Aircraft network service quality dynamic optimization method, device, equipment and medium

Abstract

The invention relates to the technical field of communication, and provides an aircraft network service quality dynamic optimization method, device, equipment and medium, which can construct a network service quality quantitative model based on index fusion according to a weight coefficient of a transport layer network index. Therefore, the granularity of the analysis is finer, the real-time performance is stronger, the influence of the application is smaller, and the comprehensive network condition can be reflected; a dual-Critic network is constructed based on an MATD3 algorithm and a network service quality quantification model, and a target network service quality quantification value is determined in combination with a transmission layer network index value and a motion parameter, so that a network resource scheduling strategy of a network to which a target aircraft belongs is adjusted. Through real-time monitoring and intelligent resource scheduling of transmission layer indexes and aircraft motion states, dynamic optimization of network service quality is realized.

Description

Technical Field

[0001] The present invention relates to the field of communication technologies, and in particular, to a method, device, equipment and medium for dynamically optimizing the quality of service of an aircraft network. Background Art

[0002] In the field of low-altitude economy, aircraft such as unmanned aerial vehicles (UAVs) usually use 5G (5th Generation Mobile Communication Technology) and 5G-A (5G-Advanced) communication technologies to transmit data to and from a ground station. The data to be transmitted includes control signaling, audio and video data, etc.

[0003] However, affected by various factors such as weather, 5G or 5G-A coverage, and 5G or 5G-A signal strength, the communication channel quality between the UAV and the ground station is unstable. It is necessary to evaluate the QoS (Quality of Service) of 5G or 5G-A communication in real time and dynamically adjust the bandwidth allocated to different service data, so as to ensure the transmission of key data such as control signaling.

[0004] In view of the above problems, in terms of QoS guarantee, the existing technologies mainly have the following defects: 1. Traditional QoS guarantee schemes are only based on application layer metrics (such as the number of video freezes, etc.) and cannot reflect the changes in the transmission layer link quality in real time; 2. Using a fixed threshold to judge the network state does not consider the impact of the dynamic flight characteristics of the UAV on the channel quality; 3. The bandwidth adjustment method does not distinguish service priorities, which may cause the transmission of key control instructions to be blocked; 4. Traditional schemes calculate information such as delay, jitter, and packet loss rate at the application layer, and there are also the following defects: 1) Most application layers are built on a certain reliable transport protocol, so underlying network problems such as packet loss and retransmission cannot be directly seen. Even if the network is unstable, the reliable protocol will automatically perform retransmission and recovery, and the application layer can only see the data stream after recovery; 2) When calculating delay information at the application layer, a timestamp is usually inserted when generating a request or response. The calculated delay includes not only the transmission time in the network link, but also the time consumed by the application in generating, processing, and sending the request. This makes it difficult to clearly determine whether the problem lies in the network layer (such as routing, link delay) or the application layer (such as request processing time) when performance problems occur, thus affecting the efficiency and accuracy of fault troubleshooting.

[0005] Therefore, how to reasonably optimize the network service quality of the aircraft has become an urgent problem to be solved. Summary of the Invention

[0006] In view of the above, it is necessary to provide a method, device, equipment and medium for dynamically optimizing the network service quality of an aircraft, aiming to solve the problem that the network service quality of the aircraft cannot be ensured.

[0007] A method for dynamically optimizing the network service quality of an aircraft, the method for dynamically optimizing the network service quality of the aircraft includes: In response to a dynamic optimization instruction for the network service quality of the target aircraft, construct a network service quality quantization model based on index fusion according to the weight coefficients of the transport layer network metrics; Construct a dual-Critic network based on the MATD3 algorithm and the network service quality quantization model; wherein, the dual-Critic network includes a real-time Critic network and a delay Critic network; Use the probes deployed on the target aircraft side to collect the transport layer network metric values in real time, and collect the motion parameters of the target aircraft in real time; Input the transport layer network metric values and the motion parameters into the real-time Critic network, and dynamically adjust the weight coefficients of the transport layer network metrics according to the motion parameters in the real-time Critic network; In the real-time Critic network, input the adjusted weight coefficients and the transport layer network metric values into the network service quality quantization model to obtain a real-time network service quality quantization value; After a preset delay, synchronize the real-time network service quality quantization value to the delay Critic network to obtain a delay network service quality quantization value; At any moment during the real-time execution of the network service quality dynamic optimization instruction, obtain the current real-time network service quality quantization value output by the real-time Critic network and the current delay network service quality quantization value output by the delay Critic network; Obtain the smaller value of the current real-time network service quality quantization value and the current delay network service quality quantization value as the target network service quality quantization value; Adjust the network resource scheduling strategy of the network to which the target aircraft belongs according to the target network service quality quantization value.

[0008] According to a preferred embodiment of the present invention, the constructing a network service quality quantization model based on index fusion according to the weight coefficients of the transport layer network metrics includes: Obtain the delay, jitter, and packet loss rate in the transport layer network metrics; Based on the delay, the jitter, and the packet loss rate, the following formula is used to construct the network service quality quantization model: Q_score = α·Delay + β·Jitter + γ·Loss; Among them, Q_score represents the network service quality quantization value, Delay represents the value of the delay, Jitter represents the value of the jitter, Loss represents the value of the packet loss rate, α represents the first weight coefficient corresponding to the delay, Jitter represents the second weight coefficient corresponding to the jitter, and γ represents the third weight coefficient corresponding to the packet loss rate.

[0009] According to the preferred embodiment of the present invention, the dynamic adjustment of the weight coefficient of the transport layer network index by the real-time Critic network according to the motion parameters includes: Obtain the real-time speed of the target aircraft from the motion parameters; When the real-time speed is greater than the speed threshold, the following formula is used to calculate the adjusted first weight coefficient: ; Among them, represents the adjusted first weight coefficient, the initial value of the first weight coefficient, represents the speed coefficient, represents the real-time speed, represents the maximum speed.

