Communication method for broadband communication and satellite fusion

Abstract

The invention discloses a communication method for broadband communication and satellite fusion, which comprises the following steps of: classifying data streams, ensuring priority transmission of high-priority data streams by adopting a self-adaptive flow control protocol, carrying out load balancing according to link quality in combination with a satellite and a ground communication link, and when the communication link fluctuates, outputting the data streams. The method can automatically switch to a backup link or adjust a transmission mode, guarantees the communication stability, predicts the satellite communication time delay through deep learning, carries out the time delay compensation, optimizes the communication efficiency, intelligently allocates bandwidth resources according to the user demands and geographic positions, optimizes the bandwidth utilization rate, reduces the data transmission amount through a self-adaptive compression algorithm, and improves the bandwidth utilization rate. Therefore, the problems of non-uniform bandwidth utilization, unstable link, delay fluctuation and insufficient flow control in the prior art are solved, and through intelligent prediction and real-time adjustment, the system can efficiently utilize bandwidth resources, ensure communication stability, reduce delay fluctuation and improve overall communication quality.

Description

Technical Field

[0001] The present invention relates to the field of communication technologies, and particularly to a communication method for broadband communication and satellite integration. Background Art

[0002] At present, broadband communication and satellite communication technologies have been widely applied in multiple fields. Especially in the context of the growing global communication demand, satellite communication provides an efficient solution. Traditional broadband communication networks usually rely on terrestrial networks such as optical fibers and DSLs to provide high-bandwidth and low-latency data transmission. Satellite communication is connected to ground stations through satellite relay stations and can achieve data transmission in a wide coverage area. Especially in remote areas and special scenarios such as the ocean and the air, it makes up for the deficiencies of traditional terrestrial networks. Existing technologies combine satellite communication and broadband communication and are committed to achieving efficient communication over long distances and providing users with all-round network coverage. However, in existing systems for broadband communication and satellite integration, due to the limited bandwidth of satellite communication and significant interference from the space environment, there are prone to time-delay fluctuations and unstable links. Especially when the satellite link load is high, the communication quality is poor. Secondly, the bandwidth resource allocation strategy in existing systems is relatively single and cannot be dynamically adjusted according to real-time network load and user needs, resulting in uneven use of bandwidth and the communication quality of some users cannot be guaranteed. Moreover, existing flow control protocols lack sufficient flexibility in the case of network congestion or poor link quality and cannot effectively adjust the priority of data streams, leading to an increase in the transmission delay of important data. Finally, existing time-delay compensation mechanisms fail to effectively adapt to the dynamically changing communication environment and often cannot quickly respond to the time-delay changes caused by link quality fluctuations, further affecting the stability and response speed of the communication system. Summary of the Invention

[0003] The present invention aims to solve at least one of the technical problems in the related technologies to some extent.

[0004] To this end, the purpose of the present invention is to propose a communication method for broadband communication and satellite integration, which can solve the problems of uneven bandwidth utilization, unstable links, time-delay fluctuations, and insufficient flow control in the existing technology through technologies such as intelligent bandwidth prediction, dynamic resource allocation, adaptive flow control, and time-delay compensation, and significantly improve the efficiency, stability, and user experience of the communication system.

[0005] To achieve the above object, the present invention proposes a communication method for broadband communication and satellite integration, including the following steps: S1: Establish a data channel between the satellite communication network and the ground station, and optimize bandwidth utilization by using frequency reuse technology; S2: Predict the bandwidth requirements using a reinforcement learning model and dynamically allocate bandwidth resources based on the real-time network conditions; S3: Classify the data streams and adopt an adaptive flow control protocol to ensure the priority transmission of high-priority data streams; S4: Combine satellite and terrestrial communication links, adjust the data transmission rate in real time, and perform load balancing according to the link quality; S5: When the communication link fluctuates, automatically switch to the backup link or adjust the transmission mode to ensure stable communication; S6: Predict the satellite communication delay through deep learning and perform delay compensation to optimize the communication efficiency; S7: Intelligently allocate bandwidth resources according to user requirements and geographical locations to optimize the bandwidth utilization rate; S8: Adopt an adaptive compression algorithm to reduce the data transmission volume and improve the bandwidth utilization rate.

