Flight control system and resource allocation method thereof

Abstract

The invention relates to the technical field of aircrafts, and discloses a flight control system and a resource allocation method thereof, and the method is applied to the flight control system. The flight control system comprises a sensor accessed to a core network and an ADS-B receiver. The method comprises the following steps: acquiring spatial characteristics and state information of a target aircraft; the spatial features are collected by the sensor; the state information is received by the ADS-B receiver; determining a risk level of an area where the target aircraft is located based on the spatial features and the state information; and setting a network slice allocation strategy based on the risk level of the area where the target aircraft is located so as to adjust communication resources of the target aircraft. According to the scheme, the efficiency is high and the accuracy is good when the resource allocation function is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft, and particularly relates to a flight control system and a resource allocation method thereof. Background Art

[0002] Airspace management of low-altitude aircraft refers to the process of planning, coordinating, and supervising flight activities within a certain altitude range above the ground. With the wide application of low-altitude aircraft, airspace management is particularly important for ensuring flight safety, avoiding air traffic conflicts, and promoting the rational use of low-altitude resources.

[0003] In the related art, airspace management of low-altitude aircraft is carried out through a low-altitude aircraft monitoring and tracking system. Data of low-altitude aircraft within the management area is collected and processed through a single sensor in the system to obtain the positions and states of the low-altitude aircraft, and relevant flight instructions are sent to the low-altitude aircraft based on preset airspace division and actual requirements.

[0004] However, the various devices included in the system in the above solution have heterogeneous protocols and scattered data, making it difficult to achieve low-latency and high-precision airspace management, and it is also difficult to conduct targeted management of low-altitude aircraft in different regions with different authorities, affecting management efficiency and accuracy, and causing waste of system resources. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a flight control system and a resource allocation method thereof to solve the problems of low efficiency and low accuracy in airspace management, resulting in waste of system resources.

[0006] In a first aspect, the present invention provides a resource allocation method in a flight control system, which is applied to the flight control system; the flight control system includes sensors connected to the core network and an ADS-B receiver; the method includes:

[0007] Obtain the spatial characteristics and status information of the target aircraft; the spatial characteristics are collected by the sensors; the status information is received by the ADS-B receiver;

[0008] Based on the spatial characteristics and the status information, determine the risk level of the area where the target aircraft is located;

[0009] Set a network slice allocation strategy based on the risk level of the area where the target aircraft is located to adjust the communication resources of the target aircraft.

[0010] In an optional implementation manner, the determining the risk level of the area where the target aircraft is located based on the spatial characteristics and the status information includes:

[0011] Based on the spatial characteristics of the target aircraft and the information of the preset flight restricted area, determine the overlap rate between the area where the target aircraft is located and the preset flight restricted area, and the continuous residence time of the target aircraft in the preset flight restricted area;

[0012] Based on the overlap rate and the continuous residence time, determine whether the target aircraft is in the flight restricted area, and obtain the determination result of the flight restricted area;

[0013] Based on the determination result of the flight restricted area and the status information of the target aircraft, determine the risk level of the area where the target aircraft is located.

[0014] In an alternative embodiment, the determining the risk level of the area where the target aircraft is located based on the determination result of the flight restricted area and the status information of the target aircraft includes:

[0015] Determine a first risk value based on the determination result of the flight restricted area;

[0016] Compare the status information of the target aircraft with a preset status threshold to determine a second risk value;

[0017] Based on the first risk value, the second risk value, and a preset weight, determine the risk level of the area where the target aircraft is located.

[0018] In an alternative embodiment, the method further includes:

[0019] Predict the motion trajectory of the target aircraft based on the spatial characteristics and the status information to obtain a trajectory prediction result;

[0020] Based on the performance data of the flight control system, the network status, and the trajectory prediction result, update the network slice allocation strategy and adjust the working status of the sensor and the ADS-B receiver.

[0021] In an alternative embodiment, the adjusting the working status of the sensor and the ADS-B receiver includes:

[0022] Based on the trajectory prediction result, determine the relative distance between the target aircraft and the flight control system and the speed of the target aircraft;

[0023] Based on the relative distance between the target aircraft and the flight control system and the speed of the target aircraft, adjust the acquisition frequency and power consumption mode of the sensor and the ADS-B receiver.

[0024] Second aspect, the present invention provides a resource allocation device in a flight control system, which is applied to a flight control system; the flight control system includes a sensor accessing the core network and an ADS-B receiver; the device includes:

[0025] A data acquisition module, configured to acquire the spatial characteristics and status information of a target aircraft; the spatial characteristics are collected by the sensor; the status information is received by the ADS-B receiver;

[0026] A risk level determination module, configured to determine the risk level of the area where the target aircraft is located based on the spatial characteristics and the status information;

[0027] A resource adjustment module, configured to set a network slice allocation policy based on the risk level of the area where the target aircraft is located to adjust the communication resources of the target aircraft.

[0028] Third aspect, the present invention provides a flight control system, the system includes:

[0029] The core network;

[0030] A sensor, configured to access the core network; the sensor is used to collect the spatial characteristics of the target aircraft;

[0031] An ADS-B receiver, configured to access the core network; the ADS-B receiver is used to collect the status information of the target aircraft;

[0032] A data acquisition module, configured to acquire the spatial characteristics and status information of the target aircraft; the spatial characteristics are collected by the sensor; the status information is received by the ADS-B receiver;

[0033] A risk level determination module, configured to determine the risk level of the area where the target aircraft is located based on the spatial characteristics and the status information;

[0034] A resource adjustment module, configured to set a network slice allocation policy based on the risk level of the area where the target aircraft is located to adjust the communication resources of the target aircraft.

[0035] Fourth aspect, the present invention provides a computer device, including: a memory and a processor, which are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to execute the resource allocation method in the flight control system according to the first aspect or any corresponding implementation manner thereof.

