Novel low-altitude traffic management method based on satellite-based technology

Abstract

The invention provides a novel low-altitude traffic management method based on a satellite-based technology, and the method comprises the steps: obtaining the initial position data of a low-altitude aircraft through a satellite-based navigation system, carrying out the multi-source fusion positioning through Beidou or GPS satellite signals, and obtaining the high-precision initial positioning data; according to the initial positioning data, correcting the positioning data in real time by adopting a satellite-based enhancement system to obtain high-precision positioning data; the method comprises the following steps: acquiring flight states and route information of aircrafts through real-time communication data, and judging potential conflict risks among the aircrafts for a high-density airspace or a cross-regional flight scene to obtain conflict early warning data; and according to the conflict early warning data, extracting a target route and a speed constraint of the aircraft from the real-time communication data to obtain optimized route data. The problems of accurate positioning, stable communication, conflict avoidance, whole-process management and the like of the low-altitude aircraft in a complex environment are effectively solved, and the safety and efficiency of low-altitude flight are improved.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a novel low-altitude traffic management method based on satellite-based technology. Background Art

[0002] Low-altitude flight management is a key area of the modern air transportation system. Its importance lies in ensuring the safe and orderly operation of low-altitude aircraft and promoting the efficient development of low-altitude economy and emergency rescue scenarios. With the rapid growth of drones, general aviation aircraft and low-altitude transportation vehicles, the complexity and management needs of low-altitude airspace have increased dramatically. However, existing low-altitude traffic management methods have significant limitations. They mainly rely on traditional radar and ground communication systems, which are difficult to deal with signal interruptions caused by complex terrain, sea areas or sudden disasters, and lack support for real-time, accuracy and full-area coverage. These limitations restrict the safety and management efficiency of low-altitude flight activities.

[0003] The core challenge stems from the urgent need for high-precision positioning and real-time communication in low-altitude flight environments. Low-altitude aircraft need to achieve accurate positioning in complex environments, especially in mountainous areas, sea areas, or disaster scenarios. Traditional positioning technologies are easily affected by terrain or signal interruptions, resulting in the loss of aircraft position data. The instability of positioning data further exacerbates the difficulty of real-time communication. Data exchange between ground stations and aircraft is often interrupted due to insufficient signal coverage, affecting the timeliness of flight instructions and route push. Communication interruptions directly restrict the low-altitude air traffic control system's comprehensive perception of flight situations and conflict warning capabilities. Especially in high-density airspace or cross-regional flights, the system lacks real-time data support and finds it difficult to effectively coordinate and guide aircraft.

[0004] Therefore, how to achieve high-precision positioning, stable real-time communication and full-process traffic management of low-altitude aircraft in complex environments by integrating satellite-based technologies has become a key issue in improving the safety and efficiency of low-altitude flight. Summary of the Invention

[0005] This invention provides a new low-altitude traffic management method based on satellite-based technology, which effectively solves the problems of accurate positioning, stable communication, conflict avoidance and full-process management of low-altitude aircraft in complex environments, and improves the safety and efficiency of low-altitude flight. It includes: Obtain the initial position data of low-altitude aircraft through the satellite-based navigation system, and use Beidou or GPS satellite signals for multi-source fusion positioning to obtain high-precision initial positioning data; Based on the initial positioning data, the satellite-based augmentation system is used to perform real-time correction on the positioning data to obtain high-precision positioning data; Extract the aircraft's real-time position and velocity information from high-precision positioning data, and use satellite-based communication links to establish a data transmission channel between the aircraft and the ground station to obtain stable real-time communication data. Through real-time communication data, obtain aircraft flight status and route information, determine potential conflict risks between aircraft, and obtain conflict warning data; Based on the conflict warning data, a dynamic route planning algorithm is used to extract the aircraft's target route and speed constraints from real-time communication data to obtain optimized route data. Extract aircraft coordination instructions from the optimized route data, adopt a full-process traffic management algorithm, and send real-time flight instructions and airspace allocation information to aircraft via satellite-based communication links to obtain full-process coordination data; Based on the full-process coordination data, the satellite-based monitoring system conducts real-time comparisons with the aircraft's actual flight trajectory. If the trajectory deviation exceeds the preset threshold T3 (T3 represents the maximum trajectory deviation, in meters), a correction instruction is sent via the satellite-based communication link to obtain the final aircraft flight management data. Using aircraft flight management data, data fusion algorithms are used to integrate positioning data, communication data, and coordination data to obtain dynamically updated airspace management data. Based on the dynamically updated airspace management data, the flight status of the aircraft and the allocation of airspace resources are continuously optimized, real-time airspace scheduling instructions are generated, and the final low-altitude flight management data is obtained.

[0006] The above method, wherein the initial position data of the low-altitude aircraft is obtained through a satellite-based navigation system, and multi-source fusion positioning is performed using Beidou or GPS satellite signals to obtain high-precision initial positioning data, includes: The initial position data of low-altitude aircraft is obtained through the satellite-based navigation system, and the Beidou signal and GPS signal are integrated for multi-source positioning to obtain preliminary positioning data; If the preliminary positioning data is affected by terrain interference or signal interruption in a complex environment, a multipath correction algorithm is used to process the Beidou signal and GPS signal to obtain corrected signal data; According to the corrected signal data, the Kalman filter algorithm is applied to fuse multi-source signals to obtain optimized positioning data; If the optimized positioning data does not meet the preset accuracy threshold, the multi-source signals are secondary fused through the weighted average algorithm to obtain high-precision positioning data; By combining high-precision positioning data with a pre-established terrain database, we can determine whether there is positioning deviation caused by terrain interference and obtain deviation correction data. Based on the deviation correction data, the initial position of the low-altitude aircraft is updated to obtain the final positioning data; The final positioning data is used to generate the position coordinates of the low-altitude aircraft and output them to the navigation system.

[0007] The above method, wherein the satellite-based augmentation system is used to perform real-time correction on the positioning data based on the initial positioning data to obtain high-precision positioning data, includes: Based on the initial positioning data, the satellite-based augmentation system is used to perform real-time correction on the positioning data. If the positioning deviation after correction is less than the preset threshold T1, high-precision positioning data is output; If the deviation is greater than or equal to T1, the differential positioning algorithm of the satellite signal is further optimized to obtain the corrected high-precision positioning data.

[0008] The above method, wherein the positioning data is corrected in real time using a satellite-based augmentation system based on the initial positioning data, and high-precision positioning data is output if the positioning deviation after correction is less than a preset threshold value T1, includes: Obtain initial positioning data from the positioning device, format the data through the pre-processing module to obtain standard positioning data; Use the satellite-based augmentation system to perform real-time correction processing on the standard positioning data to generate corrected positioning data; The deviation calculation module calculates the positioning deviation value between the corrected positioning data and the reference positioning point; If the positioning deviation value is less than the preset threshold value T1, the corrected positioning data is determined to be high-precision positioning data; For high-precision positioning data, a data output control module is used to generate formatted output data; Obtain output data, and use the accuracy verification module to determine whether the output data accuracy meets the preset standards to obtain the final positioning data; According to the final positioning data, the data storage module is used to save the data to the preset database and generate positioning data records.