[0006] A communication method for broadband communication and satellite integration according to the present invention, through frequency reuse technology and an intelligent bandwidth prediction model, the system dynamically allocates bandwidth resources in real time to ensure reasonable bandwidth guarantees for different applications and users. Secondly, through an adaptive flow control protocol, high-priority data streams are preferentially transmitted, and the data transmission rate is dynamically adjusted according to the real-time network conditions. The delay compensation mechanism predicts and compensates for the delay fluctuations of the satellite link through a deep learning model, thereby reducing the impact of delay on communication quality. Also, through a link quality evaluation mechanism, the link transmission parameters are adjusted in real time to ensure stable transmission. Finally, an adaptive compression algorithm is adopted to dynamically optimize the compression ratio according to the data type and network bandwidth changes, improving the bandwidth utilization rate, solving the problems of uneven bandwidth utilization, unstable links, delay fluctuations, and insufficient flow control in the prior art. Through intelligent prediction and real-time adjustment, the system can efficiently utilize bandwidth resources, ensure stable communication, reduce delay fluctuations, and improve the overall communication quality.

[0007] Specifically, the bandwidth prediction model uses a reinforcement learning algorithm to intelligently predict future bandwidth requirements by analyzing network historical data, real-time bandwidth requirements, and link status, and dynamically adjusts the allocation of bandwidth resources according to the prediction results, thereby effectively reducing bandwidth waste and improving the overall performance of the system.

[0008] Specifically, the flow control protocol divides the priorities of different types of data streams through an adaptive algorithm, adjusts the transmission strategy of the data streams in real time, ensures that high-priority data streams are preferentially guaranteed in the case of network congestion or insufficient bandwidth, and can achieve dynamic retransmission and rate adjustment of data packets.

[0009] Specifically, the deep learning model is trained on historical data of satellite communication latency, combines real-time link status, and uses a combination of convolutional neural network and long short-term memory network for latency prediction, so as to accurately predict the link latency and automatically adjust transmission parameters through a latency compensation mechanism to reduce the impact of latency fluctuations on communication quality.

[0010] Specifically, the latency compensation mechanism is based on a network state awareness algorithm, automatically evaluates the latency difference between the satellite and the ground link, and corrects the latency through various compensation methods to ensure that the latency change during data transmission remains within an acceptable range, thereby improving communication stability.

[0011] Specifically, the adaptive compression algorithm uses content-aware compression technology, which can dynamically select the most suitable compression method according to the characteristics of the transmitted data and the real-time change of network bandwidth. Thus, while ensuring the transmission quality, it can minimize the data volume as much as possible, reduce bandwidth occupancy, and improve the utilization rate of network resources.

[0012] Specifically, the bandwidth resource allocation strategy intelligently adjusts the bandwidth allocation according to the user's geographical location, communication requirements, and service quality requirements, combines with the user behavior analysis model, so that critical applications and high-priority users can get sufficient bandwidth guarantee, and at the same time effectively balance the bandwidth requirements of other users, avoiding resource conflicts and network bottlenecks.

[0013] Specifically, the link quality assessment mechanism monitors the link quality of satellite communication and ground broadband network in real time, uses an adaptive algorithm to dynamically adjust the modulation scheme and transmission rate of the link, and automatically selects the best link according to the network load situation and communication latency to ensure that the data packets during communication are not lost, the delay is minimized, and the bandwidth utilization is maximized.

[0014] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and / or additional aspects and advantages of the present invention will become apparent and easy to understand from the following description of the embodiments in conjunction with the drawings, wherein: Figure 1 is a flowchart of the communication method for broadband communication and satellite integration of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] Embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where like or similar reference numerals designate like or similar elements or elements having like or similar functions throughout. The embodiments described below by reference to the accompanying drawings are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention. On the contrary, the embodiments of the present invention include all variations, modifications, and equivalents that fall within the spirit and scope of the appended claims.

[0017] The communication method for broadband communication and satellite integration according to the embodiments of the present invention will be described below with reference to the accompanying drawings.