[0036] Fifth aspect, the present invention provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute the resource allocation method in the flight control system according to the first aspect or any corresponding embodiment thereof.

[0037] Sixth aspect, the present invention provides a computer program product, including computer instructions, and the computer instructions are used to cause a computer to execute the resource allocation method in the flight control system according to the first aspect or any corresponding embodiment thereof.

[0038] The technical solution provided by the present invention may include the following beneficial effects:

[0039] The resource allocation method in the flight control system provided by the present invention is applied to a flight control system, and the flight control system includes sensors connected to a core network and an ADS-B receiver; the method first obtains the spatial characteristics and status information of a target aircraft; the spatial characteristics are collected by the sensors; the status information is received by the ADS-B receiver; then, based on the spatial characteristics and the status information, the risk level of the area where the target aircraft is located is determined; finally, a network slice allocation policy is set based on the risk level of the area where the target aircraft is located to adjust the communication resources of the target aircraft. The above solution, by setting sensors and an ADS-B receiver and connecting both the sensors and the ADS-B receiver to the core network, can monitor the target aircraft more comprehensively and obtain comprehensive and low-latency data, improve the efficiency and accuracy of multi-data fusion, improve the accuracy and timeliness of determining the risk level of the area where the target aircraft is located, and then adjust the communication resources of the target aircraft based on the risk level of the area where the target aircraft is located, saving system resources while meeting the communication requirements of the target aircraft and improving system stability. Description of the Drawings

[0040] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0041] Figure 1 is a flowchart of the resource allocation method in the flight control system according to an embodiment of the present invention;

[0042] Figure 2 is a flowchart of another resource allocation method in the flight control system according to an embodiment of the present invention;

[0043] Figure 3It is a schematic structural diagram of a flight control system according to an embodiment of the present invention;

[0044] Figure 4 It is a schematic structural diagram of another flight control system according to an embodiment of the present invention;

[0045] Figure 5 It is a schematic flowchart of a resource allocation method in yet another flight control system according to an embodiment of the present invention;

[0046] Figure 6 It is a structural block diagram of a resource allocation device in a flight control system according to an embodiment of the present invention;

[0047] Figure 7 It is a schematic hardware structure diagram of a computer device according to an embodiment of the present invention. Detailed implementation manners

[0048] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0049] According to an embodiment of the present invention, an embodiment of a resource allocation method in a flight control system is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0050] In this embodiment, a resource allocation method in a flight control system is provided, which is applied to a flight control system. The flight control system includes sensors accessing the core network and an ADS-B receiver. Figure 1 It is a flowchart of a resource allocation method in a flight control system according to an embodiment of the present invention. As Figure 1 shown, the process includes the following steps:

[0051] Step S101, obtain the spatial characteristics and status information of the target aircraft.

[0052] The target aircraft can be a low-altitude aircraft flying at an altitude below 1000 meters above the ground, such as a drone, a light helicopter, a glider, etc. The flight control system is configured with core network services. The sensors and ADS-B receivers in the flight control system are pre-registered in the core network and upload device coordinates, device performance parameters, etc. to the core network, so as to obtain the spatial characteristics and status information of the target aircraft through a unified network, reduce latency, and improve data accuracy. The spatial characteristics are collected by the sensors and can include the shape of the target aircraft, its spatial positioning at different time points, its relative position relationship with surrounding objects, etc. The status information is received by the ADS-B (Automatic Dependent Surveillance-Broadcast) receiver and includes the unique identifier (ICAO address, International Civil Aviation Organization Address) of the target aircraft, position information, speed, heading, timestamp, etc.

[0053] Step S102: Based on the spatial characteristics and the status information, determine the risk level of the area where the target aircraft is located.

[0054] After obtaining the spatial characteristics and status information of the target aircraft, the location where the target aircraft is located can be determined, and then the risk level of the area where the target aircraft is located can be determined according to a preset determination criterion. Specifically, the preset determination criterion can include the risk information of each area managed by the flight control system, such as whether low-altitude aircraft are allowed to fly in the area, whether the speed of low-altitude aircraft is restricted, the degree of flight restriction, etc. Compare the spatial characteristics and status information of the target aircraft with the preset determination criterion, and determine the risk level of the area where the target aircraft is located according to the comparison result.

[0055] Step S103: Set a network slice allocation policy based on the risk level of the area where the target aircraft is located to adjust the communication resources of the target aircraft.

[0056] Since the target aircraft needs to respond in a timely manner after flying into a flight restricted area to leave in time or avoid flying into an area with a higher risk level, lower message latency and faster data collection and data processing speeds are required. Therefore, in this embodiment, a network slice allocation policy is set for the target aircraft based on the risk level of the area where the target aircraft is located to allocate a more appropriate network slice to the target aircraft and adjust the communication resources allocated to the target aircraft. For example, when the target aircraft flies into a high-risk area, a higher-performance network slice is allocated to the target aircraft to increase the communication resources of the target aircraft, enabling the target aircraft to respond faster, such as leaving the high-risk area.

[0057] Furthermore, the flight control system can monitor multiple aircraft simultaneously, distinguish them by the unique identifier of each aircraft, obtain the spatial characteristics and status information of each aircraft respectively, determine the risk level of the area where each aircraft is located, and then set the corresponding network slice allocation strategy based on the risk level of the area where each aircraft is located, so as to adjust the communication resources of each aircraft specifically, reduce system resource waste, and improve the flight control efficiency and accuracy.