[0009] In the above method, if the deviation is greater than or equal to T1, further optimizing the satellite signal differential positioning algorithm to obtain corrected high-precision positioning data includes: If the received satellite signal deviation is greater than or equal to the preset threshold T1, the original positioning data is extracted through the signal processing module to obtain the preliminary positioning coordinates; Based on the preliminary positioning coordinates, the differential positioning algorithm is used to calculate the signal deviation between the reference station and the reference station to obtain the deviation correction value; The initial positioning coordinates are optimized through the deviation correction value to obtain the corrected high-precision positioning data; If the corrected high-precision positioning data does not meet the preset accuracy requirements, the data is smoothed using the Kalman filter algorithm to obtain smoothed positioning data; Based on the smoothed positioning data, a coordinate transformation algorithm is used to map it to the target coordinate system to obtain a standardized positioning output; By comparing the standardized positioning output with the historical positioning data, we can determine whether there is abnormal drift and obtain the final verified positioning result. If the final verified positioning result meets the preset accuracy threshold, the result will be stored in the positioning database and high-precision positioning data will be output.

[0010] The above method, wherein the real-time position and velocity information of the aircraft is extracted from the high-precision positioning data, and a data transmission channel between the aircraft and the ground station is established using a satellite-based communication link to obtain stable real-time communication data, includes: Extracting the real-time position and speed information of the aircraft from the high-precision positioning data, and smoothing the data using the Kalman filter algorithm to obtain the first positioning data; If there are outliers in the first positioning data, the outliers are determined by a preset threshold value, and the outliers are removed to obtain the second positioning data; Detecting signal interruption in a complex environment based on the second positioning data; if the signal interruption time exceeds a preset threshold, establishing a data transmission channel via a satellite-based communication link of a low-orbit satellite to obtain a communication connection status; Through the communication connection status, the wide-area coverage data of low-orbit satellites is obtained, and the signal strength of multiple satellites is integrated using a weighted average algorithm to obtain stable communication data; Extract the real-time transmission data between the aircraft and the ground station from the stable communication data, verify the data integrity using the cyclic redundancy check algorithm, and obtain valid transmission data; Based on the effective transmission data, the real-time position and speed information of the aircraft is updated and stored in the database of the ground station to obtain the updated positioning information; The updated positioning information is used to generate the motion trajectory data of the aircraft, and the linear interpolation method is used to fill in the missing data points to obtain continuous trajectory data.

[0011] The above method, wherein the flight status and route information of aircraft are obtained through real-time communication data, the potential risk of conflict between aircraft is determined, and conflict warning data is obtained, includes: Acquire real-time communication data from aircraft using satellite-based technology, extract flight status and route information, and generate aircraft dynamic datasets; Based on the aircraft dynamic data set, the airspace situation awareness algorithm is used to analyze high-density airspace or cross-region flight scenarios to obtain the airspace situation distribution; If the distance between aircraft in the airspace situation distribution is lower than the preset threshold, the trajectory prediction model is used to calculate the potential conflict point and generate conflict risk assessment data; Based on the conflict risk assessment data, a clustering algorithm is used to group aircraft in high-density airspace to obtain high-risk conflict areas; If aircraft routes intersect in a high-risk conflict area, the time of conflict is determined through time window analysis to generate conflict warning data; Generate aircraft adjustment instructions based on conflict warning data, update route information, and obtain optimized airspace situation distribution; The optimized airspace situation distribution and adjustment instructions are sent to the aircraft through satellite-based technology to update the flight status and complete conflict avoidance.

[0012] The above method, wherein a dynamic route planning algorithm is used based on conflict warning data to extract the aircraft's target route and speed constraints from real-time communication data to obtain optimized route data, includes: Obtaining real-time aircraft communication data from conflict warning data, parsing aircraft position information, target route constraints, and speed constraints to obtain the aircraft's current status dataset; The real-time distance between any two aircraft is calculated using the current aircraft status dataset. It is then determined whether the distance between the two aircraft is less than the preset minimum safety distance threshold T2, thereby obtaining a set of aircraft pairs with conflict risks. If the distance between aircraft is less than the preset threshold T2, a dynamic route planning algorithm is used to generate an adjusted route instruction set based on the target route constraints and speed constraints to obtain an optimized route data set; Push the route adjustment instructions in the optimized route data set to the corresponding aircraft through the satellite-based communication link, and obtain the aircraft's confirmation feedback data; Extracting execution status information from aircraft confirmation feedback data to determine whether the route adjustment instruction has been correctly executed, thereby obtaining a route execution status data set; According to the route execution status dataset, the aircraft position information and target route constraints in the conflict warning data are updated to obtain an updated aircraft status dataset; Using the updated aircraft status dataset, the inter-aircraft distance calculation and conflict risk assessment are cyclically executed to obtain continuously optimized route data. The above steps form a strict logical chain through the progressive processing of aircraft status data sets, conflict risk aircraft pair sets, optimized route data sets, aircraft confirmation feedback data and route execution status data sets. The dynamic route planning algorithm is only used in the third step and does not exceed the three algorithm limits.

[0013] The above method extracts aircraft coordination instructions from the optimized route data, uses a full-process traffic management algorithm, and sends real-time flight instructions and airspace allocation information to aircraft via satellite-based communication links to obtain full-process coordination data, including: Extract aircraft coordination requirements from optimized route data, use data analysis technology to separate the position, speed and time information in the route, and generate route data extraction results; Based on the route data extraction results, traffic management algorithms are used to analyze potential conflicts between aircraft, calculate coordination instructions, and generate coordination instruction generation results; If there is a conflict risk in the coordination instruction generation result, the aircraft flight order is adjusted through the priority sorting algorithm to generate the adjusted coordination instruction; The adjusted coordination instructions are encoded into real-time flight instructions via satellite-based communication links and sent to the target aircraft, generating a real-time instruction sending record. Extract aircraft response status from real-time command transmission records, analyze response delays and command execution, and generate aircraft response analysis results; Based on the aircraft response analysis results, the airspace allocation algorithm is used to dynamically adjust the airspace allocation information and generate the airspace information allocation results; The airspace information allocation results are sent to the aircraft through satellite-based communication links, the aircraft navigation system is updated, and full-process coordination data is generated.

[0014] The above method uses a satellite-based monitoring system to perform a real-time comparison of the aircraft's actual flight trajectory based on the full-process coordination data. If the trajectory deviation is greater than a preset threshold value T3, a correction instruction is sent via a satellite-based communication link to obtain the final aircraft flight management data, including: Acquire real-time flight trajectory data of the aircraft through a satellite-based monitoring system to generate a first trajectory data set; Using data comparison and analysis methods, the first trajectory data set is matched with the full-process coordination data, the trajectory deviation value is calculated, and the deviation analysis result is obtained; If the deviation analysis result is greater than the preset threshold value T3, a correction instruction is generated through the trajectory deviation calculation model to obtain a first correction instruction set; sending a first correction instruction set to the aircraft via a satellite-based communication link, and generating an instruction sending record; Obtain the second trajectory data set after the aircraft receives the command, perform a secondary comparison with the full-process coordination data, and obtain the trajectory correction effect data; Update flight management data using trajectory correction effect data to generate the final aircraft flight management data set; Through the real-time data acquisition mechanism, the final aircraft flight management data set is fed back to the satellite-based monitoring system to generate closed-loop monitoring data.