[0018] As Figure 1 shown, the communication method for broadband communication and satellite integration according to the embodiments of the present invention includes: Establish a data channel between the satellite communication network and the ground station, and optimize the bandwidth utilization by using frequency reuse technology.

[0019] It should be noted that in this embodiment, establishing a data channel between the satellite communication network and the ground station and optimizing the bandwidth utilization by using frequency reuse technology first allocate signals on multiple frequency bands to improve the spectral utilization efficiency. Then, modulation and demodulation technology is used to ensure the transmission quality of satellite signals, reduce signal interference, and ensure the stable transmission of signals. Finally, according to the real-time link state, the allocation of frequency resources is dynamically adjusted to meet different network conditions and user requirements, ensuring the stable operation of the system.

[0020] Use a reinforcement learning model to predict the bandwidth demand and dynamically allocate bandwidth resources based on the real-time network conditions.

[0021] It should be noted that in this embodiment, using a reinforcement learning model to predict the bandwidth demand analyzes historical network data, real-time traffic, and link state to predict the future bandwidth demand trend. The model continuously learns and optimizes to adapt to bandwidth changes in different network environments and provides more accurate bandwidth demand predictions. Based on the real-time network conditions, the system can dynamically allocate bandwidth resources, giving priority to meeting the bandwidth demands of high-priority applications. By monitoring the network load, delay, and bandwidth utilization in real time, the bandwidth allocation strategy is adjusted to avoid network congestion and resource waste. Finally, through continuous optimization, the model not only improves the bandwidth utilization efficiency but also automatically adjusts resource allocation according to different scenarios, ensuring the stable and efficient operation of the communication system.

[0022] Classify the data stream and use an adaptive traffic control protocol to ensure the priority transmission of high-priority data streams.

[0023] It should be noted that when classifying data streams in this embodiment, the system classifies data streams into high-priority and low-priority categories according to the types and priorities of different data. Through the adaptive flow control protocol, the system analyzes the network conditions in real time and dynamically adjusts the transmission rates of various data streams. High-priority data streams, such as real-time voice or video calls, can obtain priority transmission when the network is congested to ensure their low latency and high quality. Low-priority data streams, such as large file downloads or batch data transmissions, can delay transmission or reduce the transmission rate when the bandwidth is limited to ensure that important data streams are not affected. The adaptive flow control protocol flexibly adjusts the data stream transmission strategy by continuously monitoring the link status and network load, optimizes network resource allocation, avoids traffic conflicts, and improves the overall network efficiency and user experience.

[0024] Combine satellite and ground communication links, adjust the data transmission rate in real time, and perform load balancing according to the link quality.

[0025] It should be noted that when combining satellite and ground communication links as described in this embodiment, the system dynamically adjusts the data transmission rate by real-time monitoring of the link quality to adapt to different link conditions and bandwidth requirements. According to the real-time status of the link, the system automatically selects the appropriate modulation method and transmission rate to optimize the signal transmission effect, reduce latency and data loss. The load balancing algorithm intelligently distributes traffic according to the link load, latency, and bandwidth conditions to ensure uniform load among different communication links and avoid overloading of a certain link, thereby enhancing the stability and efficiency of the overall communication system.

[0026] When the communication link fluctuates, automatically switch to the backup link or adjust the transmission mode to ensure stable communication.

[0027] It should be noted that when the communication link fluctuates as described in this embodiment, the system automatically evaluates the link stability by real-time monitoring of the link quality and quickly switches to the preset backup link when fluctuations or quality degradation are detected to maintain continuous data transmission. At the same time, the system automatically adjusts the transmission mode according to the network conditions, such as changing the modulation method or reducing the transmission rate, so as to effectively alleviate the impact of link fluctuations and ensure the stability and reliability of communication.

[0028] Predict satellite communication latency through deep learning and perform latency compensation to optimize communication efficiency.

[0029] It should be noted that in this embodiment, by analyzing the historical latency data of the satellite communication link through a deep learning model and combining the real-time network conditions, the system predicts the future latency fluctuation trend. According to the prediction results, the system automatically adjusts the latency compensation strategy, such as through a caching mechanism or adjusting transmission parameters, to reduce the impact of latency fluctuations on communication quality, thereby improving the overall communication efficiency and stability.