[0058] The resource allocation method in the flight control system provided in this embodiment is applied to the flight control system. The flight control system includes sensors and an ADS-B receiver connected to the core network. The method first obtains the spatial characteristics and status information of the target aircraft. The spatial characteristics are collected by the sensors and uploaded to the core network. The status information is received by the ADS-B receiver and uploaded to the core network. Then, based on the spatial characteristics and the status information, the risk level of the area where the target aircraft is located is determined. Finally, a network slice allocation strategy is set based on the risk level of the area where the target aircraft is located to adjust the communication resources of the target aircraft. In the above solution, by setting sensors and an ADS-B receiver and connecting both the sensors and the ADS-B receiver to the core network, more comprehensive monitoring of the target aircraft can be carried out and comprehensive and low-latency data can be obtained, improving the multi-data fusion efficiency and accuracy, improving the accuracy and timeliness of determining the risk level of the area where the target aircraft is located, and then adjusting the communication resources of the target aircraft based on the risk level of the area where the target aircraft is located, saving system resources while meeting the communication requirements of the target aircraft and improving system stability.

[0059] In this embodiment, a resource allocation method in a flight control system is provided. A network slice allocation strategy is set based on the risk level of the area where the target aircraft is located to adjust the communication resources of the target aircraft. Figure 2 It is a flowchart of the resource allocation method in the flight control system according to an embodiment of the present invention, as Figure 2 shown. The process includes the following steps:

[0060] Step S201, obtain the spatial characteristics and status information of the target aircraft.

[0061] The spatial characteristics are collected by the sensors and uploaded to the core network. The status information is received by the ADS-B receiver and uploaded to the core network.

[0062] Optionally, first register the sensor and the ADS-B receiver to the core network. Specifically, obtain the device coordinates and device performance parameters of the sensor, as well as the device coordinates and device performance parameters of the ADS-B receiver. Then, based on the device coordinates and device performance parameters of the sensor and the device coordinates and device performance parameters of the ADS-B receiver, register the sensor and the ADS-B receiver to the core network.

[0063] Optionally, the sensor includes a radar and an optoelectronic camera. The radar is used for range detection to obtain the point cloud data of the target aircraft. Specifically, the radar detects through ultra-high frequency (UHF) to generate point cloud data and generate a spatial distribution map of objects including the aircraft. The optoelectronic camera is used to capture the visual image of the aircraft, and real-time encode and compress the video stream (the clarity is determined according to requirements, such as 1080P@30fps), and transmit it to the flight control system. In the flight control system, the video stream is decoded in real time, and the dynamic target and static background are separated by the inter-frame splitting method, and the environmental noise (such as leaf shaking and light change) is eliminated, the video stream compression rate is improved, and the burden of uploading to the core network is reduced. The flight control system then reads the spatial characteristics and status information of the target aircraft from the core network.

[0064] Step S202, based on the spatial characteristics and the status information, determine the risk level of the area where the target aircraft is located.

[0065] Specifically, the above step S202 includes:

[0066] Step S2021, based on the spatial characteristics of the target aircraft and the preset flight restriction area information, determine the overlap rate between the area where the target aircraft is located and the preset flight restriction area and the continuous residence time of the target aircraft in the preset flight restriction area.

[0067] The overlap rate is used to indicate the coincidence probability between the area where the target aircraft is located and the preset flight restriction area, and the continuous residence time is used to indicate the continuous time of the target aircraft in the preset flight restriction area. The preset flight restriction area information can include the boundaries, open permissions, flight restriction standards, etc. of each area managed by the flight control system. Compare the spatial characteristics of the target aircraft with the position relationship of the preset flight restriction area to obtain the overlap rate between the two, and time the time of the target aircraft in the preset flight restriction area to obtain the continuous residence time of the target aircraft in the preset flight restriction area.

[0068] Step S2022, based on the overlap rate and the continuous residence time, determine whether the target aircraft is in the flight restriction area to obtain the flight restriction area determination result.

[0069] Optionally, compare the overlap rate of the target aircraft with a preset overlap rate threshold to obtain an overlap rate comparison result, compare the continuous residence time of the target aircraft with a preset residence time threshold to obtain a residence time comparison result, and then determine the flight restriction area determination result based on the overlap rate comparison result and the residence time comparison result. The preset overlap rate threshold and the preset residence time threshold can be set according to requirements. For example, set the preset overlap rate threshold to 85% and the preset residence time threshold to 10 s. When the overlap rate exceeds 85% and the continuous residence time exceeds 10 s, it is determined that the target aircraft is in the flight restriction area. Considering the overlap rate and the continuous residence time comprehensively can prevent occasional overlap rate exceeding the preset overlap rate threshold from causing system misjudgment, thereby improving the determination accuracy.

[0070] Step S2023, based on the flight restriction area determination result and the status information of the target aircraft, determine the risk level of the area where the target aircraft is located.

[0071] The flight restriction area determination result is used to indicate whether the target aircraft is in the flight restriction area, and the status information of the target aircraft is used to indicate the current position, speed, etc. of the target aircraft, so as to evaluate the degree of communication resource requirements of the target aircraft. For example, if the target aircraft is not in the flight restriction area and has a low speed, the degree of communication resource requirements is low; if the target aircraft is in the flight restriction area and has a high speed, the degree of communication resource requirements is high, and then determine the risk level of the area where the target aircraft is located.

[0072] Optionally, determine a first risk value based on the flight restriction area determination result. For example, if the flight restriction area determination result is "yes", the first risk value is 1; if the flight restriction area determination result is "no", the first risk value is 0. Compare the status information of the target aircraft with a preset status threshold to determine a second risk value. For example, when the status information is greater than the preset status threshold, the second risk value is 1; when the status information is less than the preset status threshold, the second risk value is 0. Finally, determine the risk level of the area where the target aircraft is located based on the first risk value, the second risk value, and a preset weight. Preset weights can be set for the first risk value and the second risk value respectively. For example, set a first weight for the first risk value and a second weight for the second risk value, add the product of the first risk value and the first weight and the product of the second risk value and the second weight to obtain the risk value of the area where the target aircraft is located, and then compare it with a preset risk level threshold to determine the risk level of the area where the target aircraft is located.