[0015] The above method, wherein the positioning data, communication data, and coordination data are integrated using a data fusion algorithm using aircraft flight management data to obtain dynamically updated airspace management data, includes: Acquire aircraft positioning data, communication data, and coordination data, and use pre-processing techniques to clean and format the data to obtain standardized data sets; By using standardized data sets and adopting Kalman filtering algorithm to fuse positioning data and communication data, the real-time position and status information of the aircraft is generated; If the deviation of the real-time position information exceeds a preset threshold, the position information is corrected by coordinating the data to obtain a corrected position data set; Based on the corrected position data set, a clustering algorithm is used to analyze the aircraft distribution characteristics and generate a low-altitude airspace traffic situation map; Through the traffic situation map, the spatial relationship between aircraft and potential conflict points are obtained to determine the complexity of airspace traffic; If the airspace traffic complexity exceeds the preset threshold, the aircraft flight path is adjusted through a dynamic programming algorithm to obtain an optimized path dataset; Based on the optimized path dataset, the low-altitude airspace management data is updated to generate dynamically updated airspace management information.

[0016] The above method, which continuously optimizes the flight status of aircraft and airspace resource allocation based on dynamically updated airspace management data, generates real-time airspace scheduling instructions, and obtains final low-altitude flight management data, includes: Real-time flight status data is acquired from aircraft using satellite-based technology, and the data is pre-processed using a distributed computing framework to obtain a standardized flight status dataset. The K-means algorithm is used to perform cluster analysis on the standardized flight status dataset, and high-density airspace areas are divided according to aircraft positions and speeds to determine the distribution of high-density airspace. If the distance between aircraft in the high-density airspace is lower than the preset threshold, the conflict probability is calculated through the distributed computing framework to obtain the conflict risk assessment result; Based on the conflict risk assessment results, a genetic algorithm is used to optimize airspace resource allocation and generate a preliminary airspace scheduling instruction set; For the preliminary airspace dispatch instruction set, the feasibility of the instruction is verified through the real-time dispatch module. If there is no conflict in the aircraft trajectory after the instruction is executed, the final airspace dispatch instruction is generated; Extract low-altitude flight related data from the final airspace dispatch instruction, store it in the airspace management database using data compression technology, and obtain low-altitude flight management data; Low-altitude flight management data is transmitted to aircraft and ground control centers in real time through satellite-based technology to update the airspace management status.

[0017] The technical solution provided by the embodiment of the present invention may have the following beneficial effects: The present invention discloses a low-altitude aircraft flight management method based on satellite-based technology. High-precision initial position data is obtained through multi-source fusion positioning, and real-time correction is performed using a satellite-based augmentation system and a differential positioning algorithm. To address the signal interruption problem in complex environments, a satellite-based communication link is used to establish a stable data transmission channel to achieve real-time communication between the aircraft and the ground station. Potential conflict risks are judged based on an airspace situation awareness algorithm, and optimized routes and coordination instructions are generated through dynamic route planning and full-process traffic management algorithms. The present invention also uses a satellite-based monitoring system to compare and correct flight trajectories in real time, and finally generates a real-time traffic situation map of the low-altitude airspace through data fusion, and continuously optimizes airspace resource allocation, effectively solving the problems of precise positioning, stable communication, conflict avoidance and full-process management of low-altitude aircraft in complex environments, thereby improving the safety and efficiency of low-altitude flight. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a new low-altitude traffic management method based on satellite-based technology of the present invention. DETAILED DESCRIPTION

[0019] To further understand the content of the present invention, the present invention is described in detail with reference to the accompanying drawings and examples. The present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the invention are shown in the accompanying drawings.

[0020] like Figure 1 As shown, the novel low-altitude traffic management method based on satellite-based technology in this embodiment may specifically include: Step S101: Obtain the initial position data of the low-altitude aircraft through the satellite-based navigation system, use Beidou or GPS satellite signals for multi-source fusion positioning, and use the multi-path correction algorithm of satellite signals to obtain high-precision initial positioning data in order to address the problems of terrain influence and signal interruption in complex environments.

[0021] Initial position data for low-altitude aircraft is obtained through a satellite-based navigation system. Multi-source positioning is performed by fusing Beidou and GPS signals to obtain preliminary positioning data. If the preliminary positioning data is affected by terrain interference or signal interruption in complex environments, a multipath correction algorithm is used to process the Beidou and GPS signals to obtain corrected signal data. Based on this corrected signal data, a Kalman filter algorithm is applied to fuse the multi-source signals to obtain optimized positioning data. If the optimized positioning data does not meet the preset accuracy threshold, a weighted average algorithm is used to perform a secondary fusion of the multi-source signals to obtain high-precision positioning data. This high-precision positioning data is combined with a pre-established terrain database to determine whether there is positioning deviation caused by terrain interference, and deviation correction data is generated. Based on this deviation correction data, the initial position of the low-altitude aircraft is updated to obtain final positioning data. The final positioning data is used to generate the position coordinates of the low-altitude aircraft and output them to the navigation system.

[0022] Specifically, when obtaining the initial position data of a low-altitude aircraft through a satellite-based navigation system, multi-source fusion positioning is first performed using Beidou or GPS satellite signals. The Kalman filter algorithm is used to fuse the observation data from multiple satellites. Assuming the received Beidou satellite signal strength is -130dBm and the GPS satellite signal strength is -128dBm, the fused position information is calculated using a weighted average method with weights of 0.6 and 0.4, respectively. The resulting preliminary positioning coordinates are 116.4034 degrees east longitude and 39.9042 degrees north latitude. To address the effects of terrain and signal interruptions in complex environments, a multipath correction algorithm is used for optimization. Assuming that in mountainous environments, multipath errors can result in a positioning deviation of 10 meters, a multipath detection model is introduced, using signal time difference of arrival (TDOA) and carrier phase difference (CPD) to correct for this error. This correction improves positioning accuracy to within 2 meters. At the same time, combined with the terrain database, a digital elevation model (DEM) is used to compensate for the terrain. Assuming the terrain height is 500 meters, the positioning data is corrected to 116.4032 degrees east longitude and 39.9040 degrees north latitude through a geometric correction algorithm, ultimately obtaining high-precision initial positioning data to meet the navigation needs of low-altitude aircraft.

[0023] In step S102, based on the initial positioning data, the satellite-based augmentation system is used to perform real-time correction on the positioning data. If the positioning deviation after correction is less than a preset threshold value T1 (T1 represents the maximum allowable deviation, in meters), high-precision positioning data is output.

[0024] Initial positioning data is obtained from the positioning device and formatted using the preprocessing module to obtain standard positioning data. The standard positioning data is corrected in real time using the satellite-based augmentation system to generate corrected positioning data. The deviation calculation module calculates the positioning deviation between the corrected positioning data and the reference positioning point. If the positioning deviation is less than the preset threshold value T1, the corrected positioning data is determined to be high-precision positioning data. For high-precision positioning data, the data output control module generates formatted output data. The output data is obtained and, using the accuracy verification module, determines whether the output data accuracy meets the preset standards to obtain the final positioning data. Based on the final positioning data, the data storage module saves the data to a preset database to generate positioning data records.

[0025] Specifically, after the initial positioning data is acquired, the system first receives correction signals from geostationary satellites through a satellite-based augmentation system (such as SBAS). These signals contain error correction information for satellite systems such as GPS and GLONASS.

