterized in that: The Kafka system performs feature configuration by using key-value format; Supports modification of features in trip data through configuration files or programs; The features that affect the performance of the Kafka system 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 to compress, batch, the number of failed retries, the message timeout, and the serialization method; The Kafka system presents the travel data in the form of a table, which includes the name, description, type, default value, valid value and importance of the feature. By analyzing the table, the effective information of the feature is obtained, so as to perform feature screening.

6. The method for obtaining an aircraft itinerary data card based on 5G enhanced positioning according to claim 1, characterized in that: In the process of obtaining the aircraft itinerary data card, the following operations are also performed: Predetermine the communication distance of each of the multiple enhanced positioning platforms and define the coverage 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, determine its location in real time based on its corresponding itinerary data; When the aircraft reaches the boundary of the first range, a boundary crossing reminder message is sent to the aircraft through the enhanced positioning platform; After receiving the over-boundary warning information, the aircraft turns on the over-boundary positioning mode and sends the final reply to the enhanced positioning platform, while returning the current coordinate position information; After receiving the reply, 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 when it lost contact.

7. The method for obtaining an aircraft itinerary data card based on 5G enhanced positioning according to claim 6, characterized in that: When the aircraft reaches the boundary of the first range, the enhanced positioning platform also performs the following operations: According to the coordinate position information and time of the last return of multiple aircraft determined and recorded by multiple enhanced positioning platforms, statistics are collected on the data of the aircraft that lost contact outside the first range; 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 is greater than a preset density threshold, a miniaturized enhanced positioning platform is deployed to the aircraft; After deploying the miniaturized enhanced positioning platform, the aircraft actively initiates positioning communication with the aircraft that lost contact outside the first range and obtains one-time trip data and returns it to the enhanced positioning platform; After the lost aircraft has acquired 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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