[0030] Intelligently allocate bandwidth resources according to user needs and geographical location to optimize bandwidth utilization.

[0031] It should be noted that the system described in this embodiment analyzes the geographical location, usage behavior, and communication requirements of users, dynamically evaluates the bandwidth requirements of each user in combination with intelligent algorithms, and automatically adjusts the allocation of bandwidth resources according to the real-time network load and user needs to ensure that high-demand areas and high-priority users can obtain sufficient bandwidth while avoiding resource waste, thereby improving the overall bandwidth utilization and user experience.

[0032] Adopt an adaptive compression algorithm to reduce the amount of data transmitted and improve bandwidth utilization.

[0033] It should be noted that in this embodiment, the adaptive compression algorithm is adopted. The system dynamically adjusts the compression ratio according to different types of data streams and network bandwidth conditions to ensure that while ensuring data quality, the amount of data transmitted is minimized. This algorithm automatically selects the most suitable compression method by analyzing data characteristics in real time, optimizes bandwidth utilization, and reduces bandwidth occupancy caused by transmitting a large amount of redundant data, thereby improving the transmission efficiency of the entire communication system.

[0034] Specifically, by combining satellite and terrestrial communication networks, intelligent algorithms such as reinforcement learning and deep learning are used to optimize bandwidth resource allocation and communication quality. In the working principle, first, a data channel is established between the satellite communication network and the ground station, and frequency reuse technology is used for bandwidth optimization, thereby improving spectrum utilization and reducing signal interference. Then, a reinforcement learning model is used to predict bandwidth requirements, and bandwidth resources are dynamically adjusted in real time according to the network state to ensure the efficient use of bandwidth. Data streams are classified and an adaptive flow control protocol is adopted to ensure that high-priority data streams are preferentially transmitted, avoiding network congestion from affecting important data. By combining satellite and terrestrial communication links, the system adjusts the data transmission rate in real time and performs load balancing according to the link quality to optimize the transmission efficiency of data streams. When the communication link fluctuates, the system will automatically switch to the backup link or adjust the transmission mode to ensure communication stability. At the same time, a deep learning model predicts the satellite communication delay and performs delay compensation to reduce the impact of delay fluctuations on communication quality. According to user needs and geographical location, the system intelligently allocates bandwidth resources to ensure that high-priority users and regions obtain sufficient bandwidth and optimize bandwidth utilization. Finally, an adaptive compression algorithm is used to reduce the amount of data transmitted, thereby improving the bandwidth utilization efficiency. This method solves the problems of uneven bandwidth utilization, unstable links, delay fluctuations, and insufficient flow control in the prior art. Through intelligent prediction, dynamic adjustment, and optimization strategies, the stability, efficiency, and user experience of the communication system are significantly improved.

[0035] Furthermore, as Figure 1As shown, the bandwidth prediction model uses a reinforcement learning algorithm to intelligently predict future bandwidth requirements by analyzing network historical data, real-time bandwidth demands, and link status, and dynamically adjusts the allocation of bandwidth resources according to the prediction results, thereby effectively reducing bandwidth waste and improving the overall system performance.

[0036] It should be noted that in this embodiment, the described bandwidth prediction model uses a reinforcement learning algorithm to first collect and analyze a large amount of historical network data, real-time bandwidth demands, and link status, including bandwidth usage under different time periods, geographical locations, and network conditions. By continuously learning the temporal variation laws and trends of these data, the model constructs an adaptive prediction model that can accurately predict the fluctuations of future bandwidth demands. Based on these prediction results, the system will intelligently adjust the bandwidth resource allocation according to the current network status and future demands, giving priority to high-demand applications and regions while avoiding resource waste, ensuring the efficient utilization of network resources, and ultimately enhancing the overall performance and user experience of the communication system.