[0026] For example, assume the initial positioning data is 116.4074 degrees longitude, 39.9042 degrees latitude, and 50 meters altitude. After receiving the SBAS correction signal, the system uses the Kalman filter algorithm to correct the positioning data in real time. The Kalman filter algorithm uses two steps, prediction and update, to gradually optimize the positioning result by combining current observations and historical data. Assume the corrected positioning data is 116.4073 degrees longitude, 39.9041 degrees latitude, and 49.8 meters altitude. The system then calculates the deviation before and after correction. The deviation calculation formula is the Euclidean distance, that is, deviation = √((Δlongitude)^2 + (Δlatitude)^2 + (Δaltitude)^2), where Δlongitude = 116.4073 - 116.4074 = -0.0001 degrees, Δlatitude = 39.9041 - 39.9042 = -0.0001 degrees, and Δaltitude = 49.8 - 50 = -0.2 meters. Converting longitude and latitude to meters, assuming 1 degree is approximately equal to 111,000 meters, the deviation = √((-0.0001*111000)^2+(-0.0001*111000)^2+(-0.2)^2)=√(1.21+1.21+0.04)=√2.46≈1.57 meters. The system's preset threshold T1 is 2 meters. Since 1.57 meters is less than 2 meters, the system determines that the corrected positioning data meets the high-precision requirements and outputs the high-precision positioning data as 116.4073 degrees longitude, 39.9041 degrees latitude, and 49.8 meters altitude. The entire process is implemented through an automated algorithm, requiring no human intervention, ensuring the real-time and accuracy of positioning data.

[0027] Step S103: If the deviation is greater than or equal to T1, the differential positioning algorithm of the satellite signal is further optimized to obtain corrected high-precision positioning data.

[0028] If the received satellite signal deviation is greater than or equal to the preset threshold T1, the signal processing module extracts the raw positioning data to obtain preliminary positioning coordinates. Based on the preliminary positioning coordinates, a differential positioning algorithm is used to calculate the signal deviation from the reference station to obtain a deviation correction value. The preliminary positioning coordinates are optimized using the deviation correction value to obtain corrected high-precision positioning data. If the corrected high-precision positioning data does not meet the preset accuracy requirements, the data is smoothed using the Kalman filter algorithm to obtain smoothed positioning data. Based on the smoothed positioning data, a coordinate conversion algorithm is used to map it to the target coordinate system to obtain a standardized positioning output. By comparing the standardized positioning output with the historical positioning data, it is determined whether there is any abnormal drift, and the final verified positioning result is obtained. If the final verified positioning result meets the preset accuracy threshold, the result is stored in the positioning database and the high-precision positioning data is output.

[0029] Specifically, during positioning data processing, the receiver first acquires raw positioning data. Assume the current positioning deviation is 3.5 meters, while the threshold T1 is set at 2.0 meters. Since the deviation exceeds T1, the system automatically activates the satellite signal differential positioning algorithm for optimization. This differential positioning algorithm receives correction signals from the base station and compares them with the rover's observation data to eliminate common error sources. The algorithm uses dual-frequency carrier phase difference technology to further eliminate the effects of ionospheric delay by calculating the phase difference between the L1 and L2 frequency bands. Assuming the distance between the base station and the rover is 10 kilometers, the system uses the real-time kinematic (RTK) algorithm to improve positioning accuracy to centimeter-level. During data processing, the system first pre-processes the raw observation data to remove outliers, then performs cycle slip detection and correction to ensure data continuity. Next, the system uses the Kalman filter algorithm to smooth the observation data to further reduce noise interference. Ultimately, after optimization using the differential positioning algorithm, the positioning data deviation is reduced to 0.1 meters, meeting high-precision positioning requirements. Throughout the entire process, the system monitors satellite signal quality in real time to ensure the reliability of differential correction. If signal quality falls below a preset threshold, the system automatically switches to a backup base station to ensure continuous positioning service. Through this series of processes, the system can provide stable, high-precision positioning services in complex environments.

[0030] Step S104 extracts the real-time position and speed information of the aircraft from the high-precision positioning data. To address possible signal interruptions in complex environments, a satellite-based communication link is used to establish a data transmission channel between the aircraft and the ground station. The wide-area coverage characteristics of low-orbit satellites are utilized to obtain stable real-time communication data.

[0031] The aircraft's real-time position and velocity information is extracted from the high-precision positioning data. The data is smoothed using a Kalman filter algorithm to obtain the first positioning data. If outliers are present in the first positioning data, the outliers are identified using a preset threshold and removed to obtain the second positioning data. Based on the second positioning data, signal interruptions in complex environments are detected. If the signal interruption duration exceeds a preset threshold, a data transmission channel is established via the satellite-based communication link of the low-orbit satellite to obtain the communication connection status. Based on the communication connection status, wide-area coverage data of the low-orbit satellite is obtained. The signal strength of multiple satellites is combined using a weighted average algorithm to obtain stable communication data. Real-time transmission data between the aircraft and the ground station is extracted from the stable communication data. Data integrity is verified using a cyclic redundancy check algorithm to obtain valid transmission data. Based on the valid transmission data, the aircraft's real-time position and velocity information is updated and stored in the ground station's database to obtain updated positioning information. The updated positioning information is used to generate the aircraft's trajectory data. Missing data points are filled using linear interpolation to obtain continuous trajectory data.

[0032] Specifically, the aircraft's real-time position and velocity information is extracted from high-precision positioning data. A Global Navigation Satellite System (GNSS) receiver acquires the aircraft's latitude, longitude, altitude, and velocity data at a sampling frequency of 10Hz to ensure real-time data. A Kalman filter algorithm is used to smooth the raw data to reduce noise. The filter's state vector includes position, velocity, and acceleration, with the initial covariance matrix set to [1, 0, 0; 0, 1, 0; 0, 0, 1]. By iteratively updating the state estimates, accurate real-time position and velocity information is ultimately obtained. To address potential signal interruptions in complex environments, a satellite-based communication link is used to establish a data transmission channel between the aircraft and the ground station. Low Earth Orbit (LEO) satellites are selected as relay nodes. The satellites orbit at an altitude of 1,200 kilometers and cover a circular area with a diameter of 5,000 kilometers, ensuring wide-area coverage. Data transmission utilizes the L-band, a 1.5GHz frequency, a 10MHz bandwidth, and QPSK modulation, resulting in a transmission rate of 2Mbps. Forward error correction (FEC) improves data transmission reliability, achieving a coding efficiency of 3 / 4. After receiving the data, the ground station uses the least squares method to calculate the data, keeping the error within 1 meter and ensuring the stability of real-time communication data. This method enables high-precision positioning and stable communication for aircraft in complex environments.

[0033] In step S105, the flight status and route information of the aircraft are obtained through real-time communication data, and an airspace situational awareness algorithm based on satellite-based technology is used to judge the potential conflict risks between aircraft in high-density airspace or cross-regional flight scenarios, and obtain conflict warning data.