[0073] Optionally, the airspace rule priority can also be set. For example, the normal airspace can be set as the first level, the temporary flight restriction area can be set as the second level, and the permanent flight restriction area can be set as the third level. Then, based on the position of the target aircraft in the area, the airspace rule priority is determined. Furthermore, based on the flight restriction area determination result, the status information of the target aircraft, and the airspace rule priority, the risk level of the area where the target aircraft is located is determined.

[0074] Step S203: Set the network slice allocation policy based on the risk level of the area where the target aircraft is located to adjust the communication resources of the target aircraft.

[0075] The higher the risk level of the area where the target aircraft is located, the better the performance of the allocated network slice, thereby increasing the communication resources of the target aircraft to meet the communication requirements of the target aircraft.

[0076] Optionally, when adjusting the communication resources of the target aircraft, it may involve obtaining relevant data, performing data processing and analysis, adjusting the network slice allocation policy, performing specific network slice allocation actions, and responding to communication resource adjustments, etc., resulting in a time difference and causing a certain delay. Therefore, the flight control system can also predict the future flight trajectory of the target aircraft to assist in adjusting the network slice allocation policy and improve the accuracy of resource allocation. When performing trajectory prediction, the motion trajectory of the target aircraft can be predicted based on the spatial characteristics and status information of the target aircraft to obtain the trajectory prediction result.

[0077] Optionally, extract the spatial distribution characteristics of the target aircraft from the point cloud data collected by the radar. The spatial distribution characteristics are used to indicate the shape of the target aircraft and its relative position to other objects in the area; extract the spatio-temporal characteristics of the target aircraft from the video stream collected by the optoelectronic camera. The spatio-temporal characteristics are used to indicate the displacement and actions of the target aircraft at different time points; perform time series encoding on the flight trajectory of the target aircraft based on the status information received by the ADS-B receiver to obtain the dynamic trajectory characteristics. The dynamic trajectory characteristics are used to indicate the position, speed, and heading of the target aircraft at different time points; fuse the spatial distribution characteristics, spatio-temporal characteristics, and dynamic trajectory characteristics to obtain the fusion data, and then predict the motion trajectory of the target aircraft based on the fusion data to obtain the trajectory prediction result. The spatial distribution characteristics of the radar, the spatio-temporal characteristics of the optoelectronic camera, and the dynamic trajectory characteristics of the ADS-B jointly provide multi-dimensional support for the flight control of the target aircraft. The flight control system can perform more accurate target trajectory prediction and output prediction results such as the predicted position, speed, and heading of the target aircraft at future time points according to the time series.

[0078] Exemplarily, the PointNet++ model is used to extract effective spatial distribution features from point cloud data. The PointNet++ model is a deep learning model specifically designed for processing 3D point cloud data. By adaptively learning local and global features, it can extract the spatial structure information of objects from point cloud data, which helps to identify the specific shape, position of the target and its relationship with other objects, and provides accurate spatial positioning information for subsequent target tracking and trajectory prediction. A 3D convolutional neural network (CNN) is used to extract spatio-temporal features. The 3D convolutional neural network can process video data simultaneously in the time and space dimensions. Therefore, it is suitable for action recognition and object tracking in video data. Through the 3D convolutional neural network, dynamic targets (target aircraft) in the video stream are identified, and the displacements and changes of the target aircraft at different time points are captured, so as to extract the spatio-temporal features of the target aircraft. The long short-term memory network (LSTM) is used to perform time series encoding on the motion trajectory of the target aircraft. The LSTM is a deep learning model that can process time series data. It can remember the long-term dependencies in the time series and effectively predict the future motion trajectory of the target. In the present invention, the LSTM analyzes the historical position, speed and heading data of the target aircraft to predict the future motion path of the target aircraft, provides an accurate modeling of the dynamic changes of the target aircraft, and obtains dynamic trajectory features.

[0079] Furthermore, based on the performance data of the flight control system, the network status and the trajectory prediction result, the network slice allocation strategy is updated. The performance data of the flight control system is used to indicate the working ability of the flight control system, such as the maximum detection range, scanning frequency, power consumption mode, etc. The network status of the flight control system is used to indicate the network speed, network resource amount, etc. By comprehensively considering the performance data, network status and trajectory prediction result, the network slice allocation strategy for the target aircraft is updated to improve the accuracy of resource allocation.

[0080] Furthermore, the working states of the sensors and the ADS-B receiver can also be adjusted according to the flight situation of the target aircraft. For example, when the target aircraft is in a non-restricted flight area and its flight speed is very slow, the flight control system can appropriately reduce the working intensity to save resources. Specifically, first, based on the trajectory prediction result, the relative distance between the target aircraft and the flight control system and the speed of the target aircraft are determined, and then based on the relative distance between the target aircraft and the flight control system and the speed of the target aircraft, the acquisition frequency and power consumption mode of the sensors and the ADS-B receiver are adjusted. For example, the closer the relative distance between the target aircraft and the flight control system and the slower the speed, the lower the acquisition frequency, and the power consumption mode can be adjusted to the low power consumption mode. The beam angle of the radar can also be adjusted according to the relative position between the target aircraft and the flight control system sub-key.

[0081] Optionally, obtain the packet loss rate of the flight control system, compare the packet loss rate with a preset packet loss rate threshold, and trigger a fault diagnosis process when the packet loss rate exceeds the preset packet loss rate threshold to diagnose and calibrate the devices in the flight control system. A packet loss duration threshold can also be set, and when the packet loss rate exceeds the preset packet loss rate threshold and the duration exceeds the packet loss duration threshold, a fault diagnosis process is triggered to prevent misjudgment by the system.

[0082] Optionally, obtain the signal strength, compare the signal strength with a preset signal strength threshold, and trigger a fault diagnosis process when the signal strength exceeds the preset signal strength threshold to diagnose and calibrate the devices in the flight control system.