[0010] According to the preferred embodiment of the present invention, the dynamic adjustment of the weight coefficient of the transport layer network index by the real-time Critic network according to the motion parameters further includes: Obtain the real-time longitudinal acceleration of the target aircraft from the motion parameters; When the real-time longitudinal acceleration is greater than the longitudinal acceleration threshold, calculate the compensation value of the second weight coefficient; Accumulate the compensation value to the second weight coefficient; Among them, the following formula is used to calculate the compensation value of the second weight coefficient; ; Among them, represents the compensation value of the second weight coefficient, represents the real-time longitudinal acceleration, represents the longitudinal acceleration safety threshold, represents the initial value of the second weight coefficient, represents the real-time pitch angle of the target aircraft, represents the maximum pitch angle.

[0011] According to a preferred embodiment of the present invention, the dynamic adjustment of the weight coefficient of the transport layer network metrics by the real-time Critic network according to the motion parameters further includes: Obtain the target altitude and real-time altitude of the target aircraft; Calculate the difference between the target altitude and the real-time altitude to obtain the altitude difference of the target aircraft; Obtain the altitude tolerance of the target aircraft, and obtain the real-time roll angle of the target aircraft from the motion parameters; According to the altitude tolerance and the real-time roll angle, calculate the adjusted third weight coefficient using the following formula: ; Wherein, ; Wherein, represents the adjusted third weight coefficient, represents the initial value of the third weight coefficient, represents the real-time roll angle, represents the altitude difference, represents the altitude tolerance.

[0012] According to a preferred embodiment of the present invention, the adjustment of the network resource scheduling strategy of the network to which the target aircraft belongs according to the target network service quality quantization value includes: When the target network service quality quantization value is greater than or equal to the first threshold and less than the second threshold, obtain the non-critical video stream corresponding to the network to which the target aircraft belongs, limit the bandwidth of the non-critical video stream to a preset ratio of the original bandwidth, and start the I-frame priority transmission mechanism; When the target network service quality quantization value is greater than or equal to the second threshold and less than the third threshold, start the encoding upgrade mechanism for converting the first digital video compression format to the second digital video compression format in the network to which the target aircraft belongs, downgrade the video bit rate transmitted in the network to which the target aircraft belongs to the configured bit rate, and turn off the video stream metadata feedback of the network to which the target aircraft belongs; When the target network service quality quantization value is greater than or equal to the third threshold, perform a downgrade process on the resolution of the image data transmitted in the network to which the target aircraft belongs, enable the packet truncation mechanism in the network to which the target aircraft belongs, and suspend the transmission of all non-real-time data.

[0013] According to a preferred embodiment of the present invention, after the adjustment of the network resource scheduling strategy of the network to which the target aircraft belongs according to the target network service quality quantization value, the method further includes: Re-obtain the network service quality quantization value output by the dual Critic network as the network service quality update value; Compare the network service quality update value with the target network service quality quantization value; When the network service quality update value is less than the target network service quality quantization value, it is determined that there is an optimization effect after adjusting the network resource scheduling strategy of the network to which the target aircraft belongs; or When the network service quality update value is greater than or equal to the target network service quality quantization value, it is determined that there is no optimization effect after adjusting the network resource scheduling strategy of the network to which the target aircraft belongs, and an optimization prompt message is sent to the specified terminal device.

[0014] An aircraft network service quality dynamic optimization device, the aircraft network service quality dynamic optimization device includes: A construction unit, configured to respond to a network service quality dynamic optimization instruction for a target aircraft, and construct a network service quality quantization model based on index fusion according to the weight coefficients of the transport layer network metrics; The construction unit is further configured to construct a dual Critic network based on the MATD3 algorithm and the network service quality quantization model; wherein, the dual Critic network includes a real-time Critic network and a delayed Critic network; An acquisition unit, configured to use a probe deployed on the target aircraft side to collect transport layer network metric values in real time and collect the motion parameters of the target aircraft in real time; An adjustment unit, configured to input the transport layer network metric values and the motion parameters into the real-time Critic network, and dynamically adjust the weight coefficients of the transport layer network metrics according to the motion parameters in the real-time Critic network; An input unit, configured to input the adjusted weight coefficients and the transport layer network metric values into the network service quality quantization model in the real-time Critic network to obtain a real-time network service quality quantization value; A synchronization unit, configured to synchronize the real-time network service quality quantization value to the delayed Critic network after a preset delay to obtain a delayed network service quality quantization value; An acquisition unit, configured to obtain the current real-time network service quality quantization value output by the real-time Critic network and the current delayed network service quality quantization value output by the delayed Critic network at any moment during the real-time execution of the network service quality dynamic optimization instruction; The obtaining unit is further configured to obtain the smaller value between the current real-time network service quality quantization value and the current delay network service quality quantization value as the target network service quality quantization value; The adjusting unit is further configured to adjust the network resource scheduling policy of the network to which the target aircraft belongs according to the target network service quality quantization value.

[0015] A computer device, the computer device includes: A memory that stores at least one instruction; and A processor that executes the instructions stored in the memory to implement the method for dynamically optimizing the network service quality of the aircraft.

[0016] A computer-readable storage medium, in which at least one instruction is stored, and the at least one instruction is executed by a processor in a computer device to implement the method for dynamically optimizing the network service quality of the aircraft.