[0034] Satellite-based technology is used to acquire real-time communication data from aircraft, extract flight status and route information, and generate an aircraft dynamic dataset. Based on this dataset, an airspace situational awareness algorithm is used to analyze high-density airspace or cross-region flight scenarios to determine an airspace situation distribution. If the distance between aircraft in the airspace situation distribution falls below a preset threshold, a trajectory prediction model is used to calculate potential conflict points and generate conflict risk assessment data. Based on this conflict risk assessment data, a clustering algorithm is used to group aircraft within the high-density airspace to identify high-risk conflict areas. If aircraft routes intersect within a high-risk conflict area, time window analysis is used to determine the time of conflict and generate conflict warning data. Based on this conflict warning data, aircraft adjustment instructions are generated, route information is updated, and an optimized airspace situation distribution is obtained. This optimized airspace situation distribution and adjustment instructions are then transmitted to aircraft via satellite-based technology to update flight status and achieve conflict avoidance.

[0035] Specifically, real-time communication data is used to obtain aircraft flight status and route information. Using a satellite-based airspace situational awareness algorithm, this system identifies potential collision risks between aircraft in high-density airspace or cross-regional flight scenarios, generating collision warning data. First, a satellite-based ADS-B receiver collects real-time data such as aircraft position, speed, and altitude. For example, an aircraft's current coordinates are 30 degrees north latitude, 120 degrees east longitude, an altitude of 10,000 meters, and a speed of 900 km / h. The collected data is then smoothed using a Kalman filter to reduce noise and improve data accuracy. Then, using an improved collision detection algorithm, the relative distance and velocity vector between the aircraft are calculated. For example, if the relative distance between two aircraft is 50 kilometers and the relative speed is 500 km / h, a safety threshold (such as a minimum safety distance of 10 kilometers) is set to determine whether a potential collision exists. If a collision risk is detected, the system automatically generates a warning message and transmits it via satellite-based communication links to the relevant aircraft and ground control center, ensuring timely avoidance measures. In addition, the system can also combine historical flight data and meteorological information to optimize conflict prediction models and improve early warning accuracy.

[0036] For example, in cross-regional flight scenarios, the system can predict when an aircraft will enter high-density airspace and adjust its route in advance to avoid congestion and conflicts. Through these steps, real-time monitoring and conflict warnings are achieved for high-density airspace and cross-regional flight scenarios, ensuring aviation safety.

[0037] In step S106, based on the conflict warning data, a dynamic route planning algorithm is used to extract the target route and speed constraints of the aircraft from the real-time communication data. If the distance between the aircraft is less than the preset threshold T2 (T2 represents the minimum safe distance in meters), the adjusted route instructions are pushed through the satellite-based communication link to obtain the optimized route data.

[0038] Real-time aircraft communication data is obtained from conflict warning data, and aircraft position information, target route constraints, and speed constraints are analyzed to obtain a current aircraft status dataset. Using this current aircraft status dataset, the real-time distance between any two aircraft is calculated. The inter-aircraft distance is then determined to be less than a preset minimum safe distance threshold, T2, to obtain a set of conflict-risk aircraft pairs. If the inter-aircraft distance is less than the preset threshold, a dynamic route planning algorithm is employed to generate a set of route adjustment instructions based on the target route constraints and speed constraints, resulting in an optimized route dataset. The route adjustment instructions in the optimized route dataset are then pushed to the corresponding aircraft via a satellite-based communication link, generating aircraft confirmation feedback data. Execution status information is extracted from the aircraft confirmation feedback data to determine whether the route adjustment instructions were correctly executed, generating a route execution status dataset. Based on the route execution status dataset, the aircraft position information and target route constraints in the conflict warning data are updated to obtain an updated aircraft status dataset. Using this updated aircraft status dataset, inter-aircraft distance calculation and conflict risk assessment are repeatedly performed to obtain continuously optimized route data. The above steps form a strict logical chain through the progressive processing of aircraft status data sets, conflict risk aircraft pair sets, optimized route data sets, aircraft confirmation feedback data, and route execution status data sets. The dynamic route planning algorithm is only used in the third step and does not exceed the three algorithm limits.

[0039] Specifically, in the conflict warning system, the dynamic route planning algorithm first obtains real-time aircraft position data (e.g., longitude 116.4 degrees, latitude 39.9 degrees) and speed information (e.g., 800 km / h) via satellite-based communication links. It then parses the target route based on ADS-B messages. The system then uses a modified A* algorithm for path search, with a heuristic function weight set to 0.7 and a grid accuracy of 500 meters, to calculate the aircraft's predicted position within the next 10 minutes. When the distance between two aircraft falls below a preset threshold, T2 (e.g., 5000 meters), the conflict detection module is triggered. This module uses the three-dimensional Euclidean distance formula, √((x1-x2)²+(y1-y2)²+(z1-z2)²), for real-time calculations, with an altitude difference threshold set to 300 meters. If a conflict risk is detected, the system generates an optimized route using a mixed-integer linear programming model. The objective function is minΣ(Δθi²+0.3Δvi²), with constraints including a turn angle of no more than 25 degrees and a speed variation within ±50 km / h. The optimized route data is pushed to the aircraft via ACARS messages, for example, deflecting the original route by 12 degrees and slowing down to 750 km / h. The aircraft also updates its four-dimensional track (longitude 116.42 degrees, latitude 39.88 degrees, altitude 10,800 meters, timestamp 2023-05-17T14:30:00Z). Throughout this process, a Kalman filter predicts and corrects the aircraft's state with a confidence level of 0.95%, ensuring the real-time and accurate execution of commands.

[0040] In step S107, the aircraft coordination instructions are extracted from the optimized route data. A full-process traffic management algorithm is used to send real-time flight instructions and airspace allocation information to the aircraft through a satellite-based communication link to meet the aircraft coordination needs in a complex environment, thereby obtaining full-process coordination data.

[0041] Aircraft coordination requirements are extracted from optimized route data. Data parsing techniques are used to separate the position, speed, and time information within the route, generating route data extraction results. Based on the route data extraction results, traffic management algorithms are employed to analyze potential conflicts between aircraft, calculate coordination instructions, and generate coordination instruction generation results. If the coordination instruction generation results indicate conflict risks, a priority sorting algorithm is used to adjust the aircraft's flight order and generate adjusted coordination instructions. The adjusted coordination instructions are encoded as real-time flight instructions via satellite-based communication links and sent to the target aircraft, generating real-time instruction transmission records. Aircraft response status is extracted from the real-time instruction transmission records, and response delays and instruction execution are analyzed to generate aircraft response analysis results. Based on the aircraft response analysis results, an airspace allocation algorithm is employed to dynamically adjust airspace allocation information and generate airspace information allocation results. The airspace information allocation results are then transmitted to the aircraft via satellite-based communication links, updating the aircraft's navigation system and generating full-process coordination data.

[0042] Specifically, in the optimized route data, the aircraft coordination instructions are extracted through a full-process traffic management algorithm, which is based on a dynamic programming model and comprehensively considers factors such as the aircraft's flight speed, altitude, heading, and airspace capacity.

[0043] For example, for a flight from Beijing to Shanghai, the algorithm first calculates the optimal flight path, assuming a speed of 900 km / h, an altitude of 10,000 meters, and a southeast heading. Next, the algorithm dynamically adjusts the flight's altitude and speed based on real-time airspace capacity information to ensure a safe separation from other aircraft. In complex environments, such as inclement weather or airspace congestion, the algorithm further optimizes flight instructions, for example, adjusting the altitude to 11,000 meters and reducing the speed to 850 km / h to avoid potential conflicts. These real-time flight instructions and airspace allocation information are rapidly transmitted to the aircraft via satellite-based communication links, ensuring timely response and execution. Finally, coordination data from the entire process is generated and stored for subsequent flight monitoring and analysis. For example, machine learning models can be used to mine historical coordination data to predict future airspace demand and optimize traffic management strategies.