[0083] Optionally, when the packet loss rate exceeds the preset packet loss rate threshold, the duration exceeds the packet loss duration threshold, and the signal strength exceeds the preset signal strength threshold, trigger a fault diagnosis process.

[0084] Optionally, regularly obtain the operation data (such as temperature, power consumption, and scanning accuracy) of each device in the flight control system, record faults when a device fails, and predict the fault probability of each device. When the fault probability of a device exceeds the fault probability threshold, a fault alarm is issued to notify relevant technical personnel to perform maintenance or replacement in advance.

[0085] The resource allocation method in the flight control system provided in this embodiment is applied to a flight control system, which includes sensors and an ADS-B receiver connected to the core network; the method first obtains the spatial characteristics and status information of the target aircraft; the spatial characteristics are collected by the sensors; the status information is received by the ADS-B receiver; then, based on the spatial characteristics and the status information, determine the risk level of the area where the target aircraft is located; finally, set a network slice allocation policy based on the risk level of the area where the target aircraft is located to adjust the communication resources of the target aircraft. The above solution, by setting sensors and an ADS-B receiver and connecting both the sensors and the ADS-B receiver to the core network, can monitor the target aircraft more comprehensively and obtain comprehensive and low-latency data, improve the efficiency and accuracy of multi-data fusion, improve the accuracy and timeliness of determining the risk level of the area where the target aircraft is located, and then adjust the communication resources of the target aircraft based on the risk level of the area where the target aircraft is located, saving system resources while meeting the communication requirements of the target aircraft and improving system stability.

[0086] This embodiment provides a flight control system, as Figure 3 shown, the system includes:

[0087] Core network;

[0088] A sensor for accessing the core network; the sensor is used to collect the spatial characteristics of the target aircraft;

[0089] An ADS - B receiver for accessing the core network; the ADS - B receiver is used to collect the status information of the target aircraft;

[0090] A data acquisition module for obtaining the spatial characteristics and status information of the target aircraft; the spatial characteristics are collected by the sensor; the status information is received by the ADS - B receiver;

[0091] A risk level determination module for determining the risk level of the area where the target aircraft is located based on the spatial characteristics and the status information;

[0092] A resource adjustment module for setting a network slice allocation policy based on the risk level of the area where the target aircraft is located to adjust the communication resources of the target aircraft.

[0093] As one or more specific application embodiments of the embodiments of the present invention, the optimal implementation plan or the plan that the inventor most wants to embody is described below in combination with specific application scenarios.

[0094] This embodiment provides a flight control system, as Figure 4 shown, the system includes a device registration module, a data acquisition module, a data transmission module, a data analysis module, a trajectory prediction module, and a device management module. Figure 5 It is a flowchart of the resource allocation method in the flight control system according to the embodiments of the present invention.

[0095] The device registration module is responsible for registering heterogeneous sensing devices (radar, optoelectronic camera, ADS-B receiver) to the 5G core network and uploading device parameters, including three sub-modules: device registration, parameter upload, and airspace policy synchronization. The device registration sub-module conducts device registration with the 5G core network through the NEF (Network Exposure Function), the parameter upload sub-module uploads device capability parameters to the UDR (Unified Data Repository), and the airspace policy synchronization sub-module issues wake-up instructions to heterogeneous sensing devices according to the airspace policy. Before data collection, all heterogeneous sensing devices need to register with the 5G core network through the NEF and upload device coordinates and device capability parameters (maximum detection range, scanning frequency, power consumption mode) to the UDR. After completion of registration, the AMF (Access and Mobility Management Function) issues a device wake-up instruction to the SCU (Sensor Control Unit) through the N1 interface based on the airspace policy (range of the airport's clear zone) stored in the UDR. After the device is woken up, the radar device conducts range detection to obtain point cloud data of the target data. The radar detects through ultra-high frequency (UHF) to generate point cloud data and produce a spatial distribution map of objects including the target aircraft. The radar collects 1000 data points per second and transmits them to the UPF through the N3 interface to ensure the minimum data transmission delay. The optoelectronic camera captures the visual image of the target aircraft, real-time encodes and compresses the high-definition video stream (1080P@30fps), and transmits it to the MEC node for further operations. In the MEC node, after the video stream is real-time decoded, the dynamic target and static background are separated by the inter-frame splitting method, environmental noise (such as shaking of leaves, light changes) is eliminated, the compression ratio is increased, and the burden of uploading to the core network is reduced. The ADS-B receiver captures the status information of the target aircraft (ICAO address, speed, position, and heading). Subsequently, various information captured by the heterogeneous sensing devices is uploaded to the NWDAF through the NEF.

[0096] The data collection module is responsible for data collection and preliminary processing of heterogeneous sensing devices, including three sub-modules: radar data collection, video stream collection, and ADS-B data collection. The radar data collection sub-module obtains the point cloud data of the radar; the video stream collection sub-module obtains the video stream of the optoelectronic camera and compresses it; the ADS-B data collection sub-module obtains the identification information of the target aircraft and uploads it.

[0097] The data transmission module is responsible for data transmission and optimization, including three sub-modules: radar data transmission, video data transmission, and ADS-B data transmission. The radar data transmission sub-module transmits radar data to the UPF (User Plane Function); the video data transmission sub-module transmits video streams to the MEC (Multi-Access Edge Computing) node; the ADS-B data transmission sub-module uploads aircraft information to the NWDAF (Network Data Analytics Function).