[0017] It can be seen from the above technical solutions that the present invention can construct a network service quality quantization model based on index fusion according to the weight coefficients of the transport layer network metrics. Since the transport layer network metrics analyze each data packet, the analysis granularity is finer, the real-time performance is stronger, and the influence of the application is smaller, and it can reflect a more comprehensive network condition; a dual Critic network is constructed based on the MATD3 algorithm and the network service quality quantization model, and the target network service quality quantization value is determined in combination with the transport layer network metric values and the motion parameters, so as to adjust the network resource scheduling policy of the network to which the target aircraft belongs. Through the real-time monitoring and intelligent resource scheduling of the transport layer metrics and the aircraft motion state, the dynamic optimization of the network service quality is realized. Description of the Drawings

[0018] Figure 1 is a flowchart of a preferred embodiment of the method for dynamically optimizing the network service quality of the aircraft of the present invention; Figure 2 is a functional module diagram of a preferred embodiment of the device for dynamically optimizing the network service quality of the aircraft of the present invention; Figure 3 is a schematic structural diagram of a computer device of a preferred embodiment of the method for dynamically optimizing the network service quality of the aircraft of the present invention. Detailed Embodiments

[0019] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be described in detail below with reference to the drawings and specific embodiments.

[0020] Such as Figure 1As shown, it is a flowchart of a preferred embodiment of the method for dynamically optimizing the quality of service of the aircraft network according to the present invention. According to different requirements, the order of steps in this flowchart can be changed, and some steps can be omitted.

[0021] The method for dynamically optimizing the quality of service of the aircraft network is applied to one or more computer devices. The computer device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, a microprocessor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0022] The computer device can be any electronic product that can interact with users. For example, a personal computer, a tablet computer, a smart phone, a personal digital assistant (PDA), a game console, an Internet Protocol Television (IPTV), a smart wearable device, etc.

[0023] The computer device can also include a network device and / or a user device. Among them, the network device includes, but is not limited to, a single network server, a server group composed of multiple network servers, or a cloud composed of a large number of hosts or network servers based on cloud computing.

[0024] The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0025] Among them, artificial intelligence (AI) is a theory, method, technology, and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0026] The basic technologies of artificial intelligence generally include technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation / interaction systems, and mechatronics. The software technologies of artificial intelligence mainly include several major directions such as computer vision technology, robotics, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0027] The network where the computer device is located includes but is not limited to the Internet, wide area network, metropolitan area network, local area network, virtual private network (VPN), etc.

[0028] S10, in response to a dynamic optimization instruction for the network service quality of the target aircraft, construct a network service quality quantization model based on index fusion according to the weight coefficients of the transport layer network metrics.

[0029] In this embodiment, the target aircraft may be an aircraft in a low-altitude economy scenario such as a drone.

[0030] In this embodiment, the network service quality dynamic optimization instruction may be automatically triggered when it is detected that the target aircraft starts, so as to realize the full-process monitoring of the flight process of the target aircraft.

[0031] In this embodiment, the constructing a network service quality quantization model based on index fusion according to the weight coefficients of the transport layer network metrics includes: Obtain the delay, jitter, and packet loss rate in the transport layer network metrics; Based on the delay, the jitter, and the packet loss rate, use the following formula to construct the network service quality quantization model: Q_score = α·Delay + β·Jitter + γ·Loss; Among them, Q_score represents the network service quality quantization value, Delay represents the value of the delay, Jitter represents the value of the jitter, Loss represents the value of the packet loss rate, α represents the first weight coefficient corresponding to the delay, Jitter represents the second weight coefficient corresponding to the jitter, and γ represents the third weight coefficient corresponding to the packet loss rate.

[0032] Among them, Delay, Jitter, and Loss can be normalized, and the value range of Q_score can be [0,1].

[0033] In the above embodiments, a mathematical model for generating a comprehensive network quality score by weighting delay, jitter, and packet loss rate through a weighted algorithm is used. Since the transport layer metrics analyze each packet at the bottom layer, network delay, retransmission, packet loss, congestion, jitter, etc. can be captured and analyzed. Therefore, the analysis granularity is finer and the real-time performance is stronger, thus reflecting a more comprehensive network condition. Moreover, when statistical delay information is collected at the transport layer, it is less affected by applications and the statistics are more accurate. Using transport layer metrics can not only accurately evaluate network quality but also provide an effective basis for adjusting subsequent network resource scheduling strategies.

[0034] S11. Construct a dual Critic network based on the MATD3 (Multi-Agent Twin Delayed DDPG, multi-agent double-delayed deep deterministic policy gradient) algorithm and the network service quality quantization model; wherein, the dual Critic network includes a real-time Critic network and a delay Critic network.

[0035] In this embodiment, by integrating the network service quality quantization model with the reinforcement learning model, the stability and efficiency of learning can be improved, and the evaluation ability for different strategies can be enhanced.

[0036] Among them, when building and training the dual Critic network, the following strategies can be adopted to process training samples: 1) Prioritized experience replay: Assign a 3-fold sampling weight to samples with a Q_score drop exceeding 20%. 2) State clustering storage: Store samples in the high-speed movement state (flight speed ≥ 20 m / s) separately to improve the generalization ability of the policy network in extreme or critical scenarios.

[0037] In the UAV training dataset, the proportion of samples in the high-speed state (such as flight speed ≥ 20 m / s) is usually less than 10%, resulting in insufficient decision-making ability of the policy network for high-speed scenarios. Moreover, when moving at high speed, problems such as sudden changes in channel quality and control instruction delay are significantly amplified, and targeted intensive training is required.

[0038] Therefore, targeted processing of samples with a Q_score drop exceeding 20% and samples in the high-speed movement state can collect more sufficient samples, thereby making the performance of the constructed dual Critic network better.

[0039] S12. Use the probe deployed on the target aircraft side to collect transport layer network metric values in real time and collect the motion parameters of the target aircraft in real time.

[0040] In this embodiment, the transport layer network metric values may include the values of delay, jitter, and packet loss rate.

[0041] In this embodiment, the motion parameters may include, but are not limited to: speed, acceleration, pitch angle, roll angle, etc.

[0042] S13. Input the transport layer network metric value and the motion parameters into the real-time Critic network, and dynamically adjust the weight coefficient of the transport layer network metric in the real-time Critic network according to the motion parameters.