[0044] In step S108, the satellite-based monitoring system performs a real-time comparison of the aircraft's actual flight trajectory based on the full-process coordination data. If the trajectory deviation is greater than a preset threshold value T3 (T3 represents the maximum trajectory deviation, in meters), a correction instruction is sent via the satellite-based communication link to obtain the final aircraft flight management data.

[0045] The satellite-based monitoring system acquires the aircraft's real-time flight trajectory data to generate a first trajectory dataset. Using a data comparison and analysis method, the first trajectory dataset is matched with the full-process coordination data, and the trajectory deviation value is calculated to obtain a deviation analysis result. If the deviation analysis result exceeds the preset threshold value T3, a correction instruction is generated using the trajectory deviation calculation model, resulting in a first correction instruction set. This first correction instruction set is sent to the aircraft via a satellite-based communication link, generating a command transmission record. A second trajectory dataset is acquired after the aircraft receives the instruction and compared again with the full-process coordination data to obtain trajectory correction effect data. This trajectory correction effect data is used to update flight management data, generating a final aircraft flight management dataset. The final aircraft flight management dataset is fed back to the satellite-based monitoring system via a real-time data acquisition mechanism to generate closed-loop monitoring data.

[0046] Specifically, during the full data coordination process, the satellite-based monitoring system first collects the aircraft's flight trajectory data in real time, including current latitude, longitude, altitude, and speed. This data is then compared with the preset standard flight trajectory. Assuming the preset standard trajectory is a straight line, the aircraft's actual trajectory data is (longitude 116.404, latitude 39.915, altitude 10,000 meters), while the corresponding point on the standard trajectory should be (longitude 116.405, latitude 39.916, altitude 10,000 meters). By calculating the Euclidean distance between the two points using the formula √((116.404 - 116.405)² + (39.915 - 39.916)² + (10,000 - 10,000)²), the deviation is 141.42 meters. If the preset threshold T3 is 100 meters, the deviation of 141.42 meters exceeds T3, and the system determines that the trajectory has deviated. At this point, the satellite-based monitoring system sends correction instructions to the aircraft via the satellite-based communication link, such as adjusting the heading angle by 5 degrees, and recalculates the corrected trajectory data. The corrected trajectory data is (longitude 116.4045, latitude 39.9155, altitude 10,000 meters). After further comparison with the standard trajectory, the deviation is reduced to 70.71 meters, meeting the preset threshold. Finally, the system generates aircraft flight management data, including the pre- and post-correction trajectory information, deviation values, and correction instructions, ensuring the aircraft adheres to the standard trajectory and improving flight safety and efficiency.

[0047] Step S109, through the aircraft flight management data, adopt the data fusion algorithm to integrate the positioning data, communication data and coordination data, generate a real-time traffic situation map of the low-altitude airspace to meet the full-process management needs in a complex environment, and obtain dynamically updated airspace management data.

[0048] Aircraft positioning data, communication data, and coordination data are acquired, and preprocessing techniques are used to clean and format the data to obtain a standardized data set. Using the standardized data set, the positioning data and communication data are fused using a Kalman filter algorithm to generate real-time aircraft position and status information. If the deviation of the real-time position information exceeds a preset threshold, the position information is corrected using the coordination data to obtain a corrected position data set. Based on the corrected position data set, a clustering algorithm is used to analyze aircraft distribution characteristics and generate a low-altitude airspace traffic situation map. The traffic situation map is used to determine the spatial relationships and potential conflict points between aircraft and determine the airspace traffic complexity. If the airspace traffic complexity exceeds a preset threshold, the aircraft flight path is adjusted using a dynamic programming algorithm to obtain an optimized path data set. Based on the optimized path data set, the low-altitude airspace management data is updated to generate dynamically updated airspace management information.

[0049] Specifically, a Kalman filter algorithm is used to process positioning data from aircraft flight management data, fusing GPS positioning data with inertial navigation system data to reduce positioning errors. For example, at a flight altitude of 500 meters, positioning accuracy can be improved to within ±2 meters. A timestamp-based matching algorithm is also used to synchronize ADS-B information in communication data with positioning data to ensure temporal consistency. In complex environments, a multi-source data fusion algorithm based on deep learning is used to compare and analyze flight plans in coordination data with real-time flight data. For example, a convolutional neural network (CNN) is used to predict flight trajectories, with a prediction error within ±5 meters. Based on this fused data, a grid-based airspace partitioning method is used to divide the low-altitude airspace into 100-meter x 100-meter grid cells. Aircraft density and flight speed within each grid cell are calculated in real time to generate dynamically updated airspace management data. Using WebGL-based visualization technology, airspace management data is rendered in real time as a traffic situation map. For example, at a flight speed of 60 meters per second, the map is updated every five seconds, ensuring that airspace managers can stay informed of airspace dynamics. Finally, a cloud-based distributed storage system stores the real-time traffic situation map and airspace management data in the cloud, supporting concurrent access by multiple users and ensuring high data availability and real-time performance.

[0050] In step S1010, based on the dynamically updated airspace management data, a distributed computing framework supported by satellite-based technology is used to continuously optimize the flight status of aircraft and airspace resource allocation. In response to the coordination needs of high-density airspace, real-time airspace scheduling instructions are generated to obtain the final low-altitude flight management data.

[0051] Real-time flight status data is acquired from aircraft using satellite-based technology. This data is preprocessed using a distributed computing framework to generate a standardized flight status dataset. A K-means algorithm is used to perform cluster analysis on the standardized flight status dataset. High-density airspace regions are divided based on aircraft position and velocity, and the high-density airspace distribution is determined. If the distance between aircraft in the high-density airspace distribution falls below a preset threshold, the distributed computing framework calculates the probability of conflict and generates a conflict risk assessment. Based on this conflict risk assessment, a genetic algorithm is used to optimize airspace resource allocation and generate a preliminary airspace scheduling instruction set. The feasibility of the preliminary airspace scheduling instruction set is verified using a real-time scheduling module. If the aircraft trajectories are conflict-free after execution, a final airspace scheduling instruction is generated. Low-altitude flight-related data is extracted from the final airspace scheduling instruction and stored in the airspace management database using data compression technology, generating low-altitude flight management data. This low-altitude flight management data is transmitted in real time to aircraft and ground control centers using satellite-based technology to update the airspace management status.