[0037] Furthermore, as Figure 1 shown, the traffic control protocol uses an adaptive algorithm to prioritize different types of data flows, and adjusts the transmission strategy of the data flows in real time to ensure that high-priority data flows are preferentially guaranteed in the case of network congestion or insufficient bandwidth, and can achieve dynamic retransmission and rate adjustment of data packets. The deep learning model trains on the historical data of satellite communication delay, combines the real-time link status, and uses a combination of convolutional neural network and long short-term memory network for delay prediction, so as to accurately predict the link delay and automatically adjust the transmission parameters through a delay compensation mechanism to reduce the impact of delay fluctuations on communication quality.

[0038] It should be noted that in this embodiment, the described traffic control protocol uses an adaptive algorithm to prioritize different types of data flows, and dynamically adjusts the sending order and rate of data packets based on the real-time demands of the data flows and the network conditions to ensure that high-priority data flows can be preferentially transmitted in the case of network congestion or insufficient bandwidth. This protocol can also automatically retransmit data packets according to the link quality and data transmission status to avoid communication interruption caused by packet loss and optimize the transmission rate to ensure the efficient transmission of data. The deep learning model combines convolutional neural network and long short-term memory network to train on the historical data of satellite communication delay, identifies the laws and influencing factors of delay fluctuations, and thus accurately predicts future delays. Combining the real-time link status, the system automatically adjusts the transmission parameters and optimizes the link delay through a delay compensation mechanism to reduce the impact of delay fluctuations and improve the stability of communication quality.

[0039] Furthermore, as Figure 1As shown, the delay compensation mechanism is based on a network state awareness algorithm, which automatically evaluates the delay difference between the satellite and the ground link, and corrects the delay through various compensation methods to ensure that the delay variation during data transmission remains within an acceptable range, thereby improving communication stability. The adaptive compression algorithm uses content-aware compression technology, which can dynamically select the most suitable compression method according to the characteristics of the transmitted data and the real-time change of network bandwidth, so as to minimize the data volume, reduce bandwidth occupancy and improve the utilization rate of network resources while ensuring the transmission quality.

[0040] It should be noted that in this embodiment, the described delay compensation mechanism uses a network state awareness algorithm to continuously monitor the delay difference between the satellite and the ground link, analyze multiple factors such as link quality and network load, and automatically identify the source of delay fluctuations. This mechanism uses a variety of compensation means, such as delay caching, adjusting modulation methods and optimizing routing, to timely correct the delay change and ensure that the delay fluctuation during data transmission remains within the minimum range, thereby improving the stability and response speed of the communication system. The adaptive compression algorithm is based on content-aware technology. First, it analyzes the type and importance of the transmitted data, and dynamically selects the most suitable compression method according to the characteristics of different data. This algorithm can also automatically adjust the compression ratio according to the real-time change of network bandwidth to minimize the data volume, reduce bandwidth occupancy, optimize resource use and improve the overall communication efficiency.

[0041] Furthermore, as Figure 1 shown, the bandwidth resource allocation strategy intelligently adjusts the bandwidth allocation according to the user's geographical location, communication requirements and service quality requirements, combined with the user behavior analysis model, so that critical applications and high-priority users can get sufficient bandwidth guarantee, while effectively balancing the bandwidth needs of other users, avoiding resource conflicts and network bottlenecks. The link quality assessment mechanism monitors the link quality of satellite communication and ground broadband network in real time, uses an adaptive algorithm to dynamically adjust the modulation scheme and transmission rate of the link, and automatically selects the best link according to the network load situation and communication delay to ensure that the data packets during communication are not lost, the delay is minimized and the bandwidth utilization is maximized.

[0042] It should be noted that the bandwidth resource allocation strategy described in this embodiment analyzes the bandwidth requirements and usage patterns of users in real time by combining the user's geographical location, communication requirements, and service quality requirements, and combining the user behavior analysis model, so as to ensure that sufficient bandwidth is allocated to critical applications and high-priority users. The system dynamically adjusts the bandwidth allocation according to the network status, optimizes the resource utilization rate, and avoids bandwidth conflicts and uneven allocation through intelligent algorithms to ensure the avoidance of network bottlenecks. The link quality evaluation mechanism analyzes factors such as signal strength, delay, and jitter of the link by monitoring the link quality between the satellite and the ground broadband network in real time, and dynamically adjusts the modulation scheme, transmission rate, and routing selection based on real-time data. This mechanism automatically selects the best link according to the network load, delay, etc. through an adaptive algorithm to ensure the stability and reliability of data transmission and optimize the utilization of bandwidth.