[0043] In this embodiment, the dynamically adjusting the weight coefficient of the transport layer network metric in the real-time Critic network according to the motion parameters includes: Obtain the real-time speed of the target aircraft from the motion parameters; When the real-time speed is greater than the speed threshold, calculate the adjusted first weight coefficient using the following formula: ; Where, represents the adjusted first weight coefficient, the initial value of the first weight coefficient, represents the speed coefficient, represents the real-time speed, represents the maximum speed.

[0044] Among them, the speed threshold can be configured according to experiments, such as 15 m / s.

[0045] In this embodiment, the dynamically adjusting the weight coefficient of the transport layer network metric in the real-time Critic network according to the motion parameters further includes: Obtain the real-time longitudinal acceleration of the target aircraft from the motion parameters; When the real-time longitudinal acceleration is greater than the longitudinal acceleration threshold, calculate the compensation value of the second weight coefficient; Accumulate the compensation value to the second weight coefficient; Among them, the compensation value of the second weight coefficient is calculated using the following formula; ; Where, represents the compensation value of the second weight coefficient, represents the real-time longitudinal acceleration, represents the longitudinal acceleration safety threshold, represents the initial value of the second weight coefficient, represents the real-time pitch angle of the target aircraft, represents the maximum pitch angle.

[0046] Among them, the longitudinal acceleration threshold can also be configured according to experiments, such as 4 m / s².

[0047] Among them, the configuration of the longitudinal acceleration safety threshold can prevent packet disorder caused by violent maneuvers. For example, the longitudinal acceleration safety threshold can be configured as 8 m / s².

[0048] In this embodiment, the dynamic adjustment of the weight coefficient of the transport layer network metric by the real-time Critic network according to the motion parameters further includes: Obtain the target height and real-time height of the target aircraft; Calculate the difference between the target height and the real-time height to obtain the height difference of the target aircraft; Obtain the height tolerance of the target aircraft, and obtain the real-time roll angle of the target aircraft from the motion parameters; According to the height tolerance and the real-time roll angle, use the following formula to calculate the adjusted third weight coefficient: ; Among them, ; Among them, represents the adjusted third weight coefficient, represents the initial value of the third weight coefficient, represents the real-time roll angle, represents the height difference, represents the height tolerance.

[0049] Among them, the height tolerance can be configured as 2 m.

[0050] Among them, the target height can be the ideal height during hovering.

[0051] By adjusting the third weight coefficient, the packet loss weight priority can be reduced during inclined flight.

[0052] Among them, the sum of the first weight coefficient, the second weight coefficient and the third weight coefficient is 1.

[0053] Among them, the adjustment ranges of the first weight coefficient, the second weight coefficient and the third weight coefficient all have certain limitations, so as to effectively avoid the mutation of the weight coefficient and avoid violent fluctuations. For example: the adjustment range of the first weight coefficient can be limited to [-0.12, +0.12].

[0054] Among them, the action smoothing filter can be used to smooth each weight coefficient during the entire flight process of the target aircraft to further avoid the mutation of the weight coefficient.

[0055] In the above embodiments, it is possible to quickly respond according to parameters such as flight speed, acceleration, and attitude angle. When performing high-speed maneuvers, low latency is preferentially guaranteed (such as increasing the first weight coefficient); when in stable cruise, the packet loss rate is optimized for guarantee (such as increasing the third weight coefficient); when performing violent maneuvers, network jitter needs to be suppressed (such as dynamically compensating the second weight coefficient).

[0056] Through the above embodiments, it is possible to adjust the weight coefficients of each transport layer index in real time according to the motion parameters of the aircraft. The spatio-temporal correlation analysis method between the transport layer index and the UAV motion state has established a dynamic mapping relationship between the UAV kinematic parameters and the transport layer index for the first time.

[0057] S14. In the real-time Critic network, input the adjusted weight coefficient and the transport layer network index value into the network service quality quantization model to obtain a real-time network service quality quantization value.

[0058] Through the above embodiments, it is possible to calculate a real-time network service quality quantization value according to the real-time transport layer index and the weight coefficient adjusted according to the aircraft motion parameters.

[0059] S15. After a preset delay, synchronize the real-time network service quality quantization value to the delayed Critic network to obtain a delayed network service quality quantization value.

[0060] Among them, the preset time delay can be configured according to the actual application scenario.

[0061] For example: for the current moment t, if the time delay is T, then the network service quality quantization value output by the real-time Critic network at the moment (t - T) can be synchronized to the delayed Critic network and used as the network service quality quantization value output by the delayed Critic network at the current moment t.

[0062] S16. At any moment during the real-time execution of the network service quality dynamic optimization instruction, obtain the current real-time network service quality quantization value output by the real-time Critic network and the current delayed network service quality quantization value output by the delayed Critic network.

[0063] For example: when the arbitrary moment is t, then obtain the current real-time network service quality quantization value Q output by the real-time Critic network, and obtain the current delayed network service quality quantization value Q' output by the delayed Critic network (Q' is the network service quality quantization value output by the real-time Critic network at the moment (t - T)).

[0064] S17. Obtain the smaller value between the current real-time network service quality quantization value and the current latency network service quality quantization value as the target network service quality quantization value.

[0065] For example: When Q is less than Q', then determine Q as the target network service quality quantization value.

[0066] Through the above embodiments, the problem of overestimation can be effectively suppressed.

[0067] S18. Adjust the network resource scheduling strategy of the network to which the target aircraft belongs according to the target network service quality quantization value.

[0068] In this embodiment, the adjusting the network resource scheduling strategy of the network to which the target aircraft belongs according to the target network service quality quantization value includes: When the target network service quality quantization value is greater than or equal to the first threshold and less than the second threshold, obtain the non-critical video stream corresponding to the network to which the target aircraft belongs, limit the bandwidth of the non-critical video stream to a preset ratio of the original bandwidth, and start the I-frame priority transmission mechanism; When the target network service quality quantization value is greater than or equal to the second threshold and less than the third threshold, start the encoding upgrade mechanism for converting the first digital video compression format to the second digital video compression format in the network to which the target aircraft belongs, downgrade the video bit rate transmitted in the network to which the target aircraft belongs to the configured bit rate, and turn off the video stream metadata feedback in the network to which the target aircraft belongs; When the target network service quality quantization value is greater than or equal to the third threshold, perform a downgrade process on the resolution of the image data transmitted in the network to which the target aircraft belongs, enable the packet truncation mechanism in the network to which the target aircraft belongs, and suspend the transmission of all non-real-time data.