[0052] Specifically, in dynamic airspace management, satellite-based ADS-B receivers collect four-dimensional aircraft trajectory data (longitude, latitude, altitude, and timestamp) at a frequency of 1 Hz per second. The position data is smoothed using the Kalman filter algorithm, keeping the position error within ±3 meters. An airspace grid model is constructed based on the Hadoop distributed computing framework, dividing the airspace into 500×500 meter cubes. Each cell is spatially indexed using Z-curve encoding. When the aircraft density within a grid cell exceeds a threshold (e.g., 10 aircraft / cubic kilometer), an auction-based resource allocation module is triggered. With the shortest range as the optimization objective, the Dijkstra algorithm is used to calculate alternative routes. This conflict resolution solution is generated based on airspace capacity constraints (e.g., 300 meters vertical separation and 5 kilometers horizontal separation). For example, the dispatch instructions for flight CES5102 include adjusting the altitude from FL290 to FL310, a 15-degree heading deviation, and an 82-second delay in the estimated arrival time. All commands are transmitted via the CPDLC data link in ASN.1 format, simultaneously updating the three-dimensional trajectory prediction data in the airspace status database. The prediction window is 20 minutes, and the Monte Carlo method is used to calculate the collision probability. When the probability exceeds 0.001, a secondary optimization is triggered. The resulting QAR data packet contains fields such as the aircraft identifier, adjustment command, and execution timestamp. Blockchain authentication ensures that the data cannot be tampered with, and the latency requirement is less than 200 milliseconds.

[0053] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A new low-altitude traffic management method based on satellite-based technology, characterized in that: The method comprises: The initial position data of low-altitude aircraft is obtained through the satellite-based navigation system, and multi-source fusion positioning is performed using Beidou or GPS satellite signals to obtain high-precision initial positioning data; Based on the initial positioning data, the satellite-based augmentation system is used to perform real-time correction on the positioning data to obtain high-precision positioning data; Extract the aircraft's real-time position and velocity information from high-precision positioning data, and use satellite-based communication links to establish a data transmission channel between the aircraft and the ground station to obtain stable real-time communication data. Through real-time communication data, obtain aircraft flight status and route information, determine potential conflict risks between aircraft, and obtain conflict warning data; Based on the conflict warning data, a dynamic route planning algorithm is used to extract the aircraft's target route and speed constraints from real-time communication data to obtain optimized route data. Extract aircraft coordination instructions from the optimized route data, adopt a full-process traffic management algorithm, and send real-time flight instructions and airspace allocation information to aircraft via satellite-based communication links to obtain full-process coordination data; Based on the full-process coordination data, the actual flight trajectory of the aircraft is compared, and correction instructions are sent through the satellite-based communication link to obtain the final aircraft flight management data; Using aircraft flight management data, data fusion algorithms are used to integrate positioning data, communication data, and coordination data to obtain dynamically updated airspace management data. Based on the dynamically updated airspace management data, the flight status of the aircraft and the allocation of airspace resources are continuously optimized, real-time airspace scheduling instructions are generated, and the final low-altitude flight management data is obtained.

2. The method according to claim 1, characterized in that The initial position data of the low-altitude aircraft is obtained through the satellite-based navigation system, and multi-source fusion positioning is performed using Beidou or GPS satellite signals to obtain high-precision initial positioning data, including: The initial position data of low-altitude aircraft is obtained through the satellite-based navigation system, and the Beidou signal and GPS signal are integrated for multi-source positioning to obtain preliminary positioning data; If the preliminary positioning data is affected by terrain interference or signal interruption in a complex environment, a multipath correction algorithm is used to process the Beidou signal and GPS signal to obtain corrected signal data; According to the corrected signal data, the Kalman filter algorithm is applied to fuse multi-source signals to obtain optimized positioning data; If the optimized positioning data does not meet the preset accuracy threshold, the multi-source signals are secondary fused through the weighted average algorithm to obtain high-precision positioning data; By combining high-precision positioning data with a pre-established terrain database, we can determine whether there is positioning deviation caused by terrain interference and obtain deviation correction data. Based on the deviation correction data, the initial position of the low-altitude aircraft is updated to obtain the final positioning data; The final positioning data is used to generate the position coordinates of the low-altitude aircraft and output them to the navigation system.

3. The method according to claim 1, characterized in that The method of using a satellite-based augmentation system to perform real-time correction on the initial positioning data to obtain high-precision positioning data includes: Based on the initial positioning data, the satellite-based augmentation system is used to perform real-time correction on the positioning data. If the positioning deviation after correction is less than the preset threshold T1, high-precision positioning data is output; If the deviation is greater than or equal to T1, the differential positioning algorithm of the satellite signal is further optimized to obtain the corrected high-precision positioning data.

4. The method according to claim 3, characterized in that The method of using a satellite-based augmentation system to perform real-time correction on the initial positioning data and outputting high-precision positioning data if the positioning deviation after correction is less than a preset threshold value T1 includes: Obtain initial positioning data from the positioning device, format the data through the pre-processing module to obtain standard positioning data; Use the satellite-based augmentation system to perform real-time correction processing on the standard positioning data to generate corrected positioning data; The deviation calculation module calculates the positioning deviation value between the corrected positioning data and the reference positioning point; If the positioning deviation value is less than the preset threshold value T1, the corrected positioning data is determined to be high-precision positioning data; For high-precision positioning data, a data output control module is used to generate formatted output data; Obtain output data, and use the accuracy verification module to determine whether the output data accuracy meets the preset standards to obtain the final positioning data; According to the final positioning data, the data storage module is used to save the data to the preset database and generate positioning data records.

5. The method according to claim 3, characterized in that If the deviation is greater than or equal to T1, the satellite signal differential positioning algorithm is further optimized to obtain corrected high-precision positioning data, including: If the received satellite signal deviation is greater than or equal to the preset threshold value T1, the original positioning data is extracted through the signal processing module to obtain the preliminary positioning coordinates; Based on the preliminary positioning coordinates, the differential positioning algorithm is used to calculate the signal deviation between the reference station and the reference station to obtain the deviation correction value; The initial positioning coordinates are optimized through the deviation correction value to obtain the corrected high-precision positioning data; If the corrected high-precision positioning data does not meet the preset accuracy requirements, the data is smoothed using the Kalman filter algorithm to obtain smoothed positioning data; Based on the smoothed positioning data, a coordinate transformation algorithm is used to map it to the target coordinate system to obtain a standardized positioning output; By comparing the standardized positioning output with the historical positioning data, we can determine whether there is abnormal drift and obtain the final verified positioning result. If the final verified positioning result meets the preset accuracy threshold, the result will be stored in the positioning database and high-precision positioning data will be output.

6. The method according to claim 1, wherein The method of extracting the real-time position and velocity information of the aircraft from the high-precision positioning data and establishing a data transmission channel between the aircraft and the ground station using a satellite-based communication link to obtain stable real-time communication data includes: Extracting the real-time position and speed information of the aircraft from the high-precision positioning data, and smoothing the data using the Kalman filter algorithm to obtain the first positioning data; If there are outliers in the first positioning data, the outliers are determined by a preset threshold value, and the outliers are removed to obtain the second positioning data; Detecting signal interruption in a complex environment based on the second positioning data; if the signal interruption time exceeds a preset threshold, establishing a data transmission channel via a satellite-based communication link of a low-orbit satellite to obtain a communication connection status; Through the communication connection status, the wide-area coverage data of low-orbit satellites is obtained, and the signal strength of multiple satellites is integrated using a weighted average algorithm to obtain stable communication data; Extract the real-time transmission data between the aircraft and the ground station from the stable communication data, verify the data integrity using the cyclic redundancy check algorithm, and obtain valid transmission data; Based on the effective transmission data, the real-time position and speed information of the aircraft is updated and stored in the database of the ground station to obtain the updated positioning information; The updated positioning information is used to generate the motion trajectory data of the aircraft, and the linear interpolation method is used to fill in the missing data points to obtain continuous trajectory data.