[0043] Embodiment 1: Intelligent Bandwidth Allocation and Delay Optimization System Combining Satellite and Ground Communication Networks Background: This embodiment is applicable to remote areas and urban fringe areas that require stable communication, where communication depends on the integration of satellite and ground networks. The system solves the problems of uneven utilization of satellite communication bandwidth, delay fluctuations, and unstable links through bandwidth prediction, traffic control based on reinforcement learning, and delay compensation based on deep learning, and provides efficient network services.

[0044] Detailed description of the technical solution: Data channel establishment and frequency reuse: The system establishes a data channel between the satellite communication network and the ground station, and optimizes the bandwidth through frequency reuse technology. The satellite signal is allocated on multiple frequency bands, and an efficient modulation and demodulation technology is adopted to avoid signal interference and ensure communication quality. To prevent waste of frequency resources and improve signal transmission quality, the system monitors the network status in real time and dynamically adjusts the spectrum resource allocation.

[0045] Bandwidth prediction and dynamic adjustment: The system uses a reinforcement learning algorithm to construct a bandwidth demand prediction model by collecting historical bandwidth demand data, real-time link quality, network load, and traffic patterns. This model can accurately predict future bandwidth demands and adjust the bandwidth allocation strategy in real time according to the prediction results. For example, when it is predicted that the network load will increase, the system will allocate more bandwidth to high-priority applications in advance to avoid communication quality problems caused by insufficient bandwidth.

[0046] Adaptive Flow Control: Data streams are classified according to priority. High-priority data (such as video conferencing, real-time data) is given priority for transmission, while low-priority data streams (such as background file transfer, bulk data transfer) can be transmitted when the network bandwidth is abundant and delayed or have their transmission rate reduced when the network is congested. The adaptive flow control protocol monitors the network congestion situation in real time and dynamically adjusts the sending rate and transmission order of data packets according to the network load to ensure that critical applications are guaranteed bandwidth first.

[0047] Link Quality Assessment and Load Balancing: The system monitors factors such as signal quality, latency, and packet loss rate of the satellite-ground link in real time and evaluates the current link state through a link quality assessment mechanism. When the link quality deteriorates, the system automatically adjusts the modulation scheme, transmission rate, or selects a backup link for communication to ensure that network transmission is not interrupted. The system also optimizes the link load through a load balancing algorithm to avoid overloading a certain link and affecting data transmission efficiency.

[0048] Latency Compensation and Adjustment: The system analyzes the historical latency data of the satellite link through a deep learning model and combines it with the real-time link quality to accurately predict future latency fluctuations. The deep learning model uses a combination of convolutional neural network (CNN) and long short-term memory network (LSTM) to precisely predict the change trend of latency. According to the prediction results, the system adjusts the transmission parameters of the link (such as adjusting the modulation method or routing) and uses a latency compensation mechanism to correct the link latency to ensure that the latency fluctuations during data transmission are kept within an acceptable range and optimize the communication quality.

[0049] Bandwidth Resource Allocation and Optimization: Based on factors such as the user's geographical location, communication requirements, and service quality requirements, the system uses a user behavior analysis model to evaluate the bandwidth requirements of each user in real time. The system intelligently allocates bandwidth resources to ensure that high-demand users and critical applications receive sufficient bandwidth and avoid network bottlenecks. For non-critical applications, the system automatically adjusts the bandwidth allocation according to the traffic load to optimize resource utilization.

[0050] Adaptive Compression Algorithm: During data transmission, the system adopts an adaptive compression algorithm. According to the content type of the data and the real-time bandwidth situation, the system dynamically selects an appropriate compression method to reduce the amount of data. For example, for large file transfer, the system will automatically adjust the compression ratio according to the current network bandwidth to reduce bandwidth occupancy while ensuring the integrity and quality of data transmission. In this way, bandwidth resources are utilized more effectively.