[0069] Among them, the non-critical video frames can be configured according to service agreements.

[0070] Among them, the first threshold, the second threshold, and the third threshold can be configured according to a large number of experiments in the actual scenario.

[0071] Among them, the preset ratio and the configured bit rate can also be configured according to a large number of experiments in the actual scenario.

[0072] For example: When the target network service quality quantization value is greater than or equal to the first threshold and less than the second threshold, adopt the first-level resource scheduling strategy. For example: Limit the bandwidth of the non-critical video stream to 70% of the original value and enable the I-frame priority transmission mechanism. This strategy only affects the enhanced data of the media stream.

[0073] For another example: when the target network service quality quantization value is greater than or equal to the second threshold and less than the third threshold, start H.264→H.265 transcoding (H.265 has higher compression efficiency than H.264 and saves more bandwidth for video stream transmission of the same picture quality. However, the encoding and decoding complexity of H.265 is 2-3 times that of H.264 and consumes more computing resources. Therefore, this adjustment is a dynamic process. When bandwidth resources are sufficient, use H.264 to encode the video stream to save computing resources. However, when bandwidth resources are limited, adjust the encoding algorithm to H.265 to trade computing resource consumption for low bandwidth consumption), downgrade the video bitrate to 64 kbps, and turn off the video stream metadata feedback. This policy affects the basic quality of the media stream.

[0074] For another example: when the target network service quality quantization value is greater than or equal to the third threshold, adaptively downgrade the resolution (such as downgrading the resolution from 1080p to 720p, or from 720p to 480p), enable the packet truncation mechanism (such as retaining the first 80% of the video macroblocks), and pause all non-real-time data transmissions. This policy affects all non-control services.

[0075] Through the above embodiments, it is possible to perform hierarchical scheduling of the network resources of the network to which the aircraft belongs according to the output network service quality quantization value, realize dynamic optimization of the service quality on the premise of ensuring the priority transmission of flight control commands, solve the problem of QoS (Quality of Service) fluctuations caused by dynamic environments in the communication between network base stations such as 5G base stations and aircraft such as drones in the low-altitude economic scenario, effectively reduce the communication interruption rate, reduce the standard deviation of the control command transmission delay, and at the same time ensure the basic picture quality.

[0076] In this embodiment, after adjusting the network resource scheduling policy of the network to which the target aircraft belongs according to the target network service quality quantization value, the method further includes: Re-obtain the network service quality quantization value output by the dual Critic network as the network service quality update value; Compare the network service quality update value with the target network service quality quantization value; When the network service quality update value is less than the target network service quality quantization value, it is determined that the adjustment of the network resource scheduling policy of the network to which the target aircraft belongs has an optimization effect; or When the network service quality update value is greater than or equal to the target network service quality quantization value, it is determined that the adjustment of the network resource scheduling policy of the network to which the target aircraft belongs does not have an optimization effect, and an optimization prompt message is sent to the specified terminal device.

[0077] In the above embodiments, after adjusting the network resource scheduling strategy according to the output network service quality quantization value, the available bandwidth, resolution, etc. of the audio and video streams in the network may also change accordingly, thereby possibly changing the index values of transmission layer metrics such as delay, jitter, and packet loss rate. That is to say, if the network service quality quantization value decreases correspondingly, then this adjustment has an optimization effect; otherwise, relevant personnel can be prompted to adjust the dynamic optimization scheme of the aircraft network service quality in a timely manner to achieve a better optimization effect.

[0078] This embodiment improves the communication service quality in the high-speed moving scenario of the UAV based on the dynamic weight coefficient adjustment mechanism of reinforcement learning. The core lies in dynamically adjusting the weight ratio of transmission layer metrics (such as delay, jitter, and packet loss rate) in the network service quality quantization model by real-time sensing of the UAV motion state and environmental parameters, and then obtaining the QoS evaluation result, which is used as the basis for dynamically adjusting the transmission parameters of services that occupy a large amount of bandwidth resources such as audio and video, so as to ensure the priority transmission of key instructions such as control signaling.

[0079] It can be seen from the above technical solutions that the present invention can construct a network service quality quantization model based on index fusion according to the weight coefficients of the transmission layer network metrics. Since the transmission layer network metrics analyze each data packet, the analysis granularity is finer, the real-time performance is stronger, and the influence of the application is smaller, which can reflect a more comprehensive network condition; a dual-Critic network is constructed based on the MATD3 algorithm and the network service quality quantization model, and the target network service quality quantization value is determined by combining the transmission layer network metric values and motion parameters to adjust the network resource scheduling strategy of the network to which the target aircraft belongs. Through the real-time monitoring and intelligent resource scheduling of the transmission layer metrics and the aircraft motion state, the dynamic optimization of the network service quality is realized.

[0080] Such as Figure 2 shown, it is a functional module diagram of a preferred embodiment of the aircraft network service quality dynamic optimization device of the present invention. The aircraft network service quality dynamic optimization device 11 includes a construction unit 110, a collection unit 111, an adjustment unit 112, an input unit 113, a synchronization unit 114, and an acquisition unit 115. The module / unit referred to in the present invention means a series of computer program segments that can be executed by a processor and can complete fixed functions, and are stored in the memory. In this embodiment, the functions of each module / unit will be described in detail in the subsequent embodiments.