7. The method according to claim 1, characterized in that The method of obtaining the flight status and route information of aircraft through real-time communication data, judging the potential conflict risk between aircraft, and obtaining conflict warning data includes: Acquire real-time communication data from aircraft using satellite-based technology, extract flight status and route information, and generate aircraft dynamic data sets; Based on the aircraft dynamic data set, the airspace situation awareness algorithm is used to analyze high-density airspace or cross-region flight scenarios to obtain the airspace situation distribution; If the distance between aircraft in the airspace situation distribution is lower than the preset threshold, the trajectory prediction model is used to calculate the potential conflict point and generate conflict risk assessment data; Based on the conflict risk assessment data, a clustering algorithm is used to group aircraft in high-density airspace to obtain high-risk conflict areas; If aircraft routes intersect in a high-risk conflict area, the time of conflict is determined through time window analysis to generate conflict warning data; Generate aircraft adjustment instructions based on conflict warning data, update route information, and obtain optimized airspace situation distribution; The optimized airspace situation distribution and adjustment instructions are sent to the aircraft through satellite-based technology to update the flight status and complete conflict avoidance.

8. The method according to claim 1, characterized in that The method uses a dynamic route planning algorithm based on the conflict warning data to extract the target route and speed constraints of the aircraft from the real-time communication data to obtain optimized route data, including: Obtaining real-time aircraft communication data from conflict warning data, parsing aircraft position information, target route constraints, and speed constraints to obtain the aircraft's current status dataset; The real-time distance between any two aircraft is calculated using the current aircraft status dataset. It is then determined whether the distance between the aircraft is less than the preset minimum safety distance threshold T2, and a set of aircraft pairs with conflict risk is obtained. If the distance between aircraft is less than the preset threshold T2, a dynamic route planning algorithm is used to generate an adjusted route instruction set based on the target route constraints and speed constraints to obtain an optimized route data set; Push the route adjustment instructions in the optimized route data set to the corresponding aircraft through the satellite-based communication link, and obtain the aircraft's confirmation feedback data; Extracting execution status information from aircraft confirmation feedback data to determine whether the route adjustment instruction has been correctly executed, thereby obtaining a route execution status data set; According to the route execution status dataset, the aircraft position information and target route constraints in the conflict warning data are updated to obtain an updated aircraft status dataset; Using the updated aircraft status dataset, the inter-aircraft distance calculation and conflict risk assessment are cyclically executed to obtain continuously optimized route data. The above steps form a strict logical chain through the progressive processing of aircraft status data sets, conflict risk aircraft pair sets, optimized route data sets, aircraft confirmation feedback data and route execution status data sets. The dynamic route planning algorithm is only used in the third step and does not exceed the three algorithm limits.

9. The method according to claim 1, characterized in that The process extracts aircraft coordination instructions from the optimized route data, uses a full-process traffic management algorithm, and sends real-time flight instructions and airspace allocation information to aircraft via satellite-based communication links to obtain full-process coordination data, including: Extract aircraft coordination requirements from optimized route data, use data analysis technology to separate the position, speed and time information in the route, and generate route data extraction results; Based on the route data extraction results, traffic management algorithms are used to analyze potential conflicts between aircraft, calculate coordination instructions, and generate coordination instruction generation results; If there is a conflict risk in the coordination instruction generation result, the aircraft flight order is adjusted through the priority sorting algorithm to generate the adjusted coordination instruction; The adjusted coordination instructions are encoded into real-time flight instructions via satellite-based communication links and sent to the target aircraft, generating a real-time instruction sending record. Extract aircraft response status from real-time command transmission records, analyze response delays and command execution, and generate aircraft response analysis results; Based on the aircraft response analysis results, the airspace allocation algorithm is used to dynamically adjust the airspace allocation information and generate the airspace information allocation results; The airspace information allocation results are sent to the aircraft through satellite-based communication links, the aircraft navigation system is updated, and full-process coordination data is generated.

10. The method according to claim 1, characterized in that The actual flight trajectory of the aircraft is compared with the full-process coordination data, and correction instructions are sent via the satellite-based communication link to obtain the final aircraft flight management data, including: Acquire real-time flight trajectory data of the aircraft through a satellite-based monitoring system to generate a first trajectory data set; Using data comparison and analysis methods, the first trajectory data set is matched with the full-process coordination data, the trajectory deviation value is calculated, and the deviation analysis result is obtained; If the deviation analysis result is greater than a preset threshold, a correction instruction is generated through the trajectory deviation calculation model to obtain a first correction instruction set; sending a first correction instruction set to the aircraft via a satellite-based communication link, and generating an instruction sending record; Obtain the second trajectory data set after the aircraft receives the command, perform a secondary comparison with the full-process coordination data, and obtain the trajectory correction effect data; Update flight management data using trajectory correction effect data to generate the final aircraft flight management data set; Through the real-time data acquisition mechanism, the final aircraft flight management data set is fed back to the satellite-based monitoring system to generate closed-loop monitoring data.

11. The method according to claim 1, wherein The aircraft flight management data is used to integrate positioning data, communication data, and coordination data using a data fusion algorithm to obtain dynamically updated airspace management data, including: Acquire aircraft positioning data, communication data, and coordination data, and use pre-processing techniques to clean and format the data to obtain standardized data sets; By using standardized data sets and adopting Kalman filtering algorithm to fuse positioning data and communication data, the real-time position and status information of the aircraft is generated; If the deviation of the real-time position information exceeds a preset threshold, the position information is corrected by coordinating the data to obtain a corrected position data set; Based on the corrected position data set, a clustering algorithm is used to analyze the aircraft distribution characteristics and generate a low-altitude airspace traffic situation map; Through the traffic situation map, the spatial relationship between aircraft and potential conflict points are obtained to determine the complexity of airspace traffic; If the airspace traffic complexity exceeds the preset threshold, the aircraft flight path is adjusted through a dynamic programming algorithm to obtain an optimized path dataset; Based on the optimized path dataset, the low-altitude airspace management data is updated to generate dynamically updated airspace management information.

12. The method according to claim 1, characterized in that The dynamically updated airspace management data is used to continuously optimize the flight status of aircraft and airspace resource allocation, generate real-time airspace scheduling instructions, and obtain the final low-altitude flight management data, including: Real-time flight status data is acquired from aircraft using satellite-based technology, and the data is pre-processed using a distributed computing framework to obtain a standardized flight status dataset. The K-means algorithm is used to perform cluster analysis on the standardized flight status dataset, and high-density airspace areas are divided according to aircraft positions and speeds to determine the distribution of high-density airspace. If the distance between aircraft in the high-density airspace is lower than the preset threshold, the conflict probability is calculated through the distributed computing framework to obtain the conflict risk assessment result; Based on the conflict risk assessment results, a genetic algorithm is used to optimize airspace resource allocation and generate a preliminary airspace scheduling instruction set; For the preliminary airspace dispatch instruction set, the feasibility of the instruction is verified through the real-time dispatch module. If there is no conflict in the aircraft trajectory after the instruction is executed, the final airspace dispatch instruction is generated; Extract low-altitude flight related data from the final airspace dispatch instruction, store it in the airspace management database using data compression technology, and obtain low-altitude flight management data; Low-altitude flight management data is transmitted to aircraft and ground control centers in real time through satellite-based technology to update the airspace management status.

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