[0051] Example 2: Application of the Intelligent Satellite Communication System in Urban Networks Background: This embodiment is used in high-bandwidth demand areas in urban networks, especially during peak communication periods, to provide stable Internet access services. By combining satellite communication with terrestrial broadband, the system realizes technologies such as intelligent bandwidth prediction, traffic control, and latency optimization, effectively improving the communication quality and resource utilization efficiency of urban networks.

[0052] Detailed description of the technical solution: Establishment of satellite and terrestrial communication links: In the urban network, the system first establishes a two-way communication link between the satellite communication and the terrestrial broadband network. The satellite link is used to cover communications in remote or high-demand areas, while the terrestrial broadband network processes regular urban communication traffic. Through frequency reuse technology and modulation and demodulation schemes, satellite communication signals are allocated in multiple frequency bands to ensure stable signal transmission, while reducing interference and improving bandwidth efficiency.

[0053] Intelligent bandwidth prediction and resource scheduling: The system uses reinforcement learning algorithms to predict future bandwidth demands. The model is trained with historical bandwidth usage data, real-time link status, and traffic demands to intelligently predict bandwidth demands at different times and in different regions. Based on the prediction results, the system dynamically adjusts the bandwidth resource allocation. For example, in areas with concentrated users, the system will allocate more bandwidth in advance to ensure that the communication quality is not affected during peak periods.

[0054] Traffic control and priority management: The system classifies all data streams and processes them according to their priorities. High-priority data streams (such as real-time video streams and online games) are given priority in transmission when bandwidth is tight, while low-priority data streams (such as file downloads) can be delayed or have their transmission rates adjusted. Through real-time traffic analysis, the system can automatically adjust the transmission order and rate of data streams to avoid low-priority traffic affecting the transmission of high-priority traffic.

[0055] Link quality assessment and dynamic adjustment: By monitoring the quality of satellite communication and terrestrial links in real time, the system evaluates the link status based on factors such as link latency, bandwidth, and packet loss rate. Based on this evaluation, the system can automatically adjust the transmission rate, modulation scheme, etc. of the link, or switch to a backup link when the link quality deteriorates. The dynamic adjustment and switching strategies ensure the stability and efficiency of communication.

[0056] Deep learning latency prediction and compensation: The system uses a deep learning model (a combination of CNN-LSTM) to analyze the latency data of the satellite link and, combined with the real-time link status, accurately predicts future latency fluctuations. Based on the prediction results, the system automatically adjusts the modulation method, transmission rate, and routing strategy of the link, and uses a latency compensation mechanism to correct the link latency, minimizing the impact of latency fluctuations on the user experience during the communication process.

[0057] Bandwidth Intelligent Allocation: The system combines the user's geographical location, requirements, and quality of service requirements to intelligently allocate bandwidth resources through a behavior analysis model. High-demand areas and users are given priority for bandwidth guarantee. After the system meets the bandwidth requirements of critical applications, it balances the bandwidth requirements of other users. This strategy avoids resource conflicts and network bottlenecks, ensuring the smooth operation of the network.

[0058] Data Compression and Bandwidth Optimization: During data transmission, the system adopts an adaptive compression algorithm based on the real-time bandwidth status and data type to optimize the bandwidth occupancy of data transmission. The system automatically adjusts the compression ratio according to the characteristics of different data streams (such as text, video, or file transmission) to ensure that, in the case of limited bandwidth, the data volume is minimized as much as possible and the transmission efficiency is improved.

[0059] In summary, through intelligent algorithms and adaptive mechanisms, the communication efficiency and resource utilization are optimized. First, through frequency reuse technology and an intelligent bandwidth prediction model, the system dynamically allocates bandwidth resources in real time to ensure that different applications and users receive reasonable bandwidth guarantees. Second, through an adaptive traffic control protocol, high-priority data streams are transmitted first, and the data transmission rate is dynamically adjusted according to the real-time network conditions. The delay compensation mechanism predicts and compensates for the delay fluctuations of the satellite link through a deep learning model, thereby reducing the impact of delay on communication quality. It also adjusts the link transmission parameters in real time through a link quality assessment mechanism to ensure stable transmission. Finally, an adaptive compression algorithm is adopted to dynamically optimize the compression ratio according to the data type and network bandwidth changes, improving the bandwidth utilization rate. The problems of uneven bandwidth utilization, unstable links, delay fluctuations, and insufficient traffic control in the prior art are solved. Through intelligent prediction and real-time adjustment, the system can efficiently utilize bandwidth resources, ensure stable communication, reduce delay fluctuations, and improve the overall communication quality.