[0081] Among them, the construction unit 110 is configured to respond to a network service quality dynamic optimization instruction for a target aircraft, and construct a network service quality quantization model based on index fusion according to the weight coefficients of the transmission layer network metrics; The building unit 110 is further configured to construct a dual-Critic network based on the MATD3 algorithm and the network service quality quantization model; wherein, the dual-Critic network includes a real-time Critic network and a delayed Critic network; The acquisition unit 111 is configured to use a probe deployed on the target aircraft side to collect real-time transport layer network metric values and real-time collect the motion parameters of the target aircraft; The adjustment unit 112 is configured to input the transport layer network metric values and the motion parameters into the real-time Critic network, and dynamically adjust the weight coefficients of the transport layer network metrics in the real-time Critic network according to the motion parameters; The input unit 113 is configured to input the adjusted weight coefficients and the transport layer network metric values into the network service quality quantization model in the real-time Critic network to obtain a real-time network service quality quantization value; The synchronization unit 114 is configured to synchronize the real-time network service quality quantization value to the delayed Critic network after a preset delay to obtain a delayed network service quality quantization value; The obtaining unit 115 is configured to obtain the current real-time network service quality quantization value output by the real-time Critic network and the current delayed network service quality quantization value output by the delayed Critic network at any moment during the real-time execution of the network service quality dynamic optimization instruction; The obtaining unit 115 is further configured to obtain the smaller value of the current real-time network service quality quantization value and the current delayed network service quality quantization value as the target network service quality quantization value; The adjustment unit 112 is further configured to adjust the network resource scheduling strategy of the network to which the target aircraft belongs according to the target network service quality quantization value.

[0082] It can be seen from the above technical solutions that the present invention can construct a network service quality quantization model based on index fusion according to the weight coefficients of the transport layer network metrics. Since the transport layer network metrics analyze each data packet, the analysis granularity is finer, the real-time performance is stronger, and the influence of the application is smaller, and it can reflect a more comprehensive network condition; a dual-Critic network is constructed based on the MATD3 algorithm and the network service quality quantization model, and the target network service quality quantization value is determined in combination with the transport layer network metric values and the motion parameters to adjust the network resource scheduling strategy of the network to which the target aircraft belongs. Through the real-time monitoring and intelligent resource scheduling of the transport layer metrics and the aircraft motion state, the dynamic optimization of the network service quality is realized.

[0083] Such as Figure 3As shown, it is a schematic structural diagram of a computer device according to a preferred embodiment of the method for dynamically optimizing the quality of service of an aircraft network in the present invention.

[0084] The computer device 1 may include a memory 12, a processor 13, and a bus (the arrows in the figure represent the bus), and may also include a computer program stored in the memory 12 and executable on the processor 13, such as an aircraft network service quality dynamic optimization program.

[0085] Those skilled in the art can understand that the schematic diagram is only an example of the computer device 1 and does not constitute a limitation on the computer device 1. The computer device 1 can be either a bus structure or a star structure. The computer device 1 may also include more or fewer other hardware or software than shown in the figure, or different component arrangements. For example, the computer device 1 may also include input / output devices, network access devices, etc.

[0086] It should be noted that the computer device 1 is only an example. Other existing or future possible electronic products that can be adapted to the present invention should also be included within the protection scope of the present invention and are hereby incorporated by reference.

[0087] Among them, the memory 12 includes at least one type of readable storage medium. The readable storage medium includes flash memory, mobile hard disks, multimedia cards, card-type memories (such as SD or DX memories, etc.), magnetic memories, magnetic disks, optical disks, etc. The memory 12 can be an internal storage unit of the computer device 1 in some embodiments, such as the mobile hard disk of the computer device 1. The memory 12 can also be an external storage device of the computer device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the computer device 1. Further, the memory 12 can include both an internal storage unit and an external storage device of the computer device 1. The memory 12 can be used not only to store application software installed on the computer device 1 and various types of data, such as the code of the aircraft network service quality dynamic optimization program, but also to temporarily store data that has been output or will be output.

[0088] In some embodiments, the processor 13 may be composed of an integrated circuit. For example, it may be composed of a single packaged integrated circuit, or may be composed of multiple packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 13 is the control core of the computer device 1, connecting various components of the entire computer device 1 through various interfaces and lines. By running or executing programs or modules stored in the memory 12 (such as executing the aircraft network service quality dynamic optimization program, etc.), and by calling the data stored in the memory 12, it executes various functions of the computer device 1 and processes data.

[0089] The processor 13 executes the operating system of the computer device 1 and various installed application programs. The processor 13 executes the application programs to implement the steps in the above-mentioned embodiments of the various aircraft network service quality dynamic optimization methods. For example Figure 1 The steps shown.

[0090] Exemplarily, the computer program may be divided into one or more modules / units. The one or more modules / units are stored in the memory 12 and executed by the processor 13 to complete the present invention. The one or more modules / units may be a series of computer-readable instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the computer device 1. For example, the computer program may be divided into a construction unit 110, a collection unit 111, an adjustment unit 112, an input unit 113, a synchronization unit 114, and an acquisition unit 115.

[0091] The above-mentioned integrated units implemented in the form of software function modules may be stored in a computer-readable storage medium. The above-mentioned software function modules stored in a storage medium include several instructions for causing a computer device (which may be a personal computer, a computer device, or a network device, etc.) or a processor to execute parts of the aircraft network service quality dynamic optimization methods described in the various embodiments of the present invention.

[0092] If the modules / units integrated in the computer device 1 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-mentioned method embodiments of the present invention, it can also be completed by a computer program instructing relevant hardware devices. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned method embodiments can be implemented.

[0093] Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory, etc.

[0094] Furthermore, the computer-readable storage medium mainly includes a storage program area and a storage data area. Among them, the storage program area can store an operating system, application programs required for at least one function, etc.; the storage data area can store data created according to the use of the blockchain node, etc.

[0095] The blockchain referred to in the present invention is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm. Blockchain, in essence, is a decentralized database, a string of data blocks generated by using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. The blockchain can include a blockchain underlying platform, a platform product service layer, an application service layer, etc.