[0098] The data analysis module is responsible for real-time analysis of the collected data and generating corresponding strategies. It includes three sub-modules: radar data analysis, sensitive area judgment, and strategy generation. The radar data analysis sub-module parses radar data and determines sensitive areas; the sensitive area judgment sub-module determines whether to enter a sensitive area based on rules; the strategy generation sub-module generates airspace strategies based on the analysis results. The NWDAF receives radar data from the UPF and performs real-time analysis and processing. First, it parses the GPS coordinates in the radar data and the status information captured by ABS-B to detect whether it is a flight restricted area. If the overlap rate of the data with the preset flight restricted area > 85% and the continuous residence time > 10s, it is determined to enter the flight restricted area. At this time, the NWDAF generates a corresponding analysis report, including whether it is a flight restricted area, the coordinates of the target aircraft, and the ICAO address, and sends it to the PCF (Policy Control Function). After receiving the event from the NWDAF, the PCF matches it with airspace regulations (flight altitude restrictions and restricted flight areas) and generates corresponding policy rules. The policy trigger criteria are as follows:

[0099] Table 1: Policy Trigger Parameter and Weight Table.

[0100]

[0101] The scoring formula is as follows:

[0102] Comprehensive score = Σ(parameter score × weight)

[0103] Table 2: Policy Trigger Threshold Table.

[0104] Scoring range Strategy level Slice type [0,30) Non-sensitive area eMBB [30,60) Low-risk sensitive area eMBB+ [60,80) Medium-risk sensitive area uRLLC ≥80 High-risk sensitive area uRLLC-Pro

[0105] Among them, eMBB (Enhanced Mobile Broadband), eMBB+, uRLLC (UltraReliable & Low Latency Communication), and uRLLC-Pro are used to indicate the type of network slice, corresponding to different application scenarios and performance requirements. Finally, the slice allocation policy is sent to the SMF (Session Management Function) and the AMF (Access and Mobility management Function). The SMF dynamically configures the processing rules of the user plane data stream, and the AMF coordinates the MEC nodes.

[0106] The trajectory prediction module is responsible for trajectory prediction using a deep learning model, including three sub-modules: point cloud data feature extraction, video data feature extraction, and trajectory prediction. The point cloud data feature extraction sub-module extracts spatial features from radar data; the video data feature extraction sub-module extracts spatio-temporal features in the video stream through 3D CNN; the trajectory prediction sub-module uses LSTM to predict the future trajectory of the target. The MEC node fuses different sensors (including the point cloud data of the radar, the video stream of the optoelectronic camera, and the status information of ADS-B) through multi-modal data fusion technology and uses a deep learning model for target trajectory prediction. The MEC node uses the PointNet++ model to extract effective spatial distribution features from the point cloud data of the radar, uses a 3D convolutional neural network (CNN) to extract spatio-temporal features in the video stream, and uses a long short-term memory network (LSTM) to extract the dynamic trajectory features of the target aircraft corresponding to the status information. Then, the MEC node combines the advantages of different data by fusing data from the radar, optoelectronic camera, and ADS-B. Finally, the generated trajectory prediction result is transmitted to the PCF, and the PCF will make the next policy adjustment according to the trajectory prediction result, such as judging whether it will enter a high-risk area in the future, adjusting the network slice allocation policy in advance, adjusting the allocated slice resources in advance, and adjusting sensor parameters (such as radar parameter adjustment, camera power consumption adjustment), etc.

[0107] The device management module is responsible for the status adjustment and health monitoring of heterogeneous sensing devices, including three sub-modules: device adjustment, power consumption management, and health monitoring. The device adjustment sub-module dynamically adjusts device parameters according to the movement of the target aircraft; the power consumption management sub-module adjusts the device power and frequency according to the status of the target aircraft; the health monitoring sub-module monitors the health status of the device and performs self-check and predictive maintenance. The PCF dynamically adjusts the working status of the sensing device according to the device capabilities, the real-time network status, the device capabilities, and the predicted trajectory.

[0108] Specifically include:

[0109] 1. Automatically adjust device parameters according to the target movement direction, speed, and airspace environment:

[0110] For the radar:

[0111] (1) Beam angle adjustment: The beam angle of the radar is automatically adjusted according to the movement trajectory and direction of the target. Especially when the movement direction of the target changes rapidly, the radar needs to adjust the beam angle accordingly to ensure continuous tracking of the target. When the target speed exceeds 30 m / s, or when the direction change of the target exceeds 20° within 5 seconds, the radar beam angle adjustment range will reach ±10°. At this time, the PCF instructs the radar to adjust the beam angle to ensure that the radar can track the accurate position of the target and improve the tracking accuracy.

[0112] (2) Power adjustment: The power adjustment of the radar depends on the distance and speed of the target. When the target is approaching and the movement speed is low, the power can be appropriately reduced to save energy; when the target is moving away or the movement speed is fast, the system will increase the radar power to improve the detection range and accuracy. When the distance of the target is less than 5 km and the speed is lower than 50 m / s, the radar power will be reduced by 30% to reduce energy consumption. When the target speed is greater than 60 m / s or the distance exceeds 30 km, the radar power will be increased to 150% of the original power to ensure the detection ability for fast-moving targets at long distances. The PCF adjusts the radar power output according to these thresholds to ensure the energy efficiency and detection accuracy of the system in different situations.

[0113] (3) Scanning frequency adjustment: The adjustment of the scanning frequency is based on the speed of the target. Low-speed or stationary targets do not require high-frequency scanning, so the scanning frequency can be reduced; while high-speed moving targets require a higher scanning frequency to maintain continuous tracking. When the target speed is lower than 20 m / s, the radar scanning frequency will be reduced to 40% of the original scanning frequency to reduce the system burden and save energy. When the target speed exceeds 60 m / s, the radar scanning frequency will be increased to 150% of the original scanning frequency to improve the tracking accuracy and response speed. The PCF dynamically adjusts the radar scanning frequency according to the movement of the target to ensure efficient response to targets with different speeds and optimize resource utilization at the same time.

[0114] For the optoelectronic camera: When the target speed exceeds 30 m / s, the video stream is increased from 30 fps to 60 fps to reduce motion blur; when entering a high-risk area, the infrared mode is switched to enhance the night tracking ability.