[0060] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A communication method for broadband communication and satellite integration, characterized in that: The following steps are involved: S1: Establish data channels between satellite communication networks and ground stations, and use frequency reuse technology to optimize bandwidth utilization; S2: Use reinforcement learning models to predict bandwidth demand and dynamically allocate bandwidth resources based on real-time network conditions; S3: classifies data flows and uses adaptive flow control protocols to ensure priority transmission of high-priority data flows; S4: Combines satellite and ground communication links to adjust data transmission rate in real time and perform load balancing based on link quality; S5: When the communication link fluctuates, it automatically switches to the backup link or adjusts the transmission mode to ensure stable communication; S6: Predict satellite communication delay through deep learning, perform delay compensation, and optimize communication efficiency; S7: Intelligently allocate bandwidth resources based on user needs and geographic location to optimize bandwidth utilization; S8: Adopt adaptive compression algorithm to reduce data transmission volume and improve bandwidth utilization.

2. The communication method for broadband communication and satellite integration according to claim 1, characterized in that: The bandwidth prediction model uses a reinforcement learning algorithm to analyze historical network data, real-time bandwidth demand, and link status to intelligently predict future bandwidth demand and dynamically adjust the allocation of bandwidth resources based on the prediction results, thereby effectively reducing bandwidth waste and improving overall system performance.

3. The communication method for broadband communication and satellite integration according to claim 1, characterized in that: The flow control protocol prioritizes different types of data streams through an adaptive algorithm and adjusts the data stream transmission strategy in real time to ensure that high-priority data streams are given priority in the event of network congestion or insufficient bandwidth, and can achieve dynamic retransmission and rate adjustment of data packets.

4. The communication method for broadband communication and satellite integration according to claim 1, characterized in that: The deep learning model is trained on historical data of satellite communication delays, combined with real-time link status, and uses a combination of convolutional neural networks and long short-term memory networks to predict delays, thereby accurately predicting link delays and automatically adjusting transmission parameters through a delay compensation mechanism to reduce the impact of delay fluctuations on communication quality.

5. The communication method for broadband communication and satellite integration according to claim 1, characterized in that: The delay compensation mechanism is based on a network status perception algorithm. It automatically evaluates the delay difference between the satellite and ground links, and corrects the delay through a variety of compensation methods to ensure that the delay changes during data transmission remain within an acceptable range, thereby improving communication stability.

6. The communication method for broadband communication and satellite integration according to claim 1, characterized in that: The adaptive compression algorithm adopts content-aware compression technology, which can dynamically select the most appropriate compression method according to the characteristics of the transmitted data and the real-time changes in the network bandwidth, thereby minimizing the amount of data while ensuring the transmission quality, reducing bandwidth occupancy and improving the utilization of network resources.

7. The communication method for broadband communication and satellite integration according to claim 1, characterized in that: The bandwidth resource allocation strategy intelligently adjusts bandwidth allocation based on the user's geographic location, communication needs, and service quality requirements, combined with the user behavior analysis model, so that key applications and high-priority users are guaranteed sufficient bandwidth, while effectively balancing the bandwidth needs of other users to avoid resource conflicts and network bottlenecks.

8. The communication method for broadband communication and satellite integration according to claim 1, characterized in that: The link quality assessment mechanism monitors the link quality between satellite communications and ground broadband networks in real time, uses an adaptive algorithm to dynamically adjust the modulation scheme and transmission rate of the link, and automatically selects the best link based on network load and communication delay to ensure that data packets are not lost, delays are minimized, and bandwidth utilization is maximized during the communication process.

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