[0096] The bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, in Figure 3 it is only represented by a single straight line, but it does not mean that there is only one bus or one type of bus. The bus is set to realize the connection and communication between the memory 12 and at least one processor 13, etc.

[0097] Although not shown, the computer device 1 may further include a power source (such as a battery) for powering each component. Preferably, the power source can be logically connected to the at least one processor 13 through a power management device, so as to implement functions such as charging management, discharging management, and power consumption management through the power management device. The power source may also include any components such as one or more DC or AC power sources, a recharge device, a power failure detection circuit, a power converter or inverter, and a power status indicator. The computer device 1 may also include various sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be elaborated here.

[0098] Furthermore, the computer device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the computer device 1 and other computer devices.

[0099] Optionally, the computer device 1 may also include a user interface. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display the information processed in the computer device 1 and to display a visual user interface.

[0100] It should be understood that the above embodiments are only for illustrative purposes and are not limited by this structure in the scope of the patent application.

[0101] Those skilled in the art can understand that Figure 3 the shown structure does not constitute a limitation on the computer device 1, and it may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0102] In combination with Figure 1 , the memory 12 in the computer device 1 stores multiple instructions to implement a method for dynamically optimizing the quality of service of an aircraft network. The processor 13 can execute the multiple instructions to achieve: In response to a dynamic optimization instruction for the quality of service of a target aircraft, construct a network service quality quantization model based on index fusion according to the weight coefficients of the transport layer network metrics; Construct a dual-Critic network based on the MATD3 algorithm and the network service quality quantization model; wherein, the dual-Critic network includes a real-time Critic network and a delayed Critic network; Use a probe deployed on the target aircraft side to collect transmission layer network metric values in real time and collect the motion parameters of the target aircraft in real time; Input the transmission layer network metric values and the motion parameters into the real-time Critic network, and dynamically adjust the weight coefficients of the transmission layer network metrics in the real-time Critic network according to the motion parameters; In the real-time Critic network, input the adjusted weight coefficients and the transmission layer network metric values into the network service quality quantization model to obtain a real-time network service quality quantization value; After a preset delay, synchronize the real-time network service quality quantization value to the delayed Critic network to obtain a delayed network service quality quantization value; At any moment during the real-time execution of the network service quality dynamic optimization instruction, obtain the current real-time network service quality quantization value output by the real-time Critic network and the current delayed network service quality quantization value output by the delayed Critic network; Obtain the smaller value of the current real-time network service quality quantization value and the current delayed network service quality quantization value as the target network service quality quantization value; Adjust the network resource scheduling strategy of the network to which the target aircraft belongs according to the target network service quality quantization value.

[0103] Specifically, the specific implementation method of the above instructions by the processor 13 can refer to Figure 1 The description of the relevant steps in the corresponding embodiment, which will not be repeated here.

[0104] It should be noted that all the data involved in this case are legally obtained. The non-company software tools or components appearing in the embodiments of this application are only for illustrative purposes and do not represent actual use.

[0105] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0106] The present invention can be used in numerous general-purpose or special-purpose computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0107] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0108] In addition, in each embodiment of the present invention, the functional modules can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of a combination of hardware and software functional modules.

[0109] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0110] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any associated drawing marks in the claims should not be regarded as limiting the claims involved.

[0111] In addition, obviously the word "including" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units or devices described in the present invention can also be implemented by one unit or device through software or hardware. Words such as first, second, etc. are used to represent names and do not represent any specific order.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for dynamically optimizing the quality of service of an aircraft network, characterized in that, The method for dynamically optimizing the quality of service of the aircraft network includes: In response to a dynamic optimization instruction for the quality of service of the target aircraft, a quality of service quantization model based on index fusion is constructed according to the weight coefficients of the transport layer network metrics; A dual Critic network is constructed based on the MATD3 algorithm and the quality of service quantization model; wherein, the dual Critic network includes a real-time Critic network and a delayed Critic network; Probes deployed on the target aircraft side are used to collect transport layer network metric values in real time and the motion parameters of the target aircraft in real time; The transport layer network metric values and the motion parameters are input into the real-time Critic network, and the weight coefficients of the transport layer network metrics are dynamically adjusted according to the motion parameters in the real-time Critic network; In the real-time Critic network, the adjusted weight coefficients and the transport layer network metric values are input into the quality of service quantization model to obtain a real-time quality of service quantization value; After a preset delay, the real-time quality of service quantization value is synchronized to the delayed Critic network to obtain a delayed quality of service quantization value; At any moment during the real-time execution of the dynamic optimization instruction for the quality of service, the current real-time quality of service quantization value output by the real-time Critic network and the current delayed quality of service quantization value output by the delayed Critic network are obtained; The smaller value of the current real-time quality of service quantization value and the current delayed quality of service quantization value is obtained as the target quality of service quantization value; The network resource scheduling strategy of the network to which the target aircraft belongs is adjusted according to the target quality of service quantization value.

2. The method for dynamically optimizing the quality of service of an aircraft network according to claim 1, wherein, The constructing a quality of service quantization model based on index fusion according to the weight coefficients of the transport layer network metrics includes: Obtaining the delay, jitter, and packet loss rate in the transport layer network metrics; Based on the delay, the jitter, and the packet loss rate, the following formula is used to construct the quality of service quantization model: Q_score = α·Delay + β·Jitter + γ·Loss; Wherein, Q_score represents the quality of service quantization value, Delay represents the value of the delay, Jitter represents the value of the jitter, Loss represents the value of the packet loss rate, α represents the first weight coefficient corresponding to the delay, Jitter represents the second weight coefficient corresponding to the jitter, and γ represents the third weight coefficient corresponding to the packet loss rate.