[0115] For the ADS-B receiver: When the target distance < 5 km, the signal scanning frequency is reduced to 0.5 Hz to save energy. When the network is congested, high-risk ADS-B signals are preferentially received.

[0116] 2. Automatically adjust the power consumption mode to reduce energy consumption:

[0117] (1) Low power consumption mode: When the speed of the target is lower than 20 m / s, it indicates that the target may be in a low-speed flight state and the tracking task is relatively light; or when the target is located in a non-sensitive airspace (for example, there is no temporary no-fly zone or the air density is low in the airspace), the PCF will instruct the radar to reduce the scanning frequency to 40% of the original and reduce the power to 70% of the original to reduce energy consumption and ensure low-power operation.

[0118] (2) High power consumption mode: When the speed of the target exceeds 60 m / s, it means that the target is moving fast and the tracking task becomes more urgent. It is necessary to increase the working frequency and power of the device to track the target more precisely. Or if the target enters a high-risk airspace (such as a temporary control area or a permanent no-fly zone), even if the target speed is low, a higher power consumption mode is required. The PCF will adjust the radar's scanning frequency to 150% of the original and increase the power to 150% of the original to enhance the tracking accuracy and response speed and ensure real-time monitoring of fast or high-risk targets.

[0119] 3. Equipment health monitoring and self-check process:

[0120] (1) Abnormality detection: If the packet loss rate exceeds 5%, the PCF will trigger an automatic diagnosis process and require the device to perform self-check and calibration to solve the packet loss problem. When the signal strength is lower than 70%, the PCF will start the self-check process to check the hardware status of the radar and determine whether there are device failures or transmission problems. If the scanning accuracy of the radar deviates from the set value and exceeds the predetermined error range (the error is greater than ±5°), the PCF will also trigger the calibration process to adjust the scanning angle or other hardware parameters to restore the accuracy.

[0121] (2) Self-check and calibration: If the packet loss rate continuously exceeds 5% (the duration exceeds 10 seconds), or the signal strength is lower than 70% of the original strength, the PCF will start the self-check process of the device.

[0122] (3) Equipment replacement or repair: If the health status of the device still cannot be restored after self-check, or the failure rate of the device exceeds the set allowable range (the failure rate exceeds 10%), the PCF will notify the operation and maintenance personnel to replace the device and upload the status of the faulty device to the unified data resource (UDR) if necessary, providing a basis for subsequent maintenance and analysis.

[0123] 4. Equipment health data upload and predictive maintenance:

[0124] (1) Health data upload: The operating data of the device (including temperature, power consumption, scanning frequency, etc.) is uploaded every 5 minutes. When the device encounters an anomaly (packet loss rate exceeds 5% or signal strength is lower than 70%), the relevant fault records should be uploaded to the UDR within 10 seconds after occurrence. After each maintenance or replacement of the device, the maintenance history is uploaded to the UDR for subsequent analysis and prediction.

[0125] (2) Predictive maintenance: When the failure probability of the device exceeds 80%, the system will schedule maintenance or replacement in advance. This threshold indicates a relatively high risk of device failure and may have a greater impact on the system. If the device has been continuously operating for more than the set maximum usage time (such as 1000 hours or 30 days), the system will evaluate the device's health status and schedule maintenance or replacement in advance.

[0126] (3) Device health status monitoring: When the deviation of the device's health data (such as temperature, power consumption, scanning accuracy, etc.) exceeds the set threshold (such as temperature exceeding 80°C or power consumption being higher than 120% of the rated power), an alarm is triggered and fault prediction analysis is performed.

[0127] In this embodiment, a resource allocation device in a flight control system is also provided. This device is used to implement the above-mentioned embodiments and preferred implementation manners, and those that have been described will not be elaborated again. As used hereinafter, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0128] This embodiment provides a resource allocation device in a flight control system, which is applied to a flight control system; the flight control system includes sensors accessing the core network and an ADS-B receiver. As Figure 6 shown, the device includes:

[0129] A data acquisition module 601, configured to acquire the spatial characteristics and status information of a target aircraft; the spatial characteristics are collected by the sensor; the status information is received by the ADS-B receiver;

[0130] A risk level determination module 602, configured to determine the risk level of the area where the target aircraft is located based on the spatial characteristics and the status information;

[0131] A resource adjustment module 603, configured to set a network slice allocation policy based on the risk level of the area where the target aircraft is located to adjust the communication resources of the target aircraft.

[0132] In an alternative implementation manner, the risk level determination module is further configured to:

[0133] Based on the spatial characteristics of the target aircraft and the information of the preset flight restricted area, determine the overlap rate between the area where the target aircraft is located and the preset flight restricted area, and the continuous residence time of the target aircraft in the preset flight restricted area;

[0134] Based on the overlap rate and the continuous residence time, determine whether the target aircraft is in the flight restricted area, and obtain the determination result of the flight restricted area;

[0135] Based on the determination result of the flight restricted area and the status information of the target aircraft, determine the risk level of the area where the target aircraft is located.

[0136] In an optional implementation manner, the risk level determination module is further configured to:

[0137] Determine a first risk value based on the determination result of the flight restricted area;

[0138] Compare the status information of the target aircraft with a preset status threshold to determine a second risk value;

[0139] Based on the first risk value, the second risk value, and a preset weight, determine the risk level of the area where the target aircraft is located.

[0140] In an optional implementation manner, the device further includes:

[0141] A trajectory prediction module, configured to predict the movement trajectory of the target aircraft based on the spatial characteristics and the status information, and obtain a trajectory prediction result;

[0142] A status adjustment module, configured to update the network slice allocation policy and adjust the working status of the sensor and the ADS-B receiver based on the performance data of the flight control system, the network status, and the trajectory prediction result.

[0143] In an optional implementation manner, the status adjustment module is further configured to:

[0144] Based on the trajectory prediction result, determine the relative distance between the target aircraft and the flight control system and the speed of the target aircraft;

[0145] Based on the relative distance between the target aircraft and the flight control system and the speed of the target aircraft, adjust the acquisition frequency and power consumption mode of the sensor and the ADS-B receiver.

[0146] The further function descriptions of the above-mentioned various modules and units are the same as those in the corresponding foregoing embodiments, and will not be elaborated herein.

[0147] The resource allocation device in the flight control system in this embodiment is presented in the form of a functional unit. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0148] An embodiment of the present invention further provides a computer device having the above Figure 6 resource allocation device in the flight control system shown.

[0149] Please refer to Figure 7 , Figure 7 which is a schematic structural diagram of a computer device provided by an alternative embodiment of the present invention. As shown in Figure 7 , the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system). Figure 7 In

[0150] , a processor 10 is taken as an example.

[0151] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0152] The memory 20 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device and the like. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 may optionally include a memory remotely disposed relative to the processor 10, and these remote memories may be connected to the computer device through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0153] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state drive; the memory 20 may further include a combination of the above types of memories.

[0154] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30, and the output device 40 may be connected through a bus or other means. Figure 7 Taking connection through a bus as an example.

[0155] The input device 30 can receive input digital or character information, and generate key signal inputs related to the user settings and function controls of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointing stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (such as an LED), and a tactile feedback device (such as a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light-emitting diode, a display, and a plasma display. In some alternative embodiments, the display device may be a touch screen.

[0156] Embodiments of the present invention also provide a computer-readable storage medium. The method according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code that is originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored as such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method shown in the above embodiments is implemented.

[0157] A part of the present invention can be applied as a computer program product, for example, computer program instructions, which when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should be able to understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to the computer.

[0158] Although the embodiments of the present invention are described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the protection scope of the present invention.

Claims

1. A resource allocation method in a flight control system, characterized in that Applied to a flight control system; the flight control system includes sensors connected to a core network and an ADS-B receiver; the method includes: Obtain the spatial characteristics and status information of the target aircraft; the spatial characteristics are collected by the sensors; the status information is received by the ADS-B receiver; Based on the spatial characteristics and the status information, determine the risk level of the area where the target aircraft is located; Set a network slice allocation policy based on the risk level of the area where the target aircraft is located to adjust the communication resources of the target aircraft.

2. The method according to claim 1, wherein The determining the risk level of the area where the target aircraft is located based on the spatial characteristics and the status information includes: Based on the spatial characteristics of the target aircraft and the preset flight restriction area information, determine the overlap rate between the area where the target aircraft is located and the preset flight restriction area and the continuous residence time of the target aircraft in the preset flight restriction area; Based on the overlap rate and the continuous residence time, determine whether the target aircraft is in a flight restriction area to obtain a flight restriction area determination result; Based on the flight restriction area determination result and the status information of the target aircraft, determine the risk level of the area where the target aircraft is located.

3. The method according to claim 2, wherein The determining the risk level of the area where the target aircraft is located based on the flight restriction area determination result and the status information of the target aircraft includes: Determine a first risk value based on the flight restriction area determination result; Compare the status information of the target aircraft with a preset status threshold to determine a second risk value; Based on the first risk value, the second risk value and a preset weight, determine the risk level of the area where the target aircraft is located.

4. The method according to any one of claims 1 to 3, characterized in that, The method further includes: Based on the spatial characteristics and the status information, predict the movement trajectory of the target aircraft to obtain a trajectory prediction result; Based on the performance data of the flight control system, the network status and the trajectory prediction result, update the network slice allocation policy and adjust the working status of the sensors and the ADS-B receiver.

5. The method according to claim 4, wherein The adjusting the working status of the sensors and the ADS-B receiver includes: Based on the trajectory prediction result, determine the relative distance between the target aircraft and the flight control system and the speed of the target aircraft; Based on the relative distance between the target aircraft and the flight control system and the speed of the target aircraft, adjust the acquisition frequency and power consumption mode of the sensors and the ADS-B receiver.

6. A resource allocation device in a flight control system, characterized in that Applied to a flight control system; the flight control system includes sensors connected to a core network and an ADS-B receiver; the device includes: A data acquisition module for obtaining the spatial characteristics and status information of the target aircraft; the spatial characteristics are collected by the sensors; the status information is received by the ADS-B receiver; A risk level determination module for determining the risk level of the area where the target aircraft is located based on the spatial characteristics and the status information; A resource adjustment module, configured to set a network slice allocation policy based on the risk level of the area where the target aircraft is located, so as to adjust the communication resources of the target aircraft.

7. A flight control system, characterized in that, The system includes: A core network; A sensor for accessing the core network; the sensor is used to collect the spatial characteristics of the target aircraft; An ADS-B receiver for accessing the core network; the ADS-B receiver is used to collect the status information of the target aircraft; A data acquisition module for acquiring the spatial characteristics and status information of the target aircraft; the spatial characteristics are collected by the sensor; the status information is received by the ADS-B receiver; A risk level determination module for determining the risk level of the area where the target aircraft is located based on the spatial characteristics and the status information; A resource adjustment module, configured to set a network slice allocation policy based on the risk level of the area where the target aircraft is located, so as to adjust the communication resources of the target aircraft.

8. A computer device, characterized in that, It includes: A memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to execute the resource allocation method in the flight control system according to any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that, Computer instructions are stored on the computer-readable storage medium, and the computer instructions are used to cause a computer to execute the resource allocation method in the flight control system according to any one of claims 1 to 5.

10. A computer program product, characterized in that, It includes computer instructions, and the computer instructions are used to cause a computer to execute the resource allocation method in the flight control system according to any one of claims 1 to 5.

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