3. The method for dynamically optimizing the quality of service of an aircraft network according to claim 2, characterized in that, The dynamically adjusting the weight coefficients of the transport layer network metrics according to the motion parameters in the real-time Critic network includes: Obtaining the real-time speed of the target aircraft from the motion parameters; When the real-time speed is greater than the speed threshold, the following formula is used to calculate the adjusted first weight coefficient: ； Among them, represents the first weight coefficient after adjustment, the initial value of the first weight coefficient, represents the speed coefficient, represents the real-time speed, represents the maximum speed.

4. The method for dynamically optimizing the quality of service of an aircraft network according to claim 2, wherein The dynamically adjusting the weight coefficients of the transport layer network metrics according to the motion parameters in the real-time Critic network further includes: Obtain the real-time longitudinal acceleration of the target aircraft from the motion parameters; When the real-time longitudinal acceleration is greater than the longitudinal acceleration threshold, calculate the compensation value of the second weight coefficient; Accumulate the compensation value to the second weight coefficient; Wherein, the compensation value of the second weight coefficient is calculated by the following formula; ； Among them, represents the compensation value of the second weight coefficient, represents the real-time longitudinal acceleration, represents the longitudinal acceleration safety threshold, represents the initial value of the second weight coefficient, represents the real-time pitch angle of the target aircraft, represents the maximum pitch angle.

5. The method for dynamically optimizing the quality of service of an aircraft network according to claim 2, characterized in that The dynamic adjustment of the weight coefficient of the transport layer network index by the real-time Critic network according to the motion parameters further includes: Obtain the target height and real-time height of the target aircraft; Calculate the difference between the target height and the real-time height to obtain the height difference of the target aircraft; Obtain the height tolerance of the target aircraft and obtain the real-time roll angle of the target aircraft from the motion parameters; According to the height tolerance and the real-time roll angle, calculate the adjusted third weight coefficient by the following formula: ； Among them, ; Among them, represents the adjusted third weight coefficient, represents the initial value of the third weight coefficient, represents the real-time roll angle, represents the height difference, represents the height tolerance.

6. The method for dynamically optimizing the quality of service of an aircraft network according to claim 1, wherein, The adjustment of the network resource scheduling strategy of the network to which the target aircraft belongs according to the target network service quality quantization value includes: When the target network service quality quantization value is greater than or equal to the first threshold and less than the second threshold, obtain the non-critical video stream corresponding to the network to which the target aircraft belongs, limit the bandwidth of the non-critical video stream to a preset ratio of the original bandwidth, and start the I-frame priority transmission mechanism; When the target network service quality quantization value is greater than or equal to the second threshold and less than the third threshold, start the encoding upgrade mechanism for converting the first digital video compression format to the second digital video compression format in the network to which the target aircraft belongs, degrade the video bit rate transmitted in the network to which the target aircraft belongs to the configured bit rate, and turn off the video stream metadata feedback of the network to which the target aircraft belongs; When the target network service quality quantization value is greater than or equal to the third threshold, perform a downgrade process on the resolution of the image data transmitted in the network to which the target aircraft belongs, enable the packet truncation mechanism in the network to which the target aircraft belongs, and suspend the transmission of all non-real-time data.

7. The method for dynamically optimizing the quality of service of an aircraft network according to claim 1, characterized in that, After the adjustment of the network resource scheduling strategy of the network to which the target aircraft belongs according to the target network service quality quantization value, the method further includes: Re-obtain the network service quality quantization value output by the dual Critic network as the network service quality update value; Compare the network service quality update value with the target network service quality quantization value; When the network service quality update value is less than the target network service quality quantization value, it is determined that the adjustment of the network resource scheduling strategy of the network to which the target aircraft belongs has an optimization effect; or When the network service quality update value is greater than or equal to the target network service quality quantization value, it is determined that the adjustment of the network resource scheduling strategy of the network to which the target aircraft belongs does not have an optimization effect, and an optimization prompt message is sent to the specified terminal device.

8. An apparatus for dynamically optimizing the quality of service of an aircraft network, characterized in that, The aircraft network service quality dynamic optimization device includes: A construction unit, which is used to respond to a dynamic optimization instruction for the network service quality of a target aircraft, and construct a network service quality quantization model based on index fusion according to the weight coefficients of transport layer network metrics; The construction unit is further used to construct a dual Critic network based on the MATD3 algorithm and the network service quality quantization model; wherein, the dual Critic network includes a real-time Critic network and a delayed Critic network; An acquisition unit, which is used to use a probe deployed on the target aircraft side to collect transport layer network metric values in real time and collect the motion parameters of the target aircraft in real time; An adjustment unit, which is used to input the transport layer network metric values and the motion parameters into the real-time Critic network, and the real-time Critic network dynamically adjusts the weight coefficients of the transport layer network metrics according to the motion parameters; An input unit, which is used to input the adjusted weight coefficients and the transport layer network metric values into the network service quality quantization model in the real-time Critic network to obtain a real-time network service quality quantization value; A synchronization unit, which is used to synchronize the real-time network service quality quantization value to the delayed Critic network after a preset delay to obtain a delayed network service quality quantization value; An acquisition unit, which is used to obtain the current real-time network service quality quantization value output by the real-time Critic network and the current delayed network service quality quantization value output by the delayed Critic network at any moment during the real-time execution of the network service quality dynamic optimization instruction; The acquisition unit is further used to obtain the smaller value of the current real-time network service quality quantization value and the current delayed network service quality quantization value as the target network service quality quantization value; The adjustment unit is further used to adjust the network resource scheduling strategy of the network to which the target aircraft belongs according to the target network service quality quantization value.

9. A computer device, characterized in that, The computer device includes: A memory, which stores at least one instruction; and A processor, which executes the instructions stored in the memory to implement the dynamic optimization method for the network service quality of an aircraft as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: At least one instruction is stored in the computer-readable storage medium, and the at least one instruction is executed by a processor in the computer device to implement the dynamic optimization method for the network service quality of an aircraft as described in any one of claims 1 to 7